Clinical Assessment of Malingering and Deception, Fourth Edition

CLINICAL ASSESSMENT OF MALINGERING AND DECEPTION

Also Available

Conducting Insanity Evaluations, Second Edition Richard Rogers and Daniel W. Shuman

Clinical Assessment of Malingering and Deception F O U R T H

E D I T I O N

edited by

Richard Rogers Scott D. Bender

THE GUILFORD PRESS New York  London

Copyright © 2018 The Guilford Press A Division of Guilford Publications, Inc. 370 Seventh Avenue, Suite 1200, New York, NY 10001 www.guilford.com All rights reserved No part of this book may be reproduced, translated, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, microfilming, recording, or otherwise, without written permission from the publisher. Printed in the United States of America This book is printed on acid-free paper. Last digit is print number: 9 8 7 6 5 4 3 2 1 The authors have checked with sources believed to be reliable in their efforts to provide information that is complete and generally in accord with the standards of practice that are accepted at the time of publication. However, in view of the possibility of human error or changes in behavioral, mental health, or medical sciences, neither the authors, nor the editors and publisher, nor any other party who has been involved in the preparation or publication of this work warrants that the information contained herein is in every respect accurate or complete, and they are not responsible for any errors or omissions or the results obtained from the use of such information. Readers are encouraged to confirm the information contained in this book with other sources. Library of Congress Cataloging-in-Publication Data Names: Rogers, Richard, 1950– editor. | Bender, Scott D., editor. Title: Clinical assessment of malingering and deception / edited by Richard   Rogers, Scott D. Bender. Description: Fourth edition. | New York : The Guilford Press, [2018] |   Includes bibliographical references and index. Identifiers: LCCN 2018000638 | ISBN 9781462533497 (hardback) Subjects: LCSH: Malingering—Diagnosis. | Deception. | BISAC: PSYCHOLOGY /   Forensic Psychology. | MEDICAL / Psychiatry / General. | LAW / Mental   Health. | SOCIAL SCIENCE / Social Work. Classification: LCC RA1146 .C57 2018 | DDC 616.85/2—dc23   LC record available at https://lccn.loc.gov/2018000638

About the Editors

Richard Rogers, PhD, ABPP, is Regents Professor of Psychology at the University of North Texas. He is a recipient of the Guttmacher Award from the American Psychi­atric Association, the Distinguished Contributions to Forensic Psychology Award from the American Academy of Forensic Psychologists, and the Amicus Award from the American Academy of Psychiatry and Law. In addition, Dr. Rogers is only the fourth psychologist to receive Distinguished Professional Contributions awards for both Applied Research and Public Policy from the American Psychological Association. He is the principal author of the Structured Interview of Reported Symptoms (SIRS) and its second edition (SIRS-2), often considered the premier measure for feigned mental disorders. Scott D. Bender, PhD, ABPP-CN, is Associate Professor of Psychiatry and Neuro­ behavioral Science at the University of Virginia (UVA). His primary appointment is with the I­ nstitute of Law, Psychiatry, and Public Policy at UVA, where his duties include teaching, research, and conducting forensic neuropsychological evaluations. Dr. Bender has published extensively, and his research focuses on differential diagnosis of malingering and the effects of traumatic brain injury on neurocognitive and emotional functioning. He frequently testifies on these and related matters in both criminal and civil cases.

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Contributors

Scott D. Bender, PhD, ABPP-CN, Institute of Law, Psychiatry, and Public Policy, and Department of Psychiatry and Neurobehavioral Science, University of Virginia, Charlottesville, Virginia David T. R. Berry, PhD, Department of Psychology, University of Kentucky, Lexington, Kentucky Marcus T. Boccaccini, PhD, Department of Psychology and Philosophy, Sam Houston State University, Huntsville, Texas Chelsea M. Bosch, MS, Department of Psychology, University of Kentucky, Lexington, Kentucky Stacey L. Brothers, BA, Department of Psychology, University of Kentucky, Lexington, Kentucky Abby P. Clark, MA, Department of Psychology, The University of Alabama, Tuscaloosa, Alabama Amor A. Correa, PhD, Federal Bureau of Prisons, Fort Worth, Texas Eric Y. Drogin, JD, PhD, Department of Psychiatry, Harvard Medical School, Boston, Massachusetts Marc D. Feldman, MD, Department of Psychiatry and Behavioral Medicine, The University of Alabama, Tuscaloosa, Alabama James R. Flens, PsyD, private practice, Valrico, Florida Richard Frederick, PhD, private practice, Springfield, Missouri Natasha E. Garcia-Willingham, MS, Department of Psychology, University of Kentucky, Lexington, Kentucky Nathan D. Gillard, PhD, Federal Bureau of Prisons, Oklahoma City, Oklahoma Jonathan W. Gould, PhD, private practice, Charlotte, North Carolina Robert P. Granacher, Jr., MD, MBA, Lexington Forensic Neuropsychiatry, Lexington, Kentucky vii

viii Contributors

James C. Hamilton, PhD, Department of Psychology, The University of Alabama, Tuscaloosa, Alabama Kimberly S. Harrison, PhD, Harrison Psychological Services, Austin, Texas Natalie Harrison, MA, Department of Psychology, The University of Alabama, Tuscaloosa, Alabama Jessica R. Hart, MA, Department of Psychology and Philosophy, Sam Houston State University, Huntsville, Texas Ashley C. Helle, MS, Department of Psychology, Oklahoma State University, Stillwater, Oklahoma Sarah Henry, PhD, Department of Psychology, University of North Texas, Denton, Texas William G. Iacono, PhD, Department of Psychology, University of Minnesota, Minneapolis, Minnesota Rebecca L. Jackson, PhD, Florida Civil Commitment Center, Arcadia, Florida Richard A. A. Kanaan, MD, PhD, Department of Psychiatry, University of Melbourne, Heidelberg, Australia James L. Knoll, IV, MD, Division of Forensic Psychiatry, State University of New York Upstate Medical University, Syracuse, New York Franz A. Kubak, PhD, Department of Psychiatry, Oregon State Hospital, Portland, Oregon Zina Lee, PhD, School of Criminology and Criminal Justice, University of the Fraser Valley, Abbotsford, British Columbia, Canada Julia Levashina, PhD, Department of Management and Information Systems, College of Business Administration, Kent State University, Kent, Ohio Richard J. McNally, PhD, Department of Psychology, Harvard University, Cambridge, Massachusetts Mazheruddin M. Mulla, MA, MPH, Department of Psychology, The University of Alabama, Tuscaloosa, Alabama Daniel J. Neller, PsyD, private practice, Southern Pines, North Carolina Christopher J. Patrick, PhD, Department of Psychology, Florida State University, Tallahassee, Florida Sol R. Rappaport, PhD, private practice, Libertyville, Illinois Phillip J. Resnick, MD, Department of Psychiatry, Case Western Reserve University, Cleveland, Ohio Richard Rogers, PhD, ABPP, Department of Psychology, University of North Texas, Denton, Texas Randall T. Salekin, PhD, Department of Psychology and Disruptive Behavior Clinic, The University of Alabama, Tuscaloosa, Alabama Kenneth W. Sewell, PhD, Division of Research, Oklahoma State University, Stillwater, Oklahoma Glenn Smith, PhD, Mental Health/Behavioral Sciences Service, James A. Haley Veterans’ Hospital, Tampa, Florida



Contributors ix

Lynda A. R. Stein, PhD, Department of Psychology, University of Rhode Island, Kingston, Rhode Island; Department of Behavioral and Social Sciences, Brown University School of Public Health, Providence, Rhode Island; Department of Children, Youth and Families, Rhode Island Training School, Cranston, Rhode Island Michael J. Vitacco, PhD, Department of Psychiatry and Health Behavior, Augusta University, Augusta, Georgia Brittany D. Walls, MS, Department of Psychology, University of Kentucky, Lexington, Kentucky Sara G. West, MD, Department of Psychiatry, Case Western Reserve University, Cleveland, Ohio Carol S. Williams, LLB, Department of Psychology, Aberystwyth University, Aberystwyth, Ceredigion, United Kingdom Philip H. Witt, PhD, Associates in Psychological Services, Somerville, New Jersey Chelsea N. Wooley, PhD, Federal Bureau of Prisons, Seagoville, Texas Dustin B. Wygant, PhD, Department of Psychology, Eastern Kentucky University, Richmond, Kentucky Gregory P. Yates, MA, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London, United Kingdom

Preface

This fourth edition of Clinical Assessment of Malingering and Deception represents an important advance for practitioners, researchers, and scholars invested in a sophisticated understanding of malingering and other response styles. On the 30th anniversary of the first edition, it pays homage to its rich past, while embracing the cutting-edge clinical and research methods of today. The fourth edition seeks to broaden its in-depth coverage of response styles with the addition of six valuable chapters in three broad domains. First, the conceptual framework is strengthened with chapters on neuropsychological models and cultural applications. Second, diagnostic issues are enhanced by chapters about deception as it relates to psycho­ pathy and conversion disorders. Third, and finally, two chapters on specialized applications address positive impression management in the context of contested custody evaluations and personnel selection. A major change with the fourth edition involves the addition of a coeditor, Scott D. Bender, who brings his expertise in neuropsychology to capably guide the relevant chapters. While I plan to play a leading role for years to come, Scott is committed to the continued success of Clinical Assessment of Malingering and Deception in future decades. Richard Rogers

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Contents

PART I.  CONCEPTUAL FRAMEWORK  1. An Introduction to Response Styles 3 Richard Rogers

 2. Detection Strategies for Malingering and Defensiveness 18 Richard Rogers

 3. Neuropsychological Models of Feigned Cognitive Deficits 42 Scott D. Bender and Richard Frederick

 4. Beyond Borders: Cultural and Transnational Perspectives of Feigning 61

and Other Response Styles

Amor A. Correa

PART II.  DIAGNOSTIC ISSUES  5. Syndromes Associated with Deception 83 Michael J. Vitacco

 6. Malingered Psychosis 98 Phillip J. Resnick and James L. Knoll, IV

 7. Malingered Traumatic Brain Injury 122 Scott D. Bender

 8. Denial and Misreporting of Substance Abuse 151 Lynda A. R. Stein, Richard Rogers, and Sarah Henry xiii

xiv Contents

 9. Psychopathy and Deception 174 Nathan D. Gillard

10. The Malingering of Posttraumatic Disorders 188 Phillip J. Resnick, Sara G. West, and Chelsea N. Wooley

11. Factitious Disorders in Medical and Psychiatric Practices 212 Gregory P. Yates, Mazheruddin M. Mulla, James C. Hamilton, and Marc D. Feldman

12. Conversion Disorder and Illness Deception 236 Richard A. A. Kanaan

13. Feigned Medical Presentations 243 Robert P. Granacher, Jr., and David T. R. Berry

PART III.  PSYCHOMETRIC METHODS 14. Assessment of Malingering and Defensiveness on the MMPI-2 257

and MMPI‑2‑RF

Dustin B. Wygant, Brittany D. Walls, Stacey L. Brothers, and David T. R. Berry

15. Response Style on the Personality Assessment Inventory 280

and Other Multiscale Inventories

Marcus T. Boccaccini and Jessica R. Hart

16. Dissimulation on Projective Measures: An Updated Appraisal 301

of a Very Old Question

Kenneth W. Sewell and Ashley C. Helle

17. Feigned Amnesia and Memory Problems 314 Richard Frederick

18. Assessment of Feigned Cognitive Impairment Using Standard 329

Neuropsychological Tests

Natasha E. Garcia‑Willingham, Chelsea M. Bosch, Brittany D. Walls, and David T. R. Berry

PART IV.  SPECIALIZED METHODS 19. Assessing Deception: Polygraph Techniques and Integrity Testing 361 William G. Iacono and Christopher J. Patrick

20. Recovered Memories of Childhood Sexual Abuse 387 Richard J. McNally



Contents xv

21. Detection of Deception in Sex Offenders 401 Philip H. Witt and Daniel J. Neller

22. Structured Interviews and Dissimulation 422 Richard Rogers

23. Brief Measures for the Detection of Feigning 449

and Impression Management

Glenn Smith

PART V.  SPECIALIZED APPLICATIONS 24. Deception in Children and Adolescents 475 Randall T. Salekin, Franz A. Kubak, Zina Lee, Natalie Harrison, and Abby P. Clark

25. Use of Psychological Tests in Child Custody Evaluations: 497

Effects of Validity Scale Scores on Evaluator Confidence in Interpreting Clinical Scales

Jonathan W. Gould, Sol R. Rappaport, and James R. Flens

26. Malingering: Considerations in Reporting and Testifying 514

about Assessment Results

Eric Y. Drogin and Carol S. Williams

27. Evaluating Deceptive Impression Management in Personnel Selection 530

and Job Performance

Julia Levashina

28. Assessment of Law Enforcement Personnel: The Role of Response Styles 552 Rebecca L. Jackson and Kimberly S. Harrison

PART VI. SUMMARY 29. Current Status of the Clinical Assessment of Response Styles 571 Richard Rogers

30. Researching Response Styles 592 Richard Rogers



Author Index 615 Subject Index 638

PA R T I

CONCEPTUAL FRAMEWORK

CHAPTER 1

An Introduction to Response Styles Richard Rogers, PhD

Complete and accurate self-disclosure remains a rarity even in the uniquely supportive context of a psychotherapeutic relationship. Even the most involved clients may intentionally conceal and distort important data about themselves. Baumann and Hill (2016) found that outpatient clients sometimes did not divulge personal matters related to sexual experiences, substance abuse, and relationship experiences. Despite imagining positive gains from such personal disclosures, many clients elected not to be fully forthcoming about deeply personal issues. Deceptions in therapy are not relegated to undisclosed personal issues. In surveying 547 former or current therapy clients, Blanchard and Farber (2016) found that many minimized their distress (53.9%) and symptom severity (38.8%). Regarding their therapists, appreciable percentages resorted to deceit in pretending to like their comments/suggestions (29.4%), overstating the effectiveness of therapy (28.5%), and pretending to do homework or other actions (25.6%). Even more concerning was the frequency of these therapy-focused deceptions, which occurred moderate or greater amounts of time. To put these findings in context, therapists also vary considerably in their numbers and types of selfdisclosures (Levitt et al., 2016). Deceptions routinely occur in personal relationships, including intimate relationships, with relatively few (27%) espousing the belief that complete honesty is needed for a successful romantic

relationship (Boon, & McLeod, 2001). Interestingly, these authors found that most persons believe they are much better (Cohen’s d = 0.71) than their partners at “successful” (undetected) deceptions. Even in intimate relationships, willingness to self-disclose is variable and multidetermined (Laurenceau, Barrett, & Rovine, 2005). Romantic partners may have implicitly understood rules about what dishonesties may be allowed in their intimate relationships (Roggensack & Sillars, 2014). Beyond therapy and relationships, deceptions commonly occur in the workplace, including the concealments of mental disorders. Most of the 17 to 20% of employees affected by mental disorders annually elect not to disclose their conditions due to public stigma or more specific concerns about potential damage to their careers (De Lorenzo, 2013). A national survey of professionals and managers by Ellison, Russinova, MacDonald-Wilson, and Lyass (2003) has important implications for understanding individuals’ disclosures and deceptions regarding mental disorders. The majority of these employees had disclosed their psychiatric conditions to their supervisors and coworkers. However, many disclosures were not entirely voluntary (e.g., they were given in response to pressure to explain health-related absences), and about one-third regretted their decisions because of negative repercussions. Moreover, the degree of self-disclosure (e.g., diagnosis, symptoms, or im-

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pairment) and the timing of the disclosures were highly variable. Nondisclosing employees were typically motivated by fears of job security and concerns about stigma. What are the two key implications of the study by Ellison et al.? First, decisions about response styles (disclose or deceive) are often rational and multidetermined; this theme is explored later in the context of the adaptational model. Second, these decisions are often individualized responses to interpersonal variables (e.g., a good relationship with a coworker) or situational demands (e.g., explanation of poor performance). This model of complex, individualized decisions directly counters a popular misconception that response styles are inflexible trait-like characteristics of certain individuals. For example, malingerers are sometimes misconstrued as having an invariant response style, unmodified by circumstances and personal motivations.1 Decisions to deceive or disclose are part and parcel of relationships across a spectrum of social contexts. For instance, impression management plays a complex role in the workplace, especially with reference to what has been termed concealable stigmas. Jones and King (2014) provide a penetrating analysis of determinants for whether employees disclose, conceal, or signal (i.e., “testing the waters,” p. 1471) about themselves (e.g., gender identity) and their own personal experiences (e.g., childhood traumas). Most individuals engage in a variety of response styles that reflect their personal goals in a particular setting. Certain behaviors, such as substance abuse, may be actively denied in one setting and openly expressed in another. Social desirability and impression management may prevail during the job application process but later be abandoned once hiring is completed. Clients in an evaluative context may experience internal and external influences on their selfreporting. Within a forensic context, for example, clients may respond to the adversarial effects of litigation—sometimes referred to as the lexogenic effects—in which their credibility is implicitly questioned (Rogers & Payne, 2006). As observed by Rogers and Bender (2003), these same clients may also be influenced internally by their diagnosis (e.g., borderline personality disorder), identity (e.g., avoidance of stigmatization), or intentional goals (e.g., malingering). By necessity, most chapters in this volume focus on one or more response style within a single domain (e.g., mental disorders, cognitive abilities, or medical complaints). In summary, all individuals fall short of full and accurate self-disclosure, irrespective of the social

context. To be fair, mental health professionals are often not fully forthcoming with clients about their assessment and treatment methods (Bersoff, 2008). In providing informed consent, how thoroughly do most practitioners describe therapeutic modalities, which they do not provide? This question is not intended to be provocative; it is simply a reminder that professionals and their clients alike may not fully embrace honesty at any cost. In the context of clinical assessments, mental health professionals may wish to consider what level of deception should be documented in their reports. One reasoned approach would be to record only consequential deceptions and distortions. For instance, Norton and Ryba (2010) asked coached simulators to feign incompetency on the Evaluation of Competency to Stand Trial—Revised (ECSTR; Rogers, Tillbrook, & Sewell, 2004). However, many simulators could be categorized as doublefailures; they failed to elude the ECST-R Atypical scales (i.e., screens for possible feigning) and also failed to produce anything more than normal to mild impairment (i.e., they appeared competent) on the ECST-R Competency scales. What should be done with such inconsequential distortions? In this specific case, the answer may be characterized as straightforward. Simply as screens, the ECST-R Atypical scales cast a wide net, so that few possible feigners are missed. As a result, no comment is needed, because substantial numbers of genuine responders score above the cutoff scores. The general issue of inconsequential deceptions should be considered carefully. Simply as a thought experiment, two extreme alternatives are presented: the taint hypothesis and the beyondreasonable-doubt standard. 1. Taint hypothesis: Any evidence of nongenuine responding is likely to signal a broader but presently undetected dissimulation. Therefore, practitioners have a professional responsibility to document any observed, even if isolated, deceptions. 2. Beyond-reasonable-doubt standard: Invoking the stringent standard of proof in criminal trials, only conclusive evidence of a response style, such as feigning, should be reported. Between the extremes, practitioners need to decide on a case-by-case basis how to balance the need to document sustained efforts regarding a particular response style with the sometimes very serious consequences of categorizing an examinee as a nongenuine responder.



1. An Introduction to Response Styles 5

In forensic practice, determinations of malingering are generally perceived as playing a decisive role in legal outcomes, because they fundamentally question the veracity and credibility of mental health claims. While it is likely that some genuinely disordered persons may attempt to malinger, the question remains unanswered2 whether fact finders simply dismiss all mental health issues as unsubstantiated. Mental health professionals must decide what evidence of response styles should be routinely included in clinical and forensic reports. Guided by professional and ethical considerations, their decisions are likely to be influenced by at least two dimensions: (1) accuracy versus completeness of their conclusion, and (2) use versus misuse of clinical findings by others. For example, a forensic psychologist may conclude that the examinee’s false denial of drug experimentation during his or her undergraduate years is difficult to establish and potentially prejudicial to a posttraumatic stress disorder (PTSD)-based personal injury case. As an introduction to response styles, this chapter has the primary goal of familiarizing practitioners and researchers with general concepts associated with malingering and deception. It operationalizes response styles and outlines common misconceptions associated with malingering and other forms of dissimulation. Conceptually, it distinguishes explanatory models from detection strategies. Because research designs affect the validity of clinical findings, a basic overview is provided. Finally, this chapter outlines the content and objectives of the subsequent chapters.

FUNDAMENTALS OF RESPONSE STYLES Basic Concepts and Definitions Considerable progress continues to been made in the standardization of terms and operationalization of response styles. Such standardization is essential to any scientific endeavor for ensuring accuracy and replicability. This section is organized conceptually into four categories: nonspecific terms, overstated pathology, simulated adjustment, and other response styles. Nonspecific Terms

Practitioners and researchers seek precision in the description of response styles. Why then begin the consideration of response styles with nonspecific terms? It is my hope that moving from general to

specific categories will limit decisional errors in the determination of response styles. As a consultant on malingering and related response styles, I find that a very common error appears to be the overspecification of response styles. For instance, criminal offenders are frequently miscategorized as malingerers simply because of their manipulative behavior, which may include asking for special treatment (e.g., overuse of medical call for minor complaints) or displaying inappropriate behavior (e.g., a relatively unimpaired inmate exposing his genitals). When disabled clients express ambivalence toward clinical or medical interventions, their less-than-wholehearted attitudes are sometimes misconstrued as prima facie evidence of secondary gain (see Rogers & Payne, 2006). The working assumption for errors in the overspecification of response styles is that practitioners approach this diagnostic classification by trying to determine which specific response style best fits the clinical data. Often, this approach results in the specification of a response style, even when the data are inconclusive, or even conflicting. As outlined in Box 1.1, a two-step approach is recommended. This approach asks practitioners to make an explicit decision between nonspecific or general descriptions and specific response styles. Clearly, conclusions about specific response styles are generally more helpful to clinical conclusions than simply nonspecific descriptions. Therefore, nonspecific descriptions should be considered first to reduce the understandable tendency of overreaching data when conclusions about specific response styles cannot be convincingly demonstrated. Nonspecific terms are presented in a bulleted format as an easily accessible reference. Terms are defined and often accompanied with a brief commentary: •• Unreliability is a very general term that raises questions about the accuracy of reported information. It makes no assumption about the individual’s intent or the reasons for inaccurate data. This

BOX 1.1.  Two-Step (General–Specific) Approach for Minimizing Overspecification 1. Do the clinical data support a nonspecific (e.g., “unreliable informant”) description? 2. If yes, are there ample data to determine a specific response style?

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term is especially useful when faced with conflicting clinical data. •• Nondisclosure simply describes a withholding of information (i.e., omission). Similar to unreliability, it makes no assumptions about intentionality. An individual may freely choose whether to divulge information, or alternatively, feel compelled by internal demands (e.g., command hallucinations) to withhold information. •• Self-disclosure refers to how much individuals reveal about themselves (Jourard, 1971). Persons are considered to have high self-disclosure when they evidence a high degree of openness. It is often considered an important construct within the context of reciprocal relationships (Hall, 2011). A lack of self-disclosure does not imply dishonesty but simply an unwillingness to share personal information. •• Deception is an all-encompassing term to describe any consequential attempts by individuals to distort or misrepresent their self-reporting. As operationalized, deception includes acts of deceit often accompanied by nondisclosure. Deception may be totally separate from the patient’s described psychological functioning (see dissimulation). •• Dissimulation is a general term to describe a wide range of deliberate distortions or misrepresentations of psychological symptoms. Practitioners find this term useful, because some clinical presentations are difficult to classify and clearly do not represent malingering, defensiveness, or any specific response style. Overstated Pathology

Important distinctions must be realized between malingering and other terms used to describe overstated pathology. For example, the determination of malingering requires the exclusion of factitious presentations (see Vitacco, Chapters 5, Yates, Mulla, Hamilton, & Feldman, Chapter 11, this volume). This subsection addresses three recommended terms: malingering, factitious presentations, and feigning. It also includes three quasi-constructs (secondary gain, overreporting, and suboptimal effort) that should be avoided in most clinical and forensic evaluations. Recommended terms to categorize overstated pathology: 1.  Malingering has been consistently defined by DSM nosology as “the intentional production of false or grossly exaggerated physical or psychologi-

cal symptoms, motivated by external incentives” (American Psychiatric Association, 2013, p. 726). An important consideration is magnitude of the dissimulation; it must be the fabrication or gross exaggeration of multiple symptoms. The presence of minor exaggerations or isolated symptoms does not qualify as malingering. Its requirement of external incentives does not rule out the co-occurrence of internal motivations. 2.  Factitious presentations are characterized by the “intentional production or feigning” of symptoms that is motivated by the desire to assume a “sick role” (APA, 2000, p. 517). However, the description of the motivation is no longer specified; DSM-5 (APA, 2013, p. 324) offers only the following: “The deceptive behavior is evident even in the absence of obvious external rewards.” Thus, the diagnosis of factitious disorders does not preclude external incentives but rather requires some unspecified internal motivation. This nonexclusion of external motivations makes sense, since internal and external motivations can often cooccur (Rogers, Jackson, & Kaminski, 2004). 3.  Feigning is the deliberate fabrication or gross exaggeration of psychological or physical symptoms, without any assumptions about its goals (Rogers & Bender, 2003, 2013). This term was introduced because standardized measures of response styles (e.g., psychological tests) have not been validated to assess an individual’s specific motivations. Therefore, determinations can often be made for feigned presentations but not their underlying motivations. To underscore this point, psychological tests can be used to establish feigning but not malingering. Several terms that are common to clinical and forensic practice lack well-defined and validated descriptions. This absence stems from either the lack of clear inclusion criteria, or the presence of multiple and conflicting definitions. Three terms to be avoided in clinical and forensic practice are summarized: 1.  Suboptimal effort (also referred to as incomplete or submaximal effort) is sometimes misused as a proxy for malingering (Rogers & Neumann, 2003). However, this term lacks precision and may be applied to nearly any client or professional (see Rogers & Shuman, 2005). The “best” effort of any individual may be affected by a variety of internal (e.g., an Axis I disorder or fatigue) and external (e.g., client-perceived adversarial context) factors.



1. An Introduction to Response Styles 7

2.  Overreporting simply refers to an unexpectedly high level of item endorsement, especially on multiscale inventories. It has also been called self-unfavorable reporting. Practitioners sometimes erroneously equate it with feigning. However, this descriptive term lacks clarity with respect to its content (i.e., socially undesirable characteristics, as well as psychopathology). Moreover, it has been used to describe both deliberate and unintentional acts (Greene, 2000). 3.  Secondary gain, unlike the other unacceptable terms, does have clear definitions. Its inherent problem for professional practice, however, stems from the presence of conflicting meanings (Rogers & Reinhardt, 1998). From a psychodynamic perspective, secondary gain is part of an unconscious process to protect the individual that is motivated by intrapsychic needs and defenses. From a behavioral medicine perspective, illness behaviors are perpetuated by the social context (e.g., health care providers), not by the individual. From a forensic perspective, individuals deliberately use their illness to gain special attention and material gains. Mental health professionals bear an important responsibility to use professional language that is clearly defined. Ambiguous terminology (e.g., suboptimal effort, overreporting, and secondary gain) adds unnecessary confusion to clinical and forensic assessments. Moreover, the misuse of professional language may lead to grievous errors in adjudicative settings, such as the courts. Simulated Adjustment

Three closely related terms are used to describe specific response styles that are associated with simulated adjustment. Defensiveness is operationalized as the masking of psychological difficulties, whereas the other two terms apply more broadly the concealment of undesirable characteristics. 1.  Defensiveness is defined as the polar opposite of malingering (Rogers, 1984). Specifically, this term refers to the deliberate denial or gross minimization of physical and/or psychological symptoms. Defensiveness must be distinguished from ego defenses, which involve intrapsychic processes that distort perceptions. 2.  Social desirability is the pervasive tendency for certain individuals to “present themselves in the most favorable manner relative to social norms and mores” (King & Bruner, 2000, p. 80). It in-

volves both the denial of negative characteristics and the attribution of positive qualities (Carsky, Selzer, Terkelsen, & Hurt, 1992). Not limited to psychological impairment, social desirability is a far more encompassing construct than defensiveness. Social desirability and its concomitant measurement should be carefully distinguished from defensiveness. 3.  Impression management refers to deliberate efforts to control others’ perceptions of an individual; its purposes may range from maximizing social outcomes to the portrayal of a desired identity (Leary & Kowalski, 1990). Impression management is often construed as more situationally driven than social desirability. It may often involve a specific set of circumstances, such as personnel selection (see Jackson & Harrison, Chapter 28, this volume). It can vary dramatically based on cultural expectations (Sandal et al., 2014). Although research studies often assume that impression management involves only a prosocial perspective, individuals may use this response style for a variety of purposes, such as hypercompetitiveness or “playing dumb” (Thornton, Lovley, Ryckman, & Gold, 2009). Preferred terms for simulated adjustment are likely to vary by the professional setting. Clinically, defensiveness is often the more precise term to describe an individual’s minimization of psychological difficulties. Importantly, this term applies to the concealment of psychological impairment rather than efforts to simulate a superior psychological adjustment (see Lanyon, 2001). At least theoretically, well-adjusted persons cannot engage in defensiveness. In many professional contexts that include clinical settings, efforts at self-presentation are likely to involve the concepts of social desirability and impression management. For research on social interactions, impression management is most versatile in describing different roles on a continuum from prosocial to antisocial. As a cautionary note, practitioners and researchers often need to examine the specific simulation instructions, because these terms are often used interchangeably as “fakegood” (Viswesvaran & Ones, 1999). Other Response Styles

Several additional response styles are not as well understood as malingering, defensiveness, and other approaches previously described. Four other response styles are outlined:

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1.  Irrelevant responding. This style refers to a response style in which the individual does not become psychologically engaged in the assessment process (Rogers, 1984). The given responses are not necessarily related to the content of the clinical inquiry. This process of disengagement may reflect intentional disinterest or simply carelessness. Occasionally, patterns emerge, such as the repetitive selection of the same option or an alternating response pattern (see commentary by Godinho, Kushnir, & Cunningham, 2016). 2.  Random responding. This style is a subset of irrelevant responding based entirely on chance factors. A likely example would be the completion of the Minnesota Multiphasic Personality Inventory–2 (MMPI-2) in less than 5 minutes. In this instance, the individual has probably read only a few of its 567 items and completed the remainder without any consideration of their content. 3.  Acquiescent responding. This style is commonly referred to as “yea-saying,” which is rarely experienced in its pure form (i.e., indiscriminately agreeing). Rogers, Sewell, Drogin, and Fiduccia (2012) examined acquiescent responding among pretrial detainees. Only 3% showed even a moderate level of acquiescence, but it did not occur most of the time. As an important distinction, acquiescence is clearly distinguishable from social desirability (Gudjonsson & Young, 2011). 4.  Disacquiescent responding. As the opposite of acquiescence, this style is characterized as “naysaying.” When used on scales focused on psychopathology, such as the MMPI-2, disacquiescence essentially eliminates elevations on feigning indicators (Burchett et al., 2016) and presumably for clinical scales. The reason appears to stem from the comparatively few inverted items (i.e., false responses signifying psychopathology). 5.  Role assumption. Individuals may occasionally assume the role or character of another person in responding to psychological measures. For example, Kroger and Turnbell (1975) asked undergraduates to simulate the role of commissioned officers in the air force. This response style is poorly understood but potentially important. 6.  Hybrid responding. This style describes an individual’s use of more than one response style in a particular situation (Rogers, 1984). For example, clients may evidence honest responding about most facets of their lives but engage in defensiveness with respect to substance abuse. Hybrid responding underscores the importance of consid-

ering response styles as adaptable and potentially transitory.

Domains of Dissimulation Response styles are almost never pervasive. For example, malingerers do not feign everything from viral infections to intellectual disabilities. A convenient framework for understanding and assessing response styles is the concept of domains. As I describe in detail in Chapter 2, this volume, three broad domains encompass most attempts at dissimulation: (1) feigned mental disorders, (2) feigned cognitive abilities, and (3) feigned medical complaints/symptoms. These domains are essential to assessment of response styles, because detection strategies are rarely effective across these three domains.

Common Misconceptions about Malingering Malingering is unique among response styles in its number of associated myths and misconceptions. Rogers (1998; Rogers & Bender, 2013) outlined common fallacies about malingering held by both practitioners and the public. Common misconceptions are summarized: •• Malingering is rare. Some clinicians simply ignore the possibility of malingering, perhaps erroneously equating infrequency with inconsequentiality. Large-scale surveys of more than 500 forensic experts (Rogers, Duncan, & Sewell, 1994; Rogers, Salekin, Sewell, Goldstein, & Leonard, 1998) suggest that malingering is not rare either in forensic or clinical settings.3 When the outcome of an evaluation has important consequences, malingering should be systematically evaluated. Its professional neglect is a serious omission. •• Malingering is a static response style. Some practitioners use—at least implicitly—the flawed logic, “Once a malingerer, always a malingerer.” On the contrary, most efforts at malingering appear to be related to specific objectives in a particular context. For example, descriptive data by Walters (1988) suggest that inmates rarely feign except when hoping to achieve a highly desired goal (e.g., a single cell based on psychological reasons); among those applying for parole, many inmates understandably manifest the opposite response style (i.e., defensiveness). As a corollary to static response style, researchers have sought to establish personality characteristics linked to ma-



1. An Introduction to Response Styles 9

lingering (e.g., antisocial features; see Kucharski, Falkenbach, Egan, & Duncan, 2006). •• Malingering is an antisocial act by an antisocial person. This common misperception is perpetuated by DSM-5, which attempts to use the presence of antisocial personality disorder (ASPD) as a screening indicator for malingering. As I detail in Chapter 2 (this volume; see the section “Conceptual Issues”), this serious error arises from confusing common characteristics (e.g., criminality in criminal settings) with discriminating characteristics, which consistently differentiate malingerers from nonmalingerers. •• Deception is evidence of malingering. This fallacy is apparently based on the erroneous and illogical notion that “malingerers lie; therefore, liars malinger.” Egregious cases have been observed in which the clients’ marked minimization of symptoms (i.e., defensiveness) was misreported by a practitioner as evidence of malingering. More commonly, deceptions by manipulative inpatients or treatment-seeking inmates are mistakenly equated with malingering (Vitacco & Rogers, 2010). •• Malingering is similar to the iceberg phenomenon. Like the taint hypothesis, this misconception appears to be based on the theory that any evidence of malingering is sufficient for its classification. The erroneous assumption appears to be that any observable feigning, similar to the visible tip of an iceberg, represents a pervasive pattern of malingering. •• Malingering precludes genuine disorders. An implicit assumption is that malingering and genuine disorders are mutually exclusive. This common misconception can sometimes be detected by a careful record review. The typical two-step sequence begins with description of all symptoms as genuine. After the determination of malingering, all symptoms are dismissed as bogus. A more nuanced approach is to doubt, if not discount, all genuine impairment once any feigning has been observed; this negative bias has been observed with the previously noted taint hypothesis and performance validity (see Rogers, Chapter 2, this volume). •• Syndrome-specific feigning scales measure syndrome-specific malingering. Intuitively, mental health professionals—like all persons—would like to assume that names of psychometric scales accurately reflect their descriptions. As a straightforward example, research participants asked to feign somatic problems score high on the MMPI-

2-Restructured Form (MMPI-2-RF) Fs (Infrequent Somatic Responses). Wouldn’t that indicate Fs measures feigned somatic complaints? When using Fs cutoff scores, a very different picture emerges; it is much more likely to identify feigned mental disorders than feigned somatic complaints (Sharf, Rogers, Williams, & Henry, 2017). Clearly, syndrome-specific feigning scales must be able differentiate designated syndrome-specific feigning from generic feigning. •• Malingering has stable base rates. As reported by Rogers et al. (1998), marked variations are observed in the base rates (i.e., SD = 14.4%) for malingering across forensic settings. Even within the same setting, marked variations are likely to occur depending on the referral question and individual circumstances. Within the forensic context, the motivation to malinger is dramatically lower for a child custody determination than for an insanity case. Moreover, the assessment process itself also affects applicable base rates. When malingering measures are used with all referrals, the base rate is likely to be relatively low (e.g., 10–30%) even in forensic settings. However, when validated screens (e.g., the Miller Forensic Assessment of Symptoms Test [M-FAST]) are used to identify possible malingerers, the base rate is likely to exceed 50%. Finally, efforts to “correct” base rates in malingering studies often make unbuttressed assumptions, such as the stability of sensitivity.4 The additive effects of multiple misconceptions may fundamentally damage clinicians’ abilities to evaluate malingering and render sound judgments. The effects of inadequate evaluations can be profound for misclassified malingerers and other affected parties. When untested hunches supersede science, then the professional standing of mental health specialties is called into question.

CLINICAL AND RESEARCH MODELS Motivational Basis of Response Styles This section introduces a clinical framework for understanding response styles, such as malingering. Because most response styles are conceptualized as deliberate efforts, individual motivations become a central concern. The motivational basis for response styles, sometimes referred to as explanatory models, has far-reaching implications for clinical and forensic practice. As summarized in the subsequent paragraphs, decisions to dis-

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simulate, such as acting in socially desirable ways or feigning medical complaints, can be viewed in terms of their predicted utility. Often, selection of a particular response style is based on the options available and the desired outcome. The general category of simulated adjustment is likely the most common constellation of response styles, and it encompasses defensiveness, impression management, and social desirability. For example, the minimization of suicidal ideation may serve twin goals, each with its own predicted utility: the maintenance of a positive image and the minimization of social sanctions (e.g., civil commitment). Predicted utilities may focus on others or be predominantly self-focused. As an example of the latter, a male executive may not want to acknowledge his depression, because to do so would be a personal sign of weakness. While it is possible that such defensiveness is unconscious (see, e.g., the Self-Deceptive Enhancement scale; Paulhus, 1998), data suggest that individuals can deliberately modify their “self-deceptive” responses to achieve a desired goal (see Rogers & Bender, 2003). Within the general category of overstated pathology, conceptual and empirical work has focused primarily on malingering. Again, the prevailing model relies on expected utility. Described as the adaptational model, malingerers attempt to engage in a cost–benefit analysis in choosing to feign psychological impairment. In an analysis of malingering cases from 220 forensic experts, the cost–benefit analyses within adversarial contexts were prototypical of malingerers (Rogers et al., 1998). Two other explanatory models have been posited for malingering: pathogenic and criminological (DSM-5). Influenced by psychodynamic thinking, the pathogenic model conceptualizes an underlying disorder as motivating the malingered presentation (Rogers, Sewell, & Goldstein, 1994). The malingerers, in an ineffectual effort to control their genuine impairment, voluntarily produce symptoms. As their condition deteriorates, they presumably become less able to control the feigned disorders. A distinctive feature of the pathogenic model is this prediction of further deterioration. While immediate recovery following litigation is uncommon (i.e., accident neurosis; see Resnick, West, and Payne (2008), research does not support this “further deterioration” hypothesis. Prototypical analysis (Rogers et al., 1998) of the pathogenic model indicated that it is not representative of most malingerers, especially those found in a forensic context.

DSM classifications (American Psychiatric Association, 1980, 1987, 1994, 2000, 2013) have adopted the criminological model to explain the primary motivation for malingering. Its underlying logic is that malingering is typically an antisocial act that is likely to be committed by antisocial persons. Whether this logic is persuasive, empirical data (Rogers, 1990) strongly questioned whether its current operationalization in DSM-5 as four indicators (i.e., forensic context, antisocial background, uncooperativeness, and discrepancies with objective findings) is useful. When DSM indices are evaluated in criminal forensic settings, they are wrong approximately four out of five times. According to Rogers and Shuman (2005), the DSM indicators should not be used even as a screen for potential malingering, because they produce an unacceptable error rate. The fundamental problem with the criminological model is that it relies on common rather than distinguishing characteristics of malingering (see Rogers, Chapter 2, this volume). Most malingerers in criminal forensic settings have antisocial backgrounds and are participating in a forensic consultation. However, the same conclusion is true for many nonmalingering individuals with genuine disorders. Therefore, the criminological model is not useful with criminal forensic and correctional settings. It has yet to be tested with other populations, where it may be less common yet still not distinguish characteristics of malingerers. Returning to predominant predicted-utility model, Lanyon and Cunningham (2005) provide an elegant example of how this model can apply across both domains and response styles. Simulators may attempt to maximize the predicted utility of their efforts by using both overstated pathology (e.g., malingering psychiatric symptoms and health problems) and simulated adjustment (e.g., exaggerating their personal virtues). The latter response style may serve two-related goals: (1) enhance the credibility of the disability claim (e.g., good citizens do not file false insurance reports) and (2) emphasize the magnitude of the purported loss (e.g., the avoidable suffering of an upstanding citizen).

Overview of Research Designs Many skilled practitioners and experienced researchers benefit from a quick overview of research designs as they related to response styles. This brief section highlights key differences in designs and their relevance to clinical practice; for a more



1. An Introduction to Response Styles 11

extensive treatment of response styles, see Rogers (Chapter 30, this volume). Together with Rogers (Chapter 2, this volume), this summary should facilitate the sophisticated use of response style measures presented in subsequent chapters. Four basic research designs are used in most studies of response styles (see Table 1.1). Two basic designs complement each other with their respective strengths: Simulation designs can provide unparalleled control over internal validity, whereas known-group comparisons are unequalled in their consideration of external validity (Rogers & Gillard, 2011). Because of the challenges in establishing the independent categorization required for known-group comparisons, two other designs have been introduced. These designs differ markedly in methodological rigor, from patently simplistic (i.e., differential prevalence design) to potentially sophisticated (partial criterion). The following paragraphs describe these four basic designs and provide salient examples of how each may be misused by clinicians. Simulation Design

Most research on response styles relies on simulation designs that use an analogue design, which may be augmented by additional samples (see Rogers, Chapter 29, this volume). As noted in Table 1.1, this research often has excellent internal validity, using standardized methods and relying partly on an experimental design, with the random assignment of participants to different experimental conditions. In most malingering studies, for example, community participants are randomly assigned to feigning and control (honest) conditions. To address the critical issue (genuine vs. feigned disorders), the feigning group is typically compared to a nonrandom clinical sample of convenience. The inclusion of clinical comparison groups can become more challenging for research on simulated adjustment. For example, Stein and Rogers (2008; Stein, Rogers, & Henry, Chapter 8, this volume) found that face valid screens may appear to be highly effective when administered to selfdisclosing substance abusers, but understandably, fail utterly when completed by denying substance abusers. For parents in child custody disputes, the key issue in establishing clinical comparison groups is how to distinguish “normal” parents presenting with social desirability from psychologically impaired parents engaging in defensiveness. The lack of an operationalized, clinical com-

parison sample represents a fundamental flaw in simulation research. This fundamental flaw is summarized in Box 1.2, which illustrates how simulation research can be confounded by the absence of relevant clinical samples. For feigning, MMPI-2 research has clearly demonstrated that patients with genuine PTSD can demonstrate extreme elevations on the F scale when responding honestly (Rogers, Sewell, Martin, & Vitacco, 2003). For denied psychopathy, offenders can easily suppress their psychopathy scores on self-report measures below the levels found in undergraduates (Kelsey, Rogers, & Robinson, 2015). In both examples, the failures to include relevant clinical comparison groups represent fundamental oversights in methodology. Known-Groups Comparisons

This design has been increasingly implemented, spurred by rigorously validated measures for feigned mental disorders (e.g., Structured Interview of Reported Symptoms [SIRS and SIRS-2]) and a very stringent detection strategy for feigned cognitive impairment. Regarding the latter, the detection strategy of significantly below chance performance (SBCP; see Chapter 2) can provide compelling data on feigning. To minimize misclassifications, it is critically important to remove an indeterminant group, which in this case includes protocols from slightly below to slightly above chance performance. Performance in this indeterminate range may reflect severe impairment, disengagement (e.g., filling in responses without reference to the test items), or feigning. As noted in Table 1.1, known-groups comparisons should strive for high classification rates (≥ 90%) in order to earn the designation of “known groups.” In doing so, the removal of too-close-tocall cases is essential to minimize both measurement and classification errors (see Rogers, Chapter 2, this volume). It is also imperative to completely mask researchers administering the target measures from any data about known groups. Otherwise, the study fails because of criterion contamination. Differential Prevalence Design

Because of challenges in establishing knowngroups comparisons, this design attempts to substitute an expedient proxy, such as referral status for well-established criteria. As a common example, researchers might lump all clients with litigation

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I.  Concep t ua l Fr a me work TABLE 1.1.  Researching Response Styles: An Overview of Basic Designs

1.  Simulation research a.  Description. Analogue research randomly assigns participants to different experimental conditions. Results are typically compared to relevant clinical groups. b.  Internal validity. Strong: Procedures include standardized instructions, operationalized conditions, incentives, and manipulation checks. c.  External validity. Weak: Participants do not face the often grave circumstances and consequences of succeeding or failing at a particular response style. d.  Classification. Effectively tested: With cross-validation, the accuracy of classification can be evaluated against the experimental condition for specific response styles. 2.  Known-groups comparison a.  Description. The objective is the establishment of highly accurate (≥ 90%) independent classifications of known groups in clinical or other professional settings. Initially, experts using the state-of-the-art methods were used to establish known groups. More recently, rigorous measures of response styles have been implemented with one important caveat: An indeterminant group must be excluded, so that a rigorous standard (≥ 90%) for classification can be achieved (Rogers & Gillard, 2011). b.  Internal validity. Comparatively weak: Researchers have no control over experimental assignment or investment in the investigation (e.g., manipulation checks). However, standardized procedures with aprioristic decision rules can provide systematic data. c.  External validity: Exceptionally strong: The participants, settings, issues and incentives are consistent real-world considerations. d.  Classification. Effectively tested: With cross-validation, the accuracy of classification can be evaluated for specific response styles, often by using rigorous measures and excluding an indeterminate group. 3.  Differential prevalence design a.  Description. Based on assumed incentives, greater numbers of a broadly defined group (e.g., litigation) are presumed to have a specific response style when compared to a second group (e.g., nonlitigation). b.  Internal validity. Weak: Researchers have no control over experimental assignment or other standardized procedures. c.  External validity. Weak to moderate: Participants are often involved in real-world consultations facing important consequences. These consequences could possibly influence the decision to engage in a specific response style. However, the complete lack of any independent classification of response styles stymies the ability to test its effectiveness. When tested empirically, differential prevalence design has produced unacceptably weak effect sizes (e.g., MMPI-2 meta-analysis on feigning yielded a mean d of merely 0.43; Rogers, Sewell, Martin, & Vitacco, 2003). d.  Classification. Untestable: Without knowing group membership, the accuracy of classification is impossible to establish. 4.  Partial criterion design a a.  Description. By using multiple scales or indicators, researchers seek to increase the likelihood of an accurate classification. The goal is to achieve a moderate level of classification, perhaps ≥ 75%. As a partial criterion, it sacrifices accuracy for more expedient research. b.  Internal validity. Weak: Researchers have no control over experimental assignment or other standardized procedures. c.  External validity. Moderately strong when conducted with the appropriate clinical samples. The participants, settings, issues and incentives fit real-world considerations. d.  Classification. Variable: The greatest risk is false positives, because an unknown percentage of classified dissimulators (e.g., deniers of substance abuse) do not warrant this classification.

aThe

partial criterion design was previously described as a “bootstrapping comparison” (Rogers, 2008).



1. An Introduction to Response Styles 13

BOX 1.2. Examples of Flawed Simulation Designs 1. Feigning studies without clinical comparison samples: Researchers do not know whether elevations whether feigners’ scores are any different from genuine responders with severe disorders. 2. Studies of psychopathy on self-report mea‑ sures without a clinical comparison group of defensive psychopaths. Researchers do not know whether their confidentiality-protected responses have any practical relevance to psychopaths practicing general deception or goal-oriented defensiveness.

into a “suspected feigning” group and all nonlitigating clients into a “genuine” group. Such simplism should not be tolerated in clinical research, although it may play a marginal role in advancing theory.5 The fundamental and fatal weaknesses of differential prevalence design can be convincingly illustrated with respect to interpersonal violence. Research (e.g., Whiting, Simmons, Havens, Smith, & Oka, 2009) has clearly supported the intergenerational influences on violence. But, put bluntly, would any self-respecting professional use childhood victimization with violence as an expedient proxy for categorizing all childhood victims as violent persons? However, this use of an expedient proxy is still occasionally applied to feigning; that is, all litigation equals faking, and all nonlitigation equals honest responding. Why should the differential prevalence design be categorically excluded from the classification of response styles? Even when base rates and results appear to be favorable, the fatal weakness of this design prevents its clinical use. For example, using a high estimate of malingering for forensic referrals (32%)6 does not help. It is unlikely but possible that 0.0% of malingerers were identified (i.e., all high scores are false positives); it is also as possible but even less likely that 100% of malingerers were identified. On average, we would expect that about two-thirds (100% – 32% = 68%) of the socalled “malingerers” would be wrongly classified. Partial Criterion

Researchers often provide an external criterion that is limited in its accuracy. Clearly, such research should not be equated with a known-groups comparison, simply because the accuracy of the

classification is not known. Formerly, this design was termed “bootstrapping comparison” (using one measure to improve another measure; see Rogers, 1997). More recently, Rogers and Bender (2013) recommended a more descriptive name: partial criterion design. As noted in Table 1.1, the external measure should have moderately good classification abilities, perhaps ≥ 75%. Rather than simply using the term external criterion for all levels of accuracy, researchers are provided with two designations: known-groups (high accuracy in group membership) and partial criterion (perhaps ≥ 75% accuracy in group membership). Because of its limited accuracy, the partial criterion design should not be used to evaluate utility estimates. Some readers may wonder whether both terms are really needed. As a brief illustration of the issue, Tarescavage and Glassmire (2016) described their design as a “criterion groups” comparison in examining sensitivities between the SIRS and SIRS-2. However, their primary “criterion” measure consisted of a brief feigning screen, specifically, the M-FAST (Miller, 2001). Given the purpose of the M-FAST as a screen, the designation “partial criterion” design would have accurately described this study. Determinations of response styles represent a complex, multifaceted process that includes domains, detection strategies, and measures. A critical first step in mastering assessment methods is the accurate identification of the four basic designs for dissimulation research. Knowledge of these designs allows practitioners to develop a sophisticated appreciation of empirical findings and their clinical relevance. In addition to understanding their respective strengths, mental health professionals must also be able to recognize faulty designs for clinical classification (i.e., the differential prevalence design) and flawed applications to dissimulation research.

LOOKING FORWARD This book is organized into six major sections that provide a logical progression in the examination of malingering and other forms of deception. Although chapters vary substantially in their content and scope, a unifying theme is the integration of research, theory, and clinical practice. As will become evident, chapters vary in their success at achieving this integration. This variability accurately reflects the strengths and weaknesses in our

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knowledge of response styles. For example, hundreds of studies have examined feigned mental disorders. In contrast, denial of medical complaints is a vast but largely uncharted territory. Understandably, the integration of research and clinical practice will be substantively different between well-established (e.g., feigned mental disorders) and recently considered (e.g., denial of medical complaints) areas of dissimulation research. The overriding goal of most chapters is the provision of clear, usable information that impacts directly on professional practice and clinical research. Whenever possible, specific guidelines are provided regarding the clinical applications of particular measures, scales, and detection strategies. Some dissimulation scales are especially useful for the ruling in (i.e., identification and classification) specific response styles. Other scales may serve an important purpose for the ruling out one or more response styles. When accomplished efficiently, such measures are very useful as screens. Despite our positive focus on advances in the assessment of response styles, we also consider common missteps and inaccuracies that may lead to grievous errors in the determination of dissimulation. Part I, Conceptual Framework, comprises the first four chapters, which operationalize response style terms and provide a conceptual basis for remaining chapters. The centerpiece of Chapter 2 is the description of detection strategies that are organized by response styles and domains. This examination of detection strategies constitutes the essential template for the remaining chapters. As evidence of its growing importance, Chapter 3 delves more closely into different neuropsychological models of feigning. Finally, Chapter 4 recognizes transnational growth in dissimulation research, examining issues of language and culture, and their effects on the assessment of response style. Part II, Diagnostic Issues, comprises nine chapters that address a range of disorders and syndromes for which dissimulation can become a central concern. Chapter 5 provides a broad and valuable overview of specific syndromes and clinical conditions that are frequently associated with dissimulation. Chapters 6 through 13 examine specifically diagnostic categories in which response styles are often considered, especially when consultations have significant financial or forensic relevance. Feigned psychosis (Chapter 6) and denied psychopathy (Chapter 9) represent critically important issues, especially in forensic assessments. Chapters 7 and 10 address very different aspects of traumatic events that may pro-

foundly affect neurocognitive functioning as well as produce psychological reactions, such as PTSD. Chapter 8 is essential to most professional practices, given the nearly endemic substance abuse and its widespread denial. Chapter 11 focuses on nearneighbor comparisons in distinguishing factitious presentations from the closely related construct of malingering. Finally, Chapters 12 and 13 broaden the scope of response styles to consider conversion disorders and deceptive medical presentations. Part III, Psychometric Methods comprises five chapters. Given the breadth and sophistication of dissimulation research, multiscale inventories and feigned cognitive impairment are covered in multiple chapters. In particular, each area is subdivided into two chapters: MMPI-2 and MMPI2-RF (Chapter 14), and the Personality Assessment Inventory and other inventories (Chapter 15). Likewise, cognitive feigning is organized into two chapters: memory and amnesia (Chapter 17), and neuropsychological measures (Chapter 18). Finally, Chapter 16 covers the controversies and clinical data concerning response styles and the use of projective methods. Part IV, Specialized Methods, also comprises five chapters. The usefulness of physiological and other standardized measures is considered in relationship to lie detection (Chapter 19) and sexual deviation (Chapter 21). With continued controversies, Chapter 20 discusses the usefulness and limitations of clinical methods used for the recovery of early memories. Finally, structured interviews (Chapter 22) and brief measures (Chapter 23) make substantial contributions to the assessment of response styles. In Part V, Specialized Applications, chapters are devoted to specific populations and applications. Youth (Chapter 24) and custody and family issues (Chapter 25) are discussed in relationship to response styles. Chapter 26 examines how law professionals learn and are sometimes misled with respect to malingering. Regarding deception and the workplace, Chapter 27 examines this broad and challenging topic, whereas Chapter 28 deals specifically with law enforcement. Part VI, Summary, has an integrative goal of bringing together common and diverse findings across the considerable array of chapters. Chapter 29 summarizes the key conclusions and provides useful guidelines for conducting evaluations of response styles. Chapter 30, presents detailed guidelines—when empirically warranted—on recommended practices for researching malingering and deception. Importantly, it seeks to improve



1.  An Introduction to Response Styles 15

our research methods to more effectively study the complex issues surrounding dissimulation.

NOTES 1.  As an implicit example, a report of malingering during adolescence was used as “evidence” decades later to corroborate the current classification of malingering. 2.  Kirkley (2008) represents a rare attempt to examine the effects of a malingering classification within the context of a disability case. She found that testimony on malingering strongly affected the damage awards but not the decision itself. 3.  The two surveys of mostly forensic psychologists yielded similar data for forensic (Ms of 15.7 and 17.4%) and nonforensic (Ms of 7.2 and 7.4%) referrals. However, the percentages for nonforensic cases may be skewed higher, because forensic practitioners often consult on nonforensic issues that are still highly consequential to clients (e.g., insurance disability claims). 4. These efforts implicitly assume that sensitivity is a stable estimate, whereas positive predictive power (PPP) is not. Although PPP does vary in relationship to base rates, sensitivity also evidences nonsystematic variability). 5.  More precisely, this design would be best used to discount a hypothesized relationship if predicted findings are not observed. 6.  Rogers et al. (1998) used estimates from 221 highly experienced forensic experts. For forensic referrals, the 32% prevalence assumes a rate that is approximately one standard deviation above the Rogers et al. average (M = 17.44%, SD = 14.44%).

REFERENCES American Psychiatric Association. (1980). Diagnostic and statistical manual of mental disorders (3rd ed.). Washington, DC: Author. American Psychiatric Association. (1987). Diagnostic and statistical manual of mental disorders (3rd ed., rev.). Washington, DC: Author. American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: Author. American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed., text rev.). Washington, DC: Author. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington, VA: Author. Baumann, E. C., & Hill, C. E. (2016). Client concealment and disclosure of secrets in outpatient psychotherapy. Counselling Psychology Quarterly, 29(1), 53–75.

Bersoff, D. N. (2008). Ethical conflicts in psychology (4th ed.). Washington, DC: American Psychological Association. Blanchard, M., & Farber, B. A. (2016). Lying in psychotherapy: Why and what clients don’t tell their therapist about therapy and their relationship. Counselling Psychology Quarterly, 29(1), 90–112. Boon, S. D., & McLeod, B. A. (2001). Deception in romantic relationships: Subjective estimates of success at deceiving and attitudes toward deception. Journal of Social and Personal Relationships, 18(4), 463–476. Burchett, D., Dragon, W. R., Smith-Holbert, A. M., Tarescavage, A. M., Mattson, C. A., Handel, R. W., et al. (2016). “False feigners”: Examining the impact of non-content-based invalid responding on the Minnesota Multiphasic Personality Inventory–2 Restructured Form content-based invalid responding indicators. Psychological Assessment, 28(5), 458–470. Carsky, M., Selzer, M. A., Terkelsen, K. G., & Hurt, S. W. (1992). The PEH: A questionnaire to assess acknowledgment of psychiatric illness. Journal of Nervous and Mental Disease, 180, 458–464. De Lorenzo, M. S. (2013). Employee mental illness: Managing the hidden epidemic. Employee Responsibilities and Rights Journal, 25(4), 219–238. Ellison, M. L., Russinova, Z., MacDonald-Wilson, K. L., & Lyass, A. (2003). Patterns and correlated of workplace disclosure among professionals and managers with psychiatric conditions. Journal of Vocational Rehabilitation, 18, 3–13. Godinho, A., Kushnir, V., & Cunningham, J. A. (2016). Unfaithful findings: Identifying careless responding in addictions research. Addiction, 111(6), 955–956. Greene, R. L. (2000). The MMPI-2: An interpretive manual (2nd ed.). Needham Heights, MA: Allyn & Bacon. Gudjonsson, G. H., & Young, S. (2011). Personality and deception: Are suggestibility, compliance and acquiescence related to socially desirable responding? Personality and Individual Differences, 50(2), 192–195. Hall, J. A. (2011). Sex differences in friendship expectations: A meta-analysis. Journal of Social and Personal Relationships, 28(6), 723–747. Jourard, S. M. (1971). Self-disclosure: An experimental analysis of the transparent self. New York: Wiley-Interscience. Kelsey, K. R., Rogers, R., & Robinson, E. V. (2015). Selfreport measures of psychopathy: What is their role in forensic assessments? Journal of Psychopathology and Behavioral Assessment, 37, 380–391. King, M. F., & Bruner, G. C. (2000). Social desirability bias: A neglected aspect of validity testing. Psychology and Marketing, 17, 79–103. Kirkley, S. M. (2008). The impact of neuropsychological testimony: Malingering, cognitive impairment, and language complexity. Unpublished doctoral dissertation, University of Alabama, Tuscaloosa, AL. Kroger, R. O., & Turnbull, W. (1975). Invalidity of valid-

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ity scales: The case of the MMPI. Journal of Consulting and Clinical Psychology, 43(1), 48–55. Kucharski, L. T., Falkenbach, D. M., Egan, S. S., & Duncan, S. (2006). Antisocial personality disorder and the malingering of psychiatric disorder: A study of criminal defendants. International Journal of Forensic Mental Health, 5(2), 195–204. Jones, K. P., & King, E. B. (2014). Managing concealable stigmas at work: A review and multilevel model. Journal of Management, 40(5), 1466–1494. Lanyon, R. I. (2001). Dimensions of self-serving misrepresentation in forensic assessment. Journal of Personality Assessment, 76(1), 169–179. Lanyon, R. I., & Cunningham, K. S. (2005). Construct validity of the misrepresentation scales of the Psychological Screening Inventory. Journal of Personality Assessment, 85, 197–206. Laurenceau, J.-P., Barrett, L. F., & Rovine, M. J. (2005). The interpersonal process model of intimacy in marriage: A daily-diary and multilevel modeling approach. Journal of Family Psychology, 19, 314–323. Leary, M. R., & Kowalski, R. M. (1990). Impression management: A literature review and two component model. Psychological Bulletin, 107, 34–47. Levitt, H. M., Minami, T., Greenspan, S. B., Puckett, J. A., Henretty, J. R., Reich, C. M., et al. (2016). How therapist self-disclosure relates to alliance and outcomes: A naturalistic study. Counselling Psychology Quarterly, 29(1), 7–28. Miller, H. A. (2001). MFAST: Miller Forensic Assessment of Symptoms Test professional manual. Lutz, FL: Psychological Assessment Resources. Norton, K. A., & Ryba, N. L. (2010). An investigation of the ECST-R as a measure of competence and feigning. Journal of Forensic Psychology Practice, 10(2), 91–106. Paulhus, D. L. (1998). Paulhus Deception Scales (PDS): The Balanced Inventory of Desirable Responding–7. North Tonawanda, NY: Multi-Health Systems. Resnick, P. J., West, S., & Payne, J. W. (2008). Malingering of posttraumatic disorders. In R. Rogers (Ed.), Clinical assessment of malingering and deception (3rd ed., pp. 109–127). New York: Guilford Press. Rogers, R. (1984). Towards an empirical model of malingering and deception. Behavioral Sciences and the Law, 2, 93–112. Rogers, R. (1997). Researching dissimulation. In R. Rogers (Ed.), Clinical assessment of malingering and deception (2nd ed., pp. 398–426). New York: Guilford Press. Rogers, R. (1998). Assessment of malingering on psychological measures: A synopsis. In G. P. Koocher, J. C., Norcross, & S. S. Hill, III (Eds.), Psychologist’s desk reference (pp. 53–57). New York: Oxford University Press. Rogers, R. (1990). Models of feigned mental illness. Professional Psychology, 21, 182–188. Rogers, R. (2008). Researching response styles. In R. Rogers (Ed.), Clinical assessment of malingering and

deception (3rd ed., pp. 411–434). New York Guilford Press. Rogers, R., & Bender, S. D. (2003). Evaluation of malingering and deception. In A. Goldstein & I. B. Weiner (Eds.), Handbook of psychology (Vol. 11, pp. 109–129). New Jersey: Wiley Rogers, R., & Bender, S. D. (2013). Evaluation of malingering and related response styles. In R. K. Otto, I. B. Weiner, R. K. Otto, & I. B. Weiner (Eds.), Handbook of psychology: Forensic psychology (pp. 517–540). Hoboken, NJ: Wiley. Rogers, R., Duncan, J. C., & Sewell, K. W. (1994). Prototypical analysis of antisocial personality disorder: DSM-IV and beyond. Law and Human Behavior, 18, 471–484. Rogers, R., & Gillard, N. D. (2011). Research methods for the assessment of malingering. In B. Rosenfeld, S. D. Penrod, B. Rosenfeld, & S. D. Penrod (Eds.), Research methods in forensic psychology (pp. 174–188). Hoboken, NJ: Wiley. Rogers, R., Jackson, R. L., & Kaminski, P. L. (2004). Factitious psychological disorders: The overlooked response style in forensic evaluations. Journal of Forensic Psychology Practice, 3, 115–129. Rogers, R., & Neumann, C. S. (2003). Conceptual issues and explanatory models of malingering. In P. W. Halligan, C. Bass, & D. A. Oakley (Eds.), Malingering and illness deception: Clinical and theoretical perspectives (pp. 71–82). Oxford, UK: Oxford University Press. Rogers, R., & Payne, J. W. (2006). Damages and rewards: Assessment of malingered disorders in compensation cases. Behavioral Sciences and the Law, 24, 645–658. Rogers, R., & Reinhardt, V. (1998). Conceptualization and assessment of secondary gain. In G. P. Koocher, J. C. Norcross, & S. S. Hill (Eds.), Psychologist’s desk reference (pp. 57–62). New York: Oxford University Press. Rogers, R., Salekin, R. T., Sewell, K. W., Goldstein, A., & Leonard, K. (1998). A comparison of forensic and nonforensic malingerers: A prototypical analysis of explanatory models. Law and Human Behavior, 22, 353–367. Rogers, R., Sewell, K. W., Drogin, E. Y., & Fiduccia, C. E. (2012). Standardized Assessment of Miranda Abilities (SAMA) professional manual. Lutz, FL: Psychological Assessment Resources. Rogers, R., Sewell, K. W., & Goldstein, A. (1994). Explanatory models of malingering: A prototypical analysis. Law and Human Behavior, 18, 543–552. Rogers, R., Sewell, K. W., Martin, M. A., & Vitacco, M. J. (2003). Detection of feigned mental disorders: A meta-analysis of the MMPI-2 and malingering. Assessment, 10(2), 160–177. Rogers, R., & Shuman, D. W. (2005). Fundamentals of forensic practice: Mental health and criminal law. New York: Springer. Rogers, R., Tillbrook, C. E., & Sewell, K. W. (2004).



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Evaluation of Competency to Stand Trial—Revised (ECST-R). Lutz, FL: Psychological Assessment Resources. Roggensack, K. E., & Sillars, A. (2014). Agreement and understanding about honesty and deception rules in romantic relationships. Journal of Social and Personal Relationships, 31(2), 178–199. Sandal, G. M., van de Vijver, F., Bye, H. H., Sam, D. L., Amponsah, B., Cakar, N., et al. (2014). Intended selfpresentation tactics in job interviews: A 10-country study. Journal of Cross-Cultural Psychology, 45(6), 939–958. Sharf, A. J., Rogers, R., Williams, M. M., & Henry, S. A. (2017). The effectiveness of the MMPI-2-RF in detecting feigned mental disorders and cognitive deficits: A meta-analysis. Journal of Psychopathology and Behavioral Assessment, 39(3), 441–455. Stein, L. R., & Rogers, R. (2008). Denial and misreporting of substance abuse. In R. Rogers (Ed.), Clinical assessment of malingering and deception (3rd ed., pp. 87–108). New York: Guilford Press. Tarescavage, A. M., & Glassmire, D. M. (2016). Differences between Structured Interview of Reported Symptoms (SIRS) and SIRS-2 sensitivity estimates

among forensic inpatients: A criterion groups comparison. Law and Human Behavior, 40(5), 488–502. Thornton, B., Lovley, A., Ryckman, R. M., & Gold, J. A. (2009). Playing dumb and knowing it all: Competitive orientation and impression management strategies. Individual Differences Research, 7(4), 265–271. Viswesvaran, C., & Ones, D. S. (1999). Meta-analysis of fakability estimates: Implications for personality measurement. Educational and Psychological Measurement, 59, 197–210. Vitacco, M. J., & Rogers, R. (2010). Assessment of malingering in correctional settings. In C. L. Scott & J. B. Gerbasi (Eds.), Handbook of correctional mental health (2nd ed., pp. 255–276). Washington, DC: American Psychiatric Publishing. Walters, G. D. (1988). Assessing dissimulation and denial on the MMPI in a sample of maximum security, male inmates. Journal of Personality Assessment, 52(3), 465–474. Whiting, J. B., Simmons, L. A., Havens, J. R., Smith, D. B., & Oka, M. (2009). Intergenerational transmission of violence: The influence of self-appraisals, mental disorders and substance abuse. Journal of Family Violence, 24(8), 639–648.

CHAPTER 2

Detection Strategies for Malingering and Defensiveness Richard Rogers, PhD

This chapter introduces detection strategies and provides a conceptual framework for understanding their development and validation. In this context, five essential criteria of detection strategies are examined. The second major section provides an overview of detection strategies as they are applied to specific response styles. This latter section is intended as a template for understanding the specific contributions provided in subsequent chapters. It briefly addresses the assumptions of posttest probabilities. The approach is briefly introduced in the this chapter and comprehensively examined by Bender and Frederick (Chapter 3, this volume).

CONCEPTUAL ISSUES The modern era for the systematic assessment of response styles was heralded by the empirical development of the Minnesota Multiphasic Personality Inventory (MMPI; Hathaway & McKinley, 1940). Seminal efforts on the MMPI relied on discriminating items that were uncharacteristic of normative populations. Simplest in construction was the F scale that merely relies on MMPI items infrequently endorsed by the Minnesota normative samples. Unlike its current use in determina 18

tions of feigned mental disorders, the F scale was originally intended as a measure of “carelessness and misunderstanding” (Meehl, 1946, p. 517). Lacking any conceptual underpinnings, interpretations of F scale elevations cover the gamut, from attentional difficulties and poor reading comprehension to psychotic interference, hostile noncompliance, and deliberate feigning (Dalstrom, Welsh, & Dahlstrom, 1972). Early MMPI versions went beyond carelessness to evaluate core aspects of simulated adjustment, including social desirability and defensiveness (see Rogers, Chapter 1, this volume). The L, or Lie, scale was originally intended to “identify deliberate or intentional efforts to avoid answering the test frankly and honestly” (Dalstrom et al., 1972, p. 108). However, a closer inspection of its item content reveals principally a measure of social desirability. Modeled after Hartshorne and May (1928), Hathaway and McKinley (1940) constructed 15 items that involved the denial of personal faults and foibles. As evidence that these faults are widely observed, normative samples typically endorse two-thirds of them as “true.” In contrast to the L scale, the K scale evaluates defensiveness (i.e., the denial of psychological impairment). When McKinley, Hathaway, and Meehl (1948, p. 20) observed “normal profiles” in clinically diag-



2.  Detection Strategies for Malingering and Defensiveness 19

nosed samples, they assumed that it was “suggestive of a defensive attitude in the patient’s responses.” What lessons can be learned from the MMPI validity scales? First, the initial conceptualization of the detection strategy is paramount to its subsequent interpretation. The clarity of the K scale development and interpretation is easily contrasted with the interpretational challenges faced by the F scale, which lacks sufficient conceptual underpinnings. Second, the selection of criterion groups dictates the precision of subsequent interpretations. Using the F scale as an example, scales developed simply on normative samples are fundamentally confounded as measures of malingering. Without clinical samples, test developers do not know whether the elevations could result from either feigned or genuine psychopathology. Despite its limitations, the MMPI represents the first critical stage in the development of empirically based detection strategies for response styles (Rogers & Gillard, 2011). Prior to the MMPI, most assessments of malingering and other response styles relied on unproven methods based on case studies. Two major pitfalls can easily arise from relying solely on case studies. First, without systematic investigations, clinicians can unwittingly engage in a tautological exercise: “salient” characteristics of malingering are identified with reference to suspected malingerers, who are thus identified on the basis of these “salient” characteristics (i.e., a classic example of confirmatory bias; Borum, Otto, & Golding, 1991). Second, common features of malingering can be mistaken for discriminating characteristics. Despite the development of empirically validated detection strategies, described later, common features versus discriminating characteristics remain a fundamental problem, especially for the assessment of malingering. DSM screening indices for malingering,1 first established more than 25 years ago (American Psychiatric Association, 1980), continue to misuse two common features of malingering as if they are discriminating characteristics (American Psychiatric Association, 2013). Although malingerers are commonly found in forensic evaluations and often have antisocial backgrounds, the facile use of these two common indices can produce disastrous inaccuracies. In criminal forensic evaluations, all individuals are involved in a forensic evaluation, with many also warranting the diagnosis of antisocial personality disorder (ASPD). For the purpose of this discussion, let us make two simple assumptions: (1) The

prevalence of malingering is about 20% based on extensive surveys (see Rogers, Chapter 1, this volume), and (2) the prevalence of ASPD is 50%. In conducting 1,000 forensic evaluations, 100 of 200 malingerers would be correctly identified. However, 400 of the 800 genuine responders would be wrongly classified. Being wrong four out of five times is simply catastrophic. This hypothetical example appears similar to what has been found empirically (see Rogers, 1990). This simple analysis demonstrates the devastating consequences of confusing common features with discriminating characteristics. The key distinction between common and distinguishing characteristics is illustrated by the following descriptions: • Common features, sometimes described as “clinical correlates,” are often observed in individuals with particular response styles. Even when frequencies exceed 50%, common clinical correlates do not assist in accurate classification. • Distinguishing characteristics refer to specific clinical features that reliably differentiate between relevant groups. They can facilitate accurate classification. The earlier example regarding ASPD and malingering in criminal forensic evaluations clearly illustrates why common characteristics should not be used for accurate classifications. Moreover, most clinical correlates are relatively modest (e.g., rs < .40), which markedly diminishes their practical value. Even when correlates are high (e.g., rs > .70), they do not assist in accurate classification, because other clinical conditions or other response styles may have correlations of a similar magnitude. Using the Millon Clinical Multiaxial Inventory–III (MCMI-III; Millon, 1994) as an example, Scale Z (Debasement Index or faking bad) correlates at .60 or above with six clinical scales; these same scales also correlate with other clinical scales and with Scale X (Disclosure Index or willingness to disclose problems) at the same magnitude.2 An important outgrowth of this focus on discriminating characteristics resulted in the careful formulation of detection strategies. The next major section describes empirically validated detection strategies for common response styles, including a critical examination of their applicability to professional practice.

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Description of Detection Strategies Discriminating characteristics are typically specific to a particular scale and cannot—without systematic research—be generalized to other assessment methods. For example, early research on the MMPI L scale suggested that the denial of personal shortcomings and foibles may be useful in the assessment of defensiveness. Does this finding qualify as a detection strategy? As a conditional answer, this approach only becomes a detection strategy when it has been clearly conceptualized, operationalized in terms of specific items, and rigorously tested with multiple measures across multiple settings. Building on earlier conceptualizations (e.g., Rogers, Harrell, & Liff, 1993; Rogers & Bender, 2013), the general definition of detection strategies is presented in Box 2.1. This definition includes five critical criteria, specifically (1) standardized method, (2) conceptual basis, (3) empirical validation, (4) systematic differentiation, and (5) a specific response style. Each component is briefly examined as follows: 1.  Standardized methods are essential to all scientific endeavors. Detection strategies must be operationalized to provide tailored items plus systematic scoring and administration, so that their results can be rigorously tested and cross-validated. 2. A conceptual basis must be described in order to test the underlying rationale for a specific detection strategy and evaluate competing hypotheses. Without a well-defined construct, research may be squandered on an atheoretical approach with difficult-to-interpret results. 3.  Empirical validation focuses on the use of proven methodology to establish the validity of a specific detection strategy. As I summarized in Chapter 1, the empirical validation of detection strategies optimally includes both simulation designs and known-group comparisons. It avoids flawed methodology (e.g., differential prevalence rates) and unsuitable comparisons (e.g., contrasting feigned vs. unimpaired protocols). Other BOX 2.1.  Definition of Detection Strategies A detection strategy is a conceptually based, empirically validated standardized method for systematically differentiating a specific response style (e.g., malingering or defensiveness) from other response styles (e.g., honest responding and irrelevant responding).

important methodological considerations are summarized in Rogers and Gillard (2011) and in Rogers (Chapter 29, this volume). 4.  Systematic differentiation is centered on estimations of accuracy. By itself, statistical significance constitutes an inadequate proxy for accuracy. Many studies of response styles yield results with a high probability of statistical significance but have very little practical utility in professional practice. Instead, the magnitude of difference is the critical issue (Wilkinson & the Task Force on Statistical Inference, 1999). Because of its clarity, this book uses Cohen’s d as the standard measure of effect sizes.3 Beyond effect sizes, the sine qua non of clinical accuracy is level of individual classification. Utility estimates are used to calculate the probabilities that cutoff scores can correctly identify a specific response style (see Streiner, 2003). 5. The delineation of a specific response style (SRS) is essential to the accurate interpretation of results. For example, some research on malingered cognitive impairment attempts to substitute suboptimal effort for malingering. This construct drift (i.e., broadening the conceptualization of malingering to embrace any manifestation of inadequate motivation) results in imprecise and likely misleading results. Researchers and clinicians must verify that criterion groups and instructional sets correspond to the specific response styles under consideration. What is a good detection strategy? In meeting the five previously mentioned criteria, the detection strategy should be cross-validated with different measures and consistently produce large effect sizes and accurate classifications (Rogers & Bender, 2013). If not effective across different measures, then the detection strategy is not sufficiently established and may be capitalizing on idiosyncratic features of one particular scale. If the effect sizes are only moderate, then the detection strategy is comparatively ineffective and should be avoided, unless it can be accurately applied for a circumscribed goal, such as ruling out a response style. Importantly, detection strategies for response styles must be targeted, focusing on a specific response style within a particular domain. Indeed, more recently, researchers have attempted to focus more specifically on the faking of specific diagnoses. For example, an MMPI-2 scale was developed to specifically evaluate feigned posttraumatic stress disorder (PTSD; i.e., FPTSD scale; Elhai, Ruggiero, Frueh, Beckham, & Gold, 2002).



2.  Detection Strategies for Malingering and Defensiveness 21

Focused Nature of Detection Strategies A fundamental principle is that detection strategies are not universal but must be considered within specific response styles and well-defined domains. Unquestionably, different detection strategies are needed for the evaluation of dissimilar response styles. For example, the assessment of malingering on the MMPI-2 (Rogers, Sewell, Martin, & Vitacco, 2003) uses very different detection strategies than those for the evaluation of defensiveness (Baer & Miller, 2002). Although an inverse relationship between malingering and defensiveness may occur (see, e.g., the bipolarity hypothesis; Greene, 2011), detection strategies focused on a specific response style have proven to be the most effective. To illustrate this point with the MMPI-2, Rogers et al. (2003) found large to very large effect sizes for validity scales based on detection strategies for feigning. In stark contrast, the absence of defensiveness (i.e., low scores on scales using its detection strategies) generally produced only small to moderate effect sizes for feigned responding. Detection strategies must also take into account the broad domains in which specific response styles commonly occur. Three broad domains include mental disorders, cognitive abilities, and medical presentations (Rogers & Bender, 2013). Consider malingering. Individuals feigning a schizophrenic disorder are faced with a very different task than those feigning an intellectual disability. With feigned schizophrenia, malingerers must create believable sets of symptoms and associated features. To be sophisticated, feigners must also decide on the course of the current episode, its concomitant impairment, and their insight into their disorder (e.g., awareness that psychotic behaviors are symptoms). In contrast, persons feigning an intellectual disability must put forth a convincing effort while failing on intellectual and cognitive measures. To be sophisticated, these feigners must also decide not only how badly to fail but on which measures, and how such failures should affect their day-today functioning. Because the tasks of malingerers are dissimilar, different detection strategies are needed. The medical domain is far more complex than either mental disorders or cognitive abilities. With medical malingering, patients can specialize in one debilitating symptom (e.g., pain), portray a constellation of common but distressing ailments (e.g., headaches, fatigue, and gastrointestinal difficulties), or specialize in complex syndromes (e.g.,

fibromyalgia). Researchers sometimes attempt to apply measures for genuine medical impairment to feigned cognitive impairment. For example, Waddell’s signs (i.e., indicators of nonorganic neurological findings) have been extensively misused as evidence of feigning, an application clearly not supported by the empirical literature (Fishbain, Cutler, Rosomoff, & Rosomoff, 2004). In light of the complexities of medical presentations, detection strategies for the medical domain face formidable challenges in their development and validation. As noted, two parameters are necessary in evaluating the usefulness of detection strategies: effect sizes and utility estimates. Effect sizes provide a standardized method for evaluating the comparative value of different detection strategies in distinguishing between relevant criterion groups. Utility estimates examine the effectiveness of particular cutoff scores for individual and group classification of response styles. These parameters are considered in the next two sections.

Effect Sizes and Detection Strategies Cohen’s (1988) seminal work on effect sizes was designed to consider relatively small differences as relevant to psychological research; for example, he recommended that an effect size of .80 be considered “large,” even though differences were substantially less than one pooled standard deviation. More rigorous standards are needed for professional practice, especially when the presence of a response style may serve to invalidate an individual’s clinical presentation (Ferguson, 2009). For the assessment of malingering, Rogers et al. (2003) proposed more rigorous standards for Cohen’s ds: “Moderate” ≥ .75; “Large” ≥ 1.25; and “Very Large” ≥ 1.75. Based on the meta-analysis of defensiveness by Baer and Miller (2002), the recommended standard for a “Very Large” effect size (≥ 1.50) is slightly lower than that for malingering. As a ready reference, categories for effect sizes (Cohen’s d) are presented in Box 2.2.

BOX 2.2.  Categorization of Effect Sizes Based on Cohen’s d for the Classification of Response Styles • Moderate ≥ .75 • Large ≥ 1.25 • Very Large ≥ 1.50

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Cutoff Scores for the Accurate Assessment of Response Styles This important section includes key issues that should be considered equally by practitioners and researchers. Each paragraph is briefly captioned to facilitate its use as an easily accessible resource. •• Imprecision in psychological assessment. Mental health professionals need to know the accuracy of cutoff scores and the more complex decision rules applied in clinical determinations, such as a specific response style. Even our very best psychometric measures are often imprecise and are sometimes simply inaccurate. As evidence of imprecision, consider for the moment the Wechsler Adult Intelligence Scale–IV (WAIS-IV; Wechsler, 2008), a superb psychometric measure. When taking into account its standard error of measurement (i.e., SEM = 2.16), we can say with 95% certainty that a tested WAIS-IV Full-Scale IQ (FSIQ) of 100 (the 50th percentile) falls somewhere between the 39th and 61st percentiles (see Wechsler, 2008, Appendix A.7). Imprecision is also evident on measures of psychopathology, such as the MMPI-2. Taking into account the SEM on standard MMPI-2 clinical scales (conservatively 6T points; see Rogers & Sewell, 2006), a marginal elevation at 65T has a 95% likelihood of falling between 53T (normal—no elevation) to 77T (moderate elevation). My point here is that some clinicians are overly confident in the precision of their results. Especially when making consequential decisions about response styles, clinicians should take great care to be prudent in their conclusions by taking into account the imprecision of measurement. •• Perils of single-point cutoff score. Rogers and Bender (2013) strongly recommended that psychologists and other practitioners take into account the imprecision of single-point cutoff scores (e.g., for the Personality Assessment Inventory [PAI], a 70T score represents a clinical elevation, whereas a 69T does not). They press this point by suggesting that such exacting discriminations implicitly assume the “laser accuracy myth of cut scores” (Rogers, Gillard, Wooley, & Ross, 2012, p. 79; emphasis in the original). Moreover, Rogers et al. demonstrated empirically that PAI scores, which are too close to the cutoff score (± 5T), had an error rate for classifications exceeding 50%. When measurement errors were also considered, the combined error rate was about 75%.

•• Well-defined cutoff scores. The laser accuracy myth can be mostly avoided by simply creating an indeterminate band of scores that is “too close to classify without substantial errors” (Rogers & Bender, 2013, p. 522; emphasis in the original). Well-defined cutoff scores—eliminating the narrow band of indeterminate scores (e.g., ± 5T or 1 SEM)—can substantially improve the accuracy of classification. Using Rogers et al. (2012) as an example, the single-point Negative Impression Management (NIM) score ≥ 70T for feigning (i.e., < 70T for genuine responding) becomes a well-defined cut score by removing ± 5T (i.e., increasing the cut score to ≥ 75T for feigning and lowering it to < 65T for genuine responding). In addition, well-defined categorizations may also be achieved via multiple scales constituting a decision model (e.g., the Structured Interview of Reported Symptoms–2 [SIRS-2]; Rogers, Sewell, & Gillard, 2010). •• Advantages of well-defined cutoff scores. The professional use of well-defined cutoff scores decrease both measurement and classification errors. Measurement errors occur whenever measuring complex psychological and medical constructs.4 Scores within one SEM (± 1) are especially vulnerable to measurement error. As researchers on response styles can easily attest, clearly defined bimodal distribution of scores between specific response styles simply do not occur in professional settings. As a result, the establishment of cutoff scores includes some arbitrariness that is reflected in classification errors. Thus, classification accuracy is improved by excluding a narrow range of too-close-to-classify cases. Practitioners and researchers often fail to consider the comparative advantages of single-point versus well-defined cutoff scores. For ready reference, they are delineated in Box 2.3.

BOX 2.3.  Single-Point versus Well-Defined Cutoff Scores • Single-point cutoff scores classify all exam‑ inees as being above or below a designated point; they are prone to both measurement errors and classification errors, which, com‑ bined, may exceed 50%. • Well-defined cutoff scores remove an indeter‑ minate group (e.g., ± 1 SEM) that is too close to classify as a systematic method for improv‑ ing utility estimates.



2.  Detection Strategies for Malingering and Defensiveness 23

Overview of Utility Estimates The accuracy of classifications for SRSs should be formally evaluated via utility estimates. Least helpful is the overall hit rate or overall correct classification (OCC), which may obscure important weaknesses. As an extreme example, a cutoff score could miss every single person with a factitious disorder but still achieve a 90% hit rate because of the very low prevalence for factitious disorders. For accuracy of classification, two utility estimates should be considered at a particular cutoff score: • Sensitivity is the proportion of persons with the SRS correctly identified by the cutoff score. If 18 of 20 malingerers are identified by a particular cutoff score (e.g., 65T on the MMPI-2 F scale), then the sensitivity is .90. • Positive predictive power (PPP) is the likelihood that persons meeting a particular cutoff score will be correctly identified as having the SRS. If the same cutoff score correctly identifies 18 of 20 malingerers but misclassifies 60 genuine patients, then the PPP (18/78) is only .23. This example clearly illustrates the importance of considering both sensitivity and PPP in evaluating the accuracy of particular cutoff scores. Extremely high sensitivity can be achieved at the expense of PPP. However, error rates (e.g., false positives) are very important and have been recognized by the Supreme Court as a critical component for the admissibility of expert testimony (see Daubert v. Merrell Dow Pharmaceuticals, Inc., 1993). Historically, utility estimates have overlooked genuine responding as a clinically relevant SRS. Instead, intentional distortions have represented the focal point. This neglect of genuine responding disadvantages many forensic examinees by overlooking potentially “good” news about their efforts at accurate self-representation (i.e., genuine responding). As a more balanced approach, clinicians should weigh—on the basis of the empirical evidence—intentional distortions, unintentional distortions, and genuine responding when evaluating response styles. Use of utility estimates is discussed more extensively in subsequent chapters (see also Streiner, 2003). Of critical importance, cutoff scores should take into account the professional goal for the clinical classification. A crucial distinction must be articulated between screens and clinical determinations. Screens are often effective when used to “screen out” genuine responders. With malinger-

ing as a salient example, I have elaborated on the work of Rogers, Robinson, and Gillard (2014) with the following distinction: 1. Screen-outs try to eliminate the majority of clearly genuine responders from further consideration. To ensure that only genuine responders are removed from further consideration, specificity (likelihood of genuine responding) is intentionally maximized at the expense of sensitivity (likelihood of feigned responding). Thus, by design, those not screened out typically include a substantial number of genuine responders. 2. Clinical determinations are based on comprehensive assessment of response styles. To minimize false positives, PPP is frequently emphasized at some cost to negative predictive power (NPP). Effective clinical determinations set stringent cutoff scores to avoid the misclassification of genuine responders. What are the professional perils? In seeking greater efficiency, some clinicians continue to substitute brief screens for time-consuming clinical determinations. Besides eschewing a more comprehensive multimethod approach, these professionals are substantially increasing the likelihood of false positives, with potentially devastating consequences.

DETECTION STRATEGIES FOR SPECIFIC RESPONSE STYLES Detection strategies form two general categories that capitalize on either unlikely presentations or amplified presentations. For unlikely presentations, detection strategies emphasize the presence of unusual and atypical characteristics that are not generally observed in genuine populations. For amplified presentations, detection strategies evaluate the frequency and intensity of characteristics commonly found in genuine populations. This categorization has been tested with feigned mental disorders (Rogers, Jackson, Sewell, & Salekin, 2005) and presented as a useful heuristic for feigned cognitive impairment (Rogers & Bender, 2013). At present, unlikely and amplified presentations provide us with a useful conceptual framework for evaluating the comprehensiveness of detection strategies. Research on detection strategies has focused intensively on two domains: feigned mental dis-

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orders and feigned cognitive abilities. For feigned mental disorders, the emphasis has been divided between malingering and defensiveness. For cognitive abilities, the focus has been solely on malingering, with concerns about defensiveness being largely neglected. Within the domain of cognitive abilities, mental health professionals have assumed that clients’ performance cannot be better than their actual abilities. Although mainly true, some individuals are able to conceal their cognitive deficits while performing work-related responsibilities. Concealed deficits may be either chronic (e.g., cognitive decline) or temporary (e.g., hangover effects for airline pilots; Bates, 2002). Others may not be able to conceal cognitive problems, such as memory failures, but engage in “damage control” explanations that lessen the “severity” of perceived deficits (Erber & Prager, 2000). Thus, concealed cognitive deficits remains an important but understudied area with response styles. In light of the current research, the following three major subsections examine (1) feigning and mental disorders,5 (2) defensiveness and mental disorders, and (3) feigning and cognitive abilities. Despite fewer studies of detection strategies, the fourth and final section summarizes the current data on feigning and medical presentations.

Feigning and Mental Disorders Rogers (1984) provided the original analysis of detection strategies, which combined empirical and heuristic models of malingering. From this earliest analysis, detection strategies for feigning have gradually evolved and continue to be subjected to rigorous examination (Rogers, 2008; Rogers & Bender, 2013). As summarized in Table 2.1, 10 detection strategies for malingered mental disorders6 have been validated. We begin with an examination of “unlikely presentation” detection strategies; for simplicity, this is referred to as unlikely detection strategies. Unlikely Detection Strategies

As noted, unlikely detection strategies focus on unusual or atypical clinical features that are rarely observed in genuine clinical populations (see Table 2.1). Rare symptoms best exemplify the category of unlikely detection strategies. The strategy is most effective when employing a stringent criterion for identifying rare symptoms (i.e., < 5% of patients with genuine responding) derived from

clinically complex samples (i.e., typically patients warranting multiple diagnoses of serious mental disorders plus personality disorders [PDs]). This strategy tends to produce large to very large effect sizes for specialized feigning measures, such as the SIRS-2 Rare Symptoms (RS) scale (Rogers et al., 2010), and multiscale inventories (e.g., the MMPI2 F-psychiatric or Fp scale; see Rogers et al., 2003). Quasi-rare symptoms represent a much weaker detection strategy than do rare symptoms. As a thought experiment, a naive researcher could devise a subset of quasi-rare symptoms from the Beck Depression Inventory (BDI), based on a well-adjusted community sample, only to find that it failed utterly with depressed inpatients. As illustrated with the BDI, this method of scale development is fundamentally faulty, because selected items may either reflect genuine disorders or feigned disorders. As a case in point, only 25.0% of the items on the F scale (a quasi-rare strategy) were also uncommon in clinical populations.7 Quasi-­ rare symptoms, while producing large to very large effect sizes, are difficult to interpret. They may also result in unacceptable levels of false positives (i.e., the misclassification of a genuine patient as malingering). The third detection strategy, devoted to individual symptoms and features, is improbable symptoms. Virtually by definition, endorsements of improbable symptoms cannot be veridical. However, improbable symptoms represent a trade-off. On the one hand, their fantastic quality increases the likelihood that endorsements are feigned. On the other hand, the high face validity of these items (i.e., recognizable as bogus symptoms) may decrease their effectiveness, especially with sophisticated malingerers. As observed in Table 2.1, two unlikely strategies move beyond individual items to examine infrequent pairs (symptom combinations) and scale configurations (spurious patterns of psychopathology). Given the complexities of establishing “spurious patterns,” this strategy has only been applied successfully to the PAI. However, the strategy of “symptom combinations” is very versatile and comparatively easy to implement. It represents a primary detection strategy on the SIRS-2 and was recently applied to the Structured Inventory of Malingered Symptomatology (SIMS; Widows & Smith, 2005). For the SIMS, Rogers, Robinson, and Gillard (2014) created a Symptoms Combination (SC) scale based on uncorrelated or negatively correlated item-pairs in genuine patients that were often endorsed by feigners.



2.  Detection Strategies for Malingering and Defensiveness 25

TABLE 2.1.  Detection Strategies for Feigned Mental Disorders Unlikely presentation detection strategies Rare symptoms 1. Description: This strategy capitalizes on symptoms or features, which are very infrequently reported (e.g., < 5.0%) by genuine clinical populations. Malingerers are often detected because they overreport these infrequent psychological symptoms and features. 2. Strengths: This detection strategy has been widely applied to different psychological measures; it tends to yield large to very large effect sizes. 3. Limitations: None are noted when this strategy is tested with diverse clinical populations using stringent criteria (e.g., < 5.0%). Problems may occur when it is tested on more homogenous samples (e.g., TSI-2 or with a lax criterion; e.g., < 20%). 4. Examples: (1) SIRS-2 RS (Rare Symptoms) scale, (2) MMPI-2 Fp (F-psychiatric) scale, (3), PAI NIM (Negative Impression Management) scale, and (4) PAI NDS (Negative Distortion Scale). Quasi-rare symptoms 1. Description: This strategy uses symptoms and features that are infrequently found in normative and nonclinical (e.g., community) samples. It is considered a “quasi” strategy, because infrequent items may reflect either genuine disorders or feigned disorders. 2. Strength: This detection strategy produces large to very large effect sizes. 3. Limitations: Because scale items represent infrequent problems for nonclinical groups, many are more frequently reported by patients with genuine mental disorders. Thus, the interpretation of quasi-rare items is often confounded. For example, clients with schizophrenia or posttraumatic stress disorder (PTSD) routinely have marked elevations (e.g., M scores ≥ 80T) on the MMPI-2 F and Fb scales (see Rogers et al., 2003). This confound also contributes to a wide array of cutoff scores. 4. Examples: MMPI-2 F and Fb (F-back) scales. Improbable symptoms 1. Description: This strategy represents an extreme variant of rare symptoms. It utilizes symptoms or features that have a fantastic or preposterous quality. 2. Strength: Because of their fantastic nature, most of its items could not possibly be true. Therefore, substantial endorsement of improbable symptoms typically does not reflect genuine clinical characteristics. 3. Limitation: The extremeness of improbable symptoms may limit its usefulness with sophisticated malingerers, who may be able identify their items as bogus symptoms. 4. Examples: SIRS-2 IA (Improbable and Absurd Symptoms) scale and MCMI-IV Validity Index (VI).

Symptom combinations 1. Description: This strategy utilizes symptoms and features that are common to clinical populations but rarely occur together. Malingerers often endorse a substantial number of infrequent pairs (e.g., grandiosity and increased sleep) rarely observed together in genuine clinical populations. 2. Strengths: This strategy is sophisticated and should be resistant to coaching and other forms of preparation. It also produces large effect sizes. Moreover, it is easily adapted to structured interviews and multiscale inventories, because unlikely pairs can be identified by negligible or even negative correlations in genuine clinical samples. 3. Limitation: At present, it has been tested primarily with feigning measures but could be adapted to multiscale inventories. 4. Examples: SIRS-2 SC (Symptom Combinations) scale, M-FAST RC (Rare Combinations) scale, and SIMS SC (Symptom Combinations) scale. Spurious patterns of psychopathology 1. Description: This strategy is an extensive elaboration of symptom combinations. It relies on certain scale configurations that are very uncommon in clinical populations but sometimes found with feigners. 2. Strength: Its complexity minimizes the possibility that malingerers could prepare and foil its detection strategy. It functions best when using both conceptual and empirical methods for deriving indicators (see MAL below). 3. Limitation: Because of its complexity, the strategy, spurious patterns of psychopathology, requires extensive cross-validation with diverse clinical samples to ensure that its results are not capitalizing on chance variance. Care must also be taken against overinterpretation (e.g., drawing conclusions in the absence of clinical elevations). 4. Examples: PAI MAL (Malingering) index and PAI RDF (Rogers Discriminant Function) index. Amplified detection strategies Indiscriminant symptom endorsement 1. Description: This strategy relies on the finding that some malingerers, unlike most genuine clients, tend to endorse a large proportion across a spectrum symptoms. 2. Strength: The overall proportion of endorsed symptoms is easy to calculate and may be applied to all psychological measures that cover a broad array of clinical characteristics. 3. Limitations: It has been tested mostly with structured interviews. Care must be taken that measures cover a broad array of symptoms; otherwise, its use may lead to false positives. 4. Examples: SIRS SEL (Symptom Selectivity) scale, SADS SEL (Symptom Selectivity) scale, and PAI MFI (Multiscale Feigning Index).

(continued)

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TABLE 2.1. (continued) Symptom severity 1. Description: This strategy capitalizes on the finding that even severely impaired patients experience only a discrete number of symptoms described as “unbearable” or “extreme” in intensity. Malingerers often endorse a wide array of psychological problems with extreme intensity. 2. Strengths: This strategy is easily adaptable to a wide range of structured interviews and clinical scales. It produces large effect sizes. 3. Limitation: At present, symptom severity is usually considered across elevations on multiple scales. However, it may be more effective to evaluate at an item level, such a PAI frequency count of the most severe (e.g., a score of 3, or “very true” for psychotic symptoms) pathological responses. Further research may improve its effectiveness by identifying which psychological problems are almost never characterized as “extreme” in clinical populations. 4. Examples: SIRS-2 SEV (Symptom Severity) scale; MMPI-2 LW (Lachar–Wrobel Critical Items); and MFAST ES (Extreme Symptomatology) scale. Obvious symptoms 1. Description: This strategy relies on the idea that malingerers are likely to report or endorse prominent symptoms that are clearly indicative of serious mental disorders. Obvious symptoms are either considered alone or in relation to subtle symptoms (i.e., perceived by nonprofessionals as “everyday” problems). 2. Strength: This strategy produces large to very large effect sizes. 3. Limitation: Researchers debate whether obvious symptoms should be considered alone or in relation to subtle symptoms. In the latter case, both obvious and subtle symptoms work best if converted to standard scores (e.g., MMPI-2 T scores). 4. Examples: SIRS BL (Blatant Symptoms) scale and MMPI-2 O­S (Obvious–Subtle) scales. Reported versus observed symptoms 1. Description. This strategy uses a pattern of marked discrepancies between the person’s markedly overstated account of his or her clearly visible symptoms, and a professional’s clinical observations. Malingerers can sometimes be identified by this specific pattern of discrepancies (i.e., reporting salient symptoms/features that cannot be observed). 2. Strength: With standardized observations, this strategy provides independent verification of unsubstantiated symptoms and features.

3. Limitation: Because many genuine patients lack insight about their psychopathology, standardization is essential for accurate discrimination. In addition, discrepancies must form clear pattern of markedly overstated clinical features. 4. Examples: SIRS-2 RO (Reported vs. Observed) scale and the M-FAST RO (Reported vs. Observed) scale. Erroneous stereotypes 1. Description: This strategy capitalizes on the finding that many persons, including mental health professionals, have misconceptions about which clinical characteristics are commonly associated with mental disorders. Malingerers are often identifiable by their major overendorsement of erroneous stereotypes. 2. Strength: This strategy appears resistant to preparation because even mental health professionals have difficulty detecting erroneous stereotypes. 3. Limitation: It has been mostly tested with the MMPI-2. 4. Examples: the MMPI-2 Ds (Dissimulation) scale and the PSI EPS (Erroneous Psychiatric Stereotype) scale. Requiring further validation: Close approximations to genuine symptoms 1. Description: This strategy uses apparently bogus symptoms that parallel genuine symptoms except for some important detail.

2. Strength: None is noted.

3. Limitation: Genuine patients may respond to the gist of the item and be misclassified. It has only been tested with one measure; its item content is considered proprietary. 4. Example: MPS MAL (Malingering) scale. Requiring further validation: Overly specified symptoms 1. Description: This strategy assumes that malingerers may be willing to endorse symptoms with an unrealistic level of precision that would distinguish them from patients presenting with genuine-only disorders 2. Strength: None is noted. 3. Limitation: It appears to be a measure of inattention to detail rather than a feigning scale per se. Similar to “close approximations,” genuine patients may respond to the gist or the item. 4. Example: SIRS-2 OS (Overly Specified) symptoms.

Note. The full names of measures listed by abbreviations are presented alphabetically: MCMI, Millon Clinical Multiaxial Inventory; M-FAST, Miller Forensic Assessment of Symptoms Test; MMPI, Minnesota Multiphasic Personality Inventory; MPS, Malingering Probability Scale; PAI, Personality Assessment Inventory; PSI, Psychological Screening Inventory; SADS, Schedule of Affective Disorders and Schizophrenia; SIMS, Structured Inventory of Malingered Symptomatology; SIRS, Structured Interview of Reported Symptoms; TSI, Trauma Symptom Inventory.



2.  Detection Strategies for Malingering and Defensiveness 27

Amplified Detection Strategies

In contrast to unlikely strategies, amplified detection strategies focus on the excessive degree (e.g., frequency and intensity) of purported symptoms. The two clearest examples are indiscriminant symptom endorsement and symptom severity. In the first instance, malingerers can be identified simply by the sheer number of reported symptoms. In the second instance, malingerers report a high proportion of symptoms as having extreme severity. In both instances, the marked amplification of the clinical presentation becomes the decisive factor, without any consideration of the specific item content. The remaining three strategies focus on content that may appear plausible to malingerers. Because obvious symptoms are easy to recognize and appear to be clinically significant, malingerers may endorse them in greater numbers than their genuine counterparts. While each symptom by itself is plausible, the detection strategy capitalizes on sheer number of endorsed obvious symptoms. Interestingly, malingerers do not necessarily endorse more obvious than subtle symptoms; 8 the detection strategy relies primarily on the proportion of obvious symptoms. Reported versus observed symptoms is a detection strategy that incorporates the clinician’s standardized perceptions of the patient’s self-described symptoms. Although the reported symptoms may be plausible, the detection strategy uses patterned discrepancies with much greater reported symptoms than observed psychopathology. The final detection strategy, erroneous stereotypes, deserves an extended comment. Especially on multiscale inventories (e.g., the MMPI-2), with their complex array of clinical and content scales, persons may wrongly assume that certain clinical characteristics are common among patient populations. When these misassumptions are widespread, erroneous stereotypes can be used to detect likely malingerers. The MMPI-2 Dissimulation scale represents a singular good example, because even mental health professionals make misassumptions about erroneous stereotypes (see Rogers et al., 2003). Similarly, Erroneous Psychiatric Stereotype (EPS; Lanyon, 1993) for the Psychological Screening Inventory (PSI; Lanyon, 1970) uses common misassumptions about persons with mental disorders to evaluate feigning. Although community participants and inpatients have comparable scores on the EPS (d = 0.18), simulators do not generally recognize these erroneous stereotypes. Their overendorsements produce very large

effect sizes (ds of 2.33 and 2.44, respectively). This detection strategy deserves further investigation because of its amplified presentation and possibility for excellent discriminability. Strategies with Limited Validation

Two detection strategies lack sufficient validation, which limits their use in clinical practice. The use of close approximations to genuine symptoms needs to be cross-validated by other investigators and tested on different measures. A second strategy without sufficient validation is overly specified symptoms, which has a relatively weak conceptual basis: High scores may reflect a general inattention to detail rather than feigning per se. For both strategies, a common risk involves genuine patients responding to the gist of the item and being tripped up by either the close approximation or unrealistic detail. In summary, clinicians have a wealth of detection strategies relying on both unlikely and amplified presentations. These strategies generally produce large to very large effect sizes, which are critical to accurate classifications. As noted in subsequent chapters in this volume, many scales designed to implement these detection strategies remain effective, even when feigners are coached or otherwise prepared.

Malingering and Cognitive Abilities Rogers et al. (1993) provided the first systematic review of detection strategies for feigned cognitive abilities. These strategies continue to be refined and tested with diverse clinical populations. However, two problematic trends have emerged. First, a few detection strategies (e.g., the floor effect) have gained unwarranted popularity at the expense of other sound detection strategies. Second, many researchers have concentrated their efforts with detection strategies on short-term learning and consequently have neglected other important facets of cognitive functioning. Awareness of these trends is important to practitioners so that they (1) select detection strategies based on effectiveness rather than popularity and (2) utilize methods appropriate to the purported deficits. Detection strategies of feigned cognitive abilities, similar to the domain of feigned mental disorders, may also be conceptualized as either unlikely or amplified. Rogers (2008; Rogers & Bender, 2013) originally used the term excessive impairment to describe amplified detection strategies. In this

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edition, we opted for amplified as a clear parallel to the domain of feigned mental disorders. As before, detection strategies with unlikely presentations are referred to as unlikely detection strategies. They focus on unusual and atypical response pattern, infrequently observed in patients with genuine neuropsychological impairment. In contrast, amplified detection strategies emphasize the magnitude of the purported deficits. With respect to organization, the next two subsections address unlikely and amplified detection strategies, respectively (see Table 2.2). These subsections omit neuroimaging indicators, which have yet to be extensively validated (Kingery & Schretlen, 2007). Unlikely Detection Strategies

Two unlikely detection strategies (magnitude of error and performance curve) focus on response patterns, which are very uncharacteristic of genuine patients. Rogers et al. (1993) first described magnitude of error as unexpected patterns of incorrect responses among feigners that ranges from close misses (approximate answers) to patently wrong answers. This strategy shows definite promise, because most feigners do not take into consideration which wrong responses are much less plausible than another. It has been utilized with existing neuropsychological measures (Liff, 2004) and in developing the Test of Cognitive Abilities (TOCA; see Bender & Rogers, 2004). The performance curve is an unlikely detection strategy that was first recognized by Goldstein (1945); it examines comparative success, taking item difficulty into account. This strategy is simple yet sophisticated, because malingerers are unlikely to be aware of item difficulty when deciding on which items to fail. Frederick (1997, 2003) has produced the most sophisticated application of the performance curve in developing the Validity Indicator Profile (VIP). Violation of learning principles differs from the two other unlikely detection strategies in its conceptual complexity. While representing a general construct, this strategy contains a constellation of well-established learning concepts. The most common learning principle for feigned cognitive impairment involves the comparative advantage of recognition versus recall. Feigners often violate this principle by obtaining comparable scores, despite the relative ease of simple recognition. For example, the Word Memory Test (WMT; Green, 2003) allows for delayed recognition (approxi-

mately 30 minutes) to be compared to immediate recall, delayed recall, and long delayed recall.9 As expected, effect sizes vary by specific comparisons. For 315 compensation cases evidencing good effort, a comparison of delayed recall to delayed recognition yielded a very large effect size (d = 3.09; see Green, Astner, & Allen, 1997). Clearly, more feigning research is needed to examine the effectiveness of this detection strategy beyond the WMT. Amplified Strategies

Rogers (2008; see also Rogers & Correa, 2008) described three detection strategies involving claims of excessive impairment (i.e., amplified detection strategies). Because forced-choice testing (FCT) often lacks a clear conceptual basis, it has been moved in this edition to a new category that is designated as “Nonstrategy Methods” in a later section. Of amplified detection strategies, the floor effect has been adapted to dozens of feigning measures. Simply put, malingerers sometimes claim impairment on simple cognitive tasks that are successfully completed by most cognitively compromised populations. The Test of Memory Malingering (TOMM; Tombaugh, 1996) represents a well-validated example of the floor effect strategy that has been successfully applied across various clinical populations (see Frederick, Chapter 17, this volume). However, it is still important to rule out dementias10 and other severe conditions prior to the classification of feigned cognitive impairment on the TOMM. In general, a major drawback of the floor effect, especially in stand-alone measures, is that feigners can easily be educated about how defeat it. Comorbidity may also be an important consideration. For example, persons with severe depression or dementia may lack the motivation and attentional abilities required to complete more time-intensive floor effect measures; even a modest decrement in functioning (e.g., 10–15% errors) may meet the feigning criterion for the floor effect strategy (e.g., the TOMM Retention trial). The strategy of significantly below chance performance (SBCP) was previously termed symptom validity testing (SVT; Rogers, 2008). As originally described (Pankratz, Fausti, & Peed, 1975), SVT clearly referred to SBCP. In the last decade, however, the term and its abbreviation have been used more generally to describe a variety of feigning measures and detection strategies (e.g., Dandachi-

TABLE 2.2.  Detection Strategies for Feigned Cognitive Impairment Unlikely presentation detection strategies Magnitude of error 1. Description: This strategy relies on data indicating that genuine patients often make predictable errors. Most malingerers do not focus on which incorrect answers are common; they are frequently detectable by choosing very wrong responses that are unlikely among genuine patients. 2. Strength: It is less transparent than most cognitive detection strategies and less vulnerable to coaching (Bender & Rogers, 2004). It produces large effect sizes. This strategy could easily be adapted to current forcedchoice formats of standard tests, such as the WAIS-IV Matrix Reasoning subtest. 3. Weakness: None is noted. 4. Examples: “d errors” on the “b Test” and the TOCA Magnitude of Error (MOE) scale. Performance curve 1. Description: This strategy is based on the general finding that genuine patients produce a predictable pattern: fewer successes with increased item difficulty. When plotted, this “rate of decay” forms a characteristic “performance curve.” Malingerers, unaware of this pattern, typically produce much less discrimination between easy and difficult items. 2. Strength: It is a sophisticated strategy that may prove to be resistant to coaching. 3. Weakness: It may be challenging to implement this strategy on existing measures, because it requires a broad range of item difficulty. 4. Examples: VIP Performance Curve and the TOCA Performance Curve (PC). Violation of learning principles 1. Description: This strategy is a specialized application of performance curve; some malingerers are unaware of underlying learning principles. 2. Strength: It is conceptually strong because it is based on rigorously evaluated learning principles. Malingerers may not take into consideration that different performances are expected based on learning principles: (a) recognition vs. recall, (b) cued recall vs. free recall, (c) immediate vs. delayed recall, (d) simple recall vs. cognitive transformation (e.g., “Tell us in your own words”) and (e) priming effect (Haines & Norris, 1995). 3. Weakness: Some violations of learning principles produce only modest group differences. Therefore, this detection strategy needs to be rigorously tested to minimize false positives. 4. Examples: RAVLT (see Sullivan et al., 2002), WMT Immediate Recognition versus Delayed Recognition, and WMT Delayed Recall versus Delayed Recognition. Amplified detection strategies Floor effect 1. Description: This strategy capitalizes on the finding that some malingerers do not recognize that very easy



cognitive tasks (i.e., “too simple to fail”) can be successfully completed by most impaired persons. 2. Strength: It is easily adaptable to many cognitive measures. 3. Limitation: When the strategy is used in a stand-alone measure, malingerers can easily be coached (e.g., “just succeed”). 4. Examples: Rey-15, TOMM, WMT, and LMT. Significantly below chance performance (SBCP) 1. Description: This strategy uses a forced-choice paradigm to test whether an individual’s failure rate is significantly below probability based on a chance performance. When given two equiprobable choices, even the most impaired individuals should succeed approximately 50% of the time (i.e., chance levels). 2. Strength: Failures significantly below chance provide definitive evidence of feigning. 3. Limitation: Most malingerers do not need to fail at such an unlikely level to achieve their objectives. Therefore, this strategy is typically successful in less than 25% of feigned cases. 4. Examples: PDRT, CARB, VSVT, WCT, and TOMM. Requiring further validation: Consistency across comparable items 1. Description: Genuine patients with stable mental status tend to perform consistently across items of comparable difficulties. Some malingerers are much more variable in their performance and can be identified by their marked inconsistencies. 2. Strength. With rigorous testing, discrepancies can be effective in distinguishing between criterion groups (feigners vs. genuine patients). At present, only the VIP has been rigorously tested for consistency across comparable items. 3. Weaknesses: Research sometimes addresses only group differences, without looking at intraindividual performances on comparable items. It is increasingly evident that appreciable intertest scatter is the norm, not the exception. Because of unknown effects of comorbidity, the presence of serious mental disorders or personality disorders represents a potential confound. 4. Example: VIP Equivalent Item Pairs.a Requiring further validation: Symptom frequency 1. Description: This strategy is based on the idea that some malingerers may report symptoms associated with cognitive impairment at a much higher rate than genuine populations. 2. Strength: With cross-validation, this strategy extends beyond cognitive performance to examine patients’ reported symptoms and their potential interference in day-to-day functioning. 3. Weaknesses: This approach has only been systematically evaluated with a single measure. A major concern is whether its results will be confounded by severe comorbidity. 4. Example: NSI (Gelder et al., 2002) total score; most feigners endorse more than 25% the total possible score.

Note. The full names of measures listed by abbreviations are presented alphabetically: CARB, Computerized Assessment of Response Bias; LMT, Letter Memory Test; NSI, Neuropsychological Symptom Inventory; PDRT, Portland Digit Recognition Test; RAVLT, Rey Auditory Verbal Learning Test; TOMM, Test of Memory Malingering; VIP, Validity Indicator Profile; VSVT, Victoria Symptom Validity Test; WAIS-IV, Wechsler Adult Intelligence Scale–IV; WMT, Word Memory Test. aThey are described in Frederick (1997) but not in the revised manual (Frederick, 2003).

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FitzGerald, Ponds, & Merten, 2013). Although unwieldy, a precise term will be employed: SBCP. Unique among detection strategies, SBCP can be used to accurately calculate the likelihood of false positives. In two-choice paradigms with equiprobable alternatives, binomial probabilities can be calculated to approximate the likelihood that a particular below-chance performance could occur in a nonfeigning individual. At extremely low scores (e.g., ≤ 25 on the 72-item Portland Digit Recognition Test; Binder, 1993), the probability of feigning exceeds 99%, with less than 1% of genuine responders being misclassified. The strength of SBCP (i.e., virtual certainty at extremely low scores) is also its inherent limitation. Feigning catastrophic failures on psychometric measures are not required in most clinical and forensic cases. Therefore, malingerers frequently feign severe but not extreme cognitive problems, thus eluding detection on SBCP measures. Nonetheless, practitioners continue to use SBCP (1) because of the unparalleled certainty that can be achieved, albeit in a small proportion of cases, and (2) because it can be integrated into measures employing other more sensitive (but less specific) strategies. Strategies with Limited Validation

The potential strategy, consistency across comparable items, makes conceptual sense with higher functioning examinees but is subject to error for patients with genuine cognitive impairment who exhibit variable performances (see Bender & Frederick, Chapter 3, this volume). However, significant numbers—because of attentional and motivational issues—evidence a substantial decline in cognitive functioning as their testing progresses. A pattern of decline or other variability in cognitive performance can confound this potential strategy. On this point, Frederick (1997) described Equivalent Item Pairs for the VIP, but did not feature them in the revised manual (Frederick, 2003). Detection strategies for feigned cognitive abilities—both unlikely and amplified—focus predominantly on examinees’ test performance on cognitive measures. As an alternative, Gelder, Titus, and Dean (2002) utilized the Neuropsychological Symptom Inventory (NSI) to evaluate the potential feigning of symptoms and clinical characteristics associated with the reported cognitive deficits. They devised a potential detection strategy, symptom frequency, to evaluate how often an array of neuropsychological symptoms is reported in feigners compared to those with genuine cogni-

tive deficits. Fitting within the amplified domain, an obvious concern is whether severely impaired genuine examinees might be misclassified as feigning. Nonstrategy Methods

Two clinical indicators—FCT and atypical presentation (ATP)—generally lack the sound conceptual basis required for detection strategies (see Table 2.3). FCT was intended to relax the standards for SBCP, so that more feigners would be

TABLE 2.3.  Nonstrategy Methods for Feigned Cognitive Impairment

These methods do not utilize conceptually based detection strategies. Instead, they are based on performance that is lower or otherwise different than that found in samples of presumably genuine patients. Forced-choice testing (FCT) 1.  Description: This method is based on the observation that some malingerers evidence lower performance than typically found for genuine patients with cognitive impairment. 2.  Strength: None is noted. 3.  Limitations: FCT lacks a conceptually based detection strategy. It relies simply on poor performance without specifying how that performance differs from genuine efforts. Other limitations for most FCT measures include not being tested with a full range of clinical conditions, and not being tested with serious mental disorders that may confound results. 4.  Examples: CARB and PDRT. Atypical test pattern (ATP) 1.  Description: This method is based on the observation that some malingerers perform differently on certain scales than samples of genuinely impaired persons. In its clearest form, these patterns are identified statistically without any consideration of their theoretical bases. 2.  Strength: None is noted. 3.  Weaknesses: It is a conceptually weak approach, lacking the conceptual clarity of detection strategies. Use of discriminant analysis often capitalizes on chance variation; thus, this approach requires extensive cross-validation. 4.  Examples: Approximate answers for the Ganser syndrome.

Note. The full names of measures listed by abbreviations are presented alphabetically: CARB, Computerized Assessment of Response Bias; PDRT, Portland Digit Recognition Test.



2.  Detection Strategies for Malingering and Defensiveness 31

classified. It differs from the floor effect in that its items are not typically evaluated for their simplicity. Rather than insisting on “below chance” performance, FCT simply requires “below expected” performance. How is below expected performance assessed? Typically using clinical samples of convenience, the lower range of scores for genuine patients is used to establish the cutoff scores for malingering. This expediency (simply below expected performance), characteristic of FCT, lacks the sound conceptual basis underlying most established detection strategies. Without extensive normative data including major mental disorders and cognitively compromised diagnostic groups, the rate of false positives cannot be established. Similar to FCT, ATPs typically are not informed by a specific detection strategy. Scholars have sought to capitalize on an innovative idea or a striking observation. As a salient historical example, the response pattern of “approximate answers” was apparently observed in three prisoners by Sigbert Ganser in 1897 and continues to be sporadically reported in case studies (e.g., Dwyer & Reid, 2004), despite the lack of empirical research linking it to factitious disorders. As a more recent example, Mittenberg, Theroux-Fichera, Zielinski, and Heilbronner (1995, p. 492) posited that brain-injured patients would “show similar levels of performance” on the WAIS-R Vocabulary and Digit Span subtests; therefore, greater decrements in Digit Span might be evidence of malingering. Posttest Probabilities

Frederick’s (2015) penetrating analysis shows that focusing only on counting feigning indicators to produce posttest probabilities can produce utterly spurious results. According to Larrabee (2008), three or more “positive scores” for feigning increased the odds for feigning to virtually a certainty (99.0 to 99.5%). As noted by Frederick (2015), “negative scores” (i.e., not feigning) must also be considered, which may remarkably reduce the likelihood of feigning. Moreover, applying three or more “positive scores” creates a numerical nightmare. Even if practitioners and researchers were to artificially limit their analysis to 30 feigning indicators, the possibilities are truly staggering, with a factorial of 265,252,859,812,191,058,636,308,480,000,000. Larrabee’s use of 3+ positive scores as highly indicative of malingering relies on at least four implicit assumptions that are summarized in Box 2.4.

BOX 2.4.  Implicit Assumptions of Posttest Probabilities 1. Interchangeability. It assumes that any combi‑ nation of feigning indicators is equal to any other combination of the same number (i.e., assuming that any four indicators produce identical results to any other four indicators). 2. Independence. It assumes uncorrelated indicators; otherwise, the summing of related items would be inappropriate (e.g., adding highly correlated items would be tantamount to “double-counting”). 3. Equal weighting. It assumes that the predic‑ tive power of each indicator is identical; otherwise, items would need to be weighted. 4. Universal applicability. Within the realm of feigned cognitive abilities, indicators are assumed to be equally applicable to any feigned presentation (e.g., malingered intellectual disability and feigned learning disability).

When looking across feigning indicators and specialized populations, practitioners are confronted with very heterogeneous results (see, e.g., Boone, 2007) based on cutoff scores, measures, and clinical presentations. Despite positive results on a small number of two-way and three-way combinations (e.g., Larrabee, 2003), the crucial assumptions of the posttest probabilities remain virtually untested. Therefore, the use of combined probabilities for feigning indicators—in light of these unknowns—may be inaccurate and misleading.

Simulated Adjustment and Mental Disorders As described by Rogers (Chapter 1, this volume), simulated adjustment is a broad term used to describe an overly favorable self-presentation. For detection strategies, clinical research has focused predominantly on defensiveness (denial or minimization of especially psychological symptoms) and social desirability (simulation of a much more positive self-image). Although these constructs are clearly distinguishable, they are often conflated in both the development of detection strategies and the interpretation of test findings. The prevalence of defensiveness among mentally disordered samples is unknown, but it likely exceeds malingering and other forms of dissimulation. Baer and Miller (2002) estimated the base rate of defensiveness at 30% in job applicant and

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child custody referrals. Applying the three most effective scales in Baer and Miller to Greene’s (2000) analysis of Caldwell’s dataset, estimates of defensiveness range from 16 to 33% of clinical referrals. In the community—even with the promise of anonymity—nonclinical participants may engage in defensiveness and fail to disclose subthreshold psychotic experiences (DeVylder & Hilimire, 2015). Despite its importance clinically and nonclinically, the development of detection

strategies for defensiveness is less advanced when compared to the assessment of malingering. Defensiveness is operationalized through two amplified detection strategies (see Table 2.4). The MMPI-2 K scale best represents the denial of patient characteristics in its empirical development of test items common in clinical populations. Using empirical keying, its items tend to be denied by clients with an array of mental disorders, but who produced unelevated MMPI clinical scales.

TABLE 2.4.  Detection Strategies for Simulated Adjustment: Defensiveness and Social Desirability

Defensiveness Denial of patient characteristics 1.  Description: This strategy capitalizes on research demonstrating that certain attributes are commonly endorsed by clinical populations. 2.  Strength: It is designed specifically to evaluate patients who do not acknowledge their psychological problems. Its items may be less transparent than those rationally based on idealized attributes. 3.  Limitation: Scales produce moderate effect sizes and are vulnerable to coaching. 4.  Examples: MMPI-2 K scale, and MMPI-2 Edwards Social Desirability (Esd) scale.a Spurious patterns of psychological adjustmentb 1.  Description: This strategy relies on certain scale configurations are characteristic of defensiveness but are very uncommon in clinical and community populations. 2.  Strength: Its complexity minimizes the extent to which defensive responders can foil this detection strategy. 3.  Limitation: Because of its complexity, the spurious patterns of simulated adjustment strategy requires extensive cross-validation to ensure that its results are not capitalizing on chance variance. Care must also be taken against overinterpretation (e.g., drawing conclusions when clinical elevations are present). 4.  Examples: PAI Defensiveness Index (DEF) and PAI Cashel Discriminant Function (CDF). Social desirability

2.  Strength: None is noted. 3.  Limitations: The complete denial, as contrasted with comparative statements (e.g., “better than most”), is unnecessary for clients denying maladjustment. It tends to produce moderate effect sizes and appears vulnerable to coaching. 4.  Example: MMPI-2 Lie (L) scale. Blended strategy with affirmation of virtuous behavior and denial of personal faults 1.  Description: This strategy combines the affirmation of overly positive attributes with the denial of common foibles. 2.  Strength: It produces moderate to large effect sizes. 3.  Limitation: As a blended strategy, it is difficult to know which component (i.e., affirmation or denial) is more effective. 4.  Examples: Marlowe–Crowne, PDS Impression Management (IM), and MMPI-2 Superlative (S) scale. Social desirability 1.  Description: This strategy attempts to identify persons striving to create a very favorable selfpresentation to others. 2.  Strengths: It produces larger effect sizes than most other defensive strategies. It also appears to be comparatively effective even when persons are coached about the strategy. 3.  Limitation: The term social desirability has been defined and operationalized in several different ways. This definition has been examined via the use of a single scale (Wsd). 4.  Example: MMPI-2 Wsd.

Denial of personal faults 1.  Description: This strategy is based on the idea that persons minimizing maladjustment take this to the extreme and deny any shortcomings or nonvirtuous behaviors. Note. Eds, Edwards Social Desirability scale; PDS, Paulhus Deception Scales; Wsd, Wiggins Social Desirability scale. aDespite its name, the Esd focuses on the denial of common psychological problems. bIn the 2008 edition of this volume, this strategy was referred to as simulated adjustment instead of psychological adjustment. The new term better captures the strategy’s focus on defensiveness.



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Despite its focus on denied emotional and social issues, K scale effect sizes remain comparatively modest for defensive patients and do not appear to be effective when simulators are coached (Baer & Miller, 2002). Morey’s (1996) PAI research on the Defensiveness Index (DEF) is a sophisticated attempt to identify defensive clients via the detection strategy referred to as spurious patterns of psychological adjustment. The DEF comprises two general indicators (positive impression and unwillingness to consider treatment) plus six pairwise comparisons. As an example of the latter, defensive clients tend to be more willing to endorse mildly grandiose characteristics than irritability on the PAI Mania scale. This difference is generally not observed among clients who genuinely disclose their psychological issues. Overall, this detection strategy appears to be highly effective on the DEF (Morey, 2007). Given its complexity, it appears surprisingly vulnerable to coaching for both the PAI DEF and the Cashel Discriminant Function (CDF) (Baer & Wetter, 1997). Three amplified detection strategies that have been developed for social desirability are also routinely applied to defensiveness. The simplest approach involves the general denial of faults and foibles. It is best exemplified by the MMPI-2 L (Lie) scale. As the name denotes, it was intended to measure general deception with regards to “exemplary” personal conduct. Multifaceted scales (i.e., blends of “Affirmation of Virtuous Behavior” and “Denial of Nonvirtuous Behavior”) appear to be more effective than denial alone. However, further research is needed to understand what elements of each contribute to this effectiveness. For instance, the S scale has three affirmation and two denial factors (Butcher & Han, 1995). In general, multifaceted scales appear to be moderately effective at assessing both social desirability and defensiveness. Wiggins (1959) developed a social desirability scale that bears his name (i.e., Wiggins’s Social Desirability scale [Wsd]), based conceptually on students’ ratings of individual items regarding favorable impressions. Both socially favorable (scored as true) and unfavorable (scored as false) impressions were included. This bidirectional approach is much more effective than positive-only detection strategies for simulated adjustment. With the Wsd, MMPI-2 research has demonstrated its superior effect sizes, even when individuals are coached regarding this strategy (Baer & Miller, 2002). Before leaving this section, I should note that all the detection strategies for simulated adjust-

ment fit solidly in the amplified category. This raises an important question: Could unlikely detection strategies be developed for defensiveness and social desirability? Quite possibly, the answer is “yes.” Analogous to symptom combinations, unlikely pairs of positive attributes could be identified that are very infrequent in general and clinical populations. In this vein, logical inconsistencies could be explored. For instance, thoughtfulness and decisive action can easily be construed as positive characteristics, yet they would constitute an unlikely pair of positive attributes. In summary, detection strategies for simulated adjustment lack some of the breadth and sophistication found with several malingering strategies. Still, many detection strategies have proven effective with uncoached simulators. Work on the Wsd with bidirectional—favorable and unfavorable items—demonstrate excellent results, even in the presence of coaching.

Response Styles and Specific Clinical Domains The development and validation of detection strategies has largely been limited to three domains (i.e., feigned mental disorders, feigned cognitive impairment, and simulated adjustment with mental disorders) described in previous sections. This section provides brief summaries of detection strategies for three other domains: (1) defensiveness and cognitive impairment, (2) malingering and medical presentations, and (3) defensiveness and medical presentations. These descriptions are very brief, because detection strategies are in their early stage of development. At present, most work on detection strategies is more conceptual than empirical. Defensiveness and Cognitive Abilities

Traditionally, practitioners have assumed that clinical populations cannot mask their cognitive weaknesses, because testing measures optimal functioning. Simply put, patients cannot do better than their best. Because of this widespread assumption, research has largely neglected whether the concealment of cognitive problems is achievable via preparation and coaching. Is this assumption accurate? Research has generally indicated that performance on standardized aptitude tests may be substantially enhanced by practice and preparation (Kulik, Bangert-Drowns, & Kulik, 1984), especially when identical tests are used. As evidence for the “benefits” of practice effects, WAIS-IV Processing Speed Index increases an av-

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erage of 9 points after a single readministration, even after a lengthy interval (i.e., 3 or 6 months; Estevis, Basso, & Combs, 2012). For job selection (i.e., law enforcement positions), readministrations after 12 months significantly enhanced performances on tests of both cognitive abilities and oral communication (Hausknecht, Trevor, & Farr, 2002). With the ease and availability of Webbased information, individuals can practice and prepare for enhanced performances on the cognitive measures. The other critical question remains unaddressed: “Can preparation successfully mask cognitive deficits and their concomitant impairment?” Cognitive defensiveness may involve either the masking of cognitive deficits or the false portrayal of cognitive strengths. In the former case, successful individuals may wish to conceal even minor decrements in cognitive abilities. For instance, commercial pilots may wish to mask even the slightest declines in cognitive abilities. Rebok, Li, Baker, Grabowski, and Willoughgy (2002) surveyed 1,310 airline pilots and found that almost none rated their own cognitive skills as diminished; on the contrary, a common pattern was to claim enhanced cognitive abilities. In the latter case, the false presentation of cognitive strengths may be viewed as an “asset” in securing a highly competitive position. Examples include selections for executive training, highly sought promotions, and acceptances into graduate school. In recent PsycINFO searches, no empirically validated detection strategies have been located for defensiveness and cognitive abilities. A potential detection strategy for defensiveness and cognitive abilities would be “practice-effect gains” (i.e., the lack of any improvement could be an indirect indicator of previous preparation). However, this potential strategy is very concerning, because genuine responders with marked cognitive deficits are likely to demonstrate the same pattern. Still, readministrations of brief measures should produce predictable patterns: substantial improvements on some scales and negligible differences on others. For example, Bird, Papadopoulou, Ricciardelli, Rossor, and Cipolotti (2004) found that several very brief scales evidence substantial improvement (e.g., 11.2% for verbal fluency in generating words beginning with s) whereas one subscale (i.e., Digit Symbol) evidence a slight decrement in performance (–3.2%). Conceptually based strategies, such as practice-effect gains, would need to be carefully developed and rigorously validated. The effectiveness of detection strategies must be evaluated on two dimensions: the intentional conceal-

ment of cognitive deficits and the false presentation of cognitive strengths. Malingering and Medical Presentations

Illness behavior is far more complex than malingering per se (Halligan, Bass, & Oakley, 2003). Beyond malingered and factitious presentations, patients with chronic medical complaints can adopt one of several maladaptive responses to their illnesses. According to Radley and Green (1987), these maladaptive patterns may include accommodation and resignation. With accommodation, the illness becomes incorporated into the patient’s identity thereby complicating assessment and treatment. With resignation, patients become overwhelmed by their diseases and may passively accept their illness status. Such maladaptive responses may be mistaken for deliberate efforts by patients to malinger by prolonging their medical conditions and thwarting treatment efforts (Rogers & Payne, 2006). The importance of empirically validated detection strategies for feigned medical presentations is underscored by recent investigations of Waddell’s classic signs for nonorganic pain (see Waddell, McCulloch, Kummel, & Venner, 1980). The presence of these signs was interpreted as either malingering or psychological stress (Kiester & Duke, 1999). However, Fishbain et al. (2003) conducted a comprehensive review of Waddell’s signs and the validity of chronic pain. Despite early claims, they found that Waddell’s signs generally did not discriminate between (1) organic and nonorganic pain, and (2) genuine presentation and secondary gain.11 In a further analysis, Fishbain et al. (2004) found that Waddell’s signs do not provide credible evidence of malingering or secondary gain. Their impressive work strongly questions the use of Waddell’s signs to assess “sincerity of effort,” as touted by other investigators (Lechner, Bradbury, & Bradley, 1998). As a further complication, recent research (Lloyd, Findlay, Roberts, & Nurmikko, 2014)—contrary to the “sincerity of effort” hypothesis—has uncovered significant differences in brain circuitry via functional magnetic resonance imaging for pain patients with high versus low Waddell’s signs. Tearnan and Lewandowski’s (1997) original work on feigned medical complaints resulted in the development of the Life Assessment Questionnaire (LAQ), which was intended to evaluate preliminary detection strategies. Tearnan and Ross (2012) refined and tested a large number of detection strategies, including (1) rare symptoms,



2.  Detection Strategies for Malingering and Defensiveness 35

although the criterion appears too low (i.e., < 25% of genuine patients); (2) improbable symptoms referred to as “nonsensical symptoms”; (3) symptom combinations; and (4) physician selection of items suggesting possible malingering.12 Interestingly, these four detection strategies evidenced very similar effect sizes (ds from 0.94 to 1.03) that fell consistently in the moderate level. Although addi-

tional research is needed, this seminal work is used as a template for examining preliminary detection strategies for feigned medical complaints. Detection strategies for malingered medical presentations, similar to other domains, may be conceptualized in two general categories: unlikely and amplified detection strategies (see Table 2.5). Unlikely detection strategies, as described

TABLE 2.5.  Initial Detection Strategies for Malingered Medical Presentations

Rare medical complaints

Intensity of medical complaints

1.  Description: This strategy capitalizes on reported symptoms and ailments that are infrequently described by genuine populations. 2.  Potential strengths: Within the general category of unlikely presentations, it has received the most attention in the initial development of two specialized measures and the recent adaptation of a standardized test, the MMPI-2. The initial empirical data are promising. 3. Examples: MMPI-2 Fs (Infrequent Somatic Complaints) scale and LAQ Infrequent Symptoms.

1.  Description: This strategy relies on observations that persons malingering medical problems are likely to overstate the frequency, duration, and severity of their physical complaints. 2.  Potential strengths: It combines several parameters (e.g., frequency and severity) to create a composite strategy. Some results have produced very large effect sizes. 3.  Examples: BHI-2 Self-Disclosure scale and NSI.

Improbable medical complaints 1.  Description: This strategy is an extreme variant of rare medical complaints. It utilizes symptoms or features that have fantastic or preposterous quality. 2.  Potential strength: If established, its items call into question the genuineness of the reported complaints. 3.  Example: LAQ Nonsensical Symptoms. Symptom combinations 1.  Description: This strategy relies on complaints and symptoms that are common to medical populations but rarely occur together. Malingerers are unlikely to be aware of their low co-occurrence. 2.  Potential strength: With extensive validation, this strategy is a sophisticated approach to malingered medical presentations. 3.  Example: LAQ Unusual Symptom Combinations. Indiscriminant endorsement of health problems 1.  Description: This strategy is based on the finding that some malingerers report a broad array of physical symptoms and complaints, when provided with extensive checklists. 2.  Potential strength: If systemic diseases (e.g., lupus) can be ruled out, the breadth of health-related complaints may provide excellent discrimination between genuine and malingered medical presentations. 3.  Examples: LAQ Physical Symptoms and PSI HPO (Health Problem Overstatement) scale.

Reported versus observed symptoms 1.  Description: This strategy uses marked discrepancies between the person’s own account of his or her medical complaints and corresponding observations. Malingerers can often be identified by systematic discrepancies (i.e., medical complaints unsupported by clinical observations). 2.  Potential strength: With standardized observations, this strategy provides independent verification of reported symptoms. 3.  Potential example: Comparisons of the PRS and PBC. Dependency on medical complaints 1.  Description: This strategy is based on the idea that malingerers may be willing to acknowledge positive attributes of their physical condition or disability status. 2.  Potential strength: None is noted. The willingness to acknowledge any potential motivation to malinger appears counterintuitive. 3.  Example: BHI-2 Symptom Dependency scale. Endorsement of excessive virtue 1.  Description: This strategy relies on the finding that some malingerers attempt to obfuscate response style issues (e.g., malingering) by falsely claiming overly positive attributes. 2.  Potential strength: In combination with other detection strategies, it may augment the discrimination between genuine and feigned medical complaints. 3.  Example: PSI EEV (Endorsement of Excessive Virtue) scale.

Note. The limitations for these initial detection strategies are not listed separately. All strategies require extensive validation and cross-validation. BHI-2, Battery for Health Improvement–2; LAQ, Life Assessment Questionnaire; NSI, Neuropsychological Symptom Inventory; PBC, Pain Behavior Checklist; PRS, Pain Rating Scale; PSI, Psychological Screening Inventory.

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with the LAQ, include three detection strategies. Of these, the greatest attention has been paid to rare symptoms. Empirically, Wygant, Ben-Porath, Berry, and Arbisi (2006) identified relatively uncommon symptoms on MMPI-2 protocols for more than 55,000 medical and chronic pain patients. Like Tearnan and Ross (2012), Wygant et al. (2006) adopted a lenient criterion of infrequency (i.e., < 25% of genuine protocols) for item inclusion; still, their Infrequent Somatic Complaints (Fs) scale produced large to very large effect sizes. Simulators feigning health problems as part of disability claims produced very high Fs scores, although they are also elevated on scales typically associated with feigned mental disorders (Sellbom, Wygant, & Bagby, 2012). In summary, the strategy of rare medical complaints shows strong promise for feigned medical presentations. Two other strategies for unlikely presentations (i.e., improbable medical complaints and symptom combinations) have received comparatively less attention than rare medical complaints. At present, Tearnan and Ross (2012) have demonstrated moderate effectiveness and good convergent validity. More broadly, they should be considered preliminary detection strategies. Five detection strategies for malingered medical presentations rely on amplified presentations for which the classification is based on the magnitude rather than the presence of specific indicators. The strategy, the indiscriminant endorsement of health problems, is the best researched. In an early study, Furnham and Henderson (1983) found that persons feigning medical illnesses endorsed a broad range of somatic and psychological symptoms. More recently, McGuire and Shores (2001) compared simulators to chronic pain patients on the Symptom Checklist 90—Revised (SCL-90-R; Derogatis, 1992). They found that simulators were indiscriminant in their reporting of somatic and psychological symptoms, with marked elevations on each of the clinical scales.13 As a specialized scale on the PSI, Lanyon (2003) developed the Health Problem Overstatement (HPO) scale, which assesses examinees who “overstate their health problems in general” (p. 2). The majority of items involve physical complaints, fatigue, and overall poor health. As strong evidence of discriminant validity, persons simulating severe physical problems on the HPO had a much higher endorsement level than medical inpatients (Cohen’s d = 2.10). A second established strategy for amplified presentation is the intensity of medical complaints. This strategy is operationalized in terms of time (e.g., frequency and duration) and severity, which

is often focused on distress and impairment. For example, an adaptation of the NSI concentrates on the frequency of medical and psychological symptoms. Gelder et al. (2002) found that persons feigning neurological conditions reported frequent symptoms across a broad spectrum. Other potential detection strategies that could be tested, such as symptom combinations (e.g., ringing in the ears and decreased appetite; changes in vision and reading problems) could also be tested for their co-occurrences. This strategy could also be operationalized in terms of severity and distress. In comparing likely malingerers to patients with genuine pain, Larrabee (2003) found that the intensity of pain by itself did not effectively differentiate the groups, largely due to ceiling effects for both groups. Instead, the severity of somatic and autonomic perceptions on the Modified Somatic Perception Questionnaire (MSPQ; Main, 1983) produced very large effect sizes. However, because the MSPQ items were not developed for feigning, it is unlikely that a well-defined detection strategy will emerge. The strategy of reported versus observed symptoms parallels the detection of malingered mental disorders. With operationally defined characteristics and systematic methods, correspondence between medical complaints and health care observations can be standardized. For example, Dirks, Wunder, Kinsman, McElhinny, and Jones (1993) compared pain ratings by patients with parallel ratings by health care professionals. Patients who were deliberately exaggerating pain had very frequent discrepancies (64.6%) that were very different from those of genuine pain patients (14.2%). The chief consideration with this strategy is that there be sufficient groundwork to establish normative data on what is expected from heterogeneous medical populations that vary in pain location, frequency, and intensity. Two strategies have yielded promising results, although their conceptual basis is less precise than other strategies. The strategy, dependency on medical complaints, expects that malingerers would acknowledge the undeserved benefits of their feigning. Conceptually, this strategy appears counterintuitive. The second strategy, endorsement of excessive virtue (Lanyon, 2003), does have merit, because some malingerers want to strengthen their cases for achieving their desired goals (e.g., compensation or a favorable outcome in the criminal justice system). The concern is its nonspecific nature. This strategy is potentially confounded by defensiveness (i.e., affirmation of virtuous behavior) or narcissistic personality traits. As noted in



2.  Detection Strategies for Malingering and Defensiveness 37

Table 2.5, the strategy, endorsement of excessive virtue, might best be conceptualized as an ancillary strategy that could augment the discriminability of other detection strategies. In summary, the current literature provides an excellent conceptual framework for the further study of detection strategies in the medical feigning domain. Within unlikely presentations, work on rare medical complaints has demonstrated the feasibility of this detection strategy. More research is needed on improbable medical complaints and symptom combinations to augment strategies based on unlikely presentations. Within amplified presentations, considerable progress has been made with two detection strategies: indiscriminant endorsement of health problems and the intensity of medical complaints. In addition, reported, as opposed to observed, symptoms show good potential, whereas the final two strategies (i.e., dependency on medical complaints and endorsement of excessive virtue) may require refinement on their conceptual basis. Overall, malingered medical presentations represent a critically important domain of response styles that is poised for further scale development and empirical validation.

Defensiveness and Medical Presentations The denial and gross minimization of medical complaints is rampant in North America and represents an immense public health problem (Kortte & Wegener, 2004). For example, the leading cause of death is cardiovascular illness (Hoyert, Heron, Murphy, & Hsiang-Ching, 2006), which is often treatable in early stages of the disease. Nonetheless, defensiveness is common in medical patients, even at the end stages of heart disease (Williams et al., 2000). Defensiveness plays a similar role with other common diseases, including cancer, diabetes, and substance abuse. It contributes to treatment noncompliance, estimated to be between 35 and 50% for chronic medical conditions; poor outcomes from untreated conditions add astronomically to health costs (Sokol, McGuigan, Verbrugge, & Epstein, 2005). The public dissemination of medical information has been an important step in increasing awareness of medical conditions and health-risk behaviors (e.g., smoking and unprotected sexual practices). However, defensiveness plays an important role in how media campaigns are processed. For example, smokers are likely to minimize or simply reject media presentations against smoking, while maintaining an “illusion of personal immunity” (Freeman, Hennessy, & Marzullo, 2001,

p. 425). Health care professionals and their consultants assess many patients that actively hide their medical symptoms (e.g., failing to disclose angina) or minimize their investment in treatment (Fowers, 1992). According to Bullard (2003), 95% of patients concealed relevant information from medical staff, including symptoms and unhealthy practices (e.g., poor diet or no exercise). Most deceptions in the medical context involve concealments and equivocations rather than direct lying (Burgoon, Callister, & Hunsaker, 1994). The critical issue is whether researchers can develop detection strategies to identify those patients who actively mask their medical symptoms. Early research has demonstrated the obvious with respect to medical defensiveness. Furnham and Henderson (1983) found that defensive individuals simply did not report prominent medical symptoms; they admitted to less than half the symptoms acknowledged by a presumably healthy community sample of young adults. This finding raises an interesting question: Are some physical symptoms so common that their absence could be used to identify medical defensiveness? Lees-Haley and Brown (1993) found that patients in a group family practice often report (i.e., > 50%) headaches, fatigue, nervousness, and sleeping problems. More extensive research might uncover a predictable pattern of common physical symptoms that could be used as a potential detection strategy for medical defensiveness. A second potential approach would be the systematic evaluation of health attitudes, which may provide an unobtrusive measure of medical defensiveness. Rather than query patients about their symptoms directly, questions could focus on their attitudes toward physicians, health, and illness. For instance, does a fatalistic approach to illness predict medical defensiveness? In addition, a normative approach (e.g., “What do most people think?”) to health-related attitudes may be even less obtrusive. Extrapolating from Rogers, Vitacco, Cruise, Sewell, and Neumann, (2002), expressed beliefs about how most people view antisocial behavior were useful for identifying psychopathic youth. When youth attempted to deny their own psychopathy, their expressed attitudes about others made them more identifiable. How might a normative approach be applied to medical defensiveness? Items about general health attitudes could be tested for their discriminability between genuine disclosures and medical defensiveness: “Most persons see illness as a sign of personal weakness.” Bruns, Disorbio, and Copeland-Disorbio (2003) developed the Defensiveness scale for the Battery

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for Health Improvement–2 (BHI-2) to assess physically injured patients who are unwilling to disclose medical complaints and personal problems. Its detection strategy relies on general complaints and psychological problems, and does not appear to be specific to medical symptoms and physical ailments. Discriminating between genuine patients and those faking good lacks the conceptual clarity needed for distinguishing between medical defensiveness and underreporting of psychological disorders. In summary, clinical researchers have neglected medical defensiveness in their studies of response styles. Several potential strategies appear conceptually sound and deserve empirical validation. Critical to the validation is the identification of criterion groups that either deny medical symptoms or conceal their seriousness. As with other domains, detection strategies for medical defensiveness require the operationalization and systematic testing of potential methods.

SUMMARY Detection strategies provide the structural framework for the systematic assessment of response styles. Three domains (feigned mental disorders, feigned cognitive impairment, and defensiveness for mental disorders) have been subjected to intensive investigations. As a result, the developed detection strategies and concomitant scales are conceptually sound and empirically validated. Three additional domains (defensiveness and cognitive impairment, feigned medical presentations, and defensiveness and medical presentations) are conceptually based and await the intensive investigations found with the first three domains.

viations. In most response-style research, Cohen’s d should not be affected by a restricted range (Li, 2015). 4.  In contrast to psychological constructs, comparatively simple physiological measurements (e.g., pulse and blood pressure) can evidence high accuracy in terms of instrumentation but marked variability (i.e., measurement error) across time. 5.  As a related matter, a small percentage of individuals can produce markedly “enhanced performances” on cognitive measures via coaching (see, e.g., Powers & Rock, 1999). 6.  The term mental disorders is used as shorthand for the broad category of feigning psychological and emotional difficulties that may include mental disorders and syndromes. 7. Specifically, 15 of the 60 F-scale items were retained on the Fp scale, which represents a true rare symptom strategy. 8. For example, T-score transformations—but not raw scores—produce the highly significant differences on the MMPI-2 O-S scale. 9. As an important caution, some patient groups may lack the focus and attentional abilities to complete tasks over an extended period. Focusing on patients with schizophrenia, 15.2% failed the WMT without any apparent motivation to feign (Strauss, Morra, Sullivan, & Gold, 2015). 10.  Tombaugh (1996, Table 3.7, p. 14) found a falsepositive rate of 27.0% for dementias, even when nonstandardized procedures (e.g., additional cuing) were used. 11. As an important clarification, Waddell never recommended the extension of his work to response styles. 12. Physician ratings of suspect symptoms do not represent a well-defined detection strategy. 13.  Simulators may have reasoned that psychological issues are related to health issues. It would be very valuable to specifically instruct simulators to feign medical complaints only, as a way of rigorously evaluating the discriminability of the Fs scale.

NOTES

REFERENCES

1. Please note that, according to the DSM-5, the presence of these two indices alone is considered sufficient cause to suspect malingering (American Psychiatric Association, 2013). 2.  The observant reader will note that only the first edition of the MCMI-III manual (Millon, 1994) was cited. Inexplicably, this valuable correlational matrix was omitted from subsequent editions (e.g., Millon, Davis, & Millon, 1997; Millon, Millon, Davis, & Grossman, 2009). 3. Cohen’s d examines the difference between two criterion groups (e.g., feigning and genuinely disordered) in standardized units based on the pooled standard de-

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2.  Detection Strategies for Malingering and Defensiveness 39

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methodology and conceptual framework. Sociology of Health and Illness, 9(2), 179–207. Rebok, G. W., Li, G., Baker, S. P., Grabowski, J. G., & Willoughgy, S. (2002). Self-rated changes in cognition and piloting skills: A comparison of younger and older airline pilots. Aviation, Space, and Environmental Medicine, 73, 466–471. Rogers, R. (1984). Towards an empirical model of malingering and deception. Behavioral Sciences and the Law, 2, 93–112. Rogers, R. (1990). Development of a new classificatory model of malingering. American Academy of Psychiatry and Law, 18, 323–333. Rogers, R. (2008). Detection strategies for malingering and defensiveness. In R. Rogers (Ed.), Clinical assessment of malingering and deception (3rd ed., pp. 14–35). New York: Guilford Press. Rogers, R., & Bender, S. D. (2013). Evaluation of malingering and related response styles. In R. K. Otto, I. B. Weiner Handbook of psychology: Vol. 11. Forensic psychology (2nd ed., pp. 517–540). Hoboken, NJ: Wiley. Rogers, R., & Correa, A. A. (2008). Determinations of malingering: Evolution from case-based methods to detection strategies. Psychiatry, Psychology and Law, 15(2), 213–223. Rogers, R., & Gillard, N. D. (2011). Research methods for the assessment of malingering. In B. Rosenfeld & S. D. Penrod (Eds.), Research methods in forensic psychology (pp. 174–188). New York: Wiley. Rogers, R., Gillard, N. D., Wooley, C. N., & Ross, C. A. (2012). The detection of feigned disabilities: The effectiveness of the Personality Assessment Inventory in a traumatized inpatient sample. Assessment, 19(1), 77–88. Rogers, R., Harrell, E. H., & Liff, C. D. (1993). Feigning neuropsychological impairment: A critical review of methodological and clinical considerations. Clinical Psychology Review, 13(3), 255–274. Rogers, R., Jackson, R. L., Sewell, K. W., & Salekin, K. L. (2005). Detection strategies for malingering: A confirmatory factor analysis of the SIRS. Criminal Justice and Behavior, 32(5), 511–525. Rogers, R., & Payne, J. W. (2006). Damages and rewards: Assessment of malingered disorders in compensation cases. Behavioral Sciences and the Law, 24, 645–658. Rogers, R., Robinson, E. V., & Gillard, N. D. (2014). The SIMS screen for feigned mental disorders: The development of detection-based scales. Behavioral Sciences and the Law, 32, 455–466. Rogers, R., & Sewell, K. W. (2006). MMPI-2 at the crossroads: Aging technology or radical retrofitting? Journal of Personality Assessment, 87, 175–178. Rogers, R., Sewell, K. W., & Gillard, N. D. (2010). Structured Interview of Reported Symptoms–2 (SIRS-2) and professional manual. Lutz, FL: Psychological Assessment Resources. Rogers, R., Sewell, K. W., Martin, M. A., & Vitacco, M. J. (2003). Detection of feigned mental disorders: A meta-analysis of the MMPI-2 and malingering. Assessment, 10, 160–177.

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CHAPTER 3

Neuropsychological Models of Feigned Cognitive Deficits Scott D. Bender, PhD Richard Frederick, PhD

Valid neuropsychological conclusions require that the examinee’s symptoms and test performance are themselves valid. Because malingering must be ruled out as a possible explanation for an examinee’s presentation, many tests and methods have been developed to assess performance and symptom validity, under the basic assumption that such measures can detect malingering. Our primary aim in this chapter is to review and critique the current state of neuropsychological malingering detection, including recent advances in probabilistic modeling. Though the majority of the chapter focuses on indicators of feigned impairment, we review salient markers of genuine symptomatology as well. A definition specific to malingered neurocognitive dysfunction (MND; Slick, Sherman, & Iverson, 1999) and the development of detection models involving performance validity have not only advanced our understanding of the construct of malingering but have also served as reminders of how much we still do not know. For instance, which detection strategies work best and for which types of symptoms? Does the simple presence of a substantial incentive approximate closely enough the individual’s true motivations to warrant its use as a proxy for motivation? While possible answers to the first question have emerged with research, the answer to the second has eluded researchers, at least partially, because it has not been subjected to 42

sufficient critical analysis. This chapter critically reviews recent developments within neuropsychological malingering detection, focusing on Slick and Sherman’s (2013) revised diagnostic criteria for MND, symptom validity testing (SVT), and performance validity testing (PVT). In addition, the utility of using multiple tests (i.e., chaining likelihoods) to identify malingered performance is considered. In 1978, Heaton, Smith, Lehman, and Vogt published what is now a long-famous article suggesting that neuropsychologists cannot reliably detect malingered cognitive test performance. The diagnostic accuracy of the study’s 10 neuropsychologists, who reviewed test data blindly, ranged from chance level to roughly 20% above chance level. Though the article was faulted for not including malingering as one of the diagnostic possibilities, it was the first to raise the issue; indeed, no references to previous published work on malingered cognitive deficits appear in the Heaton et al. (1978) study. Since then, however, thousands of studies of malingered neuropsychological deficits have been published (Martin, Shroeder, & Odland, 2015). Unfortunately, research investigating conceptual assumptions regarding feigned impairment and its detection has not kept pace with that of test developers. Before examining current neuropsychological models of feigned cognitive defi-



3. Neuropsychological Models of Feigned Cognitive Deficits 43

cits, we begin with two definitions of malingering, one more general and well-known, and the other specific to cognitive impairment.

DEFINITIONS Malingering As has been the case since DSM-III (American Psychiatric Association, 1980), DSM-5 (American Psychiatric Association, 2013) defines malingering as the intentional fabrication or gross exaggeration of symptoms. The concept of motivation was not included in the early definition; instead, the individual’s “circumstances” were emphasized. The second half of the definition states that the exaggeration or fabrication must be motivated by external incentives. In theory, the terms intentional and external in the current definition are key to differentiating malingering from other conditions—for instance, intentionality is present in malingering but is absent in somatic symptom disorders. And, any gain that is present must be characterized as external in malingering but internal in factitious disorder. In short, motivation is at the heart of the definition. Unfortunately, the definition of malingering seems to be stronger in theory than in practice, because such clean demarcations rarely occur in real life. For instance, arguably, the most critical aspect of the definition of malingering (i.e., whether or not the fabrication of gross exaggeration was truly motivated by external incentive) cannot be determined with absolute confidence. The existence of such unknowns is not unique to malingering detection, but the consequence of being wrong with regard to malingering may be devastatingly harmful (Drob, Meehan, & Waxman, 2009; Rogers & Bender, 2013). Misdiagnosing social anxiety as generalized anxiety disorder before sending the patient for treatment, while not ideal, would very likely not put the patient’s wellbeing at risk. The patient would still qualify for treatment, and treatment would be similar to that received otherwise. Being falsely labeled a malingerer, in sharp contrast, could result in profound and irreversible losses. DSM-5 (American Psychiatric Association, 2013) conceptualization of malingering states that avoiding work or military duty, obtaining financial compensation, evading criminal prosecution, or obtaining drugs are examples of external incentives that may lead to malingering. However, it also acknowledges that malingering can be adaptive, and even principled, in some situations (e.g.,

feigning illness while held captive in a time of war or faking symptoms to divert unwanted attention away from another person). DSM-5 cautions practitioners to suspect malingering for two or more of the following: (1) a forensic context, (2) major differences between subjective reports and objective data, (3) the examinee’s (or client’s) lack of cooperation, and (4) an antisocial personality disorder (ASPD) diagnosis. While intuitively appealing, combining any two of the four criteria leads to an unacceptably high false-positive rate (Rogers, 1990). Also, at least some of the criteria, while certainly present in some cases, do not actually appear to be closely associated with malingering. ASPD, for example, is not necessarily associated with an increased incidence of malingering (Niesten, Nentjes, Merckelbach, & Bernstein, 2015; Sumanti, Boone, Savodnik, & Gorsuch, 2006). This serves as a reminder that a potentially common characteristic is not necessarily a distinguishing characteristic. Simple reliance on common characteristics (see Rogers, Chapter 2, this volume) will inevitably result in unnecessary false-positive errors.

Malingered Neurocognitive Dysfunction Greiffenstein, Baker, and Gola (1994) appear to be the first to have proposed malingering diagnostic criteria for use in neuropsychological settings, specifically regarding memory dysfunction. But their article lacked a clear definition of malingering, certain terms were vague, and the criteria referred to memory loss only. In 1999, Slick et al. proposed a more comprehensive set of criteria specific to malingered cognitive dysfunction. The criteria became widely accepted (Heilbronner et al., 2009), subsequently became known as “the Slick criteria,” and fueled an explosion of malingering test development. Slick et al. (1999) defined malingered cognitive dysfunction as the “volitional exaggeration or fabrication of cognitive dysfunction for the purpose of obtaining substantial material gain, or avoiding or escaping formal duty or responsibility” (p. 552). The authors acknowledge the difficulty inherent in determining intention and motivation, and note that knowing these internal states in someone else may be impossible. In light of this problem and consistent with Rogers’s (1990) recommendation to use gradations of certainty when referring to malingering, the Slick criteria (1999) included possible, probable, and definite malingering as indications of professionals’ confidence in the diagnosis.

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The Slick criteria (1999) articulated a formal and comprehensive approach to the classification of MND. They were intended to (1) provide an explicit definition of malingering, (2) specify rule-out conditions, (3) include behavioral observations, (4) fully specify criteria, and (5) provide guidelines for the evaluation of neurocognitive domains other than memory. The Slick criteria have been endorsed by the American Academy of Clinical Neuropsychology as “a reliable means of operationalizing diagnostic decisions related to the determination of malingering” (Heilbronner, et al., 2009, p. 1098). Nevertheless, the Slick criteria have only been proposed as a framework for identifying malingering. In the original Slick criteria, definite malingering is identified by (1) incentive to malinger plus (2) “below-chance” performance on a forced-choice procedure. Below-chance performance is seen when test-takers score significantly lower than 50% on tasks in which choices determined by a coin flip alone would yield a score near 50% (Frederick & Speed, 2007). The classification of below-chance performance is typically reserved for those instances in which only 5% or fewer of nonmalingerers would produce the below-chance score at or below the designated level. Probable malingering is identified by “two or more types of evidence from neuropsychological testing, excluding definite negative response bias” (Slick et al., 1999, p. 552). Types of evidence include positive test scores (consistent with malingering) on “wellvalidated” psychometric tests and “discrepancies” between test data or self-report and known patterns of brain functioning, observed behavior, collateral reports, or documented background history. Two positive test scores would minimally meet the criteria for probable malingering, absent any other evidence. Positive scores on validity scales of personality tests such as the Minnesota Multiphasic Personality Inventory–2 (MMPI-2) or MMPI-2 Restructured Form (MMPI-2-RF) are also included in the analysis. Possible malingering is identified by substantial external incentive and just one positive element of the probable malingering category elements. Consequently, when using the Slick et al. (1999) criteria, a single positive test score of feigning is often sufficient to generate classifications of probable or possible MND. Limitations to the Slick Criteria

The Slick criteria have been central to an operationalized understanding of neurocognitive malingering, and their influence on the current state of

malingering detection cannot be overstated. Yet, as would be expected in any set of preliminary criteria regarding such a complex construct as malingering, several problems with the criteria emerged. For instance, Larrabee, Greiffenstein, Greve, and Bianchini (2007), while generally affirming the importance of the MND model, recommended improvements, especially with regard to determinations of probable MND (e.g., they recommended the use of an aggregate of malingering indicators). Boone (2007), in contrast, was more critical of the MND model, most notably regarding the nonspecificity of the exclusion criteria. Rogers, Bender, and Johnson (2011) were similarly critical, and noted that the criteria were potentially biased toward findings of malingering. Delis and Wetter (2007) underscored the difficulty inherent in determining intentionality as required by criterion A of the MND model. They observed that “intentionality is likely multifactorial in nature” (p. 592) and “practitioners may not have access to sufficient background information about a person’s life to assess if external incentives are operative in the case” (p. 594). In short, their analysis highlighted the criterion’s problematic tendency to equate possible incentive with true motivation. Whether psychometric tests are “well-validated” was not defined by Slick et al. (1999), and there is no consensus document from any organization that lists which tests are designated “well validated,” which are “adequately validated,” or which are “experimental.” Also, none of the Slick criteria have been empirically evaluated for their accuracy in classifying malingering. In recognition of these issues, Slick and Sherman (2013) revised the MND criteria. Though the definition of neurocognitive malingering was not changed, Slick and Sherman did propose modifications to important constructs, which we review here briefly. For example, below-chance scores are no longer necessary to diagnose definite malingering. As an important change, rule-out criteria do not need to be “fully” accounted for by psychiatric, neurological, or developmental factors; the new criteria use the term substantially instead of fully. The first of these two changes would appear to increase sensitivity, while the latter change seems to improve specificity. The category of possible malingering has been dropped altogether, apparently in recognition of its lack of diagnostic utility (see Rogers, Bender, et al., 2011). The revised Slick and Sherman (2013) criteria acknowledge the possibility that malingering may occur due to neurocognitive compromise (second-



3. Neuropsychological Models of Feigned Cognitive Deficits 45

ary malingering), and add both “compelling inconsistencies” (see Bianchini, Greve, & Glynn, 2005; Larrabee et al., 2007) and “posterior probabilities” (Larrabee, 2008) to the list of methods that can be used to detect malingered performance. In addition, it concedes that there are fundamental limitations to the concept of secondary gain. These additions to the criteria are reviewed briefly below. The use of compelling inconsistencies as potential indications of feigning was espoused by Bianchini et al. (2005) to facilitate identification of malingered pain. Such inconsistencies can be qualitative—an examinee’s complaint of severe and incapacitating memory loss as compared to intact memory observed during the interview, for example. Performance evaluations, collateral reports, and surveillance video that contradict the claimant’s report fall under this category as well. Several examples of what appear to be more quantitative compelling inconsistencies are included in the Slick and Sherman (2013) criteria; they use the term “marked and implausible discrepancies” to refer to compelling inconsistencies that are central to six specific criteria (criteria 3.2–3.7, p. 63). Slick and Sherman (2013) also recommend establishing posterior probabilities, that is, using multiple indicators of poor effort or symptom exaggeration in aggregate to aid detection. A growing body of research suggests that this approach can increase diagnostic certainty above that associated with single indicators. However, the particular mechanics and utility of this procedure are still being debated. Finally, the term secondary gain has typically been used to distinguish incentives coming from internal mechanisms (primary gain) from those involving external or material motivations. But as noted by Rogers and Reinhardt (1998), the term is often confusing due to its multiple context-dependent meanings. Slick and Sherman (2013) seem to acknowledge this in their proposal of the new diagnostic category of adjustment problem/disorder with specious symptoms, for use “in cases in which a person exaggerates or fabricates symptoms to obtain psychosocial secondary gains, rather than material–legal secondary gains” (p. 68). This is a potentially important distinction, but its clinical utility has yet to be tested. While many of the modifications to the original Slick criteria likely represent significant advances, they require validation as a unitary set of criteria for malingering. A distillation of the substantive changes to the 1999 criteria may be found in Table 3.1.

SYMPTOM VALIDITY TESTING The term symptom validity testing (SVT) originally referred to the forced-choice paradigm to which the binomial theorem could be applied to identify significantly below-chance performance (Pankratz, 1979; Bender & Rogers, 2004). Over time, SVT became synonymous with effort testing and validity scales in general, with no assumptions about significantly below-chance performance. Under this rubric, many tests of cognitive effort and validity scales were developed. However, more recently, SVT has been used to refer to tests that detect feigned psychiatric symptoms only, in an effort to distinguish them from tests developed to detect neurocognitive dysfunction. This partitioning is a clinically important one given that symptom exaggeration and poor cognitive effort are not always correlated, and that tests have been shown not to be equally effective across these domains (Rogers, Gillard, Berry, & Granacher, 2011). For a review of common SVTs used in forensic evaluations, see Rogers and Bender (2013), Young (2014), and Bender (Chapter 7, this volume).

PERFORMANCE VALIDITY TESTING Larrabee (2012) is credited with suggesting that neuropsychologists distinguish between tests that identify cognitive feigning and those used to detect feigned psychopathology, giving rise to the term performance validity testing (PVT). So-called “stand-alone” PVTs were designed expressly to detect poor effort during neurocognitive assessment. For reasons primarily involving ease (of both test development and clinical implementation), most of these tests have employed the floor effect strategy. A major strength of this strategy is that it is conducive to forced-choice formats, which allow for statistical comparisons to chance levels of performance, thereby potentially providing highly specific findings. The floor effect strategy capitalizes on the use of tests that appear to be more difficult than they actually are. Poor performance on such a test, a test on which even cognitively impaired individuals have been shown to perform quite well, suggests poor effort and possibly feigning (Schutte, Axelrod, & Montoya, 2015). While norms for those with severe traumatic brain injury (TBI) and a handful of other clinical populations have been established for comparison purposes, studies establishing norms for multiple comorbid conditions have been widely lacking for these

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TABLE 3.1.  Summary Table of Major Changes to Slick Criteria for MND

Slick, Sherman, and Iverson (1999)

Slick and Sherman (2013)

Specific language of 1999 criteria

Criterion number

How issue was addressed in 2013 criteria

Minimal level of certainty

Reliance on information that simply “suggests” exaggeration or fabrication

B4, B5, and C1

References to “suggest” have been removed

Minimal criteria for probable MND

≥ 2 criteria (≥ 20% of 10 criteria)

B2–B6 and C1–C5

Criteria now require three or more indicators of exaggeration/ fabrication (criterion 2)

Use possible incentive as sufficient for MND

External incentive is equated with motivation

A

Not addressed (criterion 1)

Denial of past history “proves” MND

Denial of psychiatric history or premorbid dysfunction

C1 and C4

No substantial change (criterion 3.5)

Feigning in a noncognitive domain still “proves” MND

“Exaggerated or fabricated psychological dysfunction”

C5

No longer a specific reference to psychological symptoms

Maximal level of certainty

“Fully accounted for” (100%)

D

“Fully accounted for” is now “substantially accounted for” (“definite” criterion 3)

High criteria for ruling-out motivation

“Significantly diminished capacity to appreciate laws and mores” or “inability to conform behavior”

D

Problematic language removed from 2012 criteria

Poor insight and impairment are not considered

Self-reported symptoms are discrepant

C2 and C3

“Secondary MND” added to criteria

Problems with 1999 criteria

Note. In a critical analysis of the Slick et al. (1999) criteria for MND, Rogers, Bender, and Johnson (2011) pointed out several problems with the criteria, including the specific language in question, and made recommendations to improve the criteria. This table reflects some of those issues and how they were addressed in Slick and Sherman (2013).

tests. The virtual neglect of comorbidity poses a formidable problem, in light of the fact that few examinees present with a single set of symptoms or a single diagnosis. Embedded PVTs are tests that, although not developed to detect poor effort, were later utilized to this end. The strength of these tests rests in their time-effectiveness and dual purpose. However, because they tend to measure actual ability, at least to some degree, they are often less specific to the effects of poor effort alone. For example, using traditional cutoff scores for the Reliable Digit Span (RDS) from the Wechsler Adult Intelligence Scale–IV (WAIS-IV) incorrectly classifies genuine patients with dementia as feigning (Zenisek, Millis, Banks, & Miller, 2016). While not a conceptually based strategy per se, research has demonstrated the potential of aggre-

gating PVTs to improve classificatory accuracy. For instance, in a known-groups design (26 litigating/ compensation-seeking participants who scored below chance levels vs. 31 patients with moderate to severe TBI), Larrabee (2008) found that posttest probabilities of malingering increased markedly when three PVTs were failed as compared to one PVT failure. The pretest odds of each test correctly classifying a performance as feigned were set at .667 (which are the odds associated with a base rate of 40%). When the score from each effort test was combined with the others, posttest probabilities increased from 60–70 to 98% (at base rates of 30% or above). Whiteside et al. (2015) used a similar aggregation strategy via logistic regression to establish a “cross-domain, logistically-derived embedded PVT (CLEP)” (p. 795). The criterion group consisted of



3. Neuropsychological Models of Feigned Cognitive Deficits 47

67 patients with mild TBI who failed two or more PVTs, while the clinical group comprised 66 patients with severe TBI. From a battery of tests, three scores entered the regression and yielded superior classification to that of each test alone, according to AUC (area under the curve) analyses. While promising as a detection strategy, it remains unknown whether other combinations of tests from the battery hold utility, and CLEP would not be helpful in cases in which a malingerer fakes a deficit in only one domain. Multiple assumptions must be made when using these complex statistical models, and the devil may be in the details, as we discuss later in this chapter.

Effort Testing For many years, malingering was thought to be determinable by experienced neuropsychologists, with no need of effort test data. In contrast, probabilistic methods involving effort tests are now considered a critical part of neuropsychological testing, particularly in forensic settings. The major governing bodies within neuropsychology have issued statements about the importance of effort testing when assessing malingering, and have indicated that the absence of effort testing must be justified (Bush et al., 2005; Heilbronner et al., 2009). The move from subjective judgment to actuarial decision making represents an important improvement. Yet despite an abundance of effort tests, regrettably few employ conceptually based detection strategies. The original rationale for formal effort testing likely began with a recognition that without sufficient examinee effort, neuropsychological test scores are invalid and uninterpretable. Over time, and despite cautions against drawing equivalences, effort tests have become more and more synonymous with malingering detection. Current tests of effort, as used in neuropsychological assessment, do not measure the full spectrum of effort. Rather, they identify poor effort only, without any regard to the cause or multiple causes of poor effort. A “normal” score does not necessarily indicate good effort, and a low score does not imply a particular cause, such as feigning. The importance of this point is highlighted in Box 3.1. As Millis (2008) stated, there is a “pressing need to determine how effort tests relate to each other and to standard cognitive measures” (p. 897). In the years since his call for a better understanding of intertest relationships, the question remains largely unanswered (see Larrabee, 2014, for an example of progress in this area).

BOX 3.1.  Twin Fallacies about Effort Testing and Malingering 1. Rule-out fallacy: “Normal” scores do not preclude poor effort or feigning. 2. Rule-in fallacy: Poor effort cannot be equated with feigning.

Base Rates of Malingering A base rate (or prevalence) can be defined simply as the percentage of a group that possesses a given characteristic (Elwood, 1993). Large epidemiological studies are needed for precise estimates. Base rates are instrumental to establishing the utility of a test; the test must prove to be superior to chance, and the odds of a correct determination must incorporate the probability of making that determination by base rate alone (i.e., with no test data). Despite what might appear to be well-established base rates for malingered mild TBI (mTBI), precise base rate estimates are probably impossible to obtain, and current estimates may be too high. Mittenberg, Patton, Canyock, and Condit (2002) reported a base rate of 38.5%, and Larrabee (2003) reported a rate of 40%. It bears noting, however, that the Mittenberg et al. (2002) data are (1) from a survey and (2) asked American Board of Clinical Neurophysiology (ABCN) diplomates to estimate the percentage of their annual cases that involved “probable symptom exaggeration or malingering” (p. 1101, emphasis added), which incorrectly suggests that the two terms are interchangeable, thereby inflating the estimate of actual malingering. Larrabee’s (2003) 40% base rate estimate is the mean of 11 past studies with base rate estimates ranging from 15 to 64%. Although Larrabee’s estimate has an element of empiricism that surveys do not, it included studies utilizing the highly problematic differential prevalence design (i.e., simple presence of litigation; see Rogers, Chapter 1, this volume) to designate “motivated performance deficit suggestive of malingering” (p. 411). Other surveys are markedly discrepant with the previously mentioned base rates. For example, Sharland and Gfeller’s (2007) survey of 188 members of the National Academy of Neuropsychology (NAN) yielded highly variable rates for probable malingering and definite malingering that depended on the wording of the question. In litigants, the median estimate of deliberate exaggeration of “cognitive impairment” was 20%, but

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the median for “definite malingering” was only 1% (Sharland & Gfeller, 2007; see Table 3, p. 216). In addition, Slick, Tan, Strauss, and Hultsch (2004) found that only 12.5% of their group of experts believed that definite malingering occurs in over 30% of cases. In stark contrast to Mittenberg et al. (2002), roughly two-thirds reported that “definite malingering” occurs in 20% or less of cases. Base rate estimates of neurocognitive malingering are based on (1) an improbably low test score suggestive of poor effort, and (2) a presumption not only that the poor effort was deliberate but also that the effort test failure reflects the examinee’s intentional and external motivation for the poor effort (i.e., to obtain an external incentive). In other words, whether or not the examinee is actually incentivized by the incentive is not considered. Whereas the first criterion seems reasonable, the latter may require an impermissibly large leap of faith based entirely on the mere presence of incentive. Deliberate failure on an effort test does not indicate or even imply the reason(s) for failure. As a striking example, even scoring significantly below chance levels on the Test of Memory Malingering (TOMM; Tombaugh, 1997) does not equate to failing expressly to obtain some material gain, even when the case involves financial compensation. This problem of determining motivation is not unique to SVT and PVT prevalence estimation; rather, it is inherent to all research on malingering. While the current definition requires that malingering be motivated by external incentives, it does not provide any advice on how to determine the causal role of motivation. Instead, it provides only token guidance regarding context and risk factors. In light of these factors, most base rate estimates of malingering are very likely to be substantial overestimates. Similarly, the fact that findings of probable malingering are often lumped in with those of definite malingering likely has inflated base rate estimates. A recent study by Ruff, Klopfer, and Blank (2016) suggests that “lumping” has significant implications with regard to classification rates. The authors disentangled levels of classificatory certainty in a group of consecutively referred personal injury cases. They examined rates of “definite” malingering independently from “probable” and “possible,” as defined by Slick et al. (1999), and applied homogenous testing methods across all participants. When analyzed by this approach, the base rates of definite and probable malingering plummeted: 4.7% for probable malingering and 2.0% for definite malingering. The percentages stand in stark contrast to previous studies that

(1) grouped definite with probable malingering, and (2) used more heterogeneous tests and methods. Interestingly, most of the claimants in their study presented with TBI, with severity ranging from mild to severe. When the group of patients with mTBI (n = 89) was evaluated independently, the rates increased only slightly (6.7% for probable and 3.4% for definite malingering). Given that prior studies have reported much higher base rates of malingering in mTBI, these data warrant replication. Note that the base rate of “possible” malingering reported in the Ruff et al. (2016) study was 21.3%; when this number is added to the other gradations of malingering from that study, the overall base rate is more in line with previous estimates (28% according to Ruff et al. vs. 32–38% according to prior estimates). In short, it appears that previous studies have relied on overinclusive criteria; using more specific criteria reduces the base rate. The Ruff et al. data are fairly well-aligned with the most comprehensive review of base rates to date by Young (2015), who reported that the base rate of malingering in forensic contexts was likely to be 10–20%.

DETECTION STRATEGIES FOR NEUROCOGNITIVE MALINGERING Numerous psychometrically derived detection strategies can be systematically applied to neurocognitive feigning detection. For example, the strategy of “severity indexing” uses information about dose–response relationships between injury severity and test performances to identify unlikely or amplified presentations. In short, performances that are indicative of a severe injury (according to normative standards), while simultaneously coupled with a mild injury, raise questions about the validity of those performances primarily because the scores appear to be marked amplifications of the actual degree of dysfunction. Amplified presentations appear to be especially well-suited for use in forced-choice testing (FCT) formats. FCT performance can be compared to expected performance levels based on clinical groups, and to below-chance levels of performance. Both can be compelling indicators of feigning but clearly have different strengths and weaknesses. For example, significantly below-chance performance is virtually synonymous with deliberate failure, but it lacks sensitivity, which means that few feigners will be detected this way. In contrast, FCT comparisons to clinically derived cutoff



3. Neuropsychological Models of Feigned Cognitive Deficits 49

scores tend to be more sensitive but lack specificity, which means that some examinees may be incorrectly categorized as feigning. Both freestanding and embedded FCTs employing the amplified presentation strategy have been incorporated into the Advanced Clinical Solutions (ACS) scoring package for the WAIS-IV and Wechsler Memory Scale–IV (WMS-IV) (Holdnack, Millis, Larrabee, & Iverson, 2013). The ACS includes five effort tests (one freestanding and four embedded), each of which relies primarily on the floor effect detection strategy, thereby falling under the umbrella of the amplified presentations strategy. The likelihood of failing one measure can be added to the likelihood of also failing others, thereby increasing the probability of a correct classification. A strength of the ACS program is that it presents its classificatory estimates in the context of a range of base rates. In other words, it shows which effort tests were failed, how often such a performance occurred in various clinical samples, and the probability of that particular number of tests being failed by chance. The importance of comparing performance to clinical groups before making a determination of feigning is evident in Table 3.2, which is taken from the ACS Clinical and Interpretive Manual. This methodology is not new to statistics, but it is new to neuropsychological assessment and represents a significant advance in psychometric detection of feigned cognitive impairment. However, users of the ACS should be aware of potential methodological issues involving the potential for inflated estimates associated with making multiple comparisons and with the degree to which the measures may be correlated. As we discuss later, each of these issues warrants further investigation. The Response Bias Scale (RBS) is an example of an amplified presentation strategy applied to the MMPI-2-RF. The RBS was developed by Gervais, Ben-Porath, Wygant, and Green (2007) as a measure of exaggerated cognitive complaints based on items that correlated with failure on the Word Memory Test (WMT; Green, 2003). The RBS is empirically derived and appears to be effective because its items capture overreporting of cognitive symptoms without being correlated to actual memory ability or memory complaints (Gervais, Ben-Porath, Wygant, & Green, 2008; Gervais, Ben-Porath, Wygant, & Sellbom, 2010). In a criterion-group design investigating intentional versus unintentional feigning, Peck et al. (2013) found that the RBS could differentiate individuals with nonepileptic epilepsy from genuine

and nongenuine (≥ 2 PVT failures) patients with TBI, especially when used in conjunction with the Symptom Validity Scale (SVS; formerly called the Fake Bad Scale [FBS]). False-positive rates ranged from good to excellent, but the sample size was small. Also, they assumed a base rate of 40% for malingering (which may be double the true base rate), thereby artificially increasing classification rates. Nonetheless, the study suggests that using the two scales together has potential, and that the RBS in particular represents a relatively rare opportunity to identify feigned symptoms of TBI via a psychiatric symptom inventory. With regard to the unlikely presentation strategy, Rogers, Robinson, and Gillard (2014) created a Symptom Combinations (SC) scale for the Structured Interview of Malingered Symptomatology (SIMS; Smith & Burger, 1997) based on item pairs that were uncorrelated or negatively correlated in patients but were frequently endorsed by feigners. The strategy resulted in an effect size of 2.01, and while it still requires further validation, it appears to be a promising means of expanding the utility of the SIMS. New technologies are also being considered. For example, Bigler (2015), while acknowledging the general utility of current effort testing methods, has proposed that neuroimaging be incorporated into the neuropsychological assessment of effort and test validity. Part of his contention stems from mounting evidence, including the Institute of Medicine’s (IOM) position paper, suggesting that data obtained from PVTs and SVTs are insufficient when determining the motivations for effort test failure (Freedman & Manly, 2015). Bigler (2015) advances a framework to test hypotheses about the neural substrates of effort and motivation. In short, he argues that because all PVTs and SVTs involve at least some degree of task engagement, and task engagement can be impaired by neurocognitive disorders, neuroimaging should be part of the formulation when determining where cutoff scores should be on PVTs and SVTs. He also notes that nonconscious factors likely play a role in motivation, and imaging could uncover these processes. Bigler’s arguments appear to be logical and to have intuitive appeal, but establishing empirical bases to his points would appear to be a huge undertaking involving hundreds of patients with TBI and other clinical groups with various neurocognitive and mental disorders. Until more research supports forensic applications, imaging should not be used as an indicator of malingering (see McBride, Crighton, Wygant, & Granacher, 2013).

50

I.  Concep t ua l Fr a me work TABLE 3.2.  Percentage of Cases in Groups of Interest Having Various Numbers of Scores below Clinical 5% Cutoff

Number of scores at 5% cutoff 1

2

3

4

5 —

Groups of interest No stimulus group

100

100

88

    .8

Simulators

 40

 24

14

10

2

Overall clinical sample

  9

  2

 0

 0

0

Traumatic brain injury

  9

  3

 0

 0

0

Temporal lobectomy

  8

  0

 0

 0

0

Schizophrenia

 15

  2

 0

 0

0

Major depressive disorder

  4

  0

 0

 0

0 —

Anxiety disorder

  0

  0

 0

 0

Intellectual disability—mild

 38

 13

 3

 3

0

Autistic disorder

 23

  5

 0

 0

0

Asperger’s disorder

  3

  0

 0

 0

0

Reading disorder

  0

  0

 0

 0

0

Mathematics disorder

 14

  0

 0

 0

0

ADHD

  6

  3

 0

 0

0

Nonclinical sample

  2

  0

 0

 0

0

Education level ≤8 years

 15

  2

 0

 0

0

9–11 years

  9

  1

 0

 0

0

12 years

  2

  0

 0

 0

0

13–15 years

  1

  0

 0

 0

0

≥16 years

  1

  0

 0

 0

0

  1

  0

 0

 0

0

Race/ethnicity White African American

  5

  1

 0

 0

0

Hispanic

  6

  1

 0

 0

0

Asian

  0

  0

 0

 0

0

General Ability Index 69 or less

 19

  6

 0

 0

0

70–84

  7

  1

 0

 0

0

85–99

  2

  0

 0

 0

0

100–114

  1

  0

 0

 0

0

115 or higher

  0

  0

 0

 0

0

Note. This table pertains to a 5% cutoff score only. The ACS also provides percentage estimates based on 2, 10, 15, and 25% cutoff scores, yielding a range of confidence levels.

Advanced Clinical Solutions for WAIS-IV and WMS-IV. Copyright © 2009 NCS Pearson, Inc. Reproduced with permission. All rights reserved. “WAIS” and “WMS” are trademarks, in the United States and/or other countries, of Pearson Education, Inc. or its affiliates(s).



3. Neuropsychological Models of Feigned Cognitive Deficits 51

THE CURRENT STATE OF VALIDITY TESTING IN NEUROPSYCHOLOGICAL ASSESSMENT As mentioned, a number of embedded and freestanding tests are sold by major publishers that, by design, evaluate feigned cognitive impairment. These cognitive feigning tests have been described in the literature in various compendia (e.g., Lezak, Howieson, Bigler, & Tranel, 2012). Additionally, Schutte and Axelrod (2013) reported on “empirically-derived embedded measures that are typically derived from commonly administered neuropsychological tests” (p. 160). According to Schutte and Axelrod, advantages of “embedded measures” include efficiency (reducing time constraints by administering stand-alone malingering tests), reducing ease of coaching, assessment in multiple domains of ability, multiple opportunities for assessing malingering, and assessment at multiple time points. Schutte and Axelrod reported on no fewer than 50 unique embedded measures for identifying feigned cognitive impairment. Likewise, Martin, Schroeder, and Odland (2015; see their Tables 21 and 22) reported a wide range of standalone and embedded tests currently employed in neuropsychological assessments. Martin et al. (2015) surveyed 316 neuropsychologists regarding their practices for using stand-alone and embedded measures of validity test measures. The mean number of validity tests administered per assessment was about six, with up to 16 stand-alone and embedded measures included in any one forensic assessment. On a smaller scale, Schroeder, Martin, and Odland (2016) surveyed 24 neuropsychologists whom they identified as experts in the assessment of feigned cognitive impairment. These experts administered an average of eight stand-alone and embedded measures, using up to 12 such measures in forensic evaluations. The authors provided respondents with a number of possible explanations for positive scores (i.e., scores suggestive of feigning) to see how the alternatives were ranked in forensic and nonforensic evaluations. None of the alternative explanations included “test unreliability.” In other words, false positives were assumed to always be related to genuine cognitive dysfunction. In standard criterion groups analysis, however, false-positive classification errors refer to a failure of the test to classify correctly on the construct used to divide the criterion groups; that is, effort or feigning—not cognitive dysfunction. A false-positive classification in this context refers to the inherent unreliability of the classification method, and that should be the

first consideration of reasons for assumptions of “false positives” in the clinic. It is correct to say: “Individuals with bona fide pathology nevertheless inaccurately generate positive scores, not because they have genuine pathology, but because the test is inherently unreliable to some degree, which is reflected in the false-positive rate.”

Classification Errors Produced by Test Decision Rules For any useful classification test, there are characteristics of the test that result in differential rates of positive scores for people with a condition (for our purposes, faking symptoms) and for people without the condition (not faking symptoms). Because some tests have more than one potential score/basis for classification, we prefer to use the term trials rather than tests. Any decision about feigning based on an objective test score will be referred to as a trial. Not all feigning receives a positive score on a feigning test trial, but the rate at which a single trial generates positive scores for feigners is the true positive rate (TPR, 0 ≤ TPR ≤ 1). Some persons who are not faking (compliant responders) nevertheless generate positive tests scores on malingering test trials—these are falsepositive classification errors. The reasons for these errors of classification include the inherent unreliability of the classification process and errors in measurement. The rate at which the trial generates incorrect positive scores is called the “falsepositive rate” (FPR, 0 ≤ FPR ≤ 1). The inherent unreliability of the classification process can be explained by the fact that compliant responders often have attributes in common with feigners on many neuropsychological assessment procedures (for a list of alternative clinical explanations for misclassification, see Martin et al., 2015; Schroeder et al., 2016). It is important to recognize that TPR and FPR are not the test information that is essential for individuals who administer tests; clinicians must consider positive predictive power (PPP; the probability that a positive test score resulted from feigning). As noted by Frederick and Bowden (2009a), values of PPP are not always obvious, as PPP depends on the probability that someone is feigning before testing even begins (i.e., the local base rate of feigning). As an example, consider the Failure to Maintain Set (FMS) score as applied by Larrabee (2008), who assumed that the base rate (BR) of feigning is 40% in compensation-seeking persons alleging TBI. Larrabee reported that FMS

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> 1 has a TPR = .48 and FPR = .129. FPR = .129 might suggest to many that there exists only a 12.9% probability that a person alleging TBI and seeking compensation would be classified as feigning despite not actually feigning impairment. The risk is actually much higher as the BR of feigning falls below 50%, as more and more compliant individuals are at risk for a false-positive classification error. For example, consider Larabee’s BR estimate of .40 for 100 examinees—40 feigners. True positive classification of feigning is found by 40 * .48, which is to say 19 true feigners are classified by FMS > 1. False-positive classifications of feigning for the 60 individuals who are compliant is 60 * .129, which means that eight individuals will be falsely classified as feigning; 27 individuals are classified as feigning—the rate of false classifications for the 100 individuals is 8/27 or 30%, which is far in excess of the estimated FPR. If the BR is actually much lower, say 15%, then the rate of false classifications is (85 * .129) / ([85 * .129] + [15 * .48]) = 11/18 = 61%. Decisions based on classification scores inherently have error. Even if TPR and FPR are known precisely (which is never the case), decisions are essentially exercises in choosing which form of error one wishes to risk. If one decides that a score is consistent with feigning, then one chooses to risk some form of false-positive classification error: “I am willing to characterize this person as feigning at the risk of incorrectly mislabeling someone who is not faking as a faker.” If one decides that a test score is not consistent with feigning, then one chooses to risk some form of false-negative error: “I am willing to label this person as compliant at the risk of not identifying a faker as a faker.” Forensic practitioners should keep in mind that error-free decisions are not possible, and it is often impossible to recognize when one is making an error.

Use of Multiple Validity Tests Common sense would argue that two positive scores on feigning tests predict feigning better than one positive score only. Given that there is usually more than one trial related to a decision in current examinations, and sometimes there are many trials, the overall rate of making a falsepositive error must be considered. Given the large numbers of tests and embedded measures that exist and apparently are administered in forensic assessment of cognitive abilities (Martin et al., 2015), it seems important to have a strategy for evaluating multiple positive test scores. Authors (Boone

& Lu, 2003; Larrabee, 2008; Ross, Putnam, Millis, Adams, & Krukowski, 2006) have discussed methods of combining test results to better evaluate the probability of malingering. Multiple Comparisons

When multiple tests are evaluated in hypothesis testing, the associated error rate is referred to as the familywise error rate (FWER; e.g., see Keselman, Miller, & Holland, 2011). Keselman et al. noted that the FWER is computed as 1 – (1 – alpha)m, where “m” is the number of hypothesis tests and alpha is the predetermined acceptable error rate (p. 421). The FWER inflates rapidly when alpha = .05 and when there are many hypothesis tests. For instance, for five hypothesis tests at alpha = .05, the overall false-positive error rate, FWER, is 1 – (1 – .05)5 = .23. It is common practice in experimentation to adjust the individual hypothesis error rates (i.e., to reduce alpha) in a series of decisions (e.g., post hoc hypothesis tests) to keep the overall error rate at or below 5%. Unlike alpha error rates chosen a priori in hypothesis testing, classification test FPRs often vary considerably. For a series of five classifications tests, FWER can be computed as FWER = 1 – [(1 – FPR1)*(1 – FPR2) *(1 – FPR3)*(1 – FPR4)*(1 – FPR5)] Larrabee (2008) was interested in demonstrating how information about TPR and FPR could be combined for multiple trials to improve the certainty about classification of malingering. He reported on four embedded measures of malingering from the Visual Form Discrimination Test (VFD; TPR = .48, FPR = .069), the finger-tapping test (FTT; TPR = .40, FPR = .065), the RDS (TPR = .50, FPR = .065), and FMS on the Wisconsin Card Sorting Test (FMS; TPR = .48, FPR = .129), plus the MMPI-2 FBS (TPR = .808, FPR = .138). Based on these FPR estimates, we can compute the FWER for combining these five classification trials: FWER = 1 – [(1 – .069) * (1 – .065) * (1 – .065) * (1 – .129) * (1 – .138)] = .389 At least 39% of non-fakers will get at least one positive score when completing this sequence of tests. However, when considering the meaning of just one positive score for these trials, Larrabee es-



3. Neuropsychological Models of Feigned Cognitive Deficits 53

timated (at BR = .40) that one positive score represented an 80–84% likelihood of faking (see Larrabee, 2008, Table 2). If 39% of non-fakers get at least one positive score for these five tests, it cannot be true that getting one score positive in five tries represents an 80–84% likelihood of faking. The error lies in the way the likelihood ratios were chained.

Chained Likelihood Ratios Calculating the probability of feigning only in terms of the number of positive classifications does not take into account the meaning of negative classifications. The probability of negative classifications must be considered in the context of the complete evaluation—if five feigning tests were used, the outcomes of all five tests must be evaluated. For example, if five feigning tests are administered and two are failed, it is easy to understand that this means something much different than if 1,000 tests are administered and two are failed. Larrabee (2008) proposed the use of likelihood ratios (LRs) to understand the information present in multiple validity tests. LRs are ratios that assess the potential utility of a classification test. When a test score is positive, LR+ = TPR/FPR. When a test score is negative, LR– = (1 – TPR) ÷ (1 – FPR). For validity tests, LR+ essentially means “increased likelihood of feigning” and is calculated only when a test score is positive. LR– essentially means “decreased likelihood of feigning,” and is calculated only when a test score is negative. “Chained likelihood ratios” refers to the process of considering how test information changes the initial likelihood of feigning represented by BR. BR is the pretest likelihood of feigning, and chaining LRs results in a posttest likelihood of feigning. The mathematics of chaining LRs essentially derives a probability value for a process, in which X tests are passed and Y are failed: 1. Start: Pretest probability of feigning (i.e., BR, converted to pretest odds of feigning) a. First test passed; pretest odds decrease; therefore there is a new value for odds of feigning b. Second test failed; odds of feigning from (a) therefore increase; new value for odds of feigning c, d, e, etc. Iterative changes to odds of feigning based on passing or failing remaining tests, until 2. End: Posttest odds of feigning (converted to post-test likelihood of feigning).

It is important to note that a likelihood expressed as a “probability of an event” is not the same as the “odds of an event,” even though the values are easily transformed from one to the other. For example, if the pretest likelihood of feigning (BR) = .4, then we compute the pretest odds of feigning, which is BR ÷ (1 – BR), or .4/.6 = .67. To “chain” likelihoods, we must convert BR to an odds value (pretest odds), then multiply the pretest odds by each test’s LR, depending on whether it was a pass or a fail. In the case in which two of five tests were failed, Pretest odds * LR1– * LR2– * LR3– * LR4+ * LR5+ = posttest odds → posttest probability of feigning The method of chaining likelihoods has existed for decades and is commonly cited in medical decision-making texts such as that of Straus, Richardson, Glasziou, and Haynes (2011). When Larrabee (2008) reported a range of probabilities for one positive score, he calculated the values this way, using the information for only the one failed test, Pretest odds * LRn+ = posttest odds → posttest probability of feigning For a more balanced estimate, the information from all the tests administered should be used (Straus et al., 2011). In Larabee’s example, test 1 is failed, but tests 2–5 are passed: Pretest odds * LR1+ * LR2– * LR3– * LR4– * LR5– = posttest odds → posttest probability of feigning Because it appeared to us that all five tests were used for all participants in Larrabee’s study, we applied his method of chaining LRs in order to recalculate Tables 1, 2, and 3 in Larrabee (2008, pp. 671–672) using both positive and negative LRs; that is, like Larrabee, we first calculated the probability of a single positive trial, but we then recalculated that probability in light of having four negative trials follow the positive trial; we chained both positive and negative LRs. For Larrabee’s Table 2, we calculated the probability of two trials being positive, but then modified the probability by the necessary implication that three trials were negative. For his Table 3, we calculated the probability of three trials positive and two trials negative. In our own Table 3.3, we report the calculated values based on Larrabee’s assertions, then we provide estimates of the likelihood of faking

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TABLE 3.3.  Probability of MND Given Number of Positive Trials out of Five Trials

Larrabee (2008)

Recalculation

One positive trial

.796 to .837

.096 to .308

Two positive trials

.936 to .973

.569 to .865

Three positive trials

.989 to .995

.927 to .988

Note. The recalculated estimates include data from both negative and positive trials.

given n positive trials by use of simulation study. It is important to note that Table 3.3 applies only to a situation in which five trials are planned and presented. The recalculated probability values for three positive trials would be much lower if there were 10 or 20 total trials, for example. Ethically, clinicians are obligated to report the outcomes for all trials that they consider appropriate indicators of validity; that is, if they use RDS < 7 on one case, they must use it consistently on all cases when validity assessment is invoked. In other words, they cannot vary the cutoff score and sometimes use RDS < 8. It is certainly inappropriate for clinicians to choose validity measures after they have seen the outcomes. Berthelson, Mulchan, Odland, Miller, and Mittenberg (2013) investigated the implications of using multiple tests to evaluate feigning. They obtained data from 22 independent studies of nonmalingering individuals without pending compensation issues or litigation, who had completed a variety of validity measures. They found substantial intercorrelation of test scores within most of the samples, with a mean correlation of .31 across all studies. Based on these findings, they provided tables (based on Monte Carlo simulations) that estimate the overall false-positive rates when using as many as 20 validity tests in an assessment. For example, with an assumption that every validity test has an FPR = .10, they show that the actual FPR for 20 tests > .10, until at least 6 tests are failed. If the FRP = .15 for each test, they show the actual FPR for 20 tests > .10, until at least 8 tests are failed. A more detailed Monte Carlo analysis can be found in Odland, Lammy, Martin, Grote, and Mittenberg (2015). Their results refuted the findings of Larrabee (2008), who claimed that even one failure is associated with an 80% chance of faking, and their results are inconsistent with the Slick and Sherman (2013) criteria that indicate feigned cognitive impairment is “probable” when two or more tests are failed.

Larrabee (2014) and Davis and Millis (2014) contended that validity tests are not nearly as correlated as the rates reported by Berthelson et al. (2013). Davis and Millis (2014) used up to nine validity tests for 87 outpatient physiatry referrals, which constituted a neurological no-incentive group, and found a nonsignificant correlation of .13 among the tests. Six of the tests had FPRs much lower than .10, two had FPRs = .10, and one test had an FPR = .15. Among the participants, 18 failed at least one test (20.7%), seven failed at least two tests (8.0%), and four failed three or more tests (4.6%). Davis and Millis reported that using two or more positive scores as a basis for a classification of MND results in a 12.6% false classification rate. Berthelson et al. (2013, Table IV) had suggested that when nine tests with FPRs = .10 were used, 22.8% would fail two or more tests. The difference between 22.8 and 12.6% might be related to the more conservative test scores used by Davis and Millis (2014), but Davis and Millis argued that Bertheleson et al. (2013) were simply wrong to assume that correlations among tests would make failure rates more likely. Larrabee (2014) also argued that the tables generated by Berthelson et al. (2013) produced inflated FPRs for multiple tests because of undue consideration of correlation among validity measures. However, as can be seen in several tables generated by Larrabee (2014), the differences in data from Larrabee and estimates from Berthelson et al. (2013) were not significantly different, which Larrabee attributed to a problem in statistical power. Pella, Hill, Shelton, Elliott, and Gouvier (2012) also showed quite high rates of false positives when using multiple validity indicators. Within a group of 478 students who had no external incentive to malinger cognitive impairment, they found that when administered eight validity indicators, 21.1% failed two or more, 4.6% failed three or more, 1.9% failed four or more, and 0.4% failed five indicators. These findings comport well with the values reported by Berthelson et al. (2013), and suggest that if the Berthleson et al. figures were wrong, they were not wrong by much. Regardless of whether the tests are highly correlated or not, Pella et al. (2012) replicated Berthelson et al.’s (2013) finding that the use of multiple validity indicators inherently leads to high rates of false-positive classification errors. Berthelson et al. (2013) reported a mean correlation coefficient of .31 to characterize the relationships of validity indicators to each other for the studies they sampled, and Odland et al. (2015) used the value of .31 to compute false-positive



3. Neuropsychological Models of Feigned Cognitive Deficits 55

rates for using multiple validity tests. But the Berthleson et al. (2013) meta-analysis included studies whose tests were not independent. For example, they included both the Rey 15-Item Test and the Rey 15-Item Recognition Test—the score for the latter test includes the score for the former test. The reported correlation for this study was .63. Additionally, Berthleson et al. included many studies that included both Digit Span scaled score and RDS. Both measures are from the same test. These studies included correlations of .83 and .92. In contrast, Davis and Millis (2014) reported r = .13 among the validity tests administered in their neurological no-incentive group, an apparently pure group of nonfeigners. When validity tests are independent in content, there is no reason to expect that trials will be significantly correlated within pure groups of nonfeigners or pure groups of feigners. Validity test scores that are obviously dependent on other measures (as in the earlier examples) should not be used to make classifications of validity. It seems reasonable to conclude that the use of multiple feigning tests sharply increases the possibility of false-positive classification of individuals who are compliant—not just when the tests are correlated—but because the false-positive error rate accumulates with the administration of each additional validity measure.

What Do These Tests Measure? Neuropsychologists and others who evaluate feigned cognitive impairment need to consider what the tests measure. Bigler (2011, 2014) has provided thoughtful analyses of what effort tests actually measure, questioning the indiscriminate use of the term effort. Frederick and Bowden (2009b) considered a number of ways to evaluate the constructs measured by the Validity Indicator Profile (VIP), finding support for more narrowly honed “effort” constructs, “intention to respond correctly” and “magnitude of effort.” Careful construction of criterion groups is not commonly accomplished. Both Frederick (2000) and Frederick and Bowden (2009a) explored problems in validity test research relating especially to problems with criterion group contamination (not knowing which members of criterion groups are actually compliant or faking). Within the neuropsychology literature, Greve and Bianchini (2004) have emphasized the importance of establishing specificity for validity indicators by carefully identifying groups of individuals who have no identifiable incentives to perform poorly but who other-

wise match potentially malingering samples with regard to the types of injuries and demographic characteristics. Some researchers have promoted methods that do not even depend on knowing a priori which participants belong to which groups. Meehl (1995) proposed numerous mathematical strategies (taxometrics) that do not depend on knowing which individual members of samples are actually feigning or not feigning. For example, Strong, Glassmire, Frederick, and Greene (2006) investigated the characteristics of the MMPI-2 F(p) validity indicator using Meehl’s taxometric mean above minus below a cutoff (MAMBAC) and maximum eigenvalue (MAXEIG) procedures. Walters et al. (2008) also evaluated the Structured Interview of Reported Symptoms (SIRS) using Meehl’s maximum covariance (MAXCOV) and MAMBAC methods. Similarly, Mossman, Wygant, and Gervais (2012) investigated the utility of latent class modeling (LCM) to identify test diagnostic statistics. They applied each of two LCM approaches. In the first, the agnostic approach, the only basis for analysis is the test scores—no knowledge of participant status is required. In the second, the partial truth approach, confident classifications of a subset of participants (in their study, about 10% of extreme cases) promoted the modeling of test behavior.

Common versus Distinguishing Characteristics and Markers of Genuine Responding Characteristics common to cognitive feigning include poor effort, exaggeration, litigation or presence of potential compensation, criminal prosecution, and adversarial proceedings. Such characteristics can be helpful in the early stages of determinations of feigning. However, they are merely correlates and do not have any clinical value as distinguishing characteristics (see Rogers, Chapter 2, this volume). This problem was illustrated earlier in the example involving antisocial personality and malingering: Antisocial personality may be fairly common among malingerers, but it is not a distinguishing characteristic of malingering. Establishing just what constitutes a distinguishing characteristic can be a challenge. For example, postconcussion syndrome (PCS) is reliably characterized by several cognitive, somatic, psychiatric symptoms—headache, irritability, sleep disturbance, attention problems, and other symptoms are very common in PCS. Research has shown,

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however, that these symptoms are also common in other conditions, and in healthy individuals as well (Maruta et al., 2016). Before a distinguishing characteristic can have any meaningful degree of utility, it must be shown to differentiate groups and individuals through studies employing appropriate criterion-group research designs. The probability of correctly recognizing genuine (and feigned) responses may be increased through the use of indeterminate groups in psychometric research. When relying on cutoff scores to differentiate individuals, occasional incorrect classifications are inevitable, especially those at or very near the cutoff score. Instead of forcing tooclose-to-call scores into a classification group, they are removed and are not included for calculation of utility estimates. While this procedure does mean that all participants cannot be classified, it also means that far fewer will be misclassified. There is certainly room for discussion about this research methodology with strengths and weakness to be debated. Rogers and Bender (2013) reviewed the implications of using this methodology for clinical decision making, and Rogers, Gillard, Wooley, and Ross (2012) applied it to the Personality Assessment Inventory (PAI) with regard to feigned posttraumatic stress disorder (PTSD). The overall classification rates improved only modestly when removing too-close-to-call cases. Nonetheless, it avoided impermissibly high error rates within the indeterminate group. For example, removing the range of Negative Impression Management (NIM) scores that were ±5T from the cutoff score (70T) removed 38.5% of the false-positive errors. There are other implications as well, but with regard to markers of genuine responding, the procedure resulted in reduced error. Practitioners should be knowledgeable about markers of feigned impairment, of which there are many. But indications that an examinee has responded honestly should be considered carefully as well. Rarely is an individual’s presentation accurately categorized as entirely invalid (or entirely valid), and the absence of invalid scores does not mean that the examinee’s presentation is therefore valid. Both effort and malingering are likely dimensional rather than dichotomous (Bigler, 2014; Walters et al., 2008). Forcing dimensional/ continuous variables into a dichotomy forces out important information. Yet this is what is typically demanded in forensic settings. Similarly, indications of invalid performance or exaggerated symptomatology are not synonymous with feign-

ing or malingering. This important distinction is even more important in forensic contexts in which examinees may have little reason to be forthcoming or honest (Rogers & Bender, 2013) but may not feel compelled to malinger. Genuine neuropsychological presentations are characterized by a general consistency both between the syndrome in question and the symptoms presented, and among test performances. But these variables depend on the phase/acuity of the disorder. A mild TBI, for example, produces generally mild symptoms. However, this may not be true in the acute phase of recovery, when symptoms may be more pronounced. Scores within the same domains that hang together are more likely to be valid reflections of performance than those that do not. This is not to say that an odd departure in a score means the performance is invalid. Rather, it simply may require follow-up. While consistent performance can be a sign of validity, inconsistent performance is not necessarily a sign of invalidity. Some degree of inconsistency in test performance is expected in healthy adults (Binder, Iverson, & Brooks, 2009). Thus, the normal vagaries of consistency make it difficult to establish in individual cases. It is incumbent on the forensic practitioner to know the general number of low scores expected given the examinee’s age and IQ that might otherwise be misattributed to cognitive dysfunction. A troubling situation arises when what was genuine effort devolves into a more cynical presentation involving effort test failure and exaggeration simply due to the pressures of protracted litigation (Bender & Matusewicz, 2013; Hall & Hall, 2012; Silver, 2015). Many questions emerge in such cases: What responsibility for malingering (if any) lies with the process of litigation and serial neuropsychological assessments, during which the examinee’s complaints are repeatedly contested? And would this suggest that the examinee is less accountable? While by no means exculpatory, Rogers’s (1990) adaptive explanatory model for malingering seems particularly germane here. Finally, if repeated neuropsychological assessments represent a risk factor for malingering (Vanderploeg, Belanger, & Kaufmann, 2014), does this imply a professional obligation to reduce the risk or otherwise prevent malingering from happening in the first place? The interested reader should see Horner, Turner, VanKirk, and Denning (2017) for an interesting, albeit preliminary, study of this issue.



3.  Neuropsychological Models of Feigned Cognitive Deficits 57

SUMMARY

TABLE 3.4.  Neuropsychological Decision Model for Feigned Cognitive Deficits

Malingering detection has been a focus of research for decades. Test development has burgeoned, and with it, our ability to detect malingering has improved. However, the growth in our understanding of malingering as a construct has been far less satisfying, and most malingering research has not pursued strategically based approaches. Nonetheless, neuropsychological models have recently emerged, and criteria specific to MND have been proposed. On the one hand, both the recent emphasis on BRs and the publication of the revised Slick criteria represent progress; on the other hand, it is humbling to see how much had been overlooked (or simply unknown) in the past. If we are to continue what has been substantial progress, existing diagnostic criteria for malingering must be validated, research must include criterion-group designs and multiple comorbidities, and both diagnostic probabilities and BRs must be accurately calculated. Moreover, with no means to measure motivation directly, the DSM (American Psychiatric Association, 2013) and Slick (Slick & Sherman, 2013) criteria are left conflating incentive with motivation, and practitioners are forced to rely on indirect indications of motivation, such as incentives. Important questions about incentives remain: Is the incentive a source of motivation for the examinee in the same way the examiner assumes it to be (Rogers & Bender, 2013)? Should the examiner attempt to establish that the incentive is indeed the source of motivation to malinger? What explains the (more common than not) absence of malingering in the presence of external incentives to malinger? In the meantime, based on the best available evidence, we recommend the following steps for forensic practitioners using symptom and performance validity tests for determinations of malingering (see Table 3.4).

1.  Determine whether the reported symptoms and/or cognitive profile(s) make neurological sense a.  Determine whether test performance fits known cognitive profiles (e.g., better performance on free recall than on recognition is unusual in genuine amnesia) b.  Is the amount of test scatter within base rate expectations for age and IQ? See Binder, Iverson, and Brooks (2009), Iverson, Brooks, and Holdnack (2008), and Schretlen, Munro, Anthony, and Pearlson (2003) for guidelines.

REFERENCES American Psychiatric Association. (1980). Diagnostic and statistical manual of mental disorders (3rd ed.). Washington, DC: Author. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington, VA: Author. Bender, S. D., & Matusewicz, M. (2013). PCS, iatrogenic symptoms, and malingering following concussion. Psychological Injury and Law, 6, 113–121. Bender, S. D., & Rogers, R. (2004). Detection of neu-

2.  Determine whether the degree of cognitive impairment reported is beyond that expected given the severity of the injury (Amplified and Unlikely Presentations) a.  Look for compelling inconsistencies (both qualitative and quantitative). b.  Is the degree of reported impairment inconsistent with the degree of functional disability? c.  Employ strategic detection tests and methods (e.g., amplified and unlikely presentation strategies) for both freestanding and embedded measures. d.  Use of posterior probabilities incorporating multiple tests, clinical comparison groups, and estimates of base rates (e.g., see ACS tables for the WAIS-IV and WMS-IV). e.  Use of multiple detection tests in aggregate but must know the number of negative trials on PVTs as well as positive. f.  Acknowledge existence of “indeterminate groups” in research. 3.  Attempt to determine whether there is evidence of motivation to perform poorly, with intent to secure material gain. a.  Evaluate the examinee’s perceptions of the incentive and whether it is incentivizing to the examinee. 4.  Remain aware of construct drift (i.e., broadening the conceptualization of malingering to embrace any manifestation of inadequate motivation) that results in imprecise and likely misleading results.

rocognitive feigning: Development of a multi-strategy assessment. Archives of Clinical Neuropsychology, 19(1), 49–60. Berthelson, L., Mulchan, S. S., Odland, A. P., Miller, L. J., & Mittenberg, W. (2013). False positive diagnosis of malingering due to the use of multiple effort tests. Brain Injury, 27(7–8), 909–916. Bianchini, K. J., Greve, K. W., & Glynn, G. (2005). On the diagnosis of malingered pain-related disability:

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Lessons from cognitive malingering research. The Spine Journal, 5(4), 404–417. Bigler, E. D. (2011). Effort–what is it, how should it be measured? Journal of the International Neuropsychological Society, 17(4), 751–752. Bigler, E. D. (2014). Effort, symptom validity testing, performance validity testing and traumatic brain injury. Brain Injury, 28(13–14), 1623–1638. Bigler, E. D. (2015). Neuroimaging as a biomarker in symptom validity and performance validity testing. Brain Imaging and Behavior, 9(3), 421–444. Binder, L. M., Iverson, G. L., & Brooks, B. L. (2009). To err is human: “Abnormal” neuropsychological scores and variability are common in healthy adults. Archives of Clinical Neuropsychology, 24(1), 31–46. Boone, K. B. (2007). A reconsideration of the Slick et al. (1999) criteria for malingered neurocognitive dysfunction. In K. B. Boone’s (Ed.), Assessment of feigned cognitive impairment (pp. 29–49). New York: Guilford Press. Boone, K. B., & Lu, P. (2003). Noncredible cognitive performance in the context of severe brain injury. Clinical Neuropsychologist, 17(2), 244–254. Bush, S. S., Ruff, R. M., Tröster, A. I., Barth, J. T., Koffler, S. P., Pliskin, N. H., et al. (2005). Symptom validity assessment: Practice issues and medical necessity NAN policy & planning committee. Archives of Clinical Neuropsychology, 20(4), 419–426. Davis, J. J., & Millis, S. R. (2014). Examination of performance validity test failure in relation to number of tests administered. Clinical Neuropsychologist, 28(2), 199–214. Delis, D. C., & Wetter, S. R. (2007). Cogniform disorder and cogniform condition: Proposed diagnoses for excessive cognitive symptoms. Archives of Clinical Neuropsychology, 22(5), 589–604. Drob, S. L., Meehan, K. B., & Waxman, S. E. (2009). Clinical and conceptual problems in the attribution of malingering in forensic evaluations. Journal of the American Academy of Psychiatry and the Law, 37(1), 98–106. Elwood, R. W. (1993). Psychological tests and clinical discriminations: Beginning to address the base rate problem. Clinical Psychology Review, 13(5), 409–419. Frederick, R. I. (2000). Mixed group validation: A method to address the limitations of criterion group validation in research on malingering detection. Behavioral Sciences and the Law, 18(6), 693–718. Frederick, R. I., & Bowden, S. C. (2009a). Evaluating constructs represented by symptom validity tests in forensic neuropsychological assessment of traumatic brain injury. Journal of Head Trauma Rehabilitation, 24, 105–122. Frederick, R. I., & Bowden, S. C. (2009b). The test validation summary. Assessment, 16(3), 215–236. Frederick, R. I., & Speed, F. M. (2007). On the interpretation of below-chance responding in forced-choice tests. Assessment, 14(1), 3–11. Freedman, D., & Manly, J. (2015). Use of normative data

and measures of performance validity and symptom validity in assessment of cognitive function. Available from www.iom.edu/~/media/files/perspectivesfiles/2015/freedmanmanlycommissioned%20paper.pdf. Gervais, R. O., Ben-Porath, Y. S., Wygant, D. B., & Green, P. (2007). Development and validation of a Response Bias Scale (RBS) for the MMPI-2. Assessment, 14(2), 196–208. Gervais, R. O., Ben-Porath, Y. S., Wygant, D. B., & Green, P. (2008). Differential sensitivity of the response bias scale (RBS) and MMPI-2 validity scales to memory complaints. Clinical Neuropsychologist, 22(6), 1061–1079. Gervais, R. O., Ben-Porath, Y. S., Wygant, D. B., & Sellbom, M. (2010). Incremental validity of the MMPI2-RF over-reporting scales and RBS in assessing the veracity of memory complaints. Archives of Clinical Neuropsychology, 25, 274–284. Green, P. (2003). Green’s Word Memory Test for Windows: User’s manual. Edmonton, Alberta, Canada: Green’s. Greiffenstein, M. F., Baker, W. J., & Gola, T. (1994). Validation of malingered amnesia measures with a large clinical sample. Psychological Assessment, 6(3), 218–224. Greve, K. W., & Bianchini, K. J. (2004). Setting empirical cut-offs on psychometric indicators of negative response bias: A methodological commentary with recommendations. Archives of Clinical Neuropsychology, 19(4), 533–541. Hall, R. C., & Hall, R. C. (2012). Compensation neurosis: A too quickly forgotten concept? Journal of the American Academy of Psychiatry and the Law, 40(3), 390–398. Heaton, R. K., Smith, H. H., Lehman, R. A., & Vogt, A. T. (1978). Prospects for faking believable deficits on neuropsychological testing. Journal of Consulting and Clinical Psychology, 46(5), 892–900. Heilbronner, R. L., Sweet, J. J., Morgan, J. E., Larrabee, G. J., Millis, S. R., & Conference Participants. (2009). American Academy of Clinical Neuropsychology consensus conference statement on the neuropsychological assessment of effort, response bias, and malingering. Clinical Neuropsychologist, 23(7), 1093–1129. Holdnack, J. A., Millis, S. R., Larrabee, G., & Iverson, G. L. (2013). Assessing performance validity with the ACS. In J, Holdnack, L. Drozdick, L. Weiss, & G. Iverson (Eds.), WAIS-IV, WMS-IV, and ACS (pp. 331–365). Bloomington, MN: Pearson. Horner, M. D., Turner, T. H., VanKirk, K. K., & Denning, J. H. (2017). An intervention to decrease the occurrence of invalid data on neuropsychological evaluation. Archives of Clinical Neuropsychology, 32(2), 228–237. Iverson, G. L., Brooks, B. L., & Holdnack, J. A. (2008). Misdiagnosis of cognitive impairment in forensic neuropsychology. In R. L. Heilbronner (Ed.), Neuropsychology in the courtroom: Expert analysis of reports



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Smith, G. P., & Burger, G. K. (1997). Detection of malingering: Validation of the Structured Inventory of Malingered Symptomatology (SIMS). Journal of the American Academy of Psychiatry and the Law, 25(2), 183–189. Straus, S. E., Richardson, W., Glasziou, P., & Haynes, R. B. (2011). Evidence-based medicine: How to practice and teach it (4th ed.). New York: Elsevier. Strong, D. R., Glassmire, D. M., Frederick, R. I., & Greene, R. L. (2006). Evaluating the latent structure of the MMPI-2 F(p) scale in a forensic sample: A taxometric analysis. Psychological Assessment, 18(3), 250–261. Sumanti, M., Boone, K. B., Savodnik, I., & Gorsuch, R. (2006). Noncredible psychiatric and cognitive symptoms in a workers’ compensation “stress” claim sample. Clinical Neuropsychologist, 20(4), 754–765. Tombaugh, T. N. (1997). The Test of Memory Malingering (TOMM): Normative data from cognitively intact and cognitively impaired individuals. Psychological Assessment, 9(3), 260–268. Vanderploeg, R. D., Belanger, H. G., & Kaufmann, P. M. (2014). Nocebo effects and mild traumatic brain injury: Legal implications. Psychological Injury and Law, 7(3), 245–254. Walters, G. D., Rogers, R., Berry, D. T., Miller, H. A., Duncan, S. A., McCusker, P. J., et al. (2008). Malingering as a categorical or dimensional construct: The latent structure of feigned psychopathology as measured by the SIRS and MMPI-2. Psychological Assessment, 20(3), 238–247. Whiteside, D. M., Gaasedelen, O. J., Hahn-Ketter, A. E., Luu, H., Miller, M. L., Persinger, V., et al. (2015). Derivation of a cross-domain embedded performance validity measure in traumatic brain injury. Clinical Neuropsychologist, 29(6), 788–803. Young, G. (2014). Resource material for ethical psychological assessment of symptom and performance validity, including malingering. Psychological Injury and Law, 7(3), 206–235. Young, G. (2015). Malingering in forensic disability-related assessments: Prevalence 15 ± 15%. Psychological Injury and Law, 8(3), 188–199. Zenisek, R., Millis, S. R., Banks, S. J., & Miller, J. B. (2016). Prevalence of below-criterion Reliable Digit Span scores in a clinical sample of older adults. Archives of Clinical Neuropsychology, 31(5), 426–433.

CHAPTER 4

Beyond Borders Cultural and Transnational Perspectives of Feigning and Other Response Styles Amor A. Correa, PhD

Psychological assessments are highly dependent on the honesty and openness of examinees. Historically, mental health practitioners developed professional relationships in therapy that built on trust and confidentiality, enabling many clients to openly express their symptoms and concerns. In the United States, the traditional professional–client relationship is sometimes overshadowed by a complex mental health system and opportunities for secondary gain for the patient (Rogers, 2008). Possible motives for falsifying symptoms include financial compensation, exemption from duty, or leniency from the criminal justice system; therefore, malingering is encountered in a variety of clinical and forensic situations (Reid, 2000). The increased complexity of mental health service delivery in other countries such as Spain may exert a similar effect on the demand for adequate feigning measures (Salvador-Carulla, Garrido, McDaid & Haro, 2006). Additionally, a “disability paradox” has been noted in the United Kingdom, where the number of disability claims in the country has risen despite evidence of an overall improvement in citizens’ health (Merten et al., 2013). Psychologists are now being asked to make important determinations of feigning and other response styles in an increasingly international context. The growing international spotlight on feigning presents an important quandary for practicing clinicians. In the realm of response styles, valida 61

tion research confirming the adequacy of current assessment measures is still catching up to the need for their use with increasingly culturally diverse clientele. As Merten et al. (2013) highlight, research on malingering was largely taboo in Germany and the United Kingdom until the early portion of the 21st century. Similarly, Spanish authors published their first book on the detection of feigning in 2012, and the majority of European Spanish feigning tests have only been published and made available over the course of the past 5 years (González Ordi, Santamaria Fernández, & Capilla Ramírez, 2012; Merten et al., 2013). This chapter will serve as a guide for practitioners conducting evaluations with culturally and linguistically diverse clients. The first section discusses major factors to consider in the field of multicultural assessment and test translation. The next section is an informative overview of currently available feigning measures. Finally, the chapter concludes with guidance on making culturally competent clinical interpretations of assessment data.

AN OVERVIEW ON PSYCHOLOGICAL ASSESSMENT AND TEST TRANSLATIONS In the United States, ethical guidelines from the American Psychological Association (2002) require that psychologists working with ethnically,

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linguistically, and culturally diverse populations recognize these characteristics as important factors affecting a person’s experiences, attitudes, and psychological presentation (Bersoff, 2004). Mental health professionals are generally aware that standardized assessment measures in the United States were developed with normative groups that comprised individuals proficient in English and sharing in mainstream American culture. Psychological assessment with culturally diverse and international populations must be conducted with the knowledge that most standard assessment instruments were not constructed or validated with these populations in mind. Additional considerations are needed to ensure test validity when a psychological measure is translated into another language. The Test Translation and Adaptation Guidelines developed by the International Test Commission (ITC) in 1992, and updated in 2005, called for test developers and publishers to apply appropriate research methods and statistical techniques to establish the validity of a test for each population the adapted version is intended to assess. First, empirical research must be conducted. The research results should be used to improve the accuracy of the translation/ adaptation process and to identify problems in the adaptation that may render the new version inadequate for use with the intended populations. Additionally, test developers should strive to establish the equivalence of the different language versions of the test, to make them as parallel to the original as possible. Last, the validity of the translated version must be determined separately from that of the original measure. It should not be assumed that a translated version has acceptable validity simply because that of the original English language version is adequate (Allalouf, 2003; Anastasi, 1988). The ITC standards are consistent with the Standards for Educational and Psychological Testing, coauthored by the American Psychological Association (American Educational Research Association, American Psychological Association, & National Council on Measurement in Education, 1999, 2014), and with adaptation guidelines set forth by the World Health Organization (www. who.int/substance_abuse/research_tools/translation/ en). These standards require that psychologists and other professionals refrain from using a translated version until the reliability and validity of that new measure has been firmly established. The danger in administering tests that have not been validated is that clinicians interpret the results based on an assumption that the test continues to

function in the intended manner (Fantoni-Salvador & Rogers, 1997). Until the reliability and validity of the translated assessment measures have been determined, mental health professionals should refrain from using them, just as they would refrain from administering any other unvalidated measure (Allalouf, 2003; Hambleton, 2001). For mental health practitioners seeking to conduct a culturally competent assessment battery, it is imperative to begin by choosing the most appropriate tests for each particular client. This decision involves choosing tests with appropriate translations, interpretive norms, and validation studies. The following sections highlight important considerations for clinicians when selecting which tests to administer.

TRANSLATION METHODS Many measures of feigning were originally created in the English language (Correa & Rogers, 2010a). Therefore, practitioners often find themselves using translated versions of widely researched measures. When clinicians are knowledgeable regarding the test translation process, they are better able to choose psychological measures with high linguistic validity, which is an important component of construct validity, because it denotes that the different versions of a test are conceptually equivalent in each of the target languages. This section aims to describe methods most commonly used in translating psychological assessment measures. The test translation process has been equated to construction of a new test, requiring evidence for construct validity, statistical support, and assessment of bias at the item level (Jeanrie & Bertrand, 1999). Test developers must be prepared to provide each of these requisite components for a valid measure. Three basic approaches are generally used in translating written documents from one language to another: one-way translation, translation by committee, and back-translation (Marín & Marín, 1991). Each technique varies in complexity and has its own set of strengths and limitations.

One-Way Translations One-way translations employ the simplest of translation techniques. Here, one bilingual individual uses dictionaries, reference materials, and his or her knowledge of both languages to create the



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translated product (Marín & Marín, 1991). This approach is appealing because it is time-efficient and cost-effective, and uses the resources of only one person to achieve a translation. However, its simplicity also serves as the basis of most criticisms. Relying on a single person to translate the material leaves the translation vulnerable to error, because the translator’s work is left unchecked. When misinterpretations make their way into the final product, quality of the translated measure is adversely affected. For instance, Berkanovic (1980) demonstrated that a health survey with one-way translation into Spanish had different psychometric properties and lower reliability than its original English-language counterpart. One sound recommendation is to focus on the quality of the translator for improving the quality of a one-way translation. On this point, Hambleton and Kanjee (1995) stress that translators should be (1) highly proficient in both languages involved and (2) familiar with cultural groups associated with both languages. The latter recommendation helps in constructing translated items that flow well in the new language, retain the intended meanings, and are readily understood by the target population. If translators also have an understanding of the construct being measured, they are better able to preserve the intended meaning of test items. One-way translations may also be made more thorough via the use of judges to evaluate the final product (Jeanrie & Bertrand, 1999). Judges may evaluate the following three areas: 1. Content equivalence: relevance to that cultural group 2. Conceptual equivalence: maintaining construct validity 3. Linguistic equivalence: maintaining as direct a translation as possible, without jeopardizing content and conceptual equivalence. Test items attaining the highest scores on the relevant constructs can be compiled and edited in the final step. This framework attempts to remedy some of the major criticisms of one-way translation. However, no published data are found to indicate whether this leads to a better oneway translation or simply takes up resources that could best be put to use in implementing a more well-researched translation model. Despite these suggestions, researchers (Brislin, 1970; Marín & Marín, 1991) tend to agree that one-way translations should not be used. Instead, they conclude

that more translators should be involved, and that back-translation should be used for quality control.

Translation by Committee A second translation technique involves a translation by committee (Marín & Marín, 1991). This approach utilizes two or more individuals who are highly conversant in both languages. Professionals independently produce their own translation without consulting the other translators. After the initial translations are complete, the coordinator can ask all translators to meet, compare their individual versions, and discuss and resolve the differences. In this manner, they create a final version incorporating the changes they have discussed. The goal of this process is to prevent problems, such as misinterpretation and awkward wording, that arise from relying too heavily on a single translator. Alternatively, the coordinator can ask one more persons (not involved in the original translations) to review each translator’s work and choose the best version (Marín & Marín, 1991). This option still falls under the rubric of “translation by committee,” because multiple translators are involved in the process. Translation by committee can be more accurate than one-way translation. Marín and Marín (1991) are quick to point out, however, that traits shared by the translators, such as cultural background, education level, and social class, might lead them to make similar errors in their independent translations. Ensuring that the committee consists of individuals with diverse cultural backgrounds within the culture of the target language reduces the risk of error caused by uniform interpretations (Martinez, Marín, & Schoua-Glusberg, 2006). However, committee discussion can never ensure that all possible mistakes are pointed out, because committee members may not feel comfortable criticizing each other’s translation (Marín & Marín, 1991).

Back-Translation A final translation procedure, commonly known as “back-translation,” is the most recommended by researchers (Brislin, 1986; Moreland, 1996), yet it remains the least used translation technique (Jeanrie & Bertrand, 1999). Its lack of use may be due to its time-consuming nature. Back-translation makes use of at least two bilingual individuals. As in one-way translation, one independently translates the original language (e.g., English) into the

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new language. At this point, a second translator takes the newly translated version and translates it back into the original language. It is critical that the translators work independently throughout this process and not be permitted to consult with one another. Two English-language versions of the measure now exist: the original version and the back-translated version. The two English versions are compared, and inconsistencies are identified. When differences are found, it is imperative to approach both translators, determine why the difference exists, and reach an agreement about the best option (Marín & Marín, 1991). A third party not involved in the original translation or backtranslation may also be commissioned to evaluate the two English versions (Brislin, 1970). Back-translation can be improved if the process is conducted more than once and with multiple translators. These iterations make the procedure more time-consuming and complex. However, the measure is reviewed by more bilingual professionals, which produces a better version of the instrument in the end (Marín & Marín, 1991). Backtranslation has been used extensively in creating Spanish-language versions of assessment tools as diverse as general health questionnaires (Marín, Perez-Stable, & Marín, 1989), structured interviews for the diagnosis of mental health problems (Burnam, Karno, Hough, Escobar, & Forsyth, 1983), and structured interviews for the assessment of feigned psychopathology (Correa & Rogers, 2010b; Rogers, Sewell, & Gillard, 2010). Marín et al. (1989) advocated the back-translation process finding the Spanish version of their survey was, indeed, equivalent to the English version after administering both versions to bilingual speakers. Likewise, Sireci, Yang, Harter, and Ehrlich (2006) conducted a study designed to determine how a more rigorous translation procedure (back-translation) compared to a simple translation procedure (one-way translation). They found that for many of the test items, back-translation produced results that were more comparable to the original English measure. Using their design, the Spanish Diagnostic Interview Schedule (DIS) was also found to be acceptably equivalent to the English DIS for bilingual participants (Burnam et al., 1983). An inherent limitation in the process of backtranslation is that it still relies on the translator’s interpretation of item meaning (Marín & Marín, 1991). For this reason, it is important to employ the same precautions that should be used for

“translation by committee.” Recruiting translators from varied educational, cultural, and social backgrounds minimizes errors caused by uniform interpretations (Martinez et al., 2006). Another criticism of back-translations involves the absence of guidelines as to how many independent translators are sufficient for a good translation (Cha, Kim, & Erlen, 2007). Some experts instead advocate using a combined technique (Jones, Lee, Phillips, Zhang, & Jaceldo, 2001) that employs back-translation and administers both versions of the test to bilingual participants in order to identify discrepancies before creating the final version. This method appears to incorporate equivalence testing (a recommended step for final validation) into the translation procedure (Hambleton, 2001).

CULTURALLY SPECIFIC RESPONSE STYLES THAT AFFECT VALIDITY SCALE SCORES Culturally specific response patterns emerge in several diagnostic measures for psychopathology. Consistent patterns of score elevations may be culturally normative for some test-takers and should not be construed as symptom exaggeration. Conversely, score elevations are often less evident for other groups due to culturally normative defensiveness, leaving existing assessment measures less sensitive in the detection of feigning for groups with the tendency to underreport. Distinct patterns are often apparent on clinical and validity scales of multiscale inventories used to detect minimization or exaggeration of symptoms (Correa & Rogers, 2010a; Molina & Franco, 1986). This section explores cultural characteristics that may affect the validity profile of an examinee’s responses on standardized assessment measures. The construct of machismo is among the response patterns that can significantly impact a patient’s self-report measures. Machismo is a gender schema consisting of behaviors, attitudes, and beliefs often espoused by Latin American men (Casas, Wagenheim, Banchero, & Mendoza-Romero, 1995). Factors of machismo contain positive aspects related to chivalry and negative aspects related to chauvinism. Despite little research in this area, available studies examining machismo, gender roles, and mental health have found that higher levels of machismo and restrictive emotionality may be associated with higher levels of depression and stress among Hispanic American men (Fragoso & Kashubeck, 2000).



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Therefore, machismo bolsters the theory that low symptom endorsement does not necessarily indicate subjective well-being among Hispanic Americans. Rather than indicate an absence of symptoms, underreporting on assessment measures may be more reflective of a culture-based hesitation in this clinical population to disclose symptoms of psychological distress (Correa & Rogers, 2010b). Cultural patterns for Hispanic males are particularly apparent on validity scales related to minimization of symptoms (Molina & Franco, 1986). Besides machismo, the conceptualization of extreme response style suggests that individuals of Hispanic and Mediterranean cultures have a tendency to respond either very low or very high when given choices on Likert-type scales, commonly used in the United States (Hui & Triandis, 1989). It is believed that these individuals consider extreme responses to be more sincere than “less decisive” responses located in the middle of a Likert-type scale. This distinction is most evident for individuals from Hispanic and Mediterranean cultures when contrasted with those from Asian cultures, who tend to respond in the middle of the scale (Zax & Takahashi, 1967). Notably, the language of a test can further magnify this cultural response style. In a study that administered the same items in two different languages to bilingual individuals, Gibbons, Zellner, and Rudek (1999) found that participants used more extreme ratings when responding in Spanish than in English. The theory of extreme response style suggests the possibility that Hispanic Americans may be just as likely to overreport as to underreport symptoms on a measure. More research is needed in this area to adequately understand the disparity in research findings to date. It is widely acknowledged that African Americans tend to score higher than European Americans on measures of social aggression and psychotic symptoms, even when studies control for age, gender, and level of education (Cuellar & Paniagua, 2000; Dana, 1993, 2000). Researchers believe that these results are closely tied to cultural identity and beliefs; as such, they are not necessarily indicative of psychopathology. Culture-related stressors, such as racism and adjustment to mainstream American culture, play an important role in the types of distress African Americans tend to report. Guarding against prejudice and residual anger toward racial tensions can manifest itself as aggression, hypervigilance, or paranoia on multiscale inventories (Marcella & Yamada, 2000; Ewart & Suchday, 2002).

LANGUAGE The effects of language are vitally important to consider when accounting for the accuracy of the assessment process. The psychometric properties of standardized assessment measures are likely to change when administered to individuals who differ culturally from the normative sample (Marín & Marín, 1991). Furthermore, multilingual individuals who are not tested in their preferred language may suffer a detachment effect (Bamford, 1991); they fail to adequately connect with the assessment questions and are unable to fully express their emotional and psychological issues. The detachment effect can result in poor communication about symptoms and less self-disclosure (Dana, 1995); however, it is often remedied when individuals are tested in their preferred language. For example, Guttfreund (1990) shows that bilingual Hispanic American patients who prefer to speak Spanish are more able to effectively express their emotions when tested in their preferred language rather than English. For multilingual individuals, clinicians must take into account a client’s language preference prior to beginning the assessment process. When a client is conversant in English and a second language but expresses only a minor preference for the second language, practitioners might choose the English version— given that extensive validation studies are more likely to be available. When a strong preference is expressed for another language and/or Englishlanguage abilities are limited, the translated version of the test is most appropriate.

ACCULTURATION In addition to the different response patterns among distinct cultures, level of acculturation should be assessed for each examinee taking a test normed on a cultural group different from their own. Acculturation is defined as the changes that occur in an individual’s beliefs and behaviors, as a result of interaction with his or her own ethnic group and another cultural group. Assessing acculturation allows clinicians to determine the applicability of interpretive norms and to decide whether culture-specific cutoff scores should be employed if they are available and well validated. Individuals with higher levels of acculturation may possess a greater understanding of their new culture and begin to accept and incorporate aspects

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of it into their daily lives. Individuals with low levels of acculturation continue chiefly to identify with the values of their ethnic group despite interaction with members of a different culture (Wagner & Gartner, 1997). In 1989, Berry, Kin, Power, Young, and Bujaki proposed a widely accepted, two-dimensional model of acculturation in which individuals feel a need to identify with both their own culture and a new culture. The individual can maintain one of four possible relationships with majority and minority cultures: • Assimilation: sole identification with the majority culture • Integration: identification with both cultures • Separation: sole identification with the minority culture • Marginality: no identification with either culture Berry et al.’s (1989) bidimensional model of acculturation takes into account an individual’s varying degrees of affiliation with minority and mainstream cultures. In contrast, unidimensional models of acculturation (e.g., Gordon, 1964) contend that one relationship must always be stronger than the other. In unidimensional models, individuals relinquish their ethnic culture, as they become more assimilated to the mainstream culture. In both models, distinct levels of acculturation increase the variety of possible response patterns, because salient differences also exist within cultures, not just between them, depending on how much an individual identifies with each of the cultures in question. However, unidimensional models might obscure the complexity of individual acculturation, by failing to recognize bicultural individuals, who identify strongly with both cultures (Ryder, Alden, & Paulhus, 2000). However, both models emphasize the notion that it is erroneous to assume that all members of the same ethnicity will respond similarly when interpreting assessment results. How acculturation affects responses to test items should also be established when characterizing new normative samples and cut scores.

APPROPRIATENESS OF AVAILABLE INTERPRETIVE NORMS An important issue in the validity of any assessment measure used with ethnic/minority or nonEnglish-speaking populations is consideration of

the etic and emic qualities of the test (Dana, 1993, 2005). Etic measures are those with “universal” applications, whose constructs are equally applicable to individuals of all different groups. It is expected that an individual’s assessment results on an etic measure can be interpreted based on the same set of norms, regardless of the individual’s membership in any particular cultural group. Emic measures, on the other hand, are culture-specific measures; their clinical applications can be specific to populations based on age, gender, ethnicity, or any other grouping classification. Emic measures are only appropriate for use with the groups for whom they were designed. Researchers (Berry 1969, 1988; Dana 2005) have observed for some time that most standardized assessment measures were created and normed on samples that comprised mainly European Americans in the United States. Based on current clinical practices, tests are administered, scored, and interpreted uniformly according to guidelines established in the manual. Since the majority of interpretive norms were developed primarily on individuals of European American heritage, these assessment measures fall into the category of imposed etic tests. Without further testing on other cultures, the tests remain empirically valid for only the European American culture. The practice of adding proportionate but comparatively small samples of minority populations to test norms improves their representativeness. However, it is not a substitute for minority group validation studies to establish culturally relevant cutoff scores and interpretation guidelines. Without such work, test developers inadvertently imply that European American–based cutoff scores are universally valid and generalize to all cultures. Indeed, Henrich, Heine, and Norenzayan (2010) have noted that researchers routinely make claims based on samples of American college students and other Western, Educated, Industrialized, Rich, and Democratic (WEIRD) societies. The omission of ethnic minorities in test development effectively forces minority individuals into the same interpretative categories as European Americans, thereby creating a substantial possibility for assessment bias, misdiagnosis, and misinterpretation of test results for individuals from different nationalities or cultural groups (Dana, 1993; Todd, 2004). Assessment bias may be minimized when clinicians are well informed about the populations they are testing, recognize limitations of their measures, and use additional cultural measures to aid in their interpretation of assessment results (Dana,



4.  Cultural and Transnational Perspectives 67

2005). Adequate knowledge of concepts such as acculturation makes clinicians better equipped to choose culturally appropriate tests, as well as understand how best to apply test norms and interpretive guidelines. Differences between the test scores of individuals from other cultural groups and those from the dominant culture become problematic when interpretive norms lead to inaccurate predictions or diagnoses for minority individuals (Graham, 1990). Thus, mental health professionals should consider the validity of assessment data derived from standardized measures. Clinicians must always weigh the cultural characteristics of the person being assessed and how such attributes likely affect a person’s responses. In this manner, professionals working in the mental health field can refine their clinical interpretations of standardized measures, in order to make more accurate assessments of individuals from diverse cultural backgrounds and create treatment recommendations that are best suited for such patients (Dana, 2005). Clinical interpretations of test data should be made with adequate awareness of the limited generalizability inherent in many currently available assessment tools. In the last two decades, normative studies have increasingly included census-matched standardization samples with representation from minority cultural groups in the United States (Correa & Rogers, 2010a). European countries have also begun to develop their own norms for use with specific international populations (Giger & Merten, 2013; Merten et al., 2013). Such developments are invaluable for making culturally sensitive interpretations of clinical data. They represent an important step in fully validating psychological measures for three related but distinct constructs: culture, ethnicity, and diverse linguistic backgrounds. To ensure culturally competent test interpretation, clinicians must consciously search for alternative normative datasets. Often, culturally specific norms are not available in the published test manuals. Mental health practitioners are tasked with searching the available literature for published validation studies, then determining which of the researched normative groups best fits the demographic characteristics and acculturation of each individual examinee (Committee on Psychological Testing, Including Validity Testing, for Social Security Administration Disability Determinations; Board on the Health of Select Populations; & Institute of Medicine, 2015). For example,

the demographically adjusted norms developed by Heaton, Miller, Taylor, and Grant (2004) provide a useful resource. Colloquially referred to as the “Heaton Norms,” the book compiled by Heaton et al. is used for the culturally sensitive interpretation of over 50 cognitive and neuropsychological tests. The norms provide alternative cutoff scores for examinees based on differences in demographic variables, including age, gender, level of education, and race/ethnicity. The goal of such an expansive collection of demographically adjusted norms is to improve diagnostic accuracy by controlling for variables that have continually proved to significantly affect test performance. A vigorous debate continues in the literature regarding the use of race-specific norms (Manly & Echemendia, 2007). Many researchers note that specialized norms improve the sensitivity, specificity, and diagnostic accuracy of psychological assessment measures (Ardila, 1995; Heaton et al., 2004; Manly & Echemendia, 2007). Others contend that race-specific norms are often lower and more lenient, which can obfuscate deficits and potentially disqualify individuals from beneficial services (Manly, 2005). When it comes to feigning, being suspected of intentionally reporting false symptoms can also have significant consequences for an examinee. In clinical settings, this classification can preclude clients from receiving mental health interventions (Rogers, 1997, 2008), because in settings where resources are scarce, many mental health professionals believe it is their responsibility to ensure that only the truly sick receive access mental health treatment (Resnick, 1984). When encountered in a forensic setting, the ramifications can be even more serious. Not only might individuals be denied mental health care but the classification of malingering could also be used to discredit them throughout the trial process (Rogers & Shuman, 2005). Once individuals have been classified as malingerers, often it is difficult for them to prove the genuineness of their disorders in future situations (Berry, Baer, Rinaldo, & Wetter, 2002). As previously discussed, several culturally specific response styles may affect a test-taker’s presentation on validity scales (Casas, et al., 1995; Correa & Rogers, 2010a; Dana 1993, 2000; Hui & Triandis, 1989). Another point of contention when using racespecific norms centers on their theoretical legitimacy. Some researchers contend that using race as the defining construct for the creation of normative groups is misleading, because it disregards cultural or educational variables that may actually

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account for the differences in test performance (Manly & Echemendia, 2007). For example, Lynn (2002) found a correlation between skin tone and scores on a vocabulary test, attributing the higher scores to an increased proportion of European American genes in those participants. Lynn’s data were reanalyzed by Hill (2002), who controlled for childhood environmental factors such as educational achievement, parental education, and geography. Skin tone and “race” no longer correlated after controlling for these other variables, indicating that the differences in scores were unrelated to participants’ race. For practicing clinicians, these findings highlight the complexities involved in deciding which interpretive norms to use for a given examinee. It is generally accepted that clinical determinations for non-English-speaking individuals will be more accurate if their performance is compared to demographically and linguistically similar individuals (Heaton et al., 2004; Manly & Echemendia, 2007). Mindt, Byrd, Saez, and Manly (2010) recommended making decisions on a case-by-case basis, as well as using the best assessment measures and norms possible. It is important to note that demographically appropriate norms are not always available, so professionals may have to choose the closest approximation and note the limitations in the reports. Intelligence testing provides an excellent example of how test norms and translation procedures may affect clinical interpretations; researchers have long since pointed out that demographic variables such as age, gender, and culture affect an individual’s performance on cognitive tests (Kaufman & Lichtenberger, 2006). Using data from the English-language Wechsler Adult Intelligent Scale–Wechsler Memory Scale (WAISWMS) co-norming project, Heaton, Taylor, and Manly (2001) found that Hispanic American individuals generally achieved lower scores than their European American counterparts when both groups were tested in English. Using standard norms, between 15 and 25% of Hispanic individuals were misclassified as “impaired” on the WAIS and WMS, even when corrections were made for other factors, such as age, gender, and level of education. In order to reduce an apparent bias in the interpretation of the measure, normative adjustments were suggested by Heaton et al. (2001). Predictably, when using the corrected cutoff scores, Hispanic American individuals have nearly the same likelihood of being misclassified as their European American counterparts.

Kaufman and Lichtenberger (2006) hypothesized that lower scores for Hispanic individuals on verbal measures reflect (1) unfair language demands placed on individuals for whom English is a second language, and (2) the cultural content of some verbal test items. Similarly, the Standards for Educational and Psychological Testing from the American Educational Research Association, American Psychological Association, and National Council on Measurement in Education (1999, 2014) specify that any oral or written test is also inherently a measure of an examinee’s verbal skills, whether it aims to measure this construct or not. Therefore, seemingly discrepant performance on translated measures may be a product of the translation process, not necessarily an examinee’s response style. At a minimum, clinicians must provide caveats while interpreting assessment data and tailor treatment recommendations to different groups of ethnically diverse clients (Correa & Rogers, 2010a). Clinicians must weigh the pros and cons of each measure in choosing the most appropriate test for their clients. Manly and Echemendia (2007) advocate using a “cost–benefit matrix” to evaluate the potential outcomes of each decision, then choosing the interpretive norms with the least cost to the assessment client. High test specificity is important in classifications of malingering, because the consequences of a false-positive result can be very damaging to the client (Rogers & Shuman, 2005). For other clinical decisions, higher test sensitivity may be desirable.

USING ACCULTURATION MEASURES TO CHOOSE TESTS AND INTERPRETIVE NORMS When norms do not fully match the characteristics of a particular client, approximations will need to be used by the clinician. Assessing acculturation as part of the evaluation aids in these approximations, as well as case conceptualization (Mindt et al., 2010). With low levels of acculturation, clients continue to identify with the values of their ethnic group despite frequent interactions with the mainstream culture/normative group (Wagner & Gartner, 1997). This process may account for within-culture differences in response patterns, personality characteristics, and psychopathology (Okazaki & Sue, 1995). Some clients have particular difficulty with the acculturation process and experience “marginality” with distress and alienation, because they do not identify with either culture (Berry et al., 1989).



4.  Cultural and Transnational Perspectives 69

Therefore, comprehensive evaluations often benefit from the inclusion of an acculturation measure as part of the assessment process (Dana, 1993; Wagner & Gartner, 1997). The use of an acculturation measure often clarifies the level of confidence of the interpretations and highlights important caveats to include in the final report. Specifically, acculturation measures provide tangible data about the limitations of interpretive test norms when the client’s current acculturation level deviates from that of the normative group. Many acculturation measures are available. Psychologists may wish to consider the Scale of Ethnic Experience (SEE; Malcarne, Chavira, Fernandez, & Liu, 2006), which has been widely adapted for diverse cultures, and the Acculturation Rating Scale for Mexican Americans–II (ARSMA-II; Cuellar, Arnold, & Maldonado, 1995), which has a widely researched and validated Spanish Language version.

MEASURES OF FEIGNED PSYCHOPATHOLOGY This section investigates properties and clinical utility of the most widely researched measures of feigned psychopathology currently available for international populations. For many of these assessment measures, there remains a dearth of available research validating clinical use with populations different than the normative groups published in test manuals. Consequently, it is difficult to estimate the extent to which individuals are affected by “imposed etic” interpretative practices. This chapter addresses only those tests with published feigning research involving international populations. An in-depth examination of the each test’s construction and validation research is included. The goal is to provide clinicians with important insights when testing clients of diverse ethnic backgrounds, as well as to discuss important caveats.

The Structured Interview of Reported Symptoms—2nd Edition The original version of the Structured Interview of Reported Symptoms (SIRS; Rogers, Bagby, & Dickens, 1992) was a comprehensive measure designed to evaluate feigned mental disorders. Items were generated for eight primary scales, with each scale devoted to a single detection strategy: Rare Symptoms, Symptom Combinations, Improbable and Absurd Symptoms, Blatant Symptoms, Subtle Symp-

toms, Selectivity of Symptoms, Severity of Symptoms, and Reported versus Observed Symptoms. More recently, the SIRS–2nd Edition (SIRS-2; Rogers, Sewell, & Gillard, 2010) has demonstrated excellent reliability, validity, and classification accuracy (sensitivity = .80; specificity = .975; positive predictive power = .91; negative predictive power = .91) that results in an excellent overall classification of .91 and a very small false-positive rate of 2.5%. The SIRS-2 retained the original primary scales and sought to reduce false-positives rates through additional scales and a new decision model. These additional scales include the Rare Symptom Total (RS-Total) Index and the Modified Total (MT) Index. Using the MT Index, the SIRS2 uses two unclassified groups: indeterminate–evaluate and indeterminate–general. Individuals in the indeterminate–evaluate group should be further screened for feigning. The indeterminate–general category is composed of individuals with a relatively low likelihood of feigning. The final index is the Supplementary Scale (SS) Index, involves the sum of four supplementary scales, one of which is a new scale on the SIRS-2. This new addition, the Improbable Failure (IF) scale is composed of simple cognitive tasks. It is important to note that whereas there are no published data to date, clinical evidence indicates that the IF scale may not be appropriate for clients with limited proficiency in English or those educated outside the United States. This important caveat is explored further in the next section. The Spanish SIRS-2

The Spanish SIRS-2 was translated from the original English-language version utilizing the backtranslation method with multiple translators. In the first phase, three bilingual psychologists independently translated the English-language SIRS to Spanish. These three translators then met and reviewed any discrepancies in language or intended meaning. They developed a consensus on the best Spanish translation. For the second phase, a fourth psychologist used that Spanish version for the backtranslation into English. To avoid bias in the translation process, this psychologist had no knowledge of the original English-language SIRS. A fifth bilingual psychologist then independently compared the original and back-translated English versions, resolving a small number of discrepancies. Before its publication, two additional bilingual psychologists reviewed the Spanish SIRS-2 for any grammatical errors (Correa & Rogers, 2010b, 2013).

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RELIABILITY, VALIDITY, AND CLINICAL UTILITY

The reliabilities of the Spanish and English SIRS2 are compared for internal consistencies (alphas) and interrater reliabilities (Table 4.1). Alpha coefficients for the Spanish SIRS-2 equal and sometimes slightly exceed the English version. A similar pattern is also observed for interrater reliabilities. On average, interrater reliability for the Spanish SIRS-2 indicates nearly perfect agreement between evaluators (mean r = .995). Spanish SIRS-2 validation has focused primarily on discriminant validity, or its ability to accurately distinguish feigners from genuine responders with true mental disorders (Correa & Rogers, 2010b; Rogers et al., 2010). The primary scales of the Spanish SIRS-2 evidence very large effect sizes, with Cohen’s ds from 1.38 to 2.47. Utility estimates for the decision model produce very similar results to the English SIRS-2 (e.g., sensitivity = .88; specificity = .92). Its false-positive rate is 8.0%. This finding underscores the importance of using more than one feigning measure in determinations of feigned mental disorders. Rogers et al. (2010) also conducted a small, ethnically diverse study of linguistic equivalence with bilingual outpatients in Miami. Each patient was administered both the Spanish and English versions of the SIRS-2. As reported in the SIRS2 manual, the differences between the English and Spanish administrations were very small for TABLE 4.1.  Reliability of the Spanish SIRS-2 in Comparison to the English SIRS-2

Alpha Scale

Spanish

SEM

English

Spanish

English

RS

.81

.81

.00

0.44

SC

.89

.80

.00

0.46

IA

.84

.83

.00

0.38

BL

.96

.91

.00

0.74

SU

.95

.91

.90

0.68

SEL

NA

NA

.00

0.66

SEV

NA

NA

.67

0.00

RO

.76

.81

.17

0.59

Average

.89

.85

.22

.49

RS-Total

.82

.86

.00

.71

Note. Derived from Rogers et al. (2010) and Correa and Rogers (2010b). English alphas do not include partial SIRS administrations.

the primary scales, averaging less than half of one point. Overall categorization of feigning versus genuine disorders remained identical across both language versions. With respect to cultural equivalence, one important caveat involves the Improbable Failure (IF) scale, which is a supplementary scale used to screen for feigned cognitive impairment. The IF scale requires examinees to quickly complete simple verbal tasks (rhyming and opposites) commonly practiced in U.S. elementary schools. These tasks are likely to be unfamiliar for native Spanish speakers with low levels of acculturation, low educational attainment, or those educated outside of the United States. The effectiveness of the Spanish IF scale remains to be investigated. Commonsensically, the Spanish IF scale should not be utilized with any examinees unfamiliar with rhyming and opposites. Fortunately, these skills can easily be tested prior to the SIRS-2 administration. The Spanish IF scale cannot be administered to participants who cannot comprehend or apply the measure’s instructions. As a broader principle, simple cognitive tasks commonly used in mental status exams (or on the IF scale) may be answered incorrectly by individuals who lack familiarity with the tasks. This point applies to those putting forth a genuine effort, even highly educated, unimpaired individuals from foreign countries (Ostrosky-Solís et al., 2007). Therefore, the IF cognitive tasks may be culturally inappropriate for some examinees, and low scores do not necessarily screen for cognitive feigning. By pretesting the required cognitive skills, clinicians may prudently omit the IF scale from their SIRS-2 administrations (Richard Rogers, personal communication, June 7, 2016). The two Spanish SIRS-2 validation studies included outpatients with varied cultural backgrounds. The Miami study (Rogers et al., 2010) predominantly included clients of Puerto Rican, Central American, and South American cultural heritage. In contrast, the monolingual (Spanish only) study included outpatients, mostly of Mexican descent, with low levels of acculturation to mainstream American culture. Looking to the future, TEA Ediciones, a leading test publisher in Spain, is developing a European Spanish version of the SIRS-2 and plans to conduct further studies in Spain and other Spanish-speaking countries. The Chinese SIRS-2

Currently, one published article (Liu et al., 2013) described two validity studies conducted on a



4.  Cultural and Transnational Perspectives 71

Chinese-language version of the SIRS-2. The researchers refer to their translation process as rigorous. In a personal communication (Richard Rogers on June 2, 2016), the process was briefly described: The English to Mandarin translation was completed by a research assistant who had no knowledge of the SIRS-2; this translation was reviewed and corrected by a clinical psychologist with knowledge of the SIRS-2. The back-translation was independently carried out by a graduate student in clinical psychology. The first study by Liu et al. (2013) was a simulation design, in which Chinese undergraduate students were asked to (1) respond honestly or (2) feign symptoms of mental illness. These two groups were then compared to a clinical comparison sample (i.e., psychiatric inpatients responding honestly). The second study, a known-groups design, compared psychiatric outpatients to a group of suspected malingerers from forensic settings in China. RELIABILITY, VALIDITY, AND CLINICAL UTILITY

For reliability, internal consistences were calculated for each of the Chinese SIRS-2 primary and supplementary scales. Cronbach’s alpha coefficients were mostly high for the primary scales (M = .83) but only moderate for Reported versus Observed (RO; alpha = .74). Supplementary scales ranged from .79 to .84. For the simulation study, convergent validity of the Chinese SIRS-2 was examined in relationship to the Chinese Minnesota Multiphasic Personality Inventory–2 (MMPI-2) F scale, producing low to moderate correlations (.22 to .48) with the SIRS-2 primary scales. Of greater significance, discriminant validity remained impressively high for Chinese SIRS-2 primary scales in both the simulation (mean d = 1.79) and known-groups (mean d = 1.79). These effect sizes strongly support the Chinese SIRS-2 ability to discriminate feigners from forensic inpatients with genuine disorders. Finally, Liu et al. (2013) tested the effectiveness of the Chinese SIRS-2 decision model. Focusing in the known-groups design, the utility estimates produced outstanding results, with a sensitivity of .85 and specificity of 1.00. Qualified psychologists and psychiatrists, fluent in Mandarin, may wish to include the Chinese SIRS-2 in their clinical assessments when feigned mental disorders are suspected. As always, assessments of malingering should include a multifaceted evaluation, with multiple measures and sources of data. As high-

lighted by Liu et al.’s research, some individual scales may vary from those reported in the SIRS-2 manual. Therefore, conclusions should be drawn from the SIRS-2 decision model rather than individual scales. As a caution, additional research is clearly needed for cultural minorities in China and Taiwan, where Mandarin is predominantly the spoken language. For Cantonese (e.g., Hong Kong) and other Chinese variants, different translations of the Chinese SIRS-2 are likely warranted.

The Personality Assessment Inventory The Personality Assessment Inventory (PAI; Morey, 1991) is a 344-item multiscale inventory designed to assess personality traits and patterns of psychopathology. The measure contains 11 clinical scales, five treatment scales, and two interpersonal scales. In addition, the PAI contains four standard validity scales for measuring response styles and profile validity (Morey, 1991, 2007). The normative samples included in the PAI manual create some limitations in interpreting results for members of different cultural groups. Ethnic differences are examined for the census-matched standardized sample but were not considered for the representative clinical sample. Clinical standardization samples, described in the more recent version of the PAI manual (Morey, 2007) are composed of 78.8% European Americans, 12.6% African Americans, and 8.6% “other” minority groups. Normative data for all ethnic/minority groups except African Americans is collapsed into a single “other” group. This grouping propagates the erroneous assumption that all other ethnic minorities are alike, except for African Americans, and potentially masks important between-culture differences in response style (see Romain, 2000; Todd, 2004). For instance, high scores for one cultural group on a particular scale might be balanced out by low scores from another group, masking disparities in scale elevations. The Spanish PAI

To date, the PAI has been published in English and Spanish. The PAI test manuals (Morey, 1991, 2007) do not describe the translation process for the Spanish version. However, its publisher, Psychological Assessment Resources, has standardized its translation process to include an independent back-translation and review/approval by the test’s author (see www4.parinc.com/prores/permis-

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sions.aspx). When a bilingual client expresses only a minor language preference, practitioners might choose the English version due to its extensive validation. When a strong preference is expressed for Spanish, or English language abilities are limited, the Spanish PAI is the most appropriate. Clinicians should note that the Spanish PAI is currently interpreted using the same norms as the English version. CULTURALLY SPECIFIC RESPONSE STYLES

Using the PAI, Hopwood, Flato, Ambwani, Garland, and Morey (2009) found evidence of socially desirable response styles in Hispanic Americans. Hispanic American undergraduates attained higher scores than mainstream culture Americans on the three PAI socially desirable response indicators: PAI Defensiveness Index (DEF), PAI Cashel Discriminant Function (CDF), and the Positive Impression Management (PIM) scale. These differences produced only modest effect sizes (ds = 0.28, 0.37, and 0.38, respectively), so it is unclear whether they indicate a cultural response style that potentially affects test validity. Romain’s (2000) dissertation indicates that more than 40% of the PAI protocols from Hispanic Americans were considered “invalid” based on the standard validity scale cutoff scores outlined in the PAI manual (Morey, 1991), as compared to 20% of the European American profiles. In her sample, Hispanic Americans had higher PIM scores in comparison to mainstream culture Americans (Cohen’s d = 0.60). Romain’s data suggest the presence of a culturally specific response style not previously discussed in the PAI literature. Hispanic Americans are scoring in a non-normative or atypical manner on items unrelated to psychopathology (i.e., Infrequency [INF] scale). Because INF elevations may reflect carelessness, confusion, or reading difficulties, psychologists may wish to consider issues of reading comprehension, knowledge of mainstream culture, and acculturation for the Spanish PAI. Given its large effect size in Romain’s sample (d = 1.00), INF scale may indicate a culturally specific response pattern beyond differences in reading abilities. Correa (2013) also found that approximately one-third of profiles were considered invalid for Spanish-speaking outpatients using the cutoff scores recommended in the PAI manual for the Inconsistency (ICN) scale and the INF scale. Significant differences in INF scores among honest, malingering, and defensive participants in

a simulation study suggest the possibility of idiosyncratic responding among Hispanic American patients in both underreporting and overreporting symptoms on the Spanish PAI. There is no clear pattern in INF elevations based on experimental condition. Each condition demonstrated endorsement of different INF items. To date, specific properties of the INF scale on the Spanish PAI and the possibility of a culturally specific response style have not been explicitly researched. Elevated ICN scores are generally evidence of inconsistent responding. However, the ICN scale was significantly elevated in a clinical sample, regardless of experimental condition. Using Morey’s (2007) general guideline, only participants with ICN scores lying 2 standard deviations above the sample mean in Correa’s (2013) study were considered significantly elevated and excluded from analysis. This practice yielded far fewer invalid protocols and may indicate a need for revised cutoff scores on the Spanish version of the PAI. RELIABILITY, VALIDITY, AND CLINICAL UTILITY

Rogers and his colleagues (Fantoni-Salvador & Rogers, 1997; Rogers, Flores, Ustad, & Sewell, 1995) conducted several studies on the clinical usefulness of the Spanish PAI. Rogers et al. (1995) conducted the first validation study on mostly first- and second-generation Mexican Americans involved in mental health services. The internal consistencies for clinical and treatment scales were generally lower than those found in the representative clinical sample and other non-Hispanic studies (see Morey, 2007). Measures of internal reliability were adequate for the clinical and treatment scales. Research has also found good test–retest reliability for the Spanish PAI. The equivalency between the English and Spanish versions of the PAI was examined for a small bilingual sample and produced moderately high correlations (Fernandez et al., 2008; Rogers et al., 1995). Only two investigations have addressed Spanish PAI validity indicators, one published study that specifically investigates feigning and defensiveness (Fernandez, Boccaccini, & Noland, 2008) and one unpublished dissertation (Correa, 2013). According to Correa, Spanish PAI validity indicators generally produced moderate to large effect sizes in a sample of Spanish-speaking patients with “traditional” levels of acculturation (mean d = 1.08; range from 0.72 to 1.35). Specifically, PAI scales utilizing rare-symptoms strategies (Negative Impression Management [NIM] and Negative Dis-



4.  Cultural and Transnational Perspectives 73

tortion Scale [NDS]) demonstrated moderate to large effect sizes. In contrast, the spurious patterns strategies (Malingering Index [MAL] and Rogers Discriminant Function [RDF]) that focus on patterns of response that are characteristic of malingering but very uncommon in clinical populations (MAL and RDF) appeared to be generally less effective, with ds < 1.00 (Correa, 2013). The discriminability of validity scales was also explored for PAI measures of defensiveness and socially desirable responding, specifically, the PIM, DEF, and CDF. Spanish PAI validity indicators demonstrated moderate to very large effect sizes (mean d = 1.27; range from 0.94 to 1.68). Notably, CDF produced the smallest effect size (d = 0.94) of all Spanish PAI validity indicators (Correa, 2013). This finding is unexpected, because it has been found to be more accurate in detecting defensiveness in the English version of the PAI than either the PIM or DEF scores alone (Cashel, Rogers, Sewell, & Martin-Cannici, 1995; Morey, 2007). Clinical utility of the Spanish PAI increases as different cutoff scores are employed. Correa (2013) suggests various optimized cut off scores for the PAI validity index for use with mostly monolingual Spanish-speaking outpatients with “traditional” levels of acculturation. Fernandez et al. (2008) suggested cutoff scores to maximize the overall classification rate in a sample of bilingual university students. Clinicians may wish to implement the cutoff scores established with the population that best matches the demographic characteristics of each particular client they assess. It is important to note that a European Spanish version of the PAI is now available with European norms (Ortiz-Tallo, Santamaria, Cardenal, & Sanchez, 2011). LINGUISTIC EQUIVALENCE

In a study using bilingual university students and community members, Fernandez et al. (2008) noted that validity scales on the English and Spanish PAI showed relatively equivalent levels of performance when differentiating honest responders and individuals asked to feign or respond defensively. The Spanish PAI clinical scales showed a moderate to good correspondence between Spanish and English versions (mean r = .72) and good test–retest reliability between Spanish language administrations (mean r = .79). Rogers et al. (1995) also found that the Spanish PAI demonstrated moderate correspondence between both language versions (mean r = .68). Additionally Rogers et al. (1995) demonstrated generally adequate alpha co-

efficients for Spanish PAI clinical scales (M = .68; range from .40 to .82) and treatment and interpersonal scales (M = .62; range from .40 to .82). For the PIM scale, Fernandez et al. (2008) found moderately high English-to-Spanish correlations for honest responders and the underreporting condition. These correlations are in stark contrast to the PIM correlation found by Rogers et al. (1995) in a population of Hispanic American patients. In addressing the disparity between these two studies, Fernandez et al. (2008) noted that marked differences in linguistic equivalence may contribute to differences in the samples of the two Spanish PAI studies. Specifically, they utilized a nonclinical, better educated sample than the Rogers et al. (1995) clinical outpatient sample. Neither study examined level of acculturation, so it is not possible to determine whether that also played a role in the disparity between the two studies. Another contributing factor may be that qualities specific to the PIM scale limit its effectiveness and stability among certain samples of Hispanic American individuals. Specifically, Rogers et al. found a modest correlation of .21 for the PIM scale, but much higher correlations for the remaining validity scales (i.e., INC, INF, and NIM), which ranged from .58 to .83. PIM was also identified as having the smallest effect size (d = 0.13) when differentiating between Hispanic American and European American students (Hopwood et al., 2009). Clinicians may wish to weigh the results of Rogers et al. (1995) more heavily, since they correspond better to the demographics of typical clinical referrals.

The Structured Inventory of Malingered Symptomatology The Structured Inventory of Malingered Symptomatology (SIMS; Smith, 1997; Smith & Burger, 1997) is a 75-item, self-report screening measure for feigned psychopathology and cognitive impairment. The Dutch SIMS

The Dutch SIMS was created in a multistep process. English SIMS items were translated to Dutch, then back-translated. Also, culturally specific references, such as American dollars, were replaced by Dutch equivalents. The interpretive cutoff score of 16, recommended by Rogers, Hinds, and Sewell (1996) was used in a validation study by Merckelbach and Smith (2003).

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RELIABILITY, VALIDITY, AND CLINICAL UTILITY

Merckelbach and Smith (2003) conducted three studies. They administered the Dutch SIMS to undergraduate students and a small sample of 10 psychiatric inpatients. In the first study, undergraduate students completed the Dutch SIMS twice, with instructions to respond honestly. Three weeks elapsed between testing sessions, so that test–retest reliability could be evaluated. In the second study, undergraduates completed the Dutch SIMS and other self-report measures of anxiety and depression under honest conditions. The third study consisted of undergraduates responding honestly, inpatients responding honestly, and undergraduate students simulating amnesia, schizophrenia, or neurological problems. Findings demonstrate that the Dutch translation of the SIMS has good test–retest reliability and internal consistency. Findings of the simulation study show that undergraduate students obtain higher SIMS scores than both the undergraduate control group and psychiatric inpatients. Sensitivity, specificity, and positive predictive power rates were all high (≥ 0.90). Finally, Merckelbach and Smith (2003) noted a 16% false-positive rate for the SIMS misclassifying undergraduates who scored high on a depression questionnaire. They noted that the SIMS is a screening measure, and a classification of malingering warrants further investigation and additional psychological testing.

The Miller Forensic Assessment of Symptoms Test The Miller Forensic Assessment of Symptoms Test (M-FAST) is a 25-item screening measure used to detect feigned psychopathology by implementing multiple detection strategies (Miller, 2001). The M-FAST has been translated into Korean and Spanish. However, most of the existing research on the M-FAST has been conducted on European Americans (Montes & Guyton, 2014). The Spanish M-FAST

The Spanish version of the M-FAST parallels the English version. The adaptation process used to create the Spanish M-FAST rigorously followed the adaptation guidelines set forth by the ITC (Hambleton, 2001). Developers used a multistep back-translation process with multiple bilingual and bicultural psychologists of diverse Latin American backgrounds. All translators worked

independently and produced a final version of the M-FAST that was highly comparable to the English M-FAST. The developers of the Spanish M-FAST have conducted the only published validation study (Montes & Guyton, 2014). Results of the study are discussed below. RELIABILITY, VALIDITY, AND CLINICAL UTILITY

Internal consistency for the Spanish M-FAST total score was very high (alpha = .97), with average interitem correlations in the optimal range (M = .42). All scales also demonstrated good internal consistency, with very high interitem correlations. In a simulation study using a sample of bilingual Hispanic American inmates, participants in both coached and uncoached malingering conditions demonstrated significantly higher total scores on the Spanish M-FAST than participants in the honest condition. Utility estimates were used to evaluate the effectiveness of both the English and Spanish M-FAST to correctly identify feigning in bilingual Hispanic incarcerated males. Notably, Montes and Guyton (2014) employed a total cutoff score of 5 instead of the cutoff score of 6 recommended by Miller (2001) in the M-FAST manual. Miller’s cutoff score produced lower sensitivity and negative predictive power estimates (.91 and .85, respectively) in the English M-FAST for this sample. However, the absence of a clinical comparison sample limits the generalizability of these results. LINGUISTIC EQUIVALENCE

For participants in the honest condition, the English and Spanish versions of the M-FAST correlated highly (r = .96, p = .01). Significant positive correlations were also found between the total scores of the English- and Spanish-language versions of the M-FAST for the uncoached and coached malingering conditions. The English M-FAST (M = 0.82, SD = 1.24) were very similar to the Spanish M-FAST (M = 0.82, SD = 1.14). Results provide evidence for good language equivalence between the English and Spanish versions of the M-FAST.

MEASURES OF FEIGNED COGNITIVE IMPAIRMENT Assessment of feigned cognitive impairment is a rapidly growing field in Europe. To date, significantly more research has been conducted in North America, as feigning studies have long been con-



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sidered taboo in international markets (Merten et al., 2013). Ruiz Sanchez de Leon and Gonzalez Marquez (2012) identify a number of neuropsychological tests that assess cognitive feigning in European Spanish speakers. A smaller amount of research has been conducted in Germany, Great Britain, The Netherlands, and Spain, and there are a handful of published studies from Austria, Portugal, and Switzerland. There is virtually no published research from other large countries, such as Italy and France. This disparity in distribution of research could be a result of the aforementioned acceptance or reluctance in some countries regarding the exploration of malingering (Merten et al., 2013). Currently, the most widely used measures of cognitive feigning fall into two camps: (1) forcedchoice/below chance performance and (2) tests using the “floor effect.” Many of the available measures have been widely researched and validated in North America, using normative groups composed of Americans, with small representations of ethnic/minority groups. North American English norms are the only interpretive guidelines currently available for several of these measures, so clinicians must remain aware of limited generalizability for international or non-English-speaking clients. The Montreal Cognitive Assessment (MoCA; Nasreddine et al., 2005) provides a good example of how test translations are sometimes widely available yet validity studies may continue to lag behind. Developed in Montreal, Canada, the MoCA is a brief neurological screen for mild cognitive impairment. It has been translated into approximately 60 different languages and dialects, but validation studies have only been conducted on 29 of the translations and specific normative data are available for five versions. The following sections focus on measures of cognitive feigning with published validity data and specialized norms for international populations.

Forced-Choice/Below-Chance Performance Research indicates that symptom validity tests appear to retain construct validity and remain effective tools when adapted into other languages and other Western cultures. The primary reason for this may be that these measures have such a low test ceiling that they are successfully performed even by patients with low IQ or significant neurological damage (Giger & Merten, 2013). Clinicians must guard against blindly generalizing this guideline, however. Research also indicates that efficacy of an adapted test is highly dependent on the quality

of the translation/adaptation. Cultural differences may still lead to misdiagnosis even when test adaptations are of high quality, because certain concepts are lost in translation. For example, concepts such as seasonal changes, school-based tasks, and visual stimuli (e.g., drawings of certain foods) may be so culturally irrelevant that these test items are missed by highly educated, neurologically healthy individuals. Often, available interpretive norms do not account for these differences in international populations, so clinicians must always interpret results with caution (Ostrosky-Solis et al., 2007). The following tests employ forced choices to assess symptom validity and have published research to bolster their suitability for use with some foreign populations. The Test of Memory Malingering

For a detailed description of the Test of Memory Malingering (TOMM; Tombaugh, 1996), please see Frederick (Chapter 17, this volume). A European Spanish version of the TOMM was published in 2011 with European Spanish norms (Vilar López, Pérez García, & Puente, 2011). The original English version of the TOMM is widely used in Great Britain, but no British validation studies have been published. There is an indication that the TOMM may also be frequently used in other European countries, but there are no validation studies on its use with international populations (Merten et al., 2013). The Word Memory Test

The Word Memory Test (WMT; Green, 2003) is available in 10 languages. Please refer to Frederick (Chapter 17, this volume) for a detailed description of the WMT. Validation research has been conducted on some of the adapted versions, but most versions rely on interpretive guidelines established for the original English-language version. THE DUTCH WMT

Rienstra, Spaan, and Schmand (2009) published reference data for the memory subtests of the WMT. They established linguistic equivalence between the English and Dutch versions of the WMT, with comparable mean subtest scores for the Canadian and Dutch samples. Unfortunately, an unknown number of participants were removed from the study because their scores demonstrated poor effort, so the performance of the Dutch

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WMT cannot be investigated as a test for feigning. Additionally, the absence of a clinical comparison sample limits its generalizability. THE BRITISH WMT

The WMT is one of the most commonly used symptom validity tests in the United Kingdom (McCarter, Walton, Brooks, & Powell, 2009). Hall, Worthington, and Venables (2014) conducted the only published U.K. study with nonlitigant, nonmalingering patients classified as having mild head injury. Patients were established as nonmalingerers through their scores on a battery of six different feigning tests. WMT performance data were analyzed only for the patients who demonstrated adequate effort on the other tests. WMT analysis demonstrated that Immediate Recognition (IR) and Delayed Recognition (DR) scales had acceptable false-positive rates of less than 10%. The Consistency Index (CI), however, yielded an unacceptably high false-positive rate of 18%. Hall et al. specified that participants who “failed” the CI managed to pass the other feigning tests in the administered battery, which implies that the WMT may misclassify genuine patients. The researchers recommended an investigation of alternative cutoff scores for populations with acute mild head injury. A cutoff score of 75% on the WMT provided more acceptable levels of specificity for their sample. In the context of this chapter, it is important to note that cultural differences between U.K. examinees and the original normative sample on the WMT may also affect the generalizability of recommended cutoff scores.

SUMMARY AND RECOMMENDATIONS FOR CLINICAL PRACTICE Validity research on the use of standardized tests with culturally and linguistically diverse clients is still in its infancy on an international level. To date, validation studies are particularly scarce for the assessment of feigning (Merten et al., 2013). As a result, test adaptations are often made available for clinical use without published research to demonstrate its cultural or linguistic appropriateness (Fernandez, Boccaccini, & Noland, 2007; Montes & Guyton, 2014). In order to responsibly choose appropriate psychometric measures, clinicians must be knowledgeable in test construction, test translation, current validation studies, and

cultural factors that potentially influence test validity. The following guidelines are meant to provide useful recommendations for mental health practitioners assessing feigned symptomatology in individuals who are culturally different than those included in the normative sample: 1.  If questioning the appropriateness of a testing measure for a particular client, administration of an acculturation measure may be helpful. Acculturation data could preclude the use of certain tests, illuminate interpretive caveats that could be included in the report, and provide guidance as to which interpretive norms may be most appropriate. 2. Cautionary statements should be included for all interpretations involving clients with low levels of acculturation. Many tests normed in the United States have not yet been extensively researched for clients identifying closely with a minority culture. 3. Many tests used to detect feigned psychopathology in the United States do not have sufficient research to indicate how the validity scales perform with international populations. In choosing which tests to administer, it is best to select a measure with published validity data to avoid imposed etics in test interpretation. If sufficiently established, acceptable validity data may include alternative cutoff scores for certain populations. 4.  When alternative cutoff scores or interpretive norms are not available for a particular population, clinicians may opt to cautiously interpret the test, using what they know about test performance for that particular cultural group. Cautionary/descriptive statements are immensely useful in clarifying any clinical interpretations that diverge from published test norms. Examples of suitable statements include a. “Research indicates elevation on this particular scale could be due to cultural differences.” b. “On this test, English norms were used for scoring, though research indicates the Spanish version performs similarly to the original English version.” c. “Demographically adjusted normative data were utilized when available, using minority norms to most accurately reflect this patient’s environmental, cultural, and educational background. Otherwise, specific test publisher norms were used.”



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5.  If a client’s background clearly does not fit into one set of available interpretive norms, it is acceptable to score the tests using more than one set of norms. A comparison and discussion of the examinee in reference to each normative group may be helpful in contextualizing how the examinee’s culture may or may not affect interpretation of results. 6.  In communicating test results to either patients or other mental health professionals, it is important to clearly convey the limitations of each test with members of international populations when the test has been designed for persons with mainstream American values.

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PA R T I I

DIAGNOSTIC ISSUES

CHAPTER 5

Syndromes Associated with Deception Michael J. Vitacco, PhD

Deception is a central component of malingering in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5; American Psychiatric Association, 2013). Forensic evaluators must be aware of the possibility that examinees may engage in deception whenever they are undergoing court-ordered evaluations. Malingering is described by DSM-5 in terms of faked presentations and external motivations. Yet in traditional psychotherapeutic settings, honesty is often taken for granted given that most clients are self-referred for treatment and express a genuine desire and interest in their personal growth. As such, the base rate of deceptive behaviors in traditional treatment settings remains unknown given the dearth of programmatic research on their detection. In treatment settings, response styles are often considered inconsequential as the goal is designed to improve client functioning. However, in adversarial situations, such as court-ordered evaluations, examinees frequently engage in a variety of response styles. In these cases, deception should not be construed as inconsequential and should be evaluated as a standard part of the assessment process. Such a consideration is unsurprising, since base rates of symptom exaggeration in forensic evaluations range from 15 to 30% (Mittenberg, Patton, Canyock, & Condit, 2002; Vitacco, Rogers, Gabel, & Munizza, 2007). This chapter focuses on various diagnoses, syndromes, and situations in which deception is para 83

mount. As discussed by Vitacco and Rogers (2005), individuals have varyious motives for deception at different times. For instance, the same person may feign psychosis in a pretrial competency-to-proceed evaluation to delay going to trial, and after conviction feign psychosis in a correctional environment for a different reason (e.g., a preferential cell placement or unwarranted medications). Finally, the same individual may become defensive and deny mental health problems as a potential parole release date draws closer. This illustrative example exemplifies several features about deception that should become evident throughout this chapter. First, deception is a multidimensional construct that manifests differently across situations and settings. Second, relatedly, deception is not taxonic; rather, it should be viewed as a dimensional construct that can change in direction and intensity. Third, deception is frequently adaptive but not always. For instance, in the earlier example, trying to obtain medications through feigning may be both adaptive and criminogenic (see Rogers, Chapter 1, this volume). Finally, individuals who engage in deceptive behaviors are not always insightful and aware of their reasons for deceiving. For example, clients with anorexia nervosa may deceive others about the calories consumed or their robustness of their health, but these same clients may still lack insight into the pervasiveness of their illnesses and a strong desire to control their body images. In the context of these syndromes, this chapter examines

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specific contexts within each syndrome in which deception is manifested. As the primary goal, this chapter addresses a variety of syndromes (and situations) associated with deceptions. The specific areas include DSM-5 disruptive and impulse control disorders, specifically, conduct and oppositional defiant disorders. Second, this chapter provides information on the relationship between deception and personality disorders, specifically, antisocial personality disorder (ASPD) and psychopathy. Third, it considers drug use and abuse, which have strong associations to deceptive behavior. Fourth, as mentioned earlier, individuals with eating disorders frequently use deception to avoid questions about their lack of caloric intake or to hide binging. Finally, individuals engage in a variety of deceptive practices related to sexual paraphilia. Again, the actual reasons for deception in sexual disorders are varied. Finally, I discuss other syndromes (e.g., false memory) and conditions (e.g., child custody) in relation to deception.

CHALLENGES AND PITFALLS WHEN EVALUATING MALINGERING AND DECEPTION A classification of malingering by itself is often the foundation for serious consequences. For example, the U.S. Code of Military Justice Article 115 provides criminal sanctions for a soldier who malingers for the purpose of avoiding work or duty. In the landmark case of the United States v. Greer (1998), the Fifth Circuit Court of Appeals allowed for enhanced punishment for malingering under the umbrella of obstruction of justice. Problematically, relying on DSM-5 screening indicators for malingering represents a surefire way to have an unacceptable number of false positives. As noted by Rogers and Vitacco (2002), the rate of false positives could be as high as 80% if relying solely on these indicators. These indicators include background (ASPD), setting (forensic), discrepancy (subjective reports unverified by objective data), and assessment attitude (uncooperative). In routinely evaluating pretrial defendants for court-ordered evaluations, most of the examinees I assess meet many, if not all, of the DSM-5 screening indicators for malingering. Yet the majority of these defendants do not appear to be engaging in intentional deception, such as malingering. Despite these enduring problems (e.g., see Rogers, 1990a, 1990b), DSM-5 continues to rely on screening indicators that are outdated and poorly validated.

In evaluating feigning and related response styles, several conceptual issues warrant consideration. First, evaluators must carefully consider examinees’ motivations. The definition of malingering requires a close review. It necessitates that feigning must be “intentional.” Factitious disorders, such as malingering, are typically viewed as occurring on a continuum (see Yates, Mulla, Hamilton, & Feldman, Chapter 11, this volume). Clinicians must consider motivations when making a diagnosis of malingering. As noted both in the Structured Interview of Reported Symptoms (SIRS; Rogers, Bagby, & Dickens, 1992) and the Structured Interview of Reported Symptoms–2 (SIRS-2; Rogers, Sewell, & Gillard, 2010)—the premiere instruments for the detection of feigned mental disorders—clinicians are asked to carefully evaluate the individual’s motivation for deceptive responding. As reported in the SIRS manuals, symptoms of malingering and factitious disorder are difficult to disentangle. A second point is that clinicians must not equate isolated test results with a classification of feigning or malingering. Psychological testing is useful, if not essential, to properly assessing malingering and deception. However, a single score on a psychological test must never be considered in a vacuum. False positives are an inherent part of any diagnostic test, be it psychological or medical, and psychological measures for evaluating response styles are not immune. Underscoring this idea, Berthelson, Mulchan, Odland, Miller, and Mittenberg (2013) reported a false-positive rate for malingering in neuropsychological evaluations to be as high as 38% (see also Larrabee, 2012). And, although superior to clinical judgment, objective measures, when used as the sole indices of malingering, continue to put clinicians at continued and serious risk for false conclusions. In general, clinicians are on empirically solid ground when using a multimodal approach to evaluating malingering (Denney, 2005). Such an approach integrates information from behavioral observations, mental health history (if any), criminal history (if any), and objective psychological testing. For instance, it would be highly unlikely for defendants with lengthy criminal histories and court proceedings to accurately claim virtually no knowledge about court or how the criminal justice system works. Historical information, when combined with careful objective testing of response styles, provides clinicians with well-rounded and comprehensive information on response styles. As a third conceptual issue, as noted earlier, the behaviors associated with malingering are not tax-



5.  Syndromes Associated with Deception 85

onic (Walters, Berry, Rogers, Payne, & Granacher, 2009; Walters et al., 2008). For instance, in a confirmatory factor analysis of the SIRS, Rogers, Jackson, Sewell, and Salekin (2005) found that two dimensional factors underpin the instrument: spurious presentation and plausible presentation. Using a similar analysis with the Miller Forensic Assessment of Symptoms Test (M-FAST; Miller, 2001), Vitacco et al. (2008) found a single factor that best represented the M-FAST structure. Beyond formal analyses, Resnick, West, and Payne (2008) subtyped malingering by the level of fabrication/exaggeration, from pure malingering (i.e., total fabrication) to false imputation (no exaggeration but misattributing symptoms to a compensable cause; see Resnick, West, & Wooley, Chapter 10, this volume). As a fourth conceptual issue, clinicians should consider explanatory models of malingering (see Rogers, Chapter 1, this volume). Table 5.1 offers a basic conceptualization of various clinical syndromes and situations in which deception is com-

mon. However, a close review of Table 5.1 demonstrates that explanatory models are not discrete categories; behaviors associated with deception can span two or even three of the explanatory models. It is clear that most types of deception are actually unrelated to malingering. What is clear is that clinicians must be aware of multiple determinants of motivations related of feigning and deceptive behaviors. For the fifth and final point, clinicians, inasmuch as possible, must be exacting in their use of language. For example, the term secondary gain was popular for many years and used by clinicians when conducting forensic evaluations. However, Rogers and Reinhardt (1998) warned against using the term secondary gain in forensic situations. By default, practically everyone undergoing a forensic evaluation has a stake in the outcome. If a clinician uses secondary gain as an indicator, it is akin to saying, “I know it is there, I just need to find it.” Based on such ambiguities and potential for misuse, Vitacco and Rogers (2005) recommended cli-

TABLE 5.1.  Explanatory Models of Motivations Related to Diagnoses and Dissimulation

Disorder

Explanatory model

Characteristics associated with deception

Conduct disorders

Criminogenic

Instrumental/poor impulse control

Reactive attachment disorder

Pathogenic

Secondary to extreme abuse and abandonment

Adaptational

Compensatory mechanisms in social situations

Criminogenic

Secondary to antisocial personality disorders

Adaptational

Financial motivations

Pathogenic

Rigidity

Criminogenic

Secondary to antisocial personality disorders

Adaptational

Escaping/avoiding responsibilities

Pathogenic

Self-medicating

Eating disorders

Pathogenic

Rigidity/distorted body image/maintaining control

Paraphilias

Criminogenic

Luring victims/maintaining offending

Pathogenic

Own abuse history leads to poor boundaries

Psychopathy

Criminogenic

Instrumental/game playing/poor impulse control

False-memory syndrome

Criminogenic

Secondary to antisocial personality disorder

Adaptational

Financial motivations (litigation)

Pathogenic

Regression/avoiding responsibilities

Criminogenic

Extortion/lying to turn child against parent

Adaptational

Strong desire to remain with child

Pathogenic

Rigidity/personality disorders

Factitious disorder

Substance abuse

Child custody

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nicians abandon the term secondary gain in favor of more empirically validated terms. In summary, evaluating malingering and deception are complicated endeavors that require a sophisticated understanding of response styles, knowledge of strengths and limitations of objective measures, and the willingness to explore motivations as they relate to deception. Drob, Meehan, and Waxman (2009) indicated that there is pressure on clinicians to find malingering, and cultural factors complicate the detection of malingering. These challenges underscore the importance of developing a full appreciation of response styles prior to conducting forensic evaluations. In the next two sections I focus on areas in which deception is common. The initial section focuses on deception as it relates to formal DSM-5 diagnoses. The second addresses other clinical syndromes and conditions in which various aspects of deception are manifested.

DSM-5 DIAGNOSES ASSOCIATED WITH DECEPTION Clinicians in general, but especially forensic clinicians, must be aware of the multiple ways deception can be manifested as a part of psychopathology. Moreover, as will be demonstrated, clinicians must be cognizant that motivations for deceptions may vary. Clinicians may prescribe malevolent motives to deceptive behavior, when, in reality, the deception may be unconscious or simply adaptive. This section begins with disorders typically diagnosed in children and adolescents, then moves to disorders of adulthood in considering a full range of explanations for deceptive behaviors within the context of DSM diagnoses.

Deception and Disorders of Childhood and Adolescence Oppositional Defiant and Conduct Disorders

Oppositional defiant disorder (ODD) and conduct disorder (CD) are now found in the section of DSM-5 labeled “Disruptive, Impulse-Control, and Conduct Disorders.” These disorders have been grouped together because they involve problems with self-control that may bring them into conflict with others. Sometimes these symptoms portray behavioral problems that continue to manifest into adulthood (Moffitt, 1993). Behaviors related to deceitfulness are a core component of both CD and ODD. For example, in CD, diagnostic criteria

explicitly place deceitfulness as part of the disorder with respect to falsehoods and conning. New DSM-5 criteria also allow for a specifier regarding whether behaviors include limited prosocial emotions, including lack of remorse and the presence of callousness. As such, for a CD diagnosis, it would be important to discern how adolescents think and feel about their deceptive behavior. In contrast to the explicit CD criteria related to deception, there are no explicit criteria linking ODD with deception. Indirectly, the deception in ODD is often manifested in poor attitudes displayed toward people in authority. Frick et al. (1991) evaluated the covariation of symptoms between ODD and CD in a sample of 177 clinicreferred boys. The authors found that lying loaded significantly on both ODD and CD across parent and teacher ratings. Although ODD and CD differ in their deceptive presentations, the deception associated with both diagnoses typically has a negative influence on the adolescent’s interpersonal relationships (Hughes & Hill, 2006). Relatedly, deception can also be part of behavior associated with psychopathic traits in adolescence. In studies evaluating traits of psychopathy in youth, deception was frequently part of the construct (Lynam et al., 2009; Lynam & Gudonis, 2005). In fact, two items on the Psychopathy Checklist: Youth Version (PCL:YV; Forth, Kosson, & Hare, 2003) are Pathological Lying and Conning. The mere act of engaging in deception portends significant problems in multiple domains of adolescents’ lives. Boys perceived as deceptive by their peers were reported to be more withdrawn, more aggressive, and less likeable (Gervais, Tremblay, & Héroux, 1998). A final consideration in this section involves how deceptions relate to sexual offending behavior with adolescents. Like adults, deception can take the form of both lying and minimization to avoid taking responsibility or to continue to allow access to victims (Barbaree, Marshall, & Hudson, 1993). Evidence supports the notion the large majority of adolescent sexual offenders do not persist into adulthood. Research (Caldwell, 2002; Caldwell & Dickinson, 2009) found sexually-based offending is not the strongest predictor of adult sex offenses. Nonetheless, deception and lying in adolescent sex offenders can be secondary to several factors including embarrassment, trying to get away with it, or minimizing their own histories of victimization. For instance, Baker, Tabacoff, Tornusciolo, and Eisenstadt (2001) compared sexually abused children with other children on welfare. Notably,



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an identified factor labeled “family deception” was composed of family myths and active lying. Longitudinally, the presence of “family deception” was associated with subsequent sexual offending. In summary, this section highlights the relationship between externalizing disorders and deceptive behaviors. In ODD and CD, deception is likely voluntary and related to either general defiance or blatant antisocial behavior. Specifically, deception is embedded within the items on the Interpersonal facet of the PCL:YV. Clinicians working with children and adolescents with conduct problems are advised not only to assess for the presence of deception but also to consider deception as a viable treatment target. Reactive Attachment Disorder

Similar to ODD and CD, children with reactive attachment disorder (RAD) are known to engage in deception. RAD is classified in DSM-5 under “Trauma and Stressor-Related Disorders”; children with this disorder often have experienced chronic neglect or abuse, leading to a disturbance in the ability to properly bond with individuals. The core feature for RAD involves emotional withdrawal and a lack of appropriate attachment. Research has linked RAD with lying and deception. Wilson (2001) characterized children with RAD as presenting with “sociopathic behavior which includes deception” (p. 42). Multiple studies have identified associations between psychopathic traits and early neglect. For example, Schraft, Kosson, and McBride (2013) found a link between exposure to violence and the presence of psychopathic traits in a sample of adolescents. Schimmenti, Di Carlo, Passanisi, and Caretti (2015) found that emotional abuse and neglect were related to psychopathy scores in a sample of inmates with violent convictions. More broadly, Gostisha et al. (2014) reported a modest relationship between early life stress and psychopathic traits in a sample of incarcerated adolescents in a secure treatment facility (see also Shirtcliff et al., 2009). Although the deceptive behaviors of RAD may appear similar to those of ODD and CD, they are fundamentally different in terms of the underlying motivation. In RAD, the deception tends to be used as an adaptive mechanism to protect the individual from what he or she perceives as dangerous social relationships. Alternatively, the deception may be pathogenic, secondary to the extreme neglect by caregivers. Clinicians are advised to explore histories of abuse and/or neglect

when evaluating behavioral problems. It is possible that what appear to be conscious attempts at deception may instead be behaviors associated with RAD, which are secondary to abuse and neglect. As evidenced by the new DSM-5 criteria, the etiology and pathogenesis of RAD remain imprecise (Zilberstein, 2006); however, research points to an association between the symptoms of RAD and various forms of disruptive behaviors, including deception.

Deception and Disorders of Adulthood Factitious Disorders

Factitious disorders are frequently confused with malingering due to their similar clinical presentations. In DSM-5, factitious disorders are found in the category “Somatic and Related Disorders.” Like malingering, a key component of factitious disorder is deception. Notably, the individual often presents to others as highly impaired or seriously ill. As noted by both the SIRS and SIRS-2 (Rogers et al., 1992, 2010), factitious disorder must be ruled out before deciding whether an individual is malingering. The key differential with factitious disorders is the absence of clear external incentive for the deceptive behavior. Consistent with malingering, the feigning of symptoms is within the patient’s control. However, no explicit guidelines on psychological measures clearly differentiate between malingering and a factitious disorder. In these cases, clinicians must rely on their professional judgment and explore the need of the individual to continue to maintain his or her identity as a sick person. The lack of objective evaluation weakens both the validity of the factitious disorder and clinicians’ ability to render an accurate diagnosis. In considering factitious disorder, several factors of the patient’s presentation likely draw attention to him or her. DSM-IV-TR (American Psychiatric Association, 2000) and DSM-5 (American Psychiatric Association, 2013) list criteria required for diagnosing a factitious disorder. These include both behavioral/clinical (i.e., atypical or dramatic presentation inconsistent with a mental disorder or medical condition, pathological lying, covert use of substances, and extensive history of traveling) and treatment-related issues (i.e., symptoms that are present only when the individual is aware that he or she is being observed; disruptive behavior on an inpatient unit; arguing excessively with nurses and physicians; evidence of multiple treatment interventions; few, if any, hospital visi-

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tors; and fluctuating hospital course). In a clinical case, I evaluated an offender for serious depression and suicidal ideations while he was serving a sentence for multiple charges of burglary. Objective psychological testing (e.g., Personality Assessment Inventory; Morey, 1991) and a specialized measure (SIRS) were consistent with feigned mental disorders. Despite feigning results, the multidisciplinary treatment team found no external incentives present. A review of the inmate’s history indicated several hospitalizations for unspecified physical and emotional disorders. Ultimately, the treatment team decided the most appropriate diagnosis was factitious disorder. This case illustrates that even in prison, where clinicians generally consider malingering first, it is prudent to consider the full range of potential motivations for deception. Second, this case is consistent with the notion that once it is determined that an individual is feigning, the next step needs to determine the motivation for the feigning (see Rogers et al., 1992). Another consideration is that factitious disorder includes various and diverse symptoms. Cunnien (1997) proposed the following as markers of factitious disorders: (1) strong masochistic needs; (2) sickness allowing regression and avoidance of adult responsibilities; (3) illness symbolic of anger or conflict with authority figures; (4) illness fulfilling dependency needs; and (5) illness symbolizing attempts at mastery of past trauma. In going beyond motivations, Cunnien provided a list of diagnoses often exhibited by individuals with factitious disorders. These include psychosis, bereavement, posttraumatic stress disorder, dissociative identity disorder, and even false claims of child abuse. Pope, Jonas, and Jones (1982) evaluated nine patients who seemingly had control over the presence of their psychotic symptoms and each was diagnosed with personality disorder(s). Such comorbidity is seemingly common with factitious disorders. The development and presentation of factitious disorders are not well understood. This is unfortunate given that factitious disorders are likely more prevalent than initially believed (Gregory & Jindal, 2006), with prevalence estimated at 1% in inpatient settings (American Psychiatric Association, 2013, p. 326). Factitious Disorder Imposed by Another (Munchausen by Proxy)

In DSM-5, factitious disorder imposed by another (FDIA) is the new term for deceptions targeting another person either in symptom falsification or

surreptitious symptom induction, while typically pretending to be a concerned caregiver. This diagnosis was previously referred to as Munchausen by proxy. Often FDIA takes the form of a parent purposely making his or her child become sick or impaired. If discovered, it can lead to legal charges levied against the parent. If undiscovered, the child can be subjected to long-term disabilities or even death. The television news show 48 Hours presented a case in April 2016 of a young mother accused of injecting her son with salt in order to make him sick. Ultimately, the child died and the mother was convicted of second-degree murder. Unfortunately, FDIA is not rare, and it has been suggested that factitious disorder imposed by another constitutes 10% of factitious disorder cases (Reich & Gottfried, 1983). Diagnosing FDIA is extremely difficult due to the level of deception a parent or caretaker undertakes to convince others that someone is very sick. Waller (1983) discussed how significant deception and convincing authorities that a caretaker is making the child sick interact to form barriers to effective identification and treatment. Waller noted that most parents continue their strong denials even when confronted with overwhelming evidence implicating them as the cause of their child’s illness. I once was involved in a case in which a mother went so far as to shave her child’s head to convince others she was undergoing chemotherapy to counteract an aggressive leukemia. The mother was caught only when relatives came to the hospital after traveling a significant distance, due to their concerns about the child’s impending death. Libow and Schreier (1986) reported three subtypes of individuals with FDIA: (1) those calling for help, (2) active inducers, and, (3) doctor addicts. Active inducers garner the most attention, because the parent’s behavior in these cases is criminal and the children under their care are at risk of harm. For instance, in the case presented on 48 Hours, the mother was found guilty of causing the death of her son, which occurred while he was in the hospital, under close medical supervision. As noted by John Stirling, Jr., and the Committee on Child Abuse and Neglect (2007), FDIA is not just a mental health disorder, it also constitutes a serious form of child abuse (see Ayoub et al., 2002). The etiology of factitious disorder and FDIAs are not well understood (Mart, 2002). One potential way to evaluate such disorders is through the application of explanatory models of malingering proposed by Rogers (1990b). To that end,



5.  Syndromes Associated with Deception 89

underlying motivations for factitious disorders, according to Cunnien (1997), may be consistent with all three explanatory models of malingering (i.e., adaptational, pathogenic, and criminogenic). Adaptational explanations include financial motivations or an attempt to bring alienated family together to bond over a common cause (i.e., a sick child). Pathogenic explanations include rigid control of family or a dysfunctional and maladaptive attachment with the child (Rogers, 1990b). Finally, the criminogenic model might be used to explain how an individual with psychopathy or ASPD would attempt to generate an illness to manipulate or steal. Despite their potential usefulness, Rogers (2004) indicated that such explanatory models for factitious disorders are underdeveloped. As such, our understanding of these disorders and how to apply explanatory models also need to be fully addressed. Eating Disorders

Patients with diagnoses of anorexia nervosa and bulimia nervosa often engage in a variety of deceptive practices that enable them to continue their obsessive behavior focused on their weight and body image. This behavior is especially dangerous, because it is designed to provide the façade the client is improving when, in fact, the eating disorder is continuing. Specific deceptive behaviors associated with eating disorders are wide ranging and include the following: • Hiding food in order to engage in bingeing behavior. • Secretly exercising, even when under close observation. • Stealing laxatives and diet supplements to avoid the embarrassment of purchasing them. • Traveling long distances to use drugstores outside of one’s typical neighborhood to avoid family and friends while purchasing diet aids. • Lying about weight gain and minimizing weight loss. • Wearing clothes to hide weight loss or trying to provide the appearance of gaining weight. In DSM-5, deception is not listed as part of the formal diagnostic criteria, because the description is focused mostly focused on body weight and selfimage. However, studies of individuals with eating disorders consistently revealed deceptive behaviors. Lacey and Evans (1986; also see Lacey, 1993) evaluated 112 individuals with bulimia and diag-

nosed with other impulse control disorders. In this study, 21% of the patients repeatedly stole. These results confirm how deception is frequently manifested in individuals with eating disorders. Family deceptiveness has been identified as a factor in development of eating disorders (Dalzell, 2000). Notably, this factor is frequently relevant in adolescents engaging in illegal sexual behavior. Deceptive family environments, potentially related to overcontrolled and rigid parenting styles, are implicated as an etiological factor for eating disorders. Such families have a history of nondisclosure and frequently deny basic flaws in their families and in each other. These families have the desire to project an outward appearance of perfection. Not surprisingly, individuals with eating disorders often employ deception to maintain control of their eating behavior and to deceive parents and treatment providers into believing that treatment is working. From an explanatory model, the pathogenic model appears to be apt in the attempt to understand deceptive behaviors associated with eating disorders. Substance Abuse and Other Addictive Behaviors

Deception in the context of substance abuse and addictive behaviors is commonplace. The use of alcohol and illicit drugs has long been associated with denial and misrepresentation. Yet professional athletes and sports participants are now also frequently engaging in elaborate schemes to avoid detection of banned substances. Lance Armstrong, the then seven-time champion of the Tour de France, admitted to Oprah Winfrey that he utilized a concoction of banned substances during his victory streak. Also revealed was the extensive system he utilized to defeat drug tests and sophisticated detection systems. Baseball has also been implicated in the performance-enhancing drug culture. The years between 1994 and 2003 are referred to as the “steroid area,” in which there was a dramatic increase in the number of home runs hit. With increased attention and implementation of more stringent drug testing, home run production dropped precipitously (Erickson, Yanke, Monson, & Romeo, 2015). Professional leagues now severely sanction players with positive drug tests, but new technologies designed to counteract drug tests seem to always be in development. In considering the criteria of a substance abuse disorders and deception, DSM-5 (American Psychiatric Association, 2013) indicates that an “in-

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dividual may spend a great deal of time obtaining the substance, using the substance, or recovering from its effects. In some instances of more severe substance abuse disorders, virtually all of the individual’s daily activities revolve around the substance” (p. 483). In order to continue to use substances, most individuals must justify their time away from work and home. Individuals abusing prescribed medications often need to fabricate new ailments or deceive new physicians in order to maintain their access to desired medications. One way for substance abusers to gain a more accurate perception of their behavior is through a 12-step program. Ferrari, Groh, Rulka, Jason, and Davis (2008) reported that participation in formal 12-step programs serves to minimize denial and minimization. Not surprisingly, extant research has identified a relationship between substance abuse and deception. Klein (2000) found a significant overlap between scores on the SIRS and substance abuse identified by the Substance Abuse Subtle Screening Inventory (SASSI; Miller, 1994a, 1994b). College students who self-report more substance abuse also report cheating on examination and having lied to avoid taking an examination (Blankenship & Whitley, 2000). In corrections, Richards and Pai (2003) evaluated 312 inmates and found varyious response styles, secondary to what the inmate believed would work best for his or her situation. Specifically, 22% of the sample faked good (denial and minimization), and almost 15% faked bad (exaggerating psychopathology). Another addictive disorder with links to deception is gambling disorder. The crux of this disorder is recurrent and problematic gambling behaviors leading to significant distress. DSM-5 lists nine specific diagnostic criteria for gambling disorder, and several directly involve deception, such as concealing gambling activities. Similar to other addictive disorders, deception is central to understanding the proposed Internet gaming disorder as lying to avoid taking responsibility and about time spent on the Internet. Application of Rogers’s explanatory models to addictive disorders provides useful information for understanding motivations. The pathogenic model may apply to persons with substance dependence on highly addictive drugs. This categorization is especially salient given the current problems many cities are having with heroin and methamphetamine. A criminogenic model might be applicable if addiction were part of a broader part of antisocial behavior (e.g., stealing to pay gambling debts

or to purchase illicit substances). Finally, the adaptational model may explain the use of substances (or gambling) as a method to cope with adversity or to escape. Awareness of motivations for addictive disorders is useful in the development of effective treatment and intervention models. Paraphilias and Sexual Abuse

DSM-5 (American Psychiatric Association, 2013) refers to paraphilias as sexual deviations or perversions, with behaviors or sexual urges focusing on unusual objects, activities, or situations. These deviations and behaviors can present in many forms (i.e., voyeurism, exhibitionism, frotteurism, masochism, pedophilism, fetishism, and transvestism). According to DSM-5, there are dozens of other paraphilias, but these appear to be the most common ones (American Psychiatric Association, 2013, p. 685). In many cases, these conditions lead to illegal behavior and lengthy prison sentences. In some states, convicted sex offenders can, after completing their criminal sentences, be civilly committed for the rest of their lives to receive mandated treatment to decrease dangerousness (Hoberman & Jackson, 2016; Phenix & Jackson, 2016). Given the potential consequences, it is relatively obvious why defensiveness, minimization, and lying are frequently observed in sex offenders. Defensiveness and lying refer to voluntary actions typically used to achieve a desired objective. Defensiveness is often manifested in treatment settings in which individuals with paraphilias are reluctant to discuss their sexually based behavior. Lying often occurs in treatment settings, but it may also manifest itself as part of a larger antisocial strategy to dupe unsuspecting victims. For instance, some offenders lie about their age to make an otherwise taboo sexual relationship appear normal. Other individuals who engage in sexual abuse lie (e.g., fabricate a career) to gain sexual access to potential victims. In contrast, cognitive distortions, often in the form of rationalizations, represent patterns of thinking, often referred to as schemas that are potentially less voluntary, yet occur quite frequently in sexual abusers. Carvalho and Nobre (2014) found that sex offenders who offend against children have several maladaptive schemas (e.g., pessimism) compared to non-sex offenders and sex offenders with adult victims. Treatment protocols should explore both deliberate acts of deception and automatic schemas that, although deceptive, appear less under the offender’s control (Carvalho & Nobre, 2014).



5.  Syndromes Associated with Deception 91

The previous discussion emphasizes the complexity of motivations regarding deception among sex offenders. The prevalence of deception in inpatient treatment settings is one of the primary reasons polygraphs are often employed in conjunction with intensive treatment. Although they are not used in court to prove guilt or innocence, polygraphs are combined with intensive treatment to monitor the patient’s report of his or her history and progress in treatment (Branaman & Gallagher, 2005; Kokish, Levenson, & Blasingame, 2005). Despite the use of the polygraph to monitor the veracity of the patient’s report, the use of the polygraph is highly controversial as an effective instrument to monitor patients. Personality Disorders and Psychopathy

Personality disorders are defined as “an enduring pattern of inner experience and behavior that deviates markedly from the expectations of the individual’s culture” (American Psychiatric Association, 2013, p. 646). These deviations can be found in cognitions, affectivity, interpersonal functioning, and impulse control. As it concerns deception, ASPD is frequently associated with lying and deception. However, other personality disorders also have been implicated. Borderline, narcissistic, and histrionic (referred to as Cluster B disorders) personality disorders are linked to deception (Rose & Wilson, 2014). Deception is also observed in dependent, schizotypal, and avoidant personality disorder. In each of these diagnoses, the deception is manifested differently. In ASPD, lying is often part of a general predilection to engage in criminal behavior. Specific deceptive behaviors include repeated lying, use of aliases, or conning others (American Psychiatric Association, 2013, p. 659). The deception in ASPD is instrumental and generally is designed to advance the agenda of the individual. In contrast, deception in borderline personality disorder frequently occurs in the context of identity disturbance (Engle & O’Donohue, 2012). Deception may occur in narcissism and histrionic personality disorders to compensate for low self-esteem by presenting a facade or to create excitement (see Mandal & Kocur, 2013). In contrast, individuals with avoidant personality disorder may engage in deception to disengage from and circumvent social situations. In dependent situations, individuals deceive in order to present as helpless, with an inability to fend for themselves, so that others can assume responsibility for them.

Psychopathy is defined by Hare (1996) as a “socially devastating personality disorder defined by a constellation of affective, interpersonal, and behavioral characteristics, including egocentricity, manipulativeness, deceitfulness, lack of empathy, guilt or remorse, and a propensity to violate social and legal expectations and norms” (p. 105). The Psychopathy Checklist—Revised (Hare, 2003) consists of four interrelated facets: Interpersonal, Affective, Lifestyle, and Antisocial tendencies. As described by Gillard (Chapter 9, this volume), the interpersonal features of psychopathy are most associated with deception, because that facet includes various traits and behaviors of glibness, superficiality, lying, and conning behavior. The Interpersonal Measure of Psychopathy (Kosson, Steuerwald, Forth, & Kirkhart, 1997) has been developed to assess these traits during clinical interviews. With a well-defined factor structure (Vitacco & Kosson, 2010), interpersonal behaviors associated with psychopathy can be identified even in relatively brief periods of time (Fowler, Lilienfeld, & Patrick, 2009). For individuals with high levels of psychopathy, professionals should expect they would engage in deceptive behaviors across a variety of settings. Psychopathy has been linked to deceptive behaviors at work (Mathieu, Hare, Jones, Babiak, & Neumann, 2013; Ragatz, Fremouw, & Baker, 2012) and malingering in forensic settings (Gacono, Meloy, Sheppard, Speth, & Roske, 1995). Although many psychopaths engage in deception, it does not appear that psychopaths are skilled at deception or successful at malingering (Rogers & Cruise, 2000). However, not all research has found a link between psychopathy and malingering. Consistent with Kropp and Rogers (1993), several studies (Kucharski, Duncan, Egan, & Falkenbach, 2006; Ray et al., 2013) indicated a lack of a relationship between psychopathic traits and feigning. Moreover, individuals with psychopathic traits did not appear incrementally effective at feigning mental health disorders. Robinson and Rogers (2015) found that many inmates, irrespective of psychopathy, demonstrate the ability to feign empathy, which may lead to disruptions in specific types of therapeutic communities. Overall, there appears to be an interesting conundrum related to psychopathy and deception. On one hand, those who score high on interpersonal features of psychopathy lie frequently and easily. On the other hand, psychopaths do not appear to be particularly adept at malingering and are as likely to be detected as other offenders. At no point should the

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term psychopathy be substituted for a classification of malingering or interpreted as evidence of the presence of deception.

OTHER CLINICAL PHENOMENA ASSOCIATED WITH DECEPTION A remarkable variety of conditions and situations are often associated with deceptive behavior. This chapter cannot possibly provide a comprehensive account of all disorders and situations associated with deception. Instead, this section focuses on factors of child custody that lead to deception and memory issues, including false-memory syndrome. In the third edition of this volume, I included chronic fatigue syndrome; however, research does not support its continued inclusion in this section on deception. The goal is that this section provide specific situations in which malingering is common and apply explanatory models to explain why deception occurs.

Contested Child Custody Evaluations Contested child custody evaluations provide several opportunities for deception, including a unique balance of denying one’s own shortfalls and exaggerating the faults of one’s ex-partner. This dynamic may be akin to an awkward dance as one makes one’s previous partner appear unfit and incapable, even despite contrary evidence (see Gould, Rappaport, & Flens, Chapter 25, this volume). The first response style is consistent with defensiveness and/or minimization. A seemingly better term for this (Strong, Greene, Hoppe, Johnston, & Olesen, 1999) is self-deceptive positivity (SDP), which refers to the fact that many individuals undergoing an evaluation to assist in the determination of custodial placement of a child act in a manner to augment their strengths and purposely minimize or deflect negative traits. Each construct is discussed separately in the next two paragraphs. In contested custody evaluations, the examinees’ goal is to create an image of themselves as excellent parents who place the need to their children first. The conscious goal is to appear well-adjusted by minimizing psychopathology and maladaptive traits (Erickson, Lilienfeld, & Vitacco, 2007). Like malingering, SDP is considered a dimensional construct (Strong et al., 1999) and should not be considered “all or none,” and it varies as a function of the individual and the custody situation. Defensive responding has been

evident in research employing multiscale inventories in contested child custody evaluations (see Ackerman & Ackerman, 1997; McCann et al., 2001; Medoff, 1999), including the Minnesota Multiphasic Personality Inventory–2 (Butcher, Dahlstrom, Graham, Tellegen, & Kaemmer, 1989) and the Millon Clinical Multiaxial Inventory–III (MCMI-III; Millon, 1994). Regarding the MCMIIII, Stolberg and Kauffman (2015) note the importance of evaluators being aware of the implications of positive impression management on test results. As a final note, the MCMI-IV has been published, but there is no research on it with parents undergoing custody evaluations. The final area to address is relevant to how some custody litigants have been alleged to attempt to alienate children from the other parent. This phenomenon, which has a lengthy history in the child custody field, has been aptly referred to as parental alienation syndrome (PAS). In PAS cases, one parent uses deception to cause a rift between the child and the other parent by belittling or even lying about the other parent to the child. However, PAS, has remained a controversial term, with limited scientific support. Emery (2005) suggested that in the absence of objective standards, PAS should be considered a hypothetical construct. In the same vein, Thomas and Richardson (2015) referred to PAS as “junk science” due to its lack of general acceptability in the field. Finally, the National Council of Juvenile and Family Court Judges found PAS to be lacking in scientific merit and encouraged the court to reject testimony about PAS (see Thomas & Richardson, 2015, p. 22). Whether PAS is a syndrome remains an empirical question, but the use of deception to influence a child in a contested child custody evaluation does occur.

False-Memory Syndrome The idea of false-memory syndrome (FMS; see McNally, Chapter 20, this volume) was first advanced in 1992 by an organization of the same name (False Memory Syndrome Foundation, 2016). Since its creation, research has been appropriately devoted to determining the reliability and validity of FMS. Much of the fanfare has stemmed from high-profile cases in which memories seemingly emerged from nowhere to implicate a person or group of people in a sensational case. FMS describes a cluster of symptoms in which traumatic memories remain in the absence of external evidence. From this perspective, FMS acts like psy-



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chodynamic concepts of regression and repression. Far from harmless, such memories have led to criminal prosecutions and civil litigation (Wakefield & Underwager, 1992). Memory itself remains problematic in legal settings, due in large part to the fallibility of human encoding and memory. Dr. Elizabeth Loftus (Loftus & Ketcham, 1994) challenged many techniques endorsed by providers who claimed to be able to recover memories. These techniques include hypnosis (for a review, see Loftus & Davis, 2006). Given the high stakes (e.g., criminal charges) that may be associated with retrieved memories, these cases are quite contentious, with both sides advocating for their specific positions. Moreover, memories have been shown to be substantially altered by police through stressful interrogative techniques. False confessions often are associated with devastating consequences (e.g., conviction of an innocent person for a serious offense). Persinger (1992) reported on six cases of questionable recall in which patients were subjected to hypnosis as part of their treatment. Williams (1994) found that approximately 38% of women brought to the hospital for sexual abuse as children did not recall the abuse many years later. Other scholars are not convinced about problems with repressed memories. Whitfield (2001) believes that perpetrators claim FMS in order to undermine memories and minimize their involvement in criminal behavior. Raitt and Zeedyk (2003) suggested that trauma-based memories are problematic because it is often females who report the recovered memories and, according to the authors, females have traditionally been viewed as less credible. In summary, the research in support of retrieved memories do not view them as stemming from deception, and instead focus on supporting the individuals recalling the memories. Clinicians should continue to consider multiple possibilities when dealing with sudden-onset memories (see McNally, Chapter 20, this volume). A careful review of history should include multiple collateral interviews. In forensic evaluations, the evaluator should determine whether the memories were recovered with the assistance of a therapist and attempt to obtain permission to speak to the therapist to determine the reliability of his or her methods. As noted by Cunnien (1997), dissimulation should be considered in litigated cases of alleged abuse. He wrote, “Factitious claims should be suspected when psychological evaluation reveals a substantial likelihood that revenge, displacement of anger, or recent abandonment triggered the abuse allegations” (p. 40).

CONCLUSIONS This chapter has focused on several critical areas of which clinicians should be cognizant when evaluating the potential for deception. This final section underscores several important findings as I discuss the context of clinical and forensic evaluations. This chapter demonstrates the broad array of child and adult diagnoses in which deception is associated with mental disorders. Key points are enumerated: 1. Deception is explicitly listed as criteria for psychopathy and ASPD. 2. Deception can subtle and motivated by selfprotection in RAD. 3. Deception occurs for a variety of reasons and may be adaptive (Rogers, 1990a, 1990b) or arise from a mental disorder; clinicians should not assume a malicious intent for the deception. 4. Deception should not be dichotomized as “all or none.” Instead, clinicians should realize that deception is a dimensional construct with gradients and is used selectively. Evaluators must ethically evaluate information from multiple sources when rendering opinions about response styles. This point is especially salient in forensic evaluations in which data from multiple collateral individuals are critical to reaching a well-developed and appropriate forensic opinion. When it is determined that an individual is engaging in deception, a thorough evaluation must consider various motivations, as well as mental disorders. As noted by Rogers (Chapter 1, this volume), malingering and genuine mental disorders are not mutually exclusive. Finally, clinicians should use standardized methods to evaluate malingering and deception (Vitacco & Tabernik, 2017). Clinicians should be aware of two useful models when evaluating deception: threshold and clinical decision models. The threshold model serves as a screen for identifying potential cases of deception that require further evaluation. The bar is intentionally set low to minimize missing persons engaged in a specific response style. In contrast, the clinical decision model represents the process whereby a definite conclusion is reached about the presence or absence of malingering (or any clinical condition for that matter). The bar for reaching a definitive diagnosis must be substantially higher

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compared to that for the threshold model. In the case of malingering and other response styles, such a definitive conclusion should not be reached without substantial evidence to support the conclusion.

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Wakefield, H., & Underwager, R. (1992). Recovered memories of alleged sexual abuse: Lawsuit against parents. Behavioral Sciences and the Law, 10, 483–507. Waller, D. A. (1983). Obstacles to the treatment of Munchausen by proxy syndrome. Journal of the American Academy of Child and Adolescent Psychiatry, 22, 80–85. Walters, G. D., Berry, D. R., Rogers, R., Payne, J. W., & Granacher, R. J. (2009). Feigned neurocognitive deficit: Taxon or dimension? Journal of Clinical and Experimental Neuropsychology, 31(5), 584–593. Walters, G. D., Rogers, R., Berry, D. R., Miller, H. A., Duncan, S. A., McCusker, P. J., et al. (2008). Malingering as a categorical or dimensional construct: The latent structure of feigned psychopathology as measured by the SIRS and MMPI-2. Psychological Assessment, 20(3), 238–247. Whitfield, C. (2001). The “false memory” defense: Using disinformation and junk science in and out of court. Journal of Child Sexual Abuse, 9, 53–78. Williams, L. M. (1994). Recall of childhood trauma: A prospective study of women’s memories of child sexual abuse. Journal of Consulting and Clinical Psychology, 62(6), 1167–1176. Wilson, S. L. (2001). Attachment disorders: Review and current status. Journal of Psychology: Interdisciplinary and Applied, 135, 37–51. Zilberstein, K. (2006). Clarifying core characteristics of attachment disorders: A review of current research and theory. American Journal of Orthopsychiatry, 76, 55–64.

CHAPTER 6

Malingered Psychosis Phillip J. Resnick, MD James L. Knoll, IV, MD

Though this be madness, yet there is method in it. —William Shakespeare, Hamlet (Act II, Scene 2)

The detection of malingered psychosis demands significant time and effort. It is a complex endeavor, requiring the clinician to take a specialized, systematic approach and consider multiple sources of data. The degree of difficulty involved depends on the skill and knowledge of the malingerer. Malingerers with relatively poor understanding of the phenomenology of genuine psychotic symptoms may be readily detected. In contrast, malingerers possessing shrewdness and detailed knowledge of psychosis may deceive even seasoned forensic clinicians. For example, reputed Mafia leader Vincent “The Chin” Gigante was alleged to have deceived “the most respected minds in forensic psychiatry” by malingering, among other things, schizophrenia (Newman, 2003). Gigante ultimately admitted to deceiving multiple psychiatrists during evaluations of his competency to stand trial from 1990 to 1997. One psychiatrist, who concluded that Gigante was malingering, observed, “When feigning is a consideration, we must be more critical and less accepting of our impressions when we conduct and interpret an examination than might otherwise be the case in a typical clinical situation” (Brodie, personal communication, May 17, 2005). Nonetheless, many clinicians are reluctant to label malingering when it is suspected (Yates, Nordquist, & Schultz-Ross, 1996). Reasons for 98

this reluctance include fears of litigation and of being assaulted. In addition, adverse consequences of an incorrect classification of malingering can include denial of needed care and stigma that may be difficult to shed (Kropp & Rogers, 1993). Thus, it is particularly important for clinicians to use a systematized approach to detect malingering, as opposed to merely forming a global impression (Kucharski, Ryan, Vogt, & Goodloe, 1998) The true prevalence of malingering is not known; however, it is reasonable to conclude that prevalence and incentives to malinger vary significantly depending on the context. A small number of studies have provided estimates and suggest that base rates vary according to setting and circumstances. Rogers (1986) established a 4.5% prevalence rate of definite malingering and a 20% rate of moderate deception or suspected malingering among defendants being evaluated for insanity, who were judged “sane.” Cornell and Hawk (1989) found that 8% of defendants referred for pretrial assessments attempted to feign psychosis. Gottfried, Hudson, Vitacco, and Carbonell (2017) have estimated that the base rates of malingering in criminal forensic evaluations range from 20 to 30%. Finally, Pollock, Quigley, Worley, and Bashford (1997) found that 32% of prisoners referred to forensic mental health services fabricated or



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exaggerated symptoms of mental illness. The accuracy of such prevalence estimates is highly questionable, since examinees who successfully fake psychosis are never included in the statistics. The apparent low prevalence of malingering in nonforensic populations may be due to a combination of low clinician suspicion and the relative lack of motivation to malinger.

DEFINING THE PROBLEM In the early 1900s, persons who malingered insanity were viewed as likely to be mentally “degenerate” and afflicted with an unsound mind. In 1905, a Massachusetts pool hall owner and bartender, known locally as “Joe the Snake,” appeared to be malingering insanity after committing murder. A reviewing psychiatrist concluded: “It seems questionable whether there ever occurs an occasion for simulation of insanity in those who are mentally completely sound” (Drew, 1908, p. 680). During the height of psychoanalytic influence, malingering was believed to be a form of mental illness. Eissler (1951, p. 252), for example, stated, “It can be rightly claimed that malingering is always a sign of disease often more severe than a neurotic disorder because it concerns an arrest of development at an early phase.” Others have found less merit to this view. For example Wertham (1949, p. 49) noted, “There is a strange, entirely unfounded superstition even among psychiatrists that if a man simulates insanity there must be something mentally wrong with him in the first place. As if a sane man would not grasp at any straw if his life were endangered by the electric chair.” At the present time, the Diagnostic and Statistical Manual of Mental Disorders (DSM) model has largely supplanted the pathogenic model of malingering (Rogers, 1990). Malingering is a condition not attributable to a mental disorder. DSM-5 (American Psychiatric Association, 2013) describes malingering in terms of a false presentation (fabrication or severe exaggeration). Malingering often requires differentiation from factitious disorder, which is listed in DSM-5 in the section Somatic Symptom and Related Disorders. It uses a similar description for false presentation, but the motivation is present even in the absence of obvious external rewards. In factitious disorder, a patient usually simulates illness with a motive to assume the sick role, which can be thought of as an internal (i.e., psychological) incentive (Kanaan & Wessely, 2010). Diagnosing factitious disorder

can also be challenging, and a traditional clinical education does not often provide clinicians with the training to understand and deal with patients whose symptoms appear to be simulated (Bass & Halligan, 2014). As a separate term, feigning is defined as the deliberate fabrication or gross exaggeration of symptoms, without any assumptions about its goals (McDermott, 2012; Rogers & Bender, 2013). Malingering can be further categorized into (1) pure malingering, (2) partial malingering, and (3) false imputation (Resnick, 1997). When an individual feigns a disorder that does not exist at all, this is referred to as pure malingering. When an individual has actual symptoms but consciously exaggerates them, it is called partial malingering. False imputation refers to the attribution of actual symptoms to a cause consciously recognized by the individual as having no relationship to the symptoms. For example, a claimant suffering from posttraumatic stress disorder (PTSD) due to an earlier trauma may falsely ascribe the symptoms to a car accident in order to receive compensation. Wooley and Rogers (2014) tested this three-subcategory model of malingering by assessing whether various types of malingerers were able to fake PTSD without being classified as feigning. The partial malingering group proved to be the best at feigning. The term pseudo malingering has been used to describe a prodromal phase of genuine psychosis in which the individual allegedly feigns psychosis in a desperate attempt to ward off decompensation into genuine psychosis. A number of authors have suggested that one should carefully consider pseudo malingering before labeling it as malingering (Berney, 1973; Bustamante & Ford, 1977; Folks & Freeman, 1985; Hay 1983; Pope, Jonas, & Jones, 1982; Schneck, 1970). In the novel The Dilemma, by Leonid Andreyev (1902), a physician committed murder with a premeditated plan to feign insanity. When the physician later began to have true hallucinations, he realized that he was genuinely psychotic. While the idea that someone might become mentally ill after avoiding criminal responsibility by malingering insanity makes for a gripping mystery story, it is extremely rare in forensic practice. Hay (1983) concluded that simulated schizophrenia was a prodromal phase of genuine psychosis that occurred in extremely deviant personalities. In this study, five patients originally thought to have feigned psychosis were evaluated after a lengthy period. Upon reevaluation, four of the five patients were believed to have developed genuine

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schizophrenia. An alternative explanation is that with time and practice, these four individuals improved their ability to malinger psychosis to the point that they were undetectable.

MOTIVATIONS TO MALINGER The motives to malinger psychosis fall into two general categories: (1) to avoid difficult situations or punishment (avoiding pain), and (2) to obtain compensation or medications (seeking pleasure). Table 6.1 lists common motives for malingering. Criminals may seek to avoid punishment by feigning incompetence to stand trial or insanity at the time of the offense. Psychotic illness may also be malingered in an effort to mitigate sentencing. Malingerers may seek to avoid military duty, undesirable military assignments, or combat. Financial gain from Social Security Disability, veteran’s benefits, worker’s compensation, or alleged psychological damages may also be motives to malinger psychosis. In nonforensic settings, malingerers may seek a psychiatric admission to secure social services, “pave the way” for future disability claims, or simply to obtain free room and board. In the correctional setting, inmates may malinger mental illness to do “easier time” or obtain prescription drugs. In certain circumstances, malingering may be an adaptive coping strategy. For example, a 14-year-old girl feigned hallucinations in order to be hospitalized to escape from sexual harassment by her mother’s new boyfriend (Greenfield, 1987). She had previously observed an older cousin’s genuine psychosis. When her family situation became intolerably chaotic, she was institutionalized on the basis of a feigned psychosis. She eventually acknowledged to hospital staff that she had faked her psychotic symptoms. TABLE 6.1.  Common Motives of Malingerers

Avoid pain •• Avoid arrest •• Avoid criminal prosecution •• Avoid conscription into the military Seek pleasure •• •• •• ••

Obtain controlled substances Obtain free room and board Obtain disability or worker’s compensation Obtain compensation for alleged psychological injury

RESEARCH ON MALINGERED PSYCHOSIS If sanity and insanity exist, how shall we know them? —David Rosenhan (1973, p. 250)

No research has examined the ability of clinicians to accurately detect malingered psychosis in their daily practice. There is a large body of research on detecting malingered psychosis with psychometric testing such as the Structured Interview of Reported Symptoms (SIRS; Rogers, Bagby, & Dickens, 1992), the SIRS-2 (Rogers, Sewell, & Gillard, 2010), the Stuctured Inventory of Malingered Symptoms (SIMS; Smith & Burger, 1997), and the Personality Assessment Inventory (PAI) and the Minnesota Multiphasic Personality Inventory–2 (MMPI-2; Blanchard, McGrath, Pogge, & Khadivi, 2003). These instruments are addressed in other chapters. However, researchers have yet to address the effectiveness of clinical assessment alone, without the use of these specialized methods. In Rosenhan’s (1973) classic study, eight pseudopatients were admitted to psychiatric hospitals, all alleging only that they heard very atypical auditory hallucinations. Immediately upon admission, they ceased simulating any symptoms, yet all were diagnosed with schizophrenia and remained hospitalized from 9 to 52 days. Interestingly, it was asserted that genuinely disordered inpatients commonly detected the pseudopatients’ “sanity.” On the basis of his study, Rosenhan concluded that mental health professionals were unable to distinguish normality from mental illness. However, this conclusion has been challenged. Furthermore, the criteria for schizophrenia were much more elastic in DSM-II than in DSM-5, which likely resulted in less reliable diagnoses. Anderson, Trethowan, and Kenna (1959) asked 18 normal participants to simulate mental disease in order to study the phenomenon of vorbeirden (giving an approximate answer), a central symptom in Ganser syndrome. The simulators were compared with normal controls and two comparison groups: (1) subjects with organic dementia and (2) patients with pseudodementia (primary diagnosis of hysteria with conversion symptoms). The simulators most often chose to feign depression or paranoid disorders; however, their efforts did not closely resemble well-defined mental disorders. Two feigned mental retardation by maintaining an air of obtuseness, vagueness, and poverty of content. The simulators experienced difficulty, however, in suppressing correct answers to questions.



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This was attributed to a “pull of reality” that they felt throughout the interviews. Fatigue or difficulty sustaining the response style apparently caused simulators to become increasingly normal during the prolonged interviews. Many simulators in the Anderson et al. (1959) study gave approximate answers, because they believed they should not give the “right” answers. In an effort to avoid the impression of spuriousness, they gave nearly correct answers, in contrast to the more obvious errors by patients with actual dementia. The subjects with genuine organic dementia demonstrated substantial perseveration, which was evident when progressing from one question to the next and during serial-seven subtractions. In contrast, malingerers demonstrated no significant perseveration. The authors concluded that perseveration is a strong indication of true, rather than simulated, organic impairment. The fact that the simulators gave approximate answers lends indirect support to the theory that Ganser syndrome is a form of malingering. Ganser syndrome remains controversial and understudied. In 1897, Sigbert Ganser described three inmates who exhibited symptoms characterized by approximate answers to simple questions, dulling of consciousness, hysterical neurological changes, and hallucinations (De Dios Francos, Sanz Granado, & de la Gándara Martín, 1995). The onset and remission of these symptoms were abrupt and followed by amnesia and bewilderment. The symptom of giving approximate answers has also been described as vorbeireden (talking around). However, this symptom is not pathognomonic, as it can appear in other disorders, such as dementia and schizophrenia. Ganser himself did not view Ganser syndrome as a form of malingering. Rather, he viewed it as a hysterical dissociative reaction, resulting from an unconscious effort to escape an intolerable situation (Jiménez Gómez, & Quintero, 2012). While there are similarities and differences among Ganser syndrome, factitious disorders, and malingering, little scientific research is available to reliably clarify its underpinnings. Cornell and Hawk (1989) studied 39 criminal defendants classified by experienced forensic psychologists as malingering psychotic symptoms. The prevalence of malingering was 8.0% for 314 consecutive evaluations in a forensic hospital. The authors acknowledged that a barrier to developing diagnostic criteria was the lack of an unequivocal “gold standard” for determining malingering. Individuals classified as malingering were more likely to claim bogus symptoms, suicidal ideas,

visual hallucinations, and memory problems. Furthermore, their symptoms did not cluster into any known diagnostic entities. Powell (1991) compared 40 mental health facility employees instructed to malinger symptoms of schizophrenia and 40 inpatients with genuine schizophrenia. Both groups were administered the Mini-Mental Status Examination (MMSE; Folstein, Folstein, & McHugh, 1975), which screens for basic cognitive function. Malingerers were significantly more likely than patients with genuine schizophrenia to give one or more approximate answers on the MMSE. Malingerers also reported a higher occurrence of visual hallucinations, particularly with dramatic and atypical content (e.g., not ordinary human beings). Finally, the malingerers more often called attention to their delusions.

Hallucinations Persons with atypical hallucinations should be questioned about them in great detail. Before discussing the impact of hallucinations on current functioning, examinees should be asked to describe past hallucinations and their responses to them. Specifically, patients should be questioned about content, vividness, and other characteristics of the hallucinations (Seigel & West, 1975). Hallucinations are usually (88%) associated with delusions (Lewinsohn, 1970). Table 6.2 lists potential topics of clinical inquiry when malingered auditory hallucinations are suspected. The detection of malingered mental illness can be conceptualized as an advanced clinical skill, due to the fact that the clinician must already possess a detailed knowledge about the phenomenology of genuine psychiatric symptoms. In the case of malingered psychosis, a thorough understanding of how actual psychotic symptoms present themselves is the clinician’s greatest asset in recognizing simulated hallucinations. Auditory Hallucinations

As a first step, clinicians need to differentiate between psychotic and nonpsychotic hallucinations. About 10–15% of members of the healthy population sometimes experience auditory hallucinations (Sommer et al., 2010). Nonpsychotic hallucinations usually have a childhood onset, with a median age of 12, whereas psychotic hallucinations begin at a median age of 21. Nonpsychotic hallucinations are often attributed to family members, spirits of dead people, or guardian angels

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TABLE 6.2.  Topics of Inquiry in Suspect Auditory Hallucinations

•• Gender

Male or female

•• Age

Child or adult

•• Vocal characteristics

Single or multiple voices, clear/ vague/inaudible, loudness

•• Frequency/ timing

Continuous or intermittent, time of day, during sleep

•• Familiarity

Known/unknown person, familiar/ unfamiliar

•• Type of language

Commands, stilted language, speaking in second or third person

•• Response

Degree of insight, ability to disregard, emotional response, converses with them

•• Associated characteristics

Hallucinations in other sensory modalities, delusions, other psychotic symptoms

rather than real people, such as a member of the Secret Service, or malevolent neighbors (Larøi, 2012). Nonpsychotic hallucinations contain very little negative content, whereas almost all schizophrenic patients report some negative content. Nonpsychotic voices do not tend to cause distress or disturbance in the daily life of the individual (Larøi, 2012). Goodwin, Alderson, and Rosenthal (1971), who studied 116 hallucinating patients, provide helpful data on the characteristics of genuine hallucinations. Both male and female voices were heard by 75% of their patients. For the majority of the time (88%), both familiar and unfamiliar voices were heard. About two-thirds of hallucinating subjects could identify the person speaking (Goodwin et al., 1971; Kent & Wahass, 1996; Leudar, Thomas, McNally, & Glinski, 1997; McCarthy-Jones et al., 2014). The content of hallucinations was accusatory in over one-third of the cases. In persons with genuine auditory hallucinations, 71% could recall the first time they heard voices (Hoffman, Varanko, Gilmore, & Mishara, 2008). It is important for clinicians to pay attention to the temporal course of onset and resolution of suspected auditory hallucinations. Some malingerers may allege that their hallucinations ceased after 1 or 2 days of treatment with antipsychotic medication. However, the first time a psychotic

patient is given antipsychotics, the median length of time for hallucinations to completely clear is 27 days (Gunduz-Bruce et al., 2005). Thus, individuals who allege complete clearing after 1 or 2 days of treatment should be viewed with suspicion. In patients treated for schizophrenic hallucinations, after 1 month, the voices became less loud and less distressing (Schneider, Jelinek, Lincoln, & Moritz, 2011). After 6 months of antipsychotics, they heard the voices less frequently and felt that they had more control of them. Many also recognized that the voices were self-generated (Schneider et al., 2011). The majority (82%) of patients with genuine hallucinations describe having them in more than one modality (McCarthy-Jones et al., 2014). Atypical features of auditory hallucinations were reported in less than 5% of patients with auditory hallucinations (McCarthy-Jones et al., 2014). Atypical voices included (1) a voice whose normal speaking tone is yelling, (2) only female voices, (3) only children’s voices, and (4) never hearing the same voice twice. Stephane, Pellizzer, Roberts, and McCalannahan (2006) identified atypical content of auditory hallucinations, such as claiming to hear the voices of animals, voices sounding robotic, voices referring to them as “Mr.” or “Mrs.” and voices occurring whenever they open a window. Hallucinated voices were most often perceived as originating from outside of the head (86%) in the Goodwin et al. (1971) study. However, Junginger and Frame (1985) reported that only 50% of patients with schizophrenia reported auditory hallucinations as originating from outside of the head. Further research has suggested that many patients with psychosis hear voices both internally and externally, and there appears to be no consistent differential effect of internal versus external auditory hallucinations (McCarthy-Jones et al., 2014; Copoloy, Trauer, & Mackinnon, 2004). A growing body of research theorizes that persons with genuine schizophrenia who experience auditory hallucinations have difficulty determining the source (self vs. other) of verbal information (Arguedas, Stevenson, & Langdon, 2012). Such source monitoring errors (e.g., external misattribution of self-generated information) appear to correlate with positive symptoms, in particular, auditory hallucinations, thought intrusion, and alien control symptoms (Ferchiou et al., 2010). Thus, questions about whether voices originate internally or externally have limited utility. Furthermore, it is possible that persons with chronic schizophrenia may have gained enough insight



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into their illness to attribute hallucinated voices to an internal source. Baethge et al. (2005) studied hallucinations in 4,972 psychiatrically hospitalized patients. At admission, the prevalence of hallucinations by disorder was the following: schizophrenia (61.1%), bipolar mixed (22.9%), bipolar manic (11.2%), bipolar depressed (10.5%), and unipolar depressed (5.9%). Across all diagnoses, hallucinations—particularly olfactory—were significantly associated with delusions. Clinicians should be aware that the content of auditory hallucinations may vary with the individual’s culture. For example, Kent and Wahass (1996) found that the auditory hallucinations of Saudi patients were of a religious and superstitious nature, whereas instructional themes and running commentary were common in U.K. patients. Some patients view their hallucinated voices as omnipotent and omniscient (Chadwick & Birchwood, 1994). Evidence of omniscience is based on the voices knowing that person’s thoughts and being able to predict the person’s future. Hallucinated voices are often perceived as malevolent (Chadwick & Birchwood, 1994). Patients with genuine malevolent hallucinations usually develop some strategies to decrease them (McCarthy-Jones & Resnick, 2014). Patients commonly said that evil commands were evidence that the voice was “bad.” Malevolent voices evoke negative emotions (anger, fear, depression, anxiety). Patients often respond by arguing, shouting, noncompliance, and avoidance of cues that trigger voices. Benevolent voices often use kind, protective words. They usually provoke positive emotions (amusement, reassurance, calm, happiness). Patients often respond to benevolent voices by elective listening, willing compliance, and doing things to bring on the voices. Most persons with genuine auditory hallucinations (81%) report that they are worried or upset by their hallucinations (Carter, Mackinnon, & Copoloy, 1996). The major themes of auditory hallucinations in schizophrenia are usually persecutory or instructive (Small, Small, & Andersen, 1966). Auditory hallucinations in schizophrenia tend to consist of personal insults, abuse, and derogatory comments about the patient or the activities of others (Goodwin et al., 1971; Oulis, Mavrea, Mamounas, & Stefanis, 1995; Leudar et al., 1997; Nayani & David, 1996). Larøi (2012) also found that auditory hallucinations in a clinical population were associated with significant distress and negative emotional content. Nayani and David (1996) found that some female subjects describe

terms of abuse conventionally directed at women (e.g., “slut”), while men describe male insults such as those imputing a slur about homosexuality (e.g., “queer”). Such voices are not likely to be faked, because they are unflattering and fail to exculpate criminal defendants. About one-third of persons with auditory hallucinations reported that voices asked them questions such as “Why are you smoking?” or “Why didn’t you do your essay?” (Leudar et al., 1997). Genuine hallucinated questions tend to chastise rather than seek information. The classic running commentary or voices conversing with each other is sometimes reported (Andreasen, 1987). However, hallucinations of music are rare in psychotic disorders (Fischer, Marchie, & Norris, 2004). Their onset is often related to organic brain pathology, aging, and sensory impairment. If the origin is due to brain disease, insight is more common than if the hallucination is the result of a mental disorder (Berrios, 1991). COMMAND AUDITORY HALLUCINATIONS

Command hallucinations are auditory hallucinations that instruct a person to act in a certain manner. McCarthy-Jones et al. (2014) found that 76% of their patients said they were able to resist their command hallucinations. Junginger (1990) reported that patients with hallucination-related delusions and hallucinatory voices were more likely to comply with the commands. In contrast, Kasper, Rogers, and Adams (1996) found that 84% of psychiatric inpatients with command hallucinations had obeyed them within the last 30 days. Among those reporting command hallucinations in a forensic population, 74% indicated that they acted in response to some of their commands during the episode of illness (Thompson, Stuart, & Holden, 1992). Junginger (1995) studied the relationship between command hallucinations and dangerousness. He found that 43% reported full compliance with their most recent command hallucination. People are more likely to obey their command hallucinations if (1) the voice is familiar, (2) there are hallucination-related delusions (Junginger, 1990), and (3) the voice is perceived as powerful (Fox, Gray, & Lewis, 2004; Shawyer et al., 2008). Compliance is less likely if the commands are dangerous (Junginger, 1995; Kasper et al., 1996). Noncommand auditory hallucinations (85%) and delusions (75%) are usually present with command hallucinations (Thompson et al., 1992).

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The majority of commands to commit dangerous acts are not obeyed; therefore, the examiner must be alert to the possibility that a defendant may (1) fake an exculpatory command hallucination, or (2) lie about an inability to refrain from a hallucinatory command. Hellerstein, Frosch, and Koenigsberg (1987), in a retrospective chart review, found that 38% of all patients with auditory hallucinations reported commands. Studies of schizophrenic auditory hallucinations found that 30–67% included commands (Small et al., 1966; Goodwin et al., 1971; Hellerstein et al., 1987; Mott, Small, & Andersen, 1965; McCarthy-Jones et al., 2014). Command hallucinations occurred in 30 (Goodwin et al., 1971) to 40% (Mott et al. 1965) of alcoholic withdrawal hallucinations. Patients with affective disorders reported that 46% of their hallucinations were commands (Goodwin et al., 1971). Hellerstein et al. (1987) reported that the content of command hallucinations included 52% suicide, 5% homicide, 12% nonlethal injury of self or others, 14% nonviolent acts, and 17% unspecified. However, their research involved reviewing charts rather than making direct inquiries, which probably increased the relative proportion of violent commands, since these are likely to be charted. Furthermore, command hallucinations are unlikely to produce action without other psychological variables (e.g., beliefs about the voices, coexisting delusions) mediating the process (Braham, Trower, & Birchwood, 2004). Hence, someone alleging an isolated command hallucination in the absence of other psychotic symptoms should be viewed with suspicion. Leudar and colleagues (1997) found that most patients engage in an internal dialogue with their hallucinations. Many cope with chronic hallucinations by incorporating them into their daily life as a kind of internal advisor. Interestingly, sometimes patients report that their hallucinated voices insist on certain actions after patients refuse to carry them out. The voices rephrase their requests, speak louder, or curse the patient for being noncompliant. In contrast, malingerers are more likely to claim that they were compelled to obey commands without further consideration. Most patients (98%) reported experiencing significant adverse effects of their hallucinations (Miller, O’Connor, & DiPasquale, 1993), such as difficulty holding a job, emotional distress, and feeling threatened. Yet approximately half of patients also reported some positive effects of their hallucinations, such as companionship, finding

them relaxing, and making it easier to receive disability benefits. Therefore, attitudes toward hallucinations appear unhelpful in discriminating between genuine and malingered hallucinations. Persons with genuine schizophrenia typically develop a variety of coping strategies to deal with their hallucinations. Genuine hallucinations of schizophrenia tend to diminish when patients are involved in activities (Falloon & Talbot, 1981; Goodwin et al., 1971). Carter et al. (1996) found that 66% of patients with auditory hallucinations reported ways of managing the voices, and 69% described at least some success using one or more strategies. Coping strategies may involve engaging in activities (e.g., working, watching TV), changes in posture (e.g., lying down, walking), seeking out interpersonal contact, or taking medications (Falloon & Talbot, 1981; Kanas & Barr, 1984). The most common strategy for dealing with dangerous command hallucinations is prayer. Therefore, persons suspected of malingered auditory hallucinations should be asked what they do to make the voices go away or diminish in intensity. Patients with malevolent voices are likely to have developed a strategy, whereas those with benevolent voices may not be motivated to reduce their voices. The suspected malingerer may also be asked what makes the voices worse. Eighty percent of persons with genuine hallucinations reported that being alone worsened their hallucinations (Nayani & David, 1996). Voices may also be made worse by listening to the radio and watching television (Leudar et al., 1997). Genuine auditory hallucinations are characterized by a wide range of intensity, from whispers to shouting. This range is sometimes experienced within the same patient; however, the cadence of the speech is typically normal. In contrast, malingerers may report auditory hallucinations that consist of stilted or implausible language. For example, a malingerer charged with attempted rape alleged that voices said, “Go commit a sex offense.” Malingerers may also allege implausible or far-fetched commands, such as a bank robber who alleged that voices kept screaming, “Stick up, stick up, stick up!” Both examples contain language that is very questionable for genuine hallucinations, in addition to providing “psychotic justification” for an illegal act that has a rational alternative motive. Visual Hallucinations

The incidence of visual hallucinations in psychotic individuals is estimated at only 24 (Mott et



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al., 1965) to 30% (Kanas & Barr, 1984). However, Duncan (1995) found that 54.4% of inpatients with symptoms of active schizophrenia experienced visual hallucinations during their most recent acute episode. Persons with genuine visual hallucinations report that they are humanoid about 70% of the time (Goodwin et al., 1971). A minority of visual hallucinations include animals or objects. About 95% of the time, the visions are not something that the hallucinator has actually seen before. Over 80% of persons with visual hallucinations report that their response to their first visual hallucination was to be overwhelmed or fearful (Gaunlett-Gilbert & Kuipers, 2003). Occasionally, small (Lilliputian) people are seen in alcoholic, organic (Cohen, Adler, Alfonso, & Haque, 1994), or toxic psychoses (Lewis, 1961), especially anticholinergic drug toxicity (Asaad, 1990; Contardi et al., 2007). The little people are sometimes 1 or 2 inches tall; at other times, they are up to 4 feet in height. Only 5% of visual hallucinations in the study by Goodwin et al. (1971) consisted of miniature or giant figures. Psychotic visual hallucinations do not typically change if the person’s eyes are closed or open. In contrast, drug-induced hallucinations are more readily seen with the eyes closed, or in darkened surroundings (Assad & Shapiro, 1986). Unformed hallucinations, such as flashes of light, shadows, or moving objects are typically associated with neurological disease and substance use (Cummings & Miller, 1987; Mitchell & Vierkant, 1991). Visual hallucinations occurring in persons over age 60 may suggest eye pathology, particularly cataracts (Beck & Harris, 1994). Dramatic or atypical visual hallucinations should arouse suspicion of malingering (Powell, 1991). For example, one defendant charged with bank robbery was evaluated for competence to stand trial. During the evaluation, he calmly reported experiencing visual hallucinations consisting of a “thirty foot tall, red giant smashing down the walls” of the interview room. When he was asked further detailed questions about his hallucinations, he frequently replied, “I don’t know.” He subsequently admitted to malingering. Distinctive Hallucinations

DSM-5 diagnostic criteria for an alcohol-induced psychotic disorder (AIPD) include the presence of delusions and/or hallucinations, with evidence that these symptoms resulted from intoxication or withdrawal (American Psychiatric Association,

2013). Individuals with AIPD are often brought to the emergency room or to an acute-care setting. However, the psychotic symptoms are typically temporary and resolve when the substance is discontinued. AIPD is most often observed after severe intoxication (Perala et al., 2010). The symptoms improve without formal treatment in a matter of days to weeks after cessation of severe intoxication and/or withdrawal. AIPD can be clinically distinguished from schizophrenia. Persons with AIPD had higher levels of depressive and anxiety symptoms, fewer disorganized symptoms, better insight and judgment, and less functional impairment compared to patients with schizophrenia (Jordaan, Nel, Hewlett, & Emsley, 2009). In a general population study of over 8,000 subjects, increased risk of AIPD was associated with young age at onset of alcohol dependence, family alcohol problems, and multiple hospital treatments (Perala et al., 2010). Most individuals had multiple episodes of AIPD, with full recovery between episodes. A majority of individuals with AIPD have comorbid psychiatric disorders. It is not clear whether this finding indicates sensitivity to AIPD or is simply an indicator of a history of more intense substance misuse. In AIPD, auditory hallucinations are most common, but the likelihood of noise, music, or unintelligible voices is greater than that with schizophrenia. The auditory hallucinations of AIPD typically consist of accusatory, threatening, or insulting voices directed at the patient (Ali, Patel, Avenido, Jabeen, & Riley, 2011; Cummings & Mega, 2003). Persons with alcohol-induced hallucinations discuss them more easily than do persons with hallucinations due to schizophrenia (Alpert & Silvers, 1970). Mott et al. (1965) found that persons hospitalized due to alcohol misuse had an 84% prevalence of hallucinations (75% auditory and 70% visual). The major themes of the alcoholic auditory hallucinations were spirituality, persecution, and instructions concerning the management of everyday affairs. The majority of patients thought the hallucinations were real at the time but later recognized their unreality. While alcoholics are typically frightened by their hallucinations, persons with schizophrenia often become comfortable with them over the course of their illness. Hallucinations due to a medical or neurological disorder may often be distinguished from schizophrenia due to the higher prevalence of prominent visual hallucinations, and the lower prevalence of thought disorders, bizarre behavior, negative symptoms, and rapid speech (Cornelius

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et al., 1991). Tactile hallucinations are frequently seen in cocaine-induced psychosis (e.g., cocaine bugs), and involve sensations of cutaneous or subcutaneous irritation, sometimes leading the individual to excoriate the skin with excessive scratching (­ Ellinwood, 1972). Unlike persons with schizophrenia, those with cocaine-induced psychosis do not generally report delusions of identity, grandiosity, or beliefs that their families are imposters (i.e., Capgras syndrome; Mitchell & Vierkant, 1991). Certain neurological syndromes can produce striking and relatively stereotyped complex visual hallucinations that often involve animals and human figures in bright colors and dramatic settings. The most common causes of complex visual hallucinations are epileptic disorders, brain stem lesions, and visual pathway lesions (Manford & Andermann, 1998). Peduncular hallucinosis is a syndrome of hallucinations and neurological symptoms due to a brain stem lesion. In this rare disorder, most patients were unable to discriminate their hallucinations from reality (Benke, 2006). Olfactory hallucinations are present in 13–17% of persons with schizophrenia (Langdon, McGuire, Stevenson, & Catts, 2011). Olfactory hallucinations and hallucinations of taste, touch, and bodily sensation frequently co-occur. Olfactory hallucinations often involve unpleasant odors, and self-smells may be associated with self-deprecating thought content. Olfactory and tactile hallucinations are commonly associated with general medical causes; however, persons with late-onset schizophrenia (onset after age 45) may also have visual, tactile, and olfactory hallucinations (Pearlson et al., 1989). Olfactory and gustatory hallucinations are likely to be of unpleasant odors and tastes (Goodwin et al., 1971). Olfactory hallucinations may also be associated with cerebral ischemia and epilepsy (Beume, Klingler, Reinhard, & Niesen, 2015). Persons with schizophrenia who experience olfactory hallucinations have difficulty determining whether the odor is real or imagined (Arguedas et al., 2012).

Delusions Genuine delusions vary in content, theme, degree of systemization, and relevance to the person’s life. Most delusions involve the following general themes: disease (somatic delusions), grandiosity, jealousy, love (erotomania), persecution, religion,

and being possessed (Spitzer, 1992). Grandiose delusions occur across a range of psychiatric illnesses. About two-thirds of persons diagnosed with bipolar disorder, and about 50% of those diagnosed with schizophrenia have grandiose delusions (Knowles, McCarthy-Jones, & Rowse, 2011). Slightly over 10% of the healthy general population experience grandiose thoughts that do not rise to the level of a grandiose delusion. Green et al. (2006) studied 70 individuals with persecutory delusions; the majority described the threat as severe and enduring. Depression was higher in those who felt less powerful than their persecutors. Persecutory delusions are more likely to be acted on than other types of delusions (Wessely et al., 1993). Generally, delusional systems reflect the intelligence level of the individual in terms of complexity. Delusions of nihilism, poverty, disease, and guilt are commonly seen in depression. Higher levels of psychomotor retardation, guilt, feelings of worthlessness, and increased suicidal ideation are found more commonly in psychotic than in nonpsychotic depression (Thakur, Hays, Ranga, & Krishnan, 1999). Delusions of technical content (e.g., computer chips, telephones, telepathy) occur seven times more frequently in men than in women (Kraus, 1994). Malingerers may claim the sudden onset or disappearance of a delusion. In reality, systematized delusions usually take weeks to develop and much longer to disappear. As with suspected auditory hallucinations, the examiner should pay attention to the time course, onset, and resolution of the alleged delusion. The median length of time for delusions to clear fully after the first initiation of antipsychotic medication is reported to be 73 days (Gunduz-Bruce et al., 2005). Thus, malingering should be suspected if a person claims that a delusion suddenly appeared or disappeared. Typically, genuine delusions become somewhat less relevant, and the individual will gradually relinquish its importance after adequate treatment (Sachs, Carpenter, & Strauss, 1974). Most individuals with schizophrenia and other psychotic disorders who demonstrate disordered speech also have odd beliefs (Harrow et al., 2003). Furthermore, the more bizarre the content of the reported delusions, the more disorganized the individual’s thinking is likely to be. Therefore, when suspect delusions are alleged, the clinician should carefully consider the associated behavior and speech patterns. Table 6.3 lists suspect hallucinations and delusions.



6. Malingered Psychosis 107 TABLE 6.3.  Suspect Hallucinations and Delusions

Atypical auditory hallucinations •• •• •• •• •• •• •• •• •• •• •• •• •• •• •• •• •• •• •• ••

Always unbearably distressing Sound mechanical or robotic Sound like voices of animals Come from inside parts of body other than head Refer to person as “Mr.” or “Mrs.” Change gender midsentence Never hearing the same voice twice Voice only yells Always vague, inaudible or mumbling Only female or only children’s voices Allegation that all command hallucinations were obeyed Hallucinations not associated with delusions No coping strategies for malevolent voices (i.e., never having any control over voices) Only auditory verbal (i.e., never any music, clicks, bangs, visual, tactile) Being alone does not increase frequency Ask questions seeking information (“What time is it?”, “What is the weather like?” versus “Why are you smoking?”, “Why didn’t you clean your room?”) Never affected by context (e.g., mood, place, circumstances) Stilted language (“go commit a sex offense”) Unable to recall first time hearing voices No behavioral evidence of distraction

Atypical visual hallucinations •• Black and white rather than color •• Dramatic, atypical visions •• “Schizophrenic” hallucinations that change when the eyes are closed •• Only visual hallucinations in “schizophrenia” •• Miniature or giant figures •• Visions unrelated to delusions or auditory hallucinations Atypical olfactory/gustatory hallucinations •• Pleasant odors or tastes Atypical delusions •• •• •• •• ••

Abrupt onset or termination Conduct inconsistent with delusions Bizarre content without disorganization Eagerness to discuss High conviction without adverse effects on daily functioning

With genuine delusions, the individual’s behavior usually conforms to the content of the delusions. For example, Russell Weston made a deadly assault on the U.S. Capitol building in Washington, DC, in 1998. He suffered from schizophrenia and had a delusional belief that Washington, DC was being destroyed by “cannibalism.” Allegations of persecutory delusions without any corresponding paranoid behaviors should arouse the clinician’s suspicion of malingering. One exception to this principle is persons with long-standing delusions, who have grown accustomed to their delusion and may no longer behave in a corresponding manner. Harrow et al. (2004) found that patients with schizophrenia and affective disorders with a high emotional commitment to their delusions showed poor work functioning and were likely to be hospitalized. Thus, persons who enthusiastically allege a firm conviction of their delusion should be carefully assessed for work performance and community functioning. The delusions seen in Alzheimer’s dementia frequently involve paranoid beliefs about caregivers stealing or being deceitful (Trabucchi & Bianchetti, 1997). In a study of 771 patients with Alzheimer’s dementia, Mizrahi and Starkstein (2006) found delusions in about 33% and hallucinations in approximately 7%. Delusions were significantly associated with depression, anosognosia (unawareness of illness), overt aggression, and agitation.

Malingered Mutism Mutism and mental illness have had a long-standing historical relationship with the issue of competence to stand trial (Daniel & Resnick, 1987). During the early Colonial period, persons who refused to enter a plea were considered to be either “mute by malice,” or mute by “visitation of God.” If defendants remained mute and did not put forth a plea, they were “pressed” for a plea by gradually increasing poundage placed on their chest. This is the origin of the phrase—to be “pressed for an answer.” Malingered mutism may occur as a solitary symptom or as part of a malingered psychosis. It is a difficult task to give up speech for a lengthy period, and it is not usually attempted unless the individual is facing a severe penalty or anticipating a large reward. Genuine mutism may occur in patients with or without catatonia. Mutism with catatonic stupor is recognized by the presence of posturing, negativism, automatic obedience, and waxy flexibility.

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Mutism without catatonia may also be seen in patients with paranoid schizophrenia who are unwilling to communicate due to paranoid distrust. In addition, corticosteroids (Kalambokis, Konitsiotis, Pappas, & Tsianos, 2006) and antihypertensive agents (Altshuler Cummings, & Mills, 1986) have been identified as possibly producing mutism without catatonia. Catatonic mutism can also occur in severe depression, mania, phencyclidine (PCP) use, and brief dissociative states. Medical etiologies include neurological disease (e.g., head injury), herpes encephalitis, tertiary syphilis, frontal lobe lesions, postictal states, akinetic mutism, and Wernicke’s encephalopathy (Altshuler et al., 1986). It is extremely common for mutism due to stroke to produce other neurological impairments. Only one case report of stroke-induced mutism without other neurological findings was published (Evyapan, 2006). Because mutism and/or catatonia are difficult states to simulate for long periods, observation of the suspected malingerer should ideally take place in an inpatient setting. Comprehensive evaluation may include the following: neurological examination, repeat interviews, observation at unsuspected times for communicative speech with peers, handwriting samples, and collateral nursing documentation. Feigned mutism may sometimes be exposed by suddenly arousing the individual from a deep sleep and immediately asking a simple question. A malingerer may reflexively reply before remembering to feign mutism (Davidson, 1952). Daniel and Resnick (1987) reported the case of a defendant who remained mute for 10 months in an effort to malinger incompetence to stand trial. The 53-year-old defendant was charged with raping and murdering an 11-year-old girl. The day after the crime, he was admitted voluntarily to a state hospital and complained of hearing voices. He stopped talking completely when told he was charged with murder. When he did not know he was being observed, he appeared to initiate conversations with fellow patients. No signs of catatonia or depression were observed. A thorough neurological workup and laboratory studies were negative. With the permission of the defendant and his attorney, a sodium amobarbital interview was conducted; the defendant described the offense and spoke for about 90 minutes but did not utter a word afterward. A careful review of collateral data revealed a pattern of voluntary admissions to psychiatric hospitals after several prior offenses, with the charges against him being dismissed.

CLINICAL INDICATORS OF MALINGERED PSYCHOSIS The best liar is he who makes the smallest amount of lying go the longest way. —Samuel Butler

Malingerers may be detected because they have inadequate or incomplete knowledge of the illness they are faking. Indeed, malingerers are like actors who can only portray a role as best they understand it (Ossipov, 1944). However, they often overact their part (Wachpress, Berenberg, & Jacobson, 1953), or mistakenly believe that the more bizarre their behavior, the more convincing they will be. Conversely, “successful” malingerers are more likely to endorse fewer symptoms and avoid endorsing bizarre or unusual symptoms (Edens et al., 2001). Jones and Llewellyn (1917, p. 80) observed that the malingerer “sees less than the blind, he hears less than the deaf, and he is more lame than the paralyzed. Determined that his insanity shall not lack multiple and obvious signs, he, so to speak, crowds the canvas, piles symptom upon symptom and so outstrips madness itself, attaining to a but clumsy caricature of his assumed role.” Malingerers are more likely to volunteer their putative symptoms in contrast to genuine patients with schizophrenia, who are often reluctant to discuss them (Ritson & Forest, 1970; Powell, 1991). For example, one male malingerer in a forensic evaluation proffered that he was an “insane lunatic” when he killed his parents at the behest of hallucinations that “told me to kill in my demented state.” Another malingering defendant who stabbed his wife to death claimed to have mistaken her for an intruder who appeared to be a dark, evil presence with a “huge skeleton head.” Without prompting, he asserted that at the time of the stabbing, he had been “crazy insane with mental illness.” A malingerer may go so far as to accuse the psychiatrist of implying that he or she is faking. Such accusations are rarely seen in genuinely psychotic individuals. DSM-5 states that the presence of antisocial personality disorder should arouse suspicions of malingering, but some studies have failed to show a relationship. Psychopathic traits were associated with malingering in one study (Edens, Buffington, & Tomicic, 2000). Yet a number of studies have suggested that antisocial or psychopathic persons are no more adept than others at malingering (Poythress, Edens, & Watkins, 2001; Rogers & Cruise, 2000) and have questioned the



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rationale for including antisocial personality disorder as a DSM screening indicator for malingering. For example, in a study of coached malingerers, subjects who scored higher on psychopathy and intelligence did not fake symptoms more successfully when compared with other participants (Demakis, Rimland, Reeve, & Ward, 2015).

Malingered Cognitive Deficits in the Setting of Malingered Psychosis Some malingerers believe that faking intellectual deficits, in addition to psychotic symptoms, will make them more believable (Bash & Alpert, 1980; Edens et al., 2001; Powell, 1991; Schretlen, 1988). Thus, malingerers may give incorrect answers to patently obvious questions that an individual with a genuine, serious cognitive disorder could answer correctly. Some malingerers may also believe that they must demonstrate serious memory deficits. They may claim impairment discordant with typical patterns seen in genuine memory impairment, such as claiming long-term memory impairment worse than short-term impairment (Soliman & Resnick, 2010). Malingerers give more evasive answers than patients with genuine schizophrenia (Powell, 1991). Malingerers may also repeat questions or answer questions slowly to give themselves time to think about how to successfully deceive the evaluator. Malingerers are more likely to give vague or hedging answers to straightforward questions. For example, when asked the gender of an alleged voice, a malingerer may reply, “It was probably a man’s voice.” Malingerers may also answer, “I don’t know” to detailed questions about psychotic symptoms. The malingerer who has never experienced the symptoms “doesn’t know” the correct answer. Malingerers have more difficulty imitating the form and process of psychotic thinking than the content of a bogus delusion (Sherman, Trief, Sprafkin, 1975). Psychotic symptoms such as derailment, neologisms, loose associations, and word salad are difficult to simulate. If malingerers are asked to repeat an idea, they may do it exactly, whereas genuine patients with schizophrenia often become tangential. As previously noted, malingerers rarely show perseveration, which is more likely to suggest brain pathology, or conversely, an extremely well-prepared malingerer. Also, malingerers are unlikely to imitate the subtle signs of schizophrenia, such as negative symptoms (e.g., flat affect, alogia, avolition), impaired relatedness, digressive speech, or peculiar thinking. In con-

trast, they find the positive symptoms (e.g., hallucinations, delusions) easier to feign because of their more obvious nature. Malingerers’ alleged symptoms may not fit into any known diagnostic category, instead representing symptoms from various psychoses. Therefore, malingering should always be considered before making a diagnosis of “unspecified schizophrenia spectrum and other psychotic disorder.” Table 6.4 lists a number of clinical factors suggestive of malingering. Persons with true schizophrenia may also malinger additional symptoms to avoid criminal responsibility or seek an increase in disability compensation. For example, a man with genuine schizophrenia in partial remission killed his mother because she would not give him money to purchase cocaine. He then alleged that the murder was due to a command hallucination from God. Such cases are very difficult to assess accurately for several reasons. First, clinicians usually have a lower index of suspicion because of the individual’s documented history of genuine psychotic symptoms. Second, these malingerers are able to draw on their own previous experience with psychotic symptoms and observations of other patients while in a psychiatric hospital. In essence, they know what questions to expect from the clinician and may be better equipped to successfully provide deceptive answers. Finally, some clinicians have a tendency to dichotomize forensic patients into either “mad or bad.” Therefore, it is important that clinicians not conceptualize malingering and genTABLE 6.4.  Clinical Factors Suggestive of Malingering

•• Absence of active or subtle signs of psychosis •• Marked inconsistencies or contradictions •• Improbable psychiatric symptoms ||Mixed symptom profile: Endorse depressive symptoms plus euphoric mood ||Overly dramatic ||Extremely unusual: “Do you believe that cars are members of organized religion?”, “Do you ever see words coming out of people’s mouths spelled out?” •• Evasiveness or noncooperation ||Excessively guarded or hesitant ||Frequently repeats questions ||Frequently replies, “I don’t know” to simple questions ||Hostile, intimidating: Seeks to control interview or refuses to participate •• Psychological testing indicates feigning: SIRS-2, M-FAST, MMPI-2

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uine psychosis as an “either–or” situation (Rogers, Sewell, & Goldstein, 1994). The following case illustrates clinical indicators of feigned psychosis. Mr. B was charged with murder and referred by his defense attorney for evaluation of a possible insanity defense. Mr. B reported that he had his brother killed because he believed his brother had betrayed the family and “violated his honor.” Upon evaluation, Mr. B was found to have no psychiatric history, and nothing that suggested insanity at the time of his offense. Mr. B’s attorney referred him for a second psychiatric evaluation. Mr. B told the second evaluator about persons wanting to destroy the family business and replacing his brother with a robot. The second defense psychiatrist accepted the robot story at face value. Interviews with family members did not confirm the robot story. Upon arrest, Mr. B had denied any involvement in his brother’s killing and made no mention of the robot story. Mr. B showed the following clinical indicators of malingering: (1) absence of past psychotic symptoms, (2) an atypical delusion, (3) nonpsychotic alternative motives of anger and greed, (4) absence of behavior consistent with his alleged delusion, and (5) contradictions in his story.

CLINICAL INTERVIEW APPROACH Because of the complexities involved in conclusions of malingering with reasonable professional certainty, a comprehensive malingering evaluation is recommended, particularly in difficult cases (Rogers, Vitacco, & Kurus, 2010; Drob, Meehan, & Waxman, 2009). An outline for the comprehensive evaluation of malingering is given in Table 6.4. Any information that might assist in supporting or refuting alleged symptoms should be carefully reviewed (e.g., prior treatment records, insurance records, police reports, collateral interviews). Clinicians should utilize multiple sources of data, including interviews, collateral sources, and psychometric tests in detecting malingering (Zapf & Grisso, 2012). Reliance on clinical interviews alone does not allow the examiner to classify malingering in any but the most obvious cases. Good interviewing techniques are critical to accurately detecting malingering. When malingering is suspected, the clinician should refrain from showing suspicion, and proceed in conducting an objective evaluation. A clinician’s annoyed or incredulous response is likely to result in the examinee becoming more defensive, thus decreasing

the ability of the clinician to detect malingering. It is important to begin the evaluation by asking open-ended questions that allow examinees to report symptoms in their own words. In the initial stage, inquiries about symptoms should be carefully phrased to avoid asking leading questions that give clues to the nature of genuine psychotic symptoms. Later in the interview, the clinician can proceed to more detailed questions of specific symptoms, as discussed below. The clinician should also attempt to ascertain whether each examinee has ever had the opportunity to observe persons with psychosis (e.g., during employment or in prior hospitalizations). The interview may be prolonged, since fatigue may diminish a malingerer’s ability to maintain fake symptoms (Anderson et al., 1959). In very difficult cases, inpatient assessment should be considered, because feigned psychotic symptoms are extremely difficult to maintain 24 hours a day (Broughton & Chesterman, 2001). Clinicians should be on alert for rare or improbable symptoms. Improbable symptoms are almost never reported, even in severely disturbed patients (Thompson, LeBourgeois, & Black, 2004). Malingerers may be asked about improbable symptoms: for example, “When people talk to you, do you see the words they speak spelled out?” (Miller, 2001), or “Have you ever believed that automobiles are members of an organized religion?” or “Do you ever have periods when you experience upside down vision?” (Rogers, 1987). Finally, some clinical strategies are controversial but may assist the clinician in very difficult cases. For example, one strategy is to mention, within earshot of the suspected malingerer, some easily imitated symptom that is not present. The rapid endorsement of that symptom would suggest malingering. Another strategy is to ask examinees a relevant question at the moment that they have “let down their guard,” such as during a break or a relaxed moment during the evaluation. In particularly difficult cases, a clinician may consider a clinical technique called set shifting, which involves interspersing questions for malingering detection within history taking, or even in informal conversation. This shifting may cause the defendant to momentarily forget or abandon the malingering role, and important data may be revealed. This strategy was used in the Gigante case to conclude that he was malingering a severe dementia. Gigante claimed that he was disoriented with regard to time. During a break in the examination, Gigante was asked in an offhand manner whether he knew



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what time it was. Without hesitation, Gigante reflexively responded with the correct time (J. D. Brodie, personal communication, May 17, 2005).

Inconsistencies Lying is like alcoholism. You are always recovering. —Steven Soderbergh

The clinician should pay close attention to evidence of inconsistency or contradiction in evaluating a suspected malingerer. Inconsistencies may be classified as either internal or external to the individual’s presentation. Table 6.5 lists examples of important internal and external inconsistencies. Internal inconsistencies are evident when malingerers report severe symptoms, such as mental confusion or memory loss, but are able to clearly articulate multiple examples of confusion or memory loss. Another type of internal inconsistency occurs when malingerers give markedly conflicting versions of their own history to the same evaluator. External inconsistency occurs between what the subject reports and the symptoms that are observed. For example, a malingerer may allege active auditory and visual hallucinations, yet show no evidence of being distracted. However, not all patients who hallucinate show external evidence of this. External inconsistency may be apparent between the examinee’s self-reported level of functioning and observations of his or her functioning by others. For example, a malingerer may behave in a disorganized or confused manner around the clinician, yet play excellent chess with other pa-

TABLE 6.5.  Inconsistencies Seen in Malingerers

Internal •• In subject’s own report of symptoms: giving a clear and articulate explanation of being confused •• In subject’s own reported history: giving conflicting versions External •• Between subject’s reported and observed symptoms •• Between subject’s reported level of functioning and observed level of functioning •• Between subject’s reported symptoms and the nature of genuine symptoms •• Between subject’s reported symptoms and psychological testing

tients. There may also be inconsistency between the subject’s reported symptoms and how genuine symptoms actually manifest themselves. For example, a malingerer may report seeing visual hallucinations in black and white, whereas genuine visual hallucinations are generally seen in color.

CLINICAL APPLICATIONS In a clinical setting, malingering should always be considered when there is the possibility of an external incentive for the patient. Otherwise, small separate clues of feigning may be overlooked that would lead to a more detailed investigation. In cases of suspected malingered psychosis, the clinician should inquire about the specific details of hallucinations and delusions, since the typical characteristics of these symptoms have been well researched. A major focus of this book is the development of clinical decision models to establish malingering. Table 6.6 offers such a model for malingered psychosis. The classification of malingering requires feigning plus the following: (1) the motivation is conscious, and (2) the motivation is an external incentive as opposed to a desire to be in the sick role. Furthermore, to reach a firm conclusion of malingered psychosis, the clinician must observe TABLE 6.6.  Model Criteria for the Assessment of Malingered Psychosis

A. Clear external incentive to malinger B. Marked variability of presentation as evidenced by at least one of the following: 1. Marked discrepancies in interview and noninterview behavior 2. Gross inconsistencies in reported psychotic symptoms 3. Blatant contradictions between reported prior episodes and documented psychiatric history C. Improbable psychiatric symptoms as evidenced by one or more of the following: 1. Elaborate psychotic symptoms that lack common paranoid, grandiose or religious themes 2. Sudden emergence of alleged psychotic symptoms to explain criminal behavior 3. Atypical hallucinations or delusions (see Table 6.3) D. Confirmation of malingered psychosis by either: 1. Admission of malingering 2. Strong corroborative data, such as psychometric testing or past proof of malingering

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(1) inconsistent or contradictory presentations, (2) improbable or incongruous clinical presentations, and (3) supportive collateral data. The following case illustrates the importance of detecting malingering in a clinical setting. Mrs. C, a 43-year-old woman, successfully malingered chronic schizophrenia over a 12-year period in order to receive Social Security Disability payments. Whenever Mrs. C went to her disability evaluations or a disability hearing, she dressed bizarrely and gave absurd answers to questions. She alleged confusion so severe that she was unable to drive or to meet her own basic needs. At her disability hearing, she testified that her entire family was dead, and refused to give any telephone numbers of third parties. Mr. and Mrs. C worked together on a number of other disability frauds using false Social Security numbers. Mrs. C was ultimately caught when it was discovered that she was in graduate school. Subsequently, she was videoed by federal agents appropriately dressed and attending graduate classes.

FORENSIC APPLICATIONS There were no real demons, no talking dogs, no satanic henchmen. I made it all up via my wild imagination so as to find some form of justification for my criminal acts against society. —“Son of Sam” serial killer David Berkowitz (in Samenow, 1984, p. 130)

Concern about defendants faking mental illness to avoid criminal responsibility dates back at least to the 10th century (Brittain, 1966; Collinson, 1812; Resnick, 1984). By the 1880s, many Americans considered physicians to be a generally impious, mercenary, and cynical lot who might participate in the “insanity dodge” (Rosenberg, 1968). After the Hinckley verdict, columnist Carl Rowan (1982, p. 10B) stated, “It is about time we faced the truth that the ‘insanity’ defense is mostly the last gasp legal maneuvering, often hoaxes, in cases where a person obviously has done something terrible.” In cases that capture national attention, a finding of insanity often results in a public outcry that forensic mental health professionals are fanciful, paid “excuse makers.”

Clinical Assessment of Criminal Defendants When evaluating criminal defendants in a forensic setting, the clinician should always consider

malingering (Glancy et al., 2015). Particularly in federal courts, there has been increased attention on the issue of malingering. Several cases have upheld sentencing enhancements for defendants who have malingered incompetence to stand trial (U.S. v. Batista. 448 F.3d 237, 238, 3rd Cir., 2007; U.S. v. Binion. 132 Fed Appx. 89, 8th Circ., 2005, U.S. v. Greer. 158 F.3d 228, 5th Cir., 1998). Prior to evaluating a defendant, the clinician should be equipped with as much background information as possible, such as police reports, witness statements, autopsy findings, past psychiatric records, statements of the defendant, and observations of correctional staff. Consultations with family members, social contacts, or witnesses are often helpful prior to the clinician’s examination. The clinician should also attempt to learn some relevant information about the defendant or crime that the defendant does not know the clinician knows. This approach provides a method of assessing veracity, in that the information can be compared to the defendant’s self-report upon questioning. For example, will the defendant honestly report past criminal activity as recorded on his “rap sheet”? Or (see Hall, 1982), how does the defendant’s version of the offense compare to victim or witness accounts? An attempt should be made to evaluate the defendant who raises psychiatric issues as a defense as soon as possible after the crime. An early evaluation reduces the likelihood that defendants will have been coached or will have observed genuine psychotic symptoms in a forensic hospital setting. Defendants will have less time to plan deceptive strategy, craft a consistent story, and rehearse fabrications. Normal memory distortions are also less likely to occur. Moreover, prompt examination enhances the clinician’s credibility in court. Defendants who present with a mixed picture of schizophrenia and antisocial features may pose difficulties for the clinician due to negative countertransference feelings. Such a scenario may cause the clinician to focus on the antisocial traits to the exclusion of a genuine comorbid illness (Travin & Protter, 1984). The clinician must guard against accepting the psychotic version at face value, or dismissing it out of hand. Any facile attempt to dichotomize a defendant into “mad” (assuming the credibility of the psychotic symptoms) or “bad” (assuming the fabrication of psychotic symptoms) may reduce the accuracy of the forensic assessment. The farsighted clinician will record in detail the defendant’s early account of the crime, even if he



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is not competent to stand trial. Once defendants are placed in a jail or forensic hospital, they may learn how to modify their story to avoid criminal responsibility (Samenow, 1984). Recording the early version also reduces the likelihood of being misled later by a defendant’s unconscious memory distortions. The clinician should take a careful history of past psychiatric illnesses, including details of prior hallucinations, before eliciting an account of the current crime. Malingerers are less likely to be on guard because they infrequently anticipate the relevance of such information to the current insanity issue. If defendants should subsequently fake hallucinations to explain his criminal conduct at the time of the offense, it will be too late to falsify their past symptoms to lend credence to the deception. Whenever possible, a defendant’s report of prior hallucinations and delusions should be confirmed by review of past hospital records. Kucharski et al. (1998) found that malingerers with no history of psychiatric treatment were likely to evidence current psychiatric presentations inconsistent with their recent Global Assessment of Functioning (GAF) as well as atypical hallucinatory experiences. Jaffe and Sharma (1998) found malingering defendants exhibited more uncommon psychiatric symptoms such as coprophagia, eating cockroaches, and seeing “little green men.”

Malingered Incompetence to Stand Trial In a review of 8,416 forensic evaluations of competence to stand trial, Warren et al. (2006) found that clinicians opined incompetence in 19% of the cases. Findings of incompetence were strongly associated with clinical findings of psychosis and organic/intellectual disorders. Criminal defendants may seek to malinger psychosis in an effort to be found incompetent to stand trial. The defendant who successfully malingers incompetence to stand trial and is found “unrestorable” may have the benefit of less scrutiny and more freedom than an insanity acquittee. In some cases, defendants’ charges are dropped altogether. Defendants adjudicated incompetent to stand trial may malinger mental illness to avoid trial and/or pave the way for an insanity defense. McDermott, Dualan, and Scott (2013) found a rate 17.5% of malingering in patients found incompetent to stand trial who had been sent to a hospital for restoration. Understandably, the highest rates of malingering were observed in patients found incompetent for more serious offenses (e.g., murder).

A systematized, multimodal approach to detecting feigned incompetence to stand trial should consider the Evaluation of Competency to Stand Trial—Revised (ECST-R), a standardized interview designed to assess competence to stand trial, as well as serve as a screening tool for feigned incompetence to stand trial (Rogers, Jackson, Sewell, & Harrison, 2004). The ECST-R contains “atypical presentation” (ATP) scales which screen for feigned incompetency. The ATP scale intersperses distractor questions (e.g., “Do you find it hard to cope with the overcrowding and noise in the jail?”) with questions that screen for feigned incompetency (e.g., “Have you ever felt like the court reporter is someone from your family, but in disguise?”). Studies of jail detainees and inpatient competency restoration patients provide evidence that the ECST-R can be a valuable tool for screening for feigned incompetence to stand trial (Fogel, Schiffman, Mumley, Tillbrook, & Grisso, 2013; Norton & Ryba, 2010; Vitacco, Rogers, Gabel, & Munizza, 2007). The following example provides an unusual window into the thinking of a defendant who repeatedly feigned psychosis for the purpose of avoiding trial. Mr. K was charged with aggravated robbery. Observations of Mr. K by correctional officers revealed no abnormal behavior. During his evaluation, Mr. K rocked back and forth and sang songs. He spoke rapidly and repeatedly interrupted the evaluator. He reported that he had ESP powers and was being tormented by the government as a political prisoner. He answered nearly all questions with questions. He alleged that all courtroom personnel were against him due to a government plot. When the clinician left the room, Mr. K stopped rocking and was quiet. Several letters that Mr. K had written to his girlfriend (an incarcerated codefendant) were available for review. The following excerpts advise his girlfriend on how also to malinger incompetence (K. Quinn, personal communication, June 8, 1985). 1.  “When the doctors see you, they only hold you for a little while. All the time you are with them, don’t hold a normal conversation with them. When they start asking you a question, interrupt them before they can finish asking. You can always use scriptures from the Bible to interrupt them with; make up your own scriptures, stare a lot at the floor, turn your head away from them and mumble to yourself.”

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2. “Start talking about any- and everything. Keep changing subjects. Don’t complete sentences with them. You don’t know the judge from the bailiff or prosecutor. You don’t fully understand what you are charged with. Accuse your lawyer of being a communist. You don’t understand the regular courtroom procedures; the voices told you that the courtroom was like a circus or zoo. . . . Talk stupid, dumb, and crazy to even your social worker.” Several clues were observed in ascertaining that Mr. K was malingering. He overacted his part and was eager to call attention to his illness. He did not maintain his psychotic behavior for 24 hours a day. He answered many questions with “I don’t know” and refused to give details. He pretended to be both psychotic and to have low intelligence about the criminal justice system. The finding of malingering was confirmed by his letters to his girlfriend.

MALINGERED INSANITY I am essentially not in madness, but mad in craft —Shakespeare, Hamlet (Act 3, Scene 4)

In Shakespeare’s play, Hamlet could be said to have assumed the role of a “madman.” With his simulated madness as a cover, Hamlet was at liberty to exact his revenge. Malingerers are likely to have nonpsychotic motives for their criminal behavior, such as revenge, jealousy, greed, or anger. In contrast, a crime without an apparent motive (e.g., random killing of a stranger) may lend some credence to the presence of true mental illness. Genuine psychotic explanations for rape, robbery, fraud, and check forging are extremely unusual. Warren, Murrie, Chauhan, Dietz, and Morris (2004) reviewed 5,175 insanity evaluations. Forensic evaluators opined that 12% of the defendants were insane. Opinions supporting insanity were associated with psychotic disorders, organic disorders, affective disorders, and a past history of psychiatric treatment. Opinions supporting sanity were associated with drug charges, personality disorders, intoxication at the time of the offense, and prior criminal history. In assessing defendants for criminal responsibility, clinicians must determine whether they are malingering psychosis at the time of the offense only or are continuing to malinger at the time of the examination (Hall, 1982; see Table 6.7). The

TABLE 6.7.  Malingered Psychosis During a Crime

A. Faking psychosis while actually committing the crime (very infrequent) B. Faking in the evaluation of “psychosis during the crime” and either: 1. Claiming to be well now 2. Still faking psychosis C. Actually psychotic during the crime, but superimposing faked exculpatory symptoms at the evaluation.

importance of the differentiation was demonstrated by Rogers et al. (1984) using the Schedule of Affective Disorders and Schizophrenia (SADS) diagnostic interview. Although the SADS summary scales successfully differentiated between sane and insane defendants at the time of their crimes, no significant differences were found at the time of their evaluations. Some malingerers mistakenly believe that they must show ongoing symptoms of psychosis in order to succeed with an insanity defense. When defendants present with current psychiatric symptoms, the clinician has the opportunity to see whether these alleged symptoms are consistent with contemporaneous psychological testing. Several clues can assist in the detection of fraudulent insanity defenses (see Table 6.8). A psychotic explanation for a crime should be ques-

TABLE 6.8.  Model Criteria for the Assessment of Malingered Psychosis in Defendants Pleading Insanity

Malingering should be suspected if any two of the following are present: 1. A nonpsychotic, alternative motive for the crime 2. Suspect hallucinations or delusions (see Table 6.3) 3. Current offense fits a pattern of prior criminal conduct 4. Absence of negative symptoms of psychosis during evaluation 5. Report of a sudden, irresistible impulse 6. Presence of a partner in the crime 7. “Double denial” of responsibility (disavowal of crime + attribution to psychosis) 8. Alleged illness inconsistent with documented level of functioning 9. Alleged intellectual deficit coupled with alleged psychosis



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tioned if the crime fits the same pattern as in previous criminal convictions. Gacono, Meloy, Sheppard, Speth, and Roske (1995) compared legitimate insanity acquittees with individuals who had successfully malingered insanity. Malingerers were significantly more likely to have a history of murder or rape, be diagnosed with antisocial personality disorder or sexual sadism, and produce higher PCL-R Factor 1, Factor 2, and total scores than insanity acquittees who did not malinger. Postacquittal, malingerers were also significantly more likely to be verbally or physically assaultive, have sexual relations with female staff, deal drugs, and be considered an escape risk within the forensic hospital. Malingerers may tell a far-fetched story in an attempt to “retro-fit” the facts of the crime into an insanity defense. For example, one malingerer with prior armed robbery convictions claimed that he robbed only upon command hallucinations and gave away all the stolen money to homeless people. Malingering of insanity should be suspected if a partner was involved in the crime because most accomplices of normal intelligence will not participate in a crime that is motivated by psychotic beliefs. In such cases, the clinician may assess the validity of the alleged insanity by questioning the codefendant. Thompson et al. (1992) found 98% of successful insanity acquittees in Michigan acted alone. Malingering defendants may present themselves as doubly blameless within the context of their feigned illness. For example, a male defendant pled insanity to a charge of stabbing a 7-year-old boy 60 times with an ice pick. He stated that he was sexually excited and intended to force homosexual acts on the victim but abandoned his plan after the boy began to cry. When he started to leave, he alleged that “10 faces in the bushes” began chanting, “Kill him, kill him, kill him.” He yelled, “No,” and repeatedly struck out at the faces with an ice pick. He alleged the next thing he knew, “the victim was covered with blood.” The defendant’s version of the offense demonstrates a double avoidance of responsibility: (1) The faces told him to kill, and (2) he claimed to have attacked the faces and not the victim. After his conviction for the offense, he confessed to six unsolved sexually sadistic murders.

Malingering in Correctional Settings The rate at which inmates in correctional settings feign mental illness is unclear. Pollock et al. (1997),

using the SIRS and MMPI-2, reported a 32% rate of malingering among prisoners referred to a medium secure unit. Mentally ill inmates must sometimes exaggerate their symptoms simply to ensure that needed treatment will be provided (Kupers, 2004). Thus, commonly used detection strategies may not be reliable in the postconviction correctional setting. Indeed, Vitacco and Rogers (2005) have noted that DSM screening indices for suspected malingering do not apply in a correctional setting. An incorrect classification of malingering may have seriously detrimental and long-lasting effects for a genuinely mentally ill inmate. A label of malingering will be extremely difficult for the inmate to overcome and may subject him to a variety of adverse outcomes. In addition to denial of needed treatment, an improper classification of malingering can actually result in disciplinary actions against the inmate in many prisons. The stigma of the malingering label may encourage correctional staff to disregard all the inmate patient’s future complaints. Many correctional facilities remain dangerous and under-resourced, causing difficulty distinguishing malingering from adaptive coping strategies. Inmates with serious mental illness may exaggerate symptoms to avoid toxic and stressful environments such as punitive isolation. Some inmates may malinger to seek the relatively protected environment of a mental health unit, particularly if they are harassed by general population inmates. Certainly, not all inmate patients malinger for adaptive reasons. Some have illicit motives such as obtaining medications to abuse or sell, avoiding appropriate disciplinary actions, or gaining transfer to other living situations. Inmates nearing the end of their sentences may malinger to establish a “documented” history of mental illness in order to obtain disability benefits in the community (Knoll, 2015). The finding that an inmate patient has malingered one or more symptoms of psychosis does not rule out the presence of true mental illness. Therefore, a determination of malingering should not exclude the inmate from receiving further mental health services. Kupers (2004) and Knoll (2015) have suggested some clinical indicators for when correctional mental health staff should use caution before making a classification of malingering. Table 6.9 lists clinical factors to consider prior to a conclusion of malingering by an inmate patient.

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TABLE 6.9.  Clinical Factors Warranting Caution before Diagnosing Malingering in a Correctional Setting

  1.  No clear external incentive

symptoms voluntarily, while others lacked insight into their illness. Those lacking insight were older and more likely to be psychotic than intentional dissimulators.

  2.  Extensive history of psychiatric treatment  3. Not self-referred   4.  Defensive, minimizes illness, or opposes treatment   5.  Shows improvement on psychiatric medications   6.  Does well or improves in a mental health unit  7. Intellectual disability   8.  Frequently requires special observations   9.  High number of objective suicide risk factors 10.  History of serious traumatic brain injury

Defensiveness and Denial of Psychotically Motivated Crimes Forensic clinicians are usually trained to be vigilant for defendants who may be malingering during a forensic evaluation. However, the opposite can occur with defendants who deny psychotic symptoms or otherwise “simulate” sanity (Rogers, 2008). Defensiveness is the concealment of genuine symptoms of mental illness in an effort to portray psychological health. To successfully dissimulate a psychological disorder, the individual must possess adequate self-control to simulate a healthy state or engage in impression management (Martino et al., 2016). The denial of psychiatric symptoms has been reported anecdotally in persons who have committed crimes (Diamond, 1994). One reason that defendants may deny psychotic symptoms is avoid the stigma and consequences of being labeled with a mental illness. For some defendants with mental illness, to admit their actions “were motivated by delusions, rather than reality . . . is a public humiliation destructive to one’s self-esteem” (Diamond, 1994, p. 166). For example, in the case of Theodore Kaczynski (i.e., the Unabomber), some experts suggested that he suffered from paranoid schizophrenia. However, he was highly averse to raising mental illness as a defense because he believed it would undermine the credibility of his antitechnology “manifesto” (Knoll, 2016). When a defendant conceals psychotic symptoms, the potential exists for a miscarriage of justice. Caruso, Benedek, Auble, and Bernet (2003) found that the minimization of symptoms could be classified as either intentional or lacking insight. Intentional dissimulators concealed their

CONCLUSIONS Identifying malingered psychosis is necessary to bring accuracy to forensic assessments and to prevent miscarriages of justice and misuse of limited healthcare resources. The detection of malingered psychosis can be difficult and requires a systematic approach. To confidently conclude that an individual is malingering psychotic symptoms, the clinician must have a detailed understanding of genuine psychotic symptoms and review data from multiple sources. The clinician must assemble clues from a thorough evaluation, clinical records, collateral data, and especially psychological testing. Although substantial effort is required, the clinician bears considerable responsibility to assist society in differentiating true psychosis from malingering.

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CHAPTER 7

Malingered Traumatic Brain Injury Scott D. Bender, PhD

Within the fields of psychological and neuropsychological assessment, research on malingering continues to outpace most other research topics. As noted in the equivalent chapter in the previous edition, neuropsychology saw a threefold increase in the number of publications on malingering between 1990 and 2000 (Sweet, King, Malina, Bergman, & Simmons, 2002). And more recent survey data (Martin, Shroeder, & Odland, 2015) indicate that almost 25% of all articles published in The Clinical Neuropsychologist and Archives of Clinical Neuropsychology between 2009 and 2014 involved effort and malingering. This equates to approximately 1,400 articles. Also, though precise numbers continue to be difficult to calculate, the vast majority of forensic cases seem to involve issues of contested mild traumatic brain injury (mTBI), a major focus of malingering research. It is now generally accepted that all forensic neuropsychological assessments should include methods for the detection of malingering (American Academy of Clinical Neuropsychology [AACN] Consensus Conference Statement; Heilbronner et al., 2009). In fact, the onus has shifted to justifying why effort testing was not conducted. Despite this increase in awareness, very little attention has been given to the precise meanings and causes of poor effort and/or symptom exaggeration. As I discuss in this chapter, this imbalance of attention and awareness has potentially far-reaching (and problematic) implications for forensic practice.

Forensic neuropsychologists have an increased awareness that a broad array of dissimulated behaviors may masquerade as neurocognitive dysfunction. In addition, symptom overlap is commonly observed among healthy populations, certain mental disorders, and mTBI. This lack of symptom specificity poses a major challenge for clinicians asked to render differential diagnoses involving brain injuries, particularly when faced with the rigorous standards of legal admissibility. In this chapter I describe genuine traumatic brain injury (TBI) subtypes and their typical courses, and also review malingering, both practically and conceptually. It is important to note at the outset that malingering is not the same construct as other response-style constructs such as deception, feigning, faking, incomplete/suboptimal/poor effort, uncooperativeness, exaggeration, or dissimulation. Most cognitive tests of “malingering” are actually measures of effort, whereas most psychiatric “malingering” tests measure symptom fabrication or gross exaggeration. This chapter provides detection strategies for the assessment of malingering. Importantly, poor effort is neither necessary nor sufficient for a determination of malingering in TBI cases. Rather, a causal relationship between poor effort (and/or exaggeration) and deliberate intention in the context of a substantial external incentive must be established. As already alluded to, this apparently simple task is actually often very challenging, primarily be-

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cause motivation—central to the concept of malingering—cannot be directly measured. Excellent reviews of effort testing in neuropsychological assessment may be found in Carone and Bush (2013), Larrabee (2012), and Nicholson and Martelli (2007). And while many of the concepts and methods may apply to pediatric neuropsychology, this chapter is limited to adults (see Kirkwood, 2015, for a review of validity testing in children). The main topics of this chapter include (1) strategies for detecting malingered TBI; (2) the special problem of postconcussional syndrome; and (3) common pitfalls in differential diagnosis, especially in the forensic setting. Due to its prevalence in forensic settings, special emphasis is placed on mild TBI. The chapter ends with an illustrative case study.

MALINGERING DEFINITION AND SUBTYPES DSM-5 (American Psychiatric Association, 2013) conceptualizes malingering in terms of feigned presentations and external motivation. It is not a diagnosis, but a classification of behavior, hence its status as a V-code (V65.2). According to seminal work by Lipman (1962), four types of malingering exist: 1. Invention: No genuine symptoms present; patient fabricates symptoms. 2. Perseveration: Genuine symptoms existed but have resolved; the patient alleges their continuance. 3. Exaggeration: Genuine symptoms currently exist but have been magnified beyond their true severity. 4. Transference: Genuine symptoms exist but are not related to the injury in question. The first type of malingering is the only one that involves outright fabrication of symptoms, and it is the rarest form. The other three types involve conscious manipulation of genuine symptoms and probably rightly illustrate the often nondichotomous nature of malingering—which is to say, a finding of malingering does not mean that there are no bona fide symptoms present.

Prevalence The true prevalence of malingering remains unknown, because successful malingerers go undetected and uncounted. Past surveys of forensic

experts suggested that feigning occurred in 7–17% of mental health assessments, with the higher estimate referring to forensic cases (Cornell & Hawk, 1989; Rogers, Salekin, Sewell, Goldstein, & Leonard, 1998; Rogers, Sewell, & Goldstein, 1994). In contrast to these older data, more recent surveys suggest that the rate is much higher, at least for mTBI cases, and approaches 40% when the category is broadened to include symptom exaggeration (e.g., Mittenberg, Patton, Canyock, & Condit, 2002). Though consistent with Larrabee’s (2003a) review of 11 neuropsychological studies involving mTBI, potential problems with these estimates exist, as discussed by Young (2015) and Rogers, Bender, and Johnson (2011a). For instance, many studies established the prevalence of malingering by calculating the percentage of patients who simply performed below generally accepted cutoff scores on tests of feigning in the presence of an incentive; very few studies have proven that the individuals in question were actually motivated by the external gain. Recent research by Ruff, Klopfer, and Blank (2016) suggests that estimates of malingering prevalence greatly depend on the operational definition of malingering being used. Their estimates are much more in line with older estimates of prevalence. This problem is not specific to these studies. The inability to know someone else’s true motivation(s) lies at the heart of the problem of malingering detection. As a result, prevalence rates of “malingering” may better be thought of as the prevalence of “feigning.” Accurate classification of malingering is further compromised by the multiple conditions that can resemble malingering, such as factitious disorder, conversion disorder, anxiety, posttraumatic stress disorder (PTSD), depression, and psychosocial factors involving exaggeration (Bender & Matusewicz, 2013). Given these problems, some researchers (e.g., Boone, 2007; Drob, Meehan, & Waxman, 2009) have argued that psychologists should not classify malingering at all, but just report on the validity (or lack thereof) of test results.

Diagnostic Criteria of Malingered Neurocognitive Deficits Slick, Sherman, and Iverson (1999) provided the first comprehensive diagnostic criteria for possible, probable, and definite malingering of cognitive dysfunction. Their publication helped standardize malingering detection in neurocognitive assessment and spurred malingering research dramatically.

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The Slick and Sherman (2013) update to the malingered neurocognitive dysfunction (MND) criteria addresses several concerns raised by researchers and clinicians in the years since 1999. For example, cogniform disorder (Delis & Wetter, 2007) and the concept of secondary malingering are now considered within the criteria. The Slick and Sherman (2013) classification of definite malingering still requires clear and compelling evidence of volitionally exaggerated or fabricated cognitive dysfunction in the absence of a plausible alternative explanation. Clinical diagnoses that might explain response patterns must be ruled out, and the behavior should not be the result of diminished mental capacity. Probable malingering requires similar features (e.g., volitional production of symptoms) but with comparatively less evidence (e.g., less than clear and compelling but still strongly suggestive evidence). As mentioned, the Slick and Sherman (2013) classification system has produced much needed research, but especially for forensic practice, several problematic issues remain (e.g., the criteria for definite malingering may be too narrow). For the interested reader, Bender and Frederick (Chapter 3, this volume) provide a comprehensive review and critique of the updated Slick criteria (see also Larrabee, Greiffenstein, Greve, & Bianchini, 2007; Rogers & Bender, 2013; and Rogers et al., 2011a). It is almost always easier to detect dissimulation than it is to determine its underlying motivation. No single score (or set of scores) can be considered “diagnostic” of malingering. A determination of malingering must include careful examination of contextual (i.e., nontest) factors. Nevertheless, certain scores and patterns are highly indicative of feigned or purposely biased performance. Importantly, the task of determining whether a certain test performance represents an intentional attempt to obtain material gain requires much more consideration than simply deriving a score within the “malingering range.” For example, the nature and possible meaning of the incentive to the patient must be considered. But before this topic or a full discussion of malingered symptoms of mTBI can be undertaken, a review of the characteristics of genuine TBI is needed.

TRAUMATIC BRAIN INJURY Based on hospital and emergency department records, the Centers for Disease Control and Pre-

vention (CDC) reported that 2.5 million cases of suspected TBI occurred in the United States in 2010 (Faul, Xu, Wald, & Coronado, 2010). This estimate does not include data from outpatient facilities, the U.S. military, or from those who did not seek medical care. As a result, it likely does not reflect the true burden of TBI in the United States. So, though at least 80% of TBIs are classified as “mild,” this number probably represents a substantial underestimate due to many unreported mTBI cases. The recent increase in public awareness of TBI may result in better prevalence estimates derived from hospitals and in forensic neuropsychological practice, where mTBI already represents the majority of cases and poses significant diagnostic challenges.

mTBI and Concussion The terms concussion and mTBI are often used interchangeably, and indeed, refer to the same general neuropathology. However, the diagnosis of mTBI remains muddy and controversial. One reason is that most definitions of concussion require only that an alteration of consciousness occur in order to meet its diagnostic criterion. Such a subjective phenomenon, often with no other party present to observe associated clinical signs, makes it difficult to establish whether this fundamental indicator of concussion even transpired. Likewise, alterations of consciousness and/or amnesia may actually be longer than those reported by the patient, leading to a misinformed impression of a milder injury. In short, the reliability of self-report in these situations is notoriously poor, which seriously complicates diagnosis of mTBI. Diagnostic agreement has been compromised further by a history of overly complex rating systems, with at least 27 gradation systems for concussion (Bender, Barth, & Irby, 2004). Definitions

In general terms, TBI is defined by the presence of (1) trauma to the brain either through blunt force to the skull or by acceleration–deceleration forces and (2) resultant signs and symptoms. In contrast to moderate and severe TBI, mTBI is usually undetectable on traditional neuroimaging. In fact, structural evidence of brain injury is usually viewed as an exclusion criterion, or as a reason to subclassify the injury (i.e., “complicated mTBI”). Despite the lack of evidence on computerized tomography (CT) and magnetic resonance imaging



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(MRI), studies have shown that diffuse physiological and chemical changes do occur with mTBI (e.g., Giza & Hovda, 2004; see Lezak, Howieson, Bigler, & Tranel, 2012). Diffuse axonal injury via shear strain effects has also been documented (Bigler, 2008; Bigler & Maxwell, 2012). However, clinical evidence of mTBI sequelae can be wideranging and variable, often with a lack of correlation between imaging and clinical signs. Notably, the use of diffusion tensor imaging (DTI) in research has grown rapidly since the 2008 version of this chapter in the previous edition, and in recent years, DTI studies have targeted mTBI (e.g., Chong & Schwedt, 2015; Meier et al., 2015). DTI, which is sensitive to white-matter damage, can detect small changes in white-matter integrity via fractional anisotropy. However, so far, experts in neuropsychology and neuroradiology (Delouche et al., 2016; Trafimow, 2014; Wortzel, Tsiouris, & Filippi, 2014) concur that DTI lacks specificity in mTBI and might even produce misleading results. The best methods for operationalizing the signs and symptoms of mTBI have been debated, but some consensus has been achieved. For example, mTBI is typically said to have occurred when one or more of the following is present: A score ≥ 13 on the Glasgow Coma Scale (GCS; Jennett, 2002), posttraumatic amnesia (PTA) of under 24 hours, and loss of consciousness (LOC) of less than 30 minutes (as delineated in the American Congress of Rehabilitation Medicine criteria [ACRM; 1993]). Of course, the definition requires that these features occur in the context of brain trauma. Though somewhat dated, most clinicians and researchers agree that the ACRM criteria for mTBI are still the standard both for research and practice (see Table 7.1 for ACRM criteria for mTBI). Natural Course of mTBI

Early estimates suggested that approximately 85– 90% of patients with mTBI recover and are asymptomatic by about 3 months postinjury (Alexander, 1995; Binder, 1997). However, these estimates are based on cases that were seen in the health care system and do not include individuals whose symptoms evidently did not require assessment or treatment. Therefore, the percentage of patients with mTBI who recover completely is likely higher than originally thought. The symptoms that occur within 3 months after an mTBI have been well-documented, but the symptoms are heterogeneous; thus, no single pro-

TABLE 7.1.  American College of Rehabilitation Medicine’s Definition of mTBI

A patient with mTBI is a person who has had a traumatically induced physiological disruption of brain function, as manifested by at least one of the following: 1. Any period of loss of consciousness 2. Any loss of memory for events immediately before or after the accident 3. Any alteration in mental state at the time of the accident (e.g., feeling dazed, disoriented, or confused) 4. Focal neurological deficit(s) that may or may not be transient, in which the severity of the injury does not exceed the following: a. Loss of consciousness of approximately 30 minutes or less b. After 30 minutes, an initial Glasgow Coma Scale (GCS) of 13–15 c. Posttraumatic amnesia (PTA) not greater than 24 hours

file is available to clinicians trying to distinguish feigned from genuine deficits. The problem of significant heterogeneity of symptoms only worsens beyond the 3-month window, and a unique profile of neuropsychological deficits in this time frame almost certainly does not exist (see Silverberg & Iverson, 2011). Acute symptoms of mTBI (i.e., less than 24 hours postinjury) include confusion and disorientation, dizziness, gait abnormality, visual disturbance (e.g., diplopia), and headache (Barth, Macciocchi, Boll, Giordani, & Rimel, 1983; Landre, Poppe, Davis, Schmaus, & Hobbs, 2006; Levin et al., 1987). Many of these symptoms can persist into the postacute phase (i.e., 7–10 days postinjury) but can also emerge postacutely, after an asymptomatic period. Symptoms that emerge after the acute phase tend to be more cognitive in nature, such as amnesia, attention deficits, and slowed cognitive processing speed, although any of the symptoms can occur during either phase. Pain is often a primary complaint both early and later in the course of recovery, and psychological disturbances are not uncommon, especially later in the course of recovery. A challenging issue facing forensic neuropsychologists is the condition assumed to be the residual phase of mTBI: postconcussion syndrome (PCS). PCS, which refers to the constellation of symptoms that follows concussion, often involves multiple wide-ranging somatic, cognitive, and psychiatric symptoms. Challenges inherent to differ-

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ential diagnoses involving PCS are discussed later in the chapter. The Course of Moderate to Severe TBI

Severe TBI is typically defined by GCS scores of 3–8 and/or posttraumatic amnesia (PTA) for 24 hours or longer. A TBI of moderate severity is usually said to have occurred when the GCS score is 9–12 and/or PTA lasts 1–24 hours. In general, TBI follows a dose–response curve, which means that the more severe the TBI, the longer the recovery period and the poorer the outcome. Penetrating injuries, subdural hematoma, and diffuse axonal injury are more often associated with severe TBI than with milder injuries. In addition, severe TBI often involves frank neurological signs (e.g., nonreactive pupils) and other traumatic injuries (e.g., spinal cord injury). Neurosurgical intervention and higher complication rates are also common in severe cases (Lezak et al., 2012). Individuals with severe brain injury produce highly variable scores on neuropsychological tests. Deficits tend to be wide-ranging and include (1) impaired learning and memory, (2) attention and concentration deficits, (3) slowed mental processing speed, and (4) dysexecutive syndromes (e.g., poor cognitive flexibility). Length of coma, or the time until the patient follows commands, strongly predicts degree of cognitive impairment, especially in severe cases (Dikmen, Machamer, Winn, & Temkin, 1995; Dikmen, Machemer, Fann, & Temkin, 2010; Jennett & Bond, 1975). For severe TBI, between 20 and 40% achieve “good” recovery (Millis et al., 2001). Although cognitive deficits due to moderate to severe TBI can persist indefinitely, they do not worsen over time. However, severe TBI has been linked to an increased incidence of Alzheimer’s disease (Institute of Medicine Committee on Gulf War and Health, 2009; Shively, Sher, Perl, & Diaz-Arrastia, 2012) and secondary psychological disturbances such as depression, which can worsen or prolong cognitive dysfunction. Interested readers should consult Dikmen et al. (2010) and Roebuck-Spencer and Sherer (2008) for excellent reviews of the neuropsychological consequences of non-mTBI. The clinical issue of whether severe TBI can be malingered has received far less scrutiny than feigned mTBI, partly due to two assumptions: (1) that patients with documented brain damage cannot successfully feign cognitive deficits, and (2) that they are not motivated to do so. However, a handful of published case studies involving severe

TBI and malingering appear to challenge this notion (e.g., Bianchini, Greve, & Love, 2003; Boone & Lu, 2003).

PCS and Controversy PCS describes the cognitive, somatic, and emotional problems reported by some patients with mTBI well beyond the expected time frame (i.e., 1–3 months). However, this term may also refer to the customary and expected symptoms that occur in the acute and postacute phases (i.e., within 3 months of the injury). Not surprisingly, these two contrasting definitions have caused confusion. It would probably help to distinguish between the two by using PCS to refer to symptoms that occur during the typical course of recovery and persisting PCS (PPCS) when referring to symptoms that persist beyond the expected time frame. However, PCS is already likely too entrenched as the term that refers to persisting symptoms for a change in terminology to be accepted now. Thus, forensic neuropsychologists should remain cognizant of this ongoing and potentially problematic conflation of terms. PCS is a controversial construct (Bender & Matusewicz, 2013). It has been difficult (1) to distinguish acute psychological effects of injury (e.g., shock, anxiety, and other impediments to cognition) from frank neurocognitive deficits, (2) to know how to interpret new neuroimaging techniques in concussion, and (3) to determine whether the disability is due to the neurological injury itself or to disruptions in routines of daily living (Davies & McMillan, 2005). PCS is also difficult to define because its persisting symptoms can be explained equally well by other psychological or neurological problems, or both. Criteria in the 10th edition of the International Statistical Classification of Diseases (ICD-10; World Health Organization, 1992) illustrate the challenges inherent to current definitions of PCS. ICD-10 offers nine diagnostic criteria for PCS, but they appear to have little clinical utility. Kashluba, Casey, and Paniak (2006) found that endorsement of ICD10 criteria correctly classified just 67% of patients 1 month post-mTBI. Only one symptom (increased susceptibility to fatigue) showed superior discrimination 1 month postinjury, and none successfully discriminated at the 3-month postinjury interval. For quite some time, critics have underscored the conceptual limitations of the PCS construct. For instance, Gouvier, Uddo-Crane, and Brown (1988) noted that the diagnostic validity of PCS



7. Malingered Traumatic Brain Injury 127

is suspect due to the high base rate of PCS-type symptoms in the general population. Symptoms such as “difficulty becoming interested” and “often irritable” were endorsed at comparable rates by normal controls and patients with PCS. Only one self-reported symptom (“subjective sense of restlessness”) differed significantly between these groups. Iverson and McCracken (1997) noted a similar problem of symptom overlap when comparing people with symptoms of PCS and those with chronic pain without mTBI. Lees-Haley and Brown (1993; see also Fox, Lees-Haley, Earnest, & Dolezal-Wood, 1995) found a very high incidence of PCS-like symptoms in litigants without a neurological injury or claim. More recently, Zakzanis and Yeung (2011) replicated findings that symptoms of PCS are very common in healthy people. They also noted that certain symptoms are more prevalent than others, depending on the culture. The authors cautioned that certain symptoms may be prone to misattribution to PCS due to their commonness. When compared to trauma controls (i.e., individuals with orthopedic but not brain trauma), PCS symptoms are reported in largely equivalent numbers for 3 or more months after the injury (e.g., 46.8% in mTBI vs. 48.3% in trauma controls; Meares et al., 2011). Dikmen et al. (2010) reported a larger discrepancy in PCS symptom endorsement between their trauma controls and patients with mTBI, but their mTBI group included injuries of greater severity than those found in most of the existing literature. Recent investigations continue to raise concerns about the diagnostic utility of PCS. Iverson (2003) and Ponsford et al. (2012) found that anxiety predicts persistence of PCS symptoms at 3 months postinjury, but presence of mTBI did not. In a striking example of the diagnostic imprecision of PCS, Donnell, Kim, Silva, and Vanderploeg (2012) found that just 32% of mTBI patients met DSM-IV-TR criteria for PCS, whereas 91% of patients with somatization disorder met these criteria. Several other DSM-IV-TR disorders were more diagnostically similar to PCS than was mTBI. Similarly, Iverson (2006) studied 64 patients with a depressive disorder but no TBI, and found that 72% endorsed three or more “moderate to severe” symptoms of PCS. Iverson cautioned that depression, chronic pain, sleep problems, litigation stress, and malingering can cause patients to report PCS symptoms. In short, a rapidly growing body of research indicates that mTBI is not a useful predictor of PCS.

Another long-standing concern regarding PCS involves the biased recall and responding that can occur in some patients after mTBI. Gunstad and Suhr (2001) described systematically lower reporting of preinjury problems relative to postinjury problems as the “good old days” bias. In comparing the self-reported symptoms of mTBI patients and orthopedic controls, Silverberg et al. (2016) found that the “good old days” bias was more pronounced in mTBI patients than in controls when reporting more post-injury symptoms. This was explained by reattribution theory, whereby mTBI patients consider their symptoms to be caused by the mTBI, and as a result do not often recall preinjury PCS-like symptoms. Patients with such an expectation may detect “symptoms” that are actually benign sensations associated with otherwise normal stressors and attribute them to mTBI (see also Lange, Iverson, & Rose, 2010; Silverberg & Iverson, 2011; Sullivan & Edmed, 2012; Vanderploeg, Belanger, & Kaufmann, 2014). Bender and Matusewicz (2013) classify the “good old days” bias as a patient-based source of error, which (along with provider-based errors) may help explain the misattribution of some symptoms following mTBI. Patient-based errors may also occur due to distorted perceptions of illness and anxiety sensitivity. The influence of illness perceptions on outcomes has been described in multiple conditions, and PCS may be particularly vulnerable to this phenomenon. For example, Whittaker, Kemp, and House (2007) discovered that patients with mTBI who believed that their symptoms were serious and long-lasting tended to report more symptoms 3 months later than those who anticipated a less negative outcome. Importantly, injury severity, anxiety, depression, and PTSD symptoms did not add to the predictive model. Hou et al. (2012) found similar results in patients with mTBI; negative perceptions about mTBI, all-or-nothing appraisals, depression, and anxiety all predicted PCS 6 months postinjury (see also Snell, Siegert, HaySmith, & Surgenor, 2011). Suhr and Wei (2013) provide an excellent review of the possible effects of expectancies on neuropsychological test performance and presentation. The term provider-based errors refers to the inadvertent misattributions of PCS symptoms to mTBI made by health care professionals, thereby endorsing the notion that the symptoms represent ongoing brain damage. Patients may then feel more despairing and helpless, which can both perpetuate these symptoms and cause new symptoms. In this scenario, there is no easy way for providers or

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patients to discern that these symptoms are not attributable to the mTBI itself. An important caution by Andrikopoulos and Greiffenstein (2011) regarding PTSD likely should be heeded for PCS as well, namely, that based on current nosology regarding mTBI and PCS, well-meaning doctors may inadvertently perpetuate a sequence of iatrogenic consequences simply by making the PCS diagnosis. Another source of response bias arises from litigation and other adversarial situations. So-called jurisogenic or lexigenic symptoms arise when the patient’s symptoms are challenged or contested. Decades ago, Weissman (1990) introduced the term jurisogenic to refer to symptoms that plaintiffs may report in increasing numbers and severity stemming from protracted legal proceedings. For example, persistent questioning of the veracity of the injury and symptoms often occurs in litigated mTBI cases (in contrast to cases of stroke, for example), and may contribute to the jurisogenic process (Hall & Hall, 2012; Silver, 2012). Interestingly, perceptions of unfairness seem to affect cognition and decision making (Sanfey, Rilling, Aronson, Nystrom, & Cohen, 2003) and may also play significant explanatory roles in protracted legal cases involving PCS. Despite these concerns, many clinicians continue to use PCS to define persisting symptoms ostensibly arising from mTBI, at least partially because few diagnostic alternatives exist. In fact, no single syndrome effectively captures the wide array of symptoms persisting beyond the expected time frame following mTBI. The use of PCS checklists and questionnaires exacerbates this problem by increasing number of symptoms without improving specificity (Iverson, Brooks, Ashton, & Lange, 2010). In summary, the constellation of symptoms known as PCS still lacks clear diagnostic boundaries and, as cautioned in the previous edition of this volume, PCS has likely become a pejorative term to many professionals. It contributes to the mislabeling of symptoms and may cause iatrogenic problems (Bender & Matusewicz, 2013; Larrabee & Rohling, 2013; Wood, 2004). Forensic neuropsychologists are urged to evaluate other potential symptom etiologies carefully before concluding that they are due to mTBI. However, it is equally important (1) not to rule out the possibility of genuine PCS, especially if within 3 months or so of the injury, and (2) to consider other possible causes of the symptoms that may still require treatment, whether or not they are due to mTBI.

EFFECT SIZES FOR mTBI AND MALINGERED mTBI The seminal meta-analysis by Binder, Rohling, and Larrabee (1997) compared neuropsychological test scores among patients with mTBI to those of healthy controls; the average effect size of impairment was small (d = 0.12). In a more recent meta-analysis of neuropsychological performance at 3 months postinjury, Rohling et al. (2011) found an effect size for mTBI so small that it did not significantly differ from zero (d = 0.07). As Larrabee and Rohling (2013) pointed out, this effect size is much smaller than those for substance abuse, PTSD, major affective disorder, and probable malingering. Meta-analytic studies have shown larger effect sizes for mTBI in the acute phase of recovery (e.g., d = 0.41 within the first 6 days after injury; Schretlen & Shapiro, 2003). By comparison, moderate to severe TBI yields large to very large effect sizes on neuropsychological tests (e.g., ds from 0.97 to 2.41; Rohling, Meyers, & Millis, 2003; Schretlen & Shapiro, 2003). As I discuss later, the strategy of “severity indexing” uses this type of information to help identify malingering. In contrast to the negligible effect sizes for genuine mTBI, feigners (i.e., suspected malingerers and simulators) have shown a moderately large effect size on neuropsychological tests. In the meta-analysis by Vickery, Berry, Inman, Harris, and Orey (2001), the effect size of malingered performance on testing (mean d = 1.13) appeared to be more commensurate with severe TBI than with mTBI (see also Green, Rohling & Lees-Haley, 2001). As Iverson (2003) has observed, these meta-analytic findings indicate that neuropsychological tests are potentially strong indicators of severe TBI and probable malingering but can be poor indicators of genuine mTBI. Effect sizes associated with patients’ expectancies have also been calculated, although only a few studies have been conducted to date. For example, in patients with mTBI, stereotyped beliefs about the consequences of the injury (“diagnosis threat”) produced a moderate effect size on average (d = 0.68) on tests of intellectual and executive functioning (Suhr & Gunstad, 2005). Subsequent studies of diagnosis threat suggest that the effect may be more dependent on the evaluation context, past history of the patient, and whether the patient received accurate feedback regarding his or her neuropsychological performance (Suhr & Wei, 2013).



7. Malingered Traumatic Brain Injury 129

As mentioned, the process of litigation plus the possibility of substantial financial gain can have profound effects on patients, including effects on their neuropsychological test performances. Binder and Rohling (1996) found that such incentives potentially lower test scores by nearly one-half standard deviation (d = 0.47), which is much more than the decrement associated with mTBI. However, this figure is a potentially misleading statistic, as it may persuade clinicians and attorneys to believe that litigation is a unitary entity that is solely responsible for the patient’s deficits (e.g., “accident neurosis”; see Miller, 1961). Importantly, litigation involves a number of stress-producing components that may be completely unrelated to any attempts at malingering. As Weissman (1990) noted, protracted litigation lowers morale, reduces effectiveness of treatment, and fosters iatrogenic conditions (see also Hall & Hall, 2012). Such effects can lower test performance and should not be mistaken for malingering. Furthermore, the repeated recounting of symptoms during litigation likely leads to some “drift” and at least minor fictionalization of symptoms (see Weissman, 1990; Hall & Hall, 2012). In summary, the complex set of psychological factors that accompany litigation may increase the likelihood of malingering but cannot be equated with it.

MASQUERADING SYNDROMES AND THE DIFFERENTIAL DIAGNOSIS OF mTBI Accurate mTBI diagnosis must consider not only malingering but also other, genuine clinical conditions. Neuropsychologists are strongly cautioned against an unsophisticated differential diagnosis that consists of simply mTBI versus malingering. Other diagnostic considerations include depression, anxiety, PTSD, and pain. Attributing poor outcome to the mTBI alone is often overly simplistic and misleading (Mooney, Speed, & Sheppard, 2005). For example, depressive disorders occur in approximately 75% of moderate to severe cases of TBI (Alway, Gould, Johnston, McKenzie, & Ponsford, 2016), but also occur in 14–35% of patients with mTBI (Busch & Alpern, 1998; Deb, Lyons, Koutzoukis, Ali, & McCarthy, 1999). Depressive symptoms can mimic symptoms of mTBI both clinically and on neuropsychological test scores. Depressed patients typically have low motivation, which traditionally is considered a correlate of malingering in the cognitive domain. Decreased attention and concentra-

tion, and slowed cognitive processing speed, are common in patients with depression and in those with brain injury. Further complicating the picture, individuals with depression frequently report symptoms of PCS, such as headache, dizziness, and blurred vision (Silver, McAllister, & Arciniegas, 2009). Meta-analyses indicate that depression is associated with cognitive dysfunction with moderate effect sizes (e.g., d = 0.52; Zakzanis, Leach, & Kaplan, 1998). Differential diagnoses of mTBI and depression rely on multiple indicators. Obviously, an indication of brain trauma is required for mTBI. Beyond that, a preexisting history of depression and cognitive deficits that fluctuate appreciably with mood changes is often indicative of depression. Deficits that resolve with time with less fluctuation are consistent with mTBI. Practitioners should be careful in their diagnosis, because mTBI may include behavioral and emotional dysregulation that lowers cognitive efficiency, especially during times of irritability. Differentiating depression from mTBI is more difficult in the postacute stages, when symptom overlap increases, and the true source of symptoms is blurred. Lange, Iverson, and Rose (2011) found that PCS symptoms were reported significantly more often by outpatients with depression than by patients with mTBI within 8 months of their injury, with a moderate effect size (d = 0.68). No accurate methods exist to determine whether symptoms represent a depressive disorder or a neuropathological component of the mTBI. But the symptoms are less likely due to mTBI itself as the time after injury increases. Mild TBI must also be differentiated from anxiety and PTSD. Anxiety is highly comorbid with depression and, when severe, can diminish cognitive efficiency in its own right (Clarke & MacLeod, 2013). Also, attention and judgment are more vulnerable in anxious individuals; perceived threats and their associated costs are more commonly reported in this group (Cisler & Koster, 2010; Nelson, Lickel, Sy, Dixon, & Deacon, 2010). The essential feature of PTSD involves the development of disabling symptoms following exposure to a potentially life-threatening traumatic event (e.g., being a victim or witnessing violence). Patients with PTSD alone often have neuropsychological symptoms such as slowed processing speed, inattention, and poor verbal learning and memory (see Kay, 1999; Scott et al., 2015). These symptoms overlap with those of mTBI and PCS, and therefore pose obvious difficulties when trying

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to determine the source of deficits in the context of mTBI. Estimates vary appreciably, but studies suggest that PTSD may be more common following mTBI than originally thought, with prevalence rates mediated by the cause of trauma (Harvey & Bryant, 1998; Koren, Norman, Cohen, Berman, & Klein, 2005). For instance, Hoffman, Dikmen, Temkin, and Bell (2012) found that 17% of civilians with mTBI also met diagnostic criteria for PTSD. The presence of bodily trauma appears to increase the risk of PTSD (Koren, Hemel, & Klein, 2006). PTSD symptoms and subclinical features also are more common in patients whose symptoms have been slow to resolve, and PTSD can have a similar course to PCS, leading to the suggestion that PTSD is a variant of PCS (Davidoff, Kessler, Laibstain, & Mark, 1988; Schneiderman, Braver, & Kang, 2008). Cases in which patients have witnessed a death or suffered a personal attack may increase the vulnerability to PTSD. Amnesia for the traumatic event may protect against PTSD but seems to depend on the severity of the TBI, duration of amnesia, and attitudes and attributions of the patient (Al-Ozairi, McCullagh, & Feinstein, 2015; Bryant et al., 2009; Hoffman et al., 2012). Importantly, recent studies suggest that PTSD does not increase the degree of cognitive impairment above that associated with mTBI alone (Gordon, Fitzpatrick, & Hilsabeck, 2011; Soble, Spanierman, & Fitzgerald-Smith, 2013). It is noteworthy that DSM-5 directs special attention to PTSD as a potential target of malingering. Rosen and Taylor (2007) have suggested that this is due to the ease with which one can feign symptoms of PTSD and the difficulty establishing whether a traumatic event actually occurred. They note that the prevalence of malingered PTSD is still unknown. Preliminary estimates from Demakis, Gervais, and Rohling (2008) suggest that prevalence rates may be near 30%. Moreover, the new diagnostic criteria for PTSD have increased the number of symptom combinations to over 636,000 (Galatzer-Levy & Bryant, 2013). The new criteria reduce diagnostic precision of PTSD, thereby also likely hindering accurate malingering detection. Given these diagnostic complexities, Kay’s (1999) four key provisions for forensic neuropsychologists working with patients with PTSD remain important: 1. PTSD can co-occur with genuine symptoms of TBI. 2. Neuropsychological deficits out of proportion

to the injury raise the probability of psychological versus neuropsychological disorder. 3. Regardless of the severity of injury and context, all plausible diagnoses must be considered, including PTSD. 4. Exaggeration by the patient does not rule out legitimate psychological conditions or mTBI. Like depression and PTSD, pain can produce symptoms and deficits similar to those of mTBI. Pain is subjective and can be a method of expressing psychological distress in patients who are not psychologically minded (e.g., patients with somatic symptom disorder). Given its subjectivity, pain is also a potential symptom to feign. Neuropsychologists must be aware of and account for the neuropsychological effects of pain but, unfortunately, neuropsychological evaluations are not designed to detect pain directly. Therefore, the veracity of pain symptoms must be inferred from performance on tests of other constructs, including tests of malingered cognitive impairment, and from behavioral observations. This determination is troubling given that (1) genuine pain is a common comorbid condition with mTBI, (2) chronic pain patients without a history of concussion often have several symptoms of PCS (e.g., over 80% of chronic pain patients without TBI report three or more PCS symptoms; see Iverson & McCracken, 1997; Nampiaparampil, 2008), and (3) the prevalence of malingered pain has been estimated to be as high as 34% (Mittenberg et al., 2002). Chronic pain complaints may be over four times more common in mTBI than in moderate to severe TBI (Uomoto & Esselman, 1993). Bianchini, Greve, and Glynn (2005) outlined their proposed criteria for malingered pain-related disability (MPRD), largely based on Slick and colleagues’ (1999) original criteria for malingered neurocognitive deficits. Interestingly, the criteria include (but are not limited to) poor effort on purely neuropsychological tests. This approach has questionable specificity and benefit, because (1) most neuropsychological tests do not assess pain, and (2) less than one-third of litigating pain patients may be expected to fail neurocognitive “validity checks” (Meyers & Diep. 2000). B ­ ianchini et al. (2005) applied the Slick et al. (1999) criteria to approach to this complex diagnostic problem. In their subsequent work, Greve, Ord, Bianchini, and Curtis (2009) found that the prevalence rate of cognitive effort test failure in pain patients is higher than prior estimates. However, the group’s methodology may be problematic. For example,



7. Malingered Traumatic Brain Injury 131

they did not establish false-positive rates for genuine pain patients; rather, they used scores from a TBI group, raising concerns that they assumed that feigning one condition (e.g., pain) is synonymous with feigning any condition. Greve, Ord, Curtis, Bianchini, and Brennan (2008) found that over half of chronic patients and nearly onethird of patients with mTBI could not be confidently categorized as malingering (or not) by three commonly used performance validity tests (PVTs): Test of Memory Malingering (TOMM), Word Memory Test (WMT), and Portland Digit Recognition Test (PDRT). Patients who failed one PVT and one symptom validity test (SVT) were assumed to be malingering, which obscures what exactly is being malingered (i.e., pain or cognitive abilities). It also falls short of more recent recommendations that at least three indicators be used to classify performance as malingered (Slick & Sherman, 2013). Pain is naturally distracting, but it also seems to produce a separate state of psychological distress that interferes with cognitive efficiency (Kewman, Vaishampayan, Zald, & Han, 1991). Clinicians working with patients with mTBI must be aware of pain syndromes, as well as somatic symptom disorders, and carefully consider the role of pain when questions of adequate effort are raised. In complex cases, consultation with a pain specialist may be needed. Chronic pain has been shown to increase attention paid to physical sensations, which may not only increase reports of PCS symptoms (Meares et al., 2011) but may also result in deficits on formal neuropsychological testing. For instance, individuals with MRI evidence of neck pain following mTBI may perform worse on neurocognitive tests than patients with mTBI without such evidence of pain (Fakhran, Qu, & Alhilali, 2016). Moreover, the mTBI was particularly mild for the entire sample (e.g., PTA < 30 minutes, LOC < 1 minute), suggesting that the differences in cognitive test scores were not attributable to mTBI. Resnick, West, and Payne (2008) suggested several characteristics that can differentiate pain and conversion symptoms from malingering. For instance, malingerers tend to be less eager to be examined or treated, whereas patients with pain disorders welcome the opportunity to obtain an explanation for their symptoms. Also, genuine mTBI and pain patients often avoid the details of the accident that caused the symptoms, but malingerers typically go into detail. Such willingness may reveal the lack of genuine emotional salience

of the purported trauma and may reflect a deliberate external goal (i.e., malingering). Although neuropsychological tests are not tests of pain per se, some cognitive measures may assist in determining the genuineness of reported pain. Etherton, Bianchini, Greve, and Ciota (2005) found that the TOMM was not impaired by the effects of laboratory-induced pain. They suggested that failed TOMM scores are caused by something other than pain, such as poor effort. Subsequent investigations by the same group (Etherton, Bianchini, Ciota, Heinly, & Greve, 2006) showed that the Working Memory Index (WMI) and the Processing Speed Index (PSI) from the Wechsler Adult Intelligence Scale–III (WAIS-III) are only modestly affected by either experimentally induced or chronic pain. In contrast, many of those feigning pain had low WMI scores. These cognitive tests are not designed to assess the validity of pain complaints but may hold utility because they measure pain-related complaints. More research is definitely needed. Bianchini et al. (2014) reported that the Pain Disability Index (PDI) and the Modified Somatic Perception Questionnaire (MSPQ) may have merit as measures of malingered pain-related disability. Just over 30% of the criterion group (known chronic pain patients with incentive to feign) met MPRD criteria for probable (27.4%) or definite (4.6%) malingering. When indeterminate scores were excluded, both the PDI and the MSPQ differentiated between pain patients with incentive and those without with acceptable specificities, but sensitivities were quite low. Studies have begun to document effect sizes and utility of certain measures of feigned cognitive impairment in individual populations. The available data (see Table 7.2) suggest that some measures of feigning may have moderate to large effect sizes when compared with these disorders, but further work is needed. Importantly, the comorbidity of mTBI and clinical conditions of depression, PTSD, and pain has not been systematically evaluated. These representative studies underscore the importance of further research for systematically evaluating comorbid conditions. In summary, depression, anxiety and PTSD, and pain represent common differential diagnoses when considering mTBI. In addition, each diagnosis can be feigned in its own right, which means that there are multiple possible outcomes from a neuropsychological evaluation: For example, the patients may (1) have a genuine brain injury, (2) have a genuine mental disorder masquerading as

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TABLE 7.2.  Specialized Measures of Feigning with Comparisons to Specific Conditions

Feigners vs. specific conditions (Cohen’s ds) Measure

Representative studies

TBI

TOMM (retention trial)

Gierok, Dickson, & Cole (2005) a

PTSD

Etherton, Bianchini, Greve, & Ciota (2005)

Weinborn, Orr, Woods, Conover, & Feix

4.65 1.42

(2003) b

0.71

Axelrod, Fichtenberg, Millis, & Wertheimer (2006)

1.21

Babikian, Boone, Lu, & Arnold (2006) c

1.05

Etherton, Bianchini, Greve, & Heinly (2005)

1.27

21-Item Test

Vickery et al. (2001) c

1.46

Category Test

Forrest, Allen, & Goldstein (2004) c

1.40

Greve, Bianchini, & Roberson (2007)d

0.42

(2007) e

0.64

Greve, Bianchini, & Roberson CVLT-II FBS/SVS

Root et al. (2006) c

0.82

Wolfe et al. (2010) f

1.06

Greiffenstein et al. (2004) g

Pain

0.95

Rees, Tombaugh, Gansler, & Moczynski (1998)

RDS

Dep

5.05

Note. Dep, depression; PTSD, posttraumatic stress disorder. aDifferential prevalence design; heterogeneous psychiatric inpatient group. bDifferential prevalence design; forensic psychiatric inpatients. cHeterogeneous clinical group, Trials 1–5 (raw score). dErrors on Subtests 1 and 2 (not MND vs. MND). eEasy items (not MND vs. MND). fTotal Recall Discriminability (standard score). gThis figure represents the mean when men and women were combined. When considered separately, the effects sizes are markedly different (d = 2.9 in men; d = 7.2 in women).

TBI, or (3) be malingering. Combinations of the three alternatives are possible as well. The forensic neuropsychologist must establish the veracity not only of the primary complaint (e.g., mTBI) but also the competing diagnoses. The accuracy of this determination is essential. It is not difficult to imagine how disastrous the consequences would be for a genuine patient who is incorrectly classified as a malingerer.

STRATEGIC IDENTIFICATION OF FEIGNED TBI The clinical history, context of injury, and other background information are critical parts of the determination of injury severity. Forensic neuropsychologists must use their knowledge of both the symptomatology and the natural course of TBI to identify neuropsychological inconsisten-

cies. Many authors have recommended a systematic assessment of patterned inconsistencies during each evaluation (e.g., Iverson & Binder, 2000; Iverson, 2006; Larrabee & Rohling, 2013; Sweet, Goldman, & Guidotti-Breting, 2013). They are patterned in the sense that they are not random; performance suggestive of malingering is always poorer than expected. Reported symptoms should match observed behavior. If, for example, the patient is reportedly “almost blind” but grasps a pen from the examiner without difficulty, there is an inconsistency between report and observation. If the patient reports severe anterograde amnesia but recalls the route to the clinician’s office, then there is a mismatch between report and observation. The examination of patterned inconsistencies permits many comparisons. Although highly accessible and intuitively appealing, patterned inconsistencies represent a qualitative approach



7. Malingered Traumatic Brain Injury 133

that is subject to biases in clinical judgment (see Iverson, Brooks, & Holdnack, 2008, for a review of common cognitive biases and logical fallacies affecting clinical judgment). The second type of assessment for cognitive feigning, often called syndrome analysis (Walsh, 1985), uses information about patients’ genuineness of presentation. With this method, the clinician compares features of the current presentation with the known syndrome. An exhaustive review is beyond the scope of this chapter, but an example would be a reported cognitive decline in the postacute stages of mTBI in the absence of an evolving bleed or other neurologic cause. Such an exacerbation would be inconsistent with the known course of mTBI and raises questions of feigning or the emergence of a genuine psychological disturbance, the latter of which occurs more commonly in forensic than in clinical cases. Likewise, a newly acquired aphasia in the context of TBI without welldocumented damage to the temporal lobe is highly unusual and deserving of a comprehensive assessment for possible response biases. Neither observation by itself is pathognomonic of malingering; as mentioned, other diagnoses must be ruled out. This approach is only a part of malingering detection, because atypical yet genuine cases do not always fit the syndrome in question. A third method of assessing feigned cognitive abilities involves the assessment of intertest inconsistency. Generally consistent performances are expected on tests of similar abilities (e.g., Arithmetic and Digit Span subtests; Finger Tapping and Grooved Pegboard), and significant deviations are difficult to explain syndromally. However, a word of caution is warranted: As several investigators have noted (e.g., Binder, Iverson, & Brooks, 2009; Schretlen, Munro, Anthony, & Pearlson, 2003), significant scatter among the Wechsler subtests is the norm, not the exception. The base rate of a difference between two tests must be considered before rendering an opinion about the likelihood that the inconsistency represents actual feigning. The fourth and final method, severity indexing, can be used to compare the current patient’s characteristics (including demographic data, injury characteristics, test scores, etc.) with those of previously studied patient groups. This approach capitalizes on the fact that a dose–response curve is associated with TBI, such that symptom load increases as severity of injury increases (Sweet et al., 2013). It is similar to the traditional use of norms but incorporates norms from TBI populations rather than healthy controls.

Rohling et al. (2003) proposed the Overall Test Battery Mean (OTBM) as such a measure of severity inconsistency. Basically, the authors transformed raw scores from neuropsychological tests into Tscores and then calculated an overall average (i.e., the OTBM). They then compared the patients’ OTBM with datasets from samples of neurocognitively impaired individuals. The authors used the outcome data from Dikmen and colleagues (1995) and from Volbrecht, Meyers, and Kaster-Bundgaard (2000) as benchmarks for performances among patients with TBI. The comparison of OTBMs to these datasets allows clinicians to determine the congruity between acute injury and residual cognitive deficits. An OTBM outside of the expected range in light of the patient’s length of loss of consciousness should be considered suspect. The severity indexing approach is promising because it yields an empirical index of the expected severity of neurocognitive deficits given the characteristics of the injury. Symptoms and ostensible deficits beyond the expected course and severity of injury raise the likelihood of dissimulation or psychologically based symptoms. Also, there are limited data regarding the prevalence of genuine but atypical neurocognitive profiles that do not fit with the existing estimates of severity. Clinicians are encouraged to explore this technique conceptually with data from Dikmen and colleagues (1995), Volbrecht et al. (2000), or Rohling et al. (2003) for specific comparisons. However, clinicians are also cautioned not to rely on this approach alone when making determinations of malingering. See Hill, Rohling, Boettcher, and Meyers (2013) for a potential complication involving intraindividual variability when using this approach.

Severe TBI and Malingering Very few case studies have investigated feigned severe TBI. As an exception, Boone and Lu (2003) described noncredible performances in two cases of severe TBI. Both patients underwent numerous multidisciplinary workups, but apparently the possibility of malingering was not considered. In their subsequent evaluations, the authors documented “odd” and “nonsensical” fluctuations in neurocognitive test performance. For instance, one patient had shown good neuropsychological recovery 6 months postinjury and had reenrolled part time in high school, but her neurocognitive scores declined significantly in the next 2 months. No identifiable brain mechanism was identified to explain this unexpected decline.

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Bianchini et al. (2003) reported three cases of litigating patients, each with documented severe TBI. Each patient performed significantly below chance on symptom validity tests and had clear incentives to fake deficits. The investigators’ published classification of “definite malingering” appears to be the first for feigned severe TBI. They reiterated Boone and Lu’s (2003) contention that tests of effort and feigning be included in all cases with potential material gain (see also Ju & Varney, 2000).

SELECTED NEUROPSYCHOLOGICAL TESTS USED IN TBI Multiple studies have demonstrated (to varying degrees) that certain neuropsychological tests may be used simultaneously to evaluate both genuine neurocognitive dysfunction and malingering. The chief advantage of these embedded measures involves time efficiency, but they may also be less recognizable as tests of malingering and therefore more resistant to coaching. A brief review of some commonly administered “double-duty” tests is provided here. More comprehensive reviews can be found in Boone (2007) and Larrabee (2007). Van Gorp and colleagues (1999) and Curtiss and Vanderploeg (2000) provide dissenting perspectives about the utility of embedded effort measures.

California Verbal Learning Test The recognition trial of the California Verbal Learning Test (CVLT; Delis, Kramer, Kaplan, & Thompkins, 1987) and CVLT-II (Delis, Kramer, Kaplan, & Ober, 2000) provides an opportunity for forced-choice testing of response bias during a test of true neurocognitive dysfunction. Although several studies have evaluated the utility of the CVLT in detecting dissimulation (e.g., Ashendorf, O’Bryant, & McCaffrey, 2003; Millis, Putnam, Adams, & Ricker, 1995; Slick, Iverson, & Green, 2000), it is difficult to synthesize their findings due to the differing criteria used to define the feigning groups. Among TBI patients, Bauer, Yantz, and Ryan (2005) used the WMT (Green, Iverson, & Allen, 1999) as the partial criterion (see Rogers, Chapter 1, this volume) for malingering (defined as performance below 82.5% on one of three WMT scores). A discriminant function using five CVLT-II variables correctly classified 75.8% of their sample. Specificity was an impressive 95.6%,

but sensitivity was just 13.5%, raising serious questions about the CVLT-II’s overall utility. Root, Robbins, Chang, and van Gorp (2006) evaluated the utility of the critical item analysis (CIA) procedure, as well as the forced-choice recognition indices from the CVLT-II. Basically, the CIA relies on the performance curve strategy (see Rogers, Chapter 2, this volume) in comparing performance on easy versus difficult items. Using a partial criterion design (PCD) they divided the entirely forensic sample into adequate- and inadequate-effort groups as defined by performance on the Validity Indicator Profile (VIP) and/or TOMM. They found that the CIA Recognition Index yielded exceptionally good specificity (100%) and positive predictive power (PPP; 100%). The CVLT-II appears to be a strong indicator of inadequate effort, but it lacks sensitivity, ranging from 4 to 60%. Although combining indices from the CVLT-II seems to improve incremental validity somewhat, clinicians are again advised not to rely on one indicator for malingering detection.

Digit Span and Reliable Digit Span Several investigators (e.g., Greiffenstein, Baker, & Gola, 1994; Iverson & Tulsky, 2003) argued that the Digit Span subtest from the WAIS-III was a useful measure of effort, because only 5% of both healthy and clinical samples score below the 5th percentile. As such, it represents an example of the floor effect strategy. Axelrod, Fichtenberg, Millis, and Wertheimer (2006) reported a cutoff score of 7 on the Digit Span subtest, and correctly classified 75% of probable malingerers and 69% of patients with mTBI. Though studies report specificities ranging from 90 to 100%, the degree of forensic utility is limited by small samples and lack of replication. Greiffenstein and colleagues (1994) introduced Reliable Digit Span (RDS) as a measure of effort, which is now an incorporated measure of effort in the Advanced Clinical Solutions program for the WAIS-IV. At least eight studies have provided estimates of specificity, with results ranging from 57 to 100%, but sensitivity is too low to be useful (e.g., 10–49%; Young, Sawyer, Roper, & Baughman, 2012). Efforts to improve the sensitivity of the RDS (the revised RDS [RDS-R]) have yielded largely similar rates (Young et al., 2012). Moreover, RDS may not be much more effective than standard Digit Span scaled score (Spencer et al., 2013) and may not be appropriate for individuals with



7. Malingered Traumatic Brain Injury 135

borderline IQs or lower. A summary of RDS sensitivities and specificities may be found in Babikian, Boone, Lu, and Arnold (2006).

Category Test The Category Test also employs the floor effect to detect feigned responding. Bolter, Picano, and Zych (1985) determined that certain items of the Category Test (i.e., “Bolter items”) were so easy that examinees with brain injuries rarely missed them. Tenhula and Sweet (1996) later developed six validity indicators for the Category Test that, in some combinations, correctly classified 88.9%, with a specificity of 92.6%. Adding more difficult items lowered specificity. Neuropsychologists should remember that whenever cutoff scores are combined, extensive cross-validation is required before they can be applied clinically. Boone (2007) provides comprehensive reviews of the Category Test and recommendations for its use in forensic practice. In general, the Bolter/easy items from the Category Test seem to hold promise for malingering detection. As can be seen, tests of genuine neuropsychological dysfunction may also be used to assess suboptimal effort. However, none should be used in isolation. Multiple independent measures (i.e., not strongly correlated) should be used. Studies have demonstrated the benefit of using multiple measures of relatively independent constructs to increase incremental validity (e.g., Rogers & Bender, 2013; Larrabee, 2014; Nelson et al., 2003).

Multiscale Inventories In this volume, Wygant, Walls, Brothers, and Berry (Chapter 14, this volume) and Boccaccini and Hart (Chapter 15, this volume) examine the use of multiscale inventories to evaluate feigning and other response styles. Rogers and Bender (2013) advanced a strong argument (see also Rogers, Chapter 2, this volume) for addressing feigned cognitive impairment as different from other domains (i.e., mental disorders and medical complaints). In essence, each domain places unique demands on malingerers and has its own detection strategies. However, some feigners may simulate personality changes and psychological impairment as arising from their putative brain traumas. Three multiscale inventories are often used with suspected cases of feigned cognitive impairment: the Minnesota Multiphasic Personality In-

ventory–2 (MMPI-2), MMPI-2-Restructured Form (MMPI-2-RF), and Personality Assessment Inventory (PAI). They are summarized briefly in the next two subsections. MMPI-2 and MMPI-2-RF

The MMPI-2 is among the most commonly administered tests in forensic neuropsychology. As Lees-Haley, Iverson, Lange, Fox, and Allen (2002) noted, the MMPI-2 not only characterizes emotional distress but also yields data regarding effort and exaggeration. It is critical to note that the MMPI-2 validity scales were not designed to evaluate the genuineness of examinees’ cognitive complaints. Nonetheless, its utility for that very purpose has been scrutinized with some potentially surprising results. In a meta-analysis of 19 studies, Nelson, Sweet, and Demakis (2006) summarized the effect sizes of the most commonly used validity scales from the MMPI-2 (e.g., L, F, K, Fp, F-K, O-S, FBS) when used to differentiate “overresponders” from honest groups. Nelson and colleagues found that several validity scales yielded large effect sizes, with the Fake Bad Scale (FBS; Lees-Haley, English, & Glenn, 1991) yielding the largest (d = 0.96). The FBS, now referred to as the SVS (Symptom Validity Scale), has also shown promise in specific neuropsychological studies, as it appears to correlate more with tests of feigned cognitive impairment (the Victoria Symptom Validity Test; VSVT) than with F, F-K, and Fp (Slick, Hopp, Strauss, & Spellacy, 1996). The SVS appears to accurately classify mildly head-injured litigants putting forth poor effort (e.g., Ross, Millis, Krukowski, Putnam, & Adams, 2004). Peck et al. (2013) found that while the SVS correctly classified only 50% of the criterion group (known TBI feigners), it misclassified only 6% of patients with conversion disorder. Sample sizes were small, and their data need to be replicated for relevant groups (e.g., TBI feigners vs. patients with conversion disorder). Larrabee (2003b) found that SVS scores of patients with moderate to severe TBI were far below those of litigants who had failed the PDRT, with an effect size of 1.81. Similarly, Dearth, Berry, and Vickery (2005) found that the SVS yielded an effect size of 1.39 between honest patients with TBI and TBI simulators. Other investigators (Arbisi & Butcher, 2004; Butcher, Gass, Cumella, Kally, & Williams, 2008) questioned the construct validity and potential

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biases of the SVS. In short, they raised questions about its potential for bias against disabled or trauma patients, and women. They cautioned psychologists about these problems and limits to its inadmissibility. Similarly, Lees-Haley and colleagues (2002) cautioned that although the MMPI-2 certainly meets the Daubert standard of general acceptance within the scientific community, its reliability and validity may not be as high as many clinicians assumed. Gervais, Ben-Porath, Wygant, and Green (2007) developed the Response Bias Scale (RBS) for the MMPI-2. MMPI-2 items were particularly relevant in establishing the credibility of cognitive effort in civil forensic groups. Based on an archival sample of over 1,200 disability claimants without head injury, the resultant 28-item scale yielded excellent specificity (95% overall; 100% for chronic pain patients) but very poor sensitivity (25%). In short, the study employed a lax criterion when predicting effort failure, which was defined as failure on only one or more cognitive feigning tests. Cross-validation appears to support the utility of the RBS when differentiating mixed psychiatric, mixed neurological, TBI, TBI with poor effort, and epilepsy groups (Schroeder et al., 2012). This study evaluated the utility of the RBS in a criterion-group design involving multiple clinical groups, which likely increases the confidence of the findings. The FBS/SVS and RBS were among the changes made during the construction of the MMPI2-RF (Ben-Porath & Tellegen, 2008). One of the MMPI-2 criticisms was that the validity scales were not independent from the clinical scales, with substantial overlap between certain scales (e.g., SVS and Scales 1 and 3). The MMPI-2-RF addressed this problem by removing items that did not load orthogonally, meaning that elevations on the validity scales are no longer as prone to conflation with elevations on clinical scales. Recent studies (McBride, Crighton, Wygant, & Granacher, 2013; Sleep, Petty, & Wygant, 2015) indicate that the revised versions of SVS and RBS are both correlated with cognitive effort test failure and are resistant to the effects of genuine brain damage. Excellent specificity, but variable sensitivities, were found. PAI

The PAI (Morey, 1991) is newer to the forensic realm than the MMPI-2, but it continues to gain widespread acceptance in forensic and clinical

practice. The PAI appears to hold certain advantages over other multiscale inventories. These advantages include (1) a fourth-grade reading level, (2) a shorter length than some other inventories (e.g., the MMPI-2), (3) nonoverlapping scales that aid differential diagnosis, and (4) a larger range of response options (i.e., “false,” “slightly true,” “mainly true,” and “very true”). The PAI (see Boccaccini & Hart, Chapter 15, this volume) includes four validity scales for assessing defensiveness and malingering. The Malingering Index (Morey, 1996) and the Rogers Discriminant Function (RDF; Rogers, Sewell, Morey, & Ustad, 1996) were developed for feigned mental disorders. As this section reviews the PAI only as it pertains to TBI, the interested reader should refer to Boccaccini and Hart (Chapter 15, this volume) for a broader critical review of the PAI and response styles. A growing body of research establishes the utility of the PAI in mTBI cases. Demakis and colleagues (2007) found that genuine TBI was associated with elevations on the Somatic Complaints, Depression, Borderline Features, Paranoia, and Schizophrenia scales, which is generally in line with similar studies using the MMPI-2 (Warriner, Rourke, Velikonja, & Metham, 2003). The social isolation and confused thinking items associated with Cluster 8 profiles (Morey, 1991) were endorsed by almost 20% of the mTBI sample, suggesting that the PAI includes items that are at least somewhat sensitive to symptoms of TBI. Perhaps not surprisingly, the Negative Impression Management (NIM) scale had one of the highest elevations, with over 24% scoring 2 standard deviations or more above the standardization sample’s mean. Such marked elevations raise a critical concern about whether genuine TBI patients might be misclassified as feigners on the PAI NIM scale. The authors did not control for litigation status, leaving open questions about the NIM scale’s utility for classifying TBI in incentivized contexts. According to previous research, milder head injuries are associated with higher scale elevations on the MMPI-2, especially on the “neurotic triad” (Youngjohn, Davis, & Wolf, 1997). Kurtz, Shealy, and Putnam (2007) replicated this “paradoxical severity effect” on four MMPI-2 scales but noted a different result on the PAI. Only two PAI scales (Somatization and Depression) were elevated among patients with mTBI, but less so than on the MMPI-2. These and other data led the authors to conclude that the PAI is both valid and useful when assessing head injury.



7. Malingered Traumatic Brain Injury 137

The PAI has few published studies on its efficacy for identifying feigned TBI. In a study of claims for worker’s compensation, Sumanti, Boone, Savodnik, and Gorsuch (2006) found that correlations between the PAI validity scales and cognitive effort tests were modest (rs from .02 to .30). The results suggest that the PAI (a measure of psychopathology) lacks utility for detecting feigned cognitive dysfunction. But recent studies may be more promising. For example, Keiski, Shore, Hamilton, and Malec (2015) found that the overreporting scales of the PAI were sensitive both to simulators feigning symptoms globally and simulators feigning more specific symptoms of TBI (though less so in the latter group). As expected, the NIM scale performed better overall than the other scales with a cutoff score of 73, yielding a sensitivity of .67 and a specificity of .83. Though intriguing, replication is needed before the PAI can be used in forensic practice. Furthermore, though other PAI studies have been conducted in TBI populations, many use a differential prevalence design (e.g., Whiteside, Galbreath, Brown, & Turnbull, 2012), rendering the results largely uninterpretable with respect to classificatory utility.

FOUR TESTS DESIGNED TO DETECT FEIGNED COGNITIVE DEFICITS No neuropsychological battery is complete without a measure designed expressly to detect feigned neurocognitive dysfunction. Dozens of tests are available, many of which containing multiple indicators. Only four tests are included here, based on their familiarity to most clinicians and their research support. Interested readers are encouraged to review Bender and Frederick (Chapter 3), Frederick (Chapter 17), and Garcia-Willingham, Bosch, Walls, and Berry (Chapter 18) in this volume, as well as other reviews (see Boone, 2007; Rogers & Bender, 2013). The four tests cover both verbal and nonverbal domains, thereby theoretically improving incremental validity when used in combination. The 21-Item Test is ostensibly a measure of verbal recall, but it has been shown to be insensitive to bona fide memory disorders, which makes it a good candidate for assessing effort (Iverson, Wilhelm, & Franzen, 1993). It also appears to be insensitive to cognitive decline in the elderly (Ryan et al., 2012), which suggests that it has potential as a measure of effort in this rapidly growing population. As a

strength, the 21-Item Test uses a detection strategy termed violations of learning principles. If the number of words in free recall exceeds the number in the recognition trial, then feigning is suspected. The Validity Indicator Profile (VIP; Frederick, 1997; Chapter 17, this volume) is also commonly used in forensic cases. It is unique in at least two ways: First, it employs multiple detection strategies, which makes it difficult for the examinee to know which strategy is being used for which items. Second, it assesses feigning in multiple cognitive domains (e.g., conceptualization, attention, and memory). The VIP Performance Curve strategy capitalizes on feigners that missing items too soon, which is indicated by a premature dip in performance. It is virtually impossible to explain improved performance in terms other than poor effort or feigning. The VSVT (Slick, Hopp, Strauss, & Thompson, 1997) requires the examinee to recognize the correct sequence of digits when provided with two choices. Performance curve can be examined by comparing performance on hard versus easy items, resulting in very good classification rates in mTBI litigants (Silk-Eglit, Lynch, & McCaffrey, 2016) and in military samples with mTBI (Jones, 2013). Finally, the TOMM, a forced-choice test of visual recognition memory, relies primarily on the floor effect. It is the most commonly used test of neurocognitive feigning, but unfortunately, suggested cutoff scores and sample reports have found their way to several websites, which raises questions about test security (Bauer & McCaffrey, 2006). Supplemental measures of effort for the TOMM have since been developed (Buddin et al., 2014; Gunner, Miele, Lynch, & McCaffrey, 2012). In short, these indices capture normatively unusual levels of inconsistency across the three trials of the TOMM. Both studies employ the atypical performance strategy to identify atypical learning and memory patterns, and have yielded slightly increased sensitivities over the traditional TOMM cutoff scores while maintaining high specificity. Vickery and colleagues (2001) conducted a meta-analysis on the aforementioned tests of feigned cognitive impairment (except the VSVT). The 21-Item Test showed an impressive average specificity of 100% but a low sensitivity of 22%. The meta-analysis also revealed large effect sizes for the 21-Item Test (mean d = 1.46). In contrast, the 15-Item Test (Rey, 1964) performed less impressively, especially with subtle feigning (Guilmette, Hart, Giuliano, & Leininger, 1994). It was also

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less successful with genuine neurocognitive disorders, including mental retardation (Goldberg & Miller, 1986; Schretlen, Brandt, Krafft, & van Gorp. 1991). In an updated meta-analysis, Sollman and Berry (2011) replicated some of the prior findings. They included neurocognitive feigning tests only if at least three contrasts of criterion-supported honest patient groups and feigners were available. As a result of this rigorous standard, the measures differed somewhat from the 2001 study. In short, the VSVT, TOMM, WMT, Letter Memory Test, and Medical Symptom Validity Test all showed large effect sizes (mean d = 1.55). Mean specificity was 0.90, and mean sensitivity was 0.69. Notably, these measures, and the vast majority of all cognitive feigning measures, focus solely on recognition memory. It is reasonable to question the degree to which such tests measure effort in other cognitive domains. Indeed, their clinical utility rests solely on the untested premise that they do. Constantinou, Bauer, Ashendorf, Fisher, and McCaffery (2005) provide a preliminary answer to this question in the mTBI population. They found that though poor performance on the TOMM correlated significantly with multiple domain scores from tests of intellectual functioning and from the Halstead–Reitan Neuropsychological Test Battery, the amount of variance in neuropsychological performance accounted for by the TOMM was small (r2 = .19–.30). This supports the use of the TOMM as a test of effort as opposed to a measure of various cognitive abilities.

EVALUATION OF FEIGNED TBI: A CASE EXAMPLE Case Findings A 43-year-old right-handed man (FM) was exiting his pickup truck when another pickup struck his vehicle from behind at 5mph, causing the door frame to strike him in the back of the head. Witnesses noted that although he did not lose consciousness, he was confused. When paramedics arrived 10 minutes later, his GCS score was 15. His past medical history was noncontributory, and CT of the head was negative, but he had a headache. He was discharged from the emergency department the same day with instructions to rest, and was prescribed naproxen for pain. When he presented for neuropsychological assessment 12 months later as part of a workers’ compensation evaluation, he reported LOC of 30 minutes and

posttraumatic amnesia of 5–7 days. FM claimed disabling attention deficits, memory problems, anxiety, and insomnia. He complained to his primary care physician (PCP) that his cognitive symptoms were worsening and said that he had never experienced any symptoms before the accident. FM indicated that he “deserved some kind of restitution.” As part of an independent medical examination (IME) 2 years postaccident, FM was administered a comprehensive neuropsychological battery. He now reported that he had been knocked out “for at least an hour” and that he “could not remember anything anymore.” His scores on the TOMM yielded a PPP of 94.3% and negative predictive power (NPP) of 87.4% (assuming a malingering base rate of 30%). Contrary to well-known learning principles, FM’s performance curve on the VIP revealed a slight improvement in performance in the middle of the curve. Finally, the RBS scale from the MMPI-2-RF strongly suggested that he exaggerated cognitive and somatic symptoms. The degree to which his scores comport with those expected in light of injury severity was also assessed. Specifically, his scores were compared to data from Volbrecht and colleagues (2000) showing that injury severity is significantly correlated with neuropsychological test performance. For illustration, Table 7.3 compares FM’s transformed scores to the T-scores and standard deviations from selected tests taken by genuine patients with TBI. The severity of deficits increases in a dose– response curve as LOC increases. FM’s T-score of 22 on the Rey Auditory Verbal Learning Test (RAVLT) falls within the range of patients whose LOC was beyond 2 weeks, which is clearly incongruent with what was reported. Also, his perceptual reasoning score (Perceptual Reasoning Index [PRI] from the WAIS-IV) would suggest that he had been unconscious for at least 2 weeks.

Case Conceptualization FM stands to gain monetarily if he is found to be disabled. The neuropsychological evaluation included specific tests of feigning and analyzed patterns of performance to assess response bias. The medical history was carefully reviewed in order to identify incongruities between reported and observed symptoms. Finally, the claimant’s test scores were compared to those of known groups of patients with brain injury. FM reported that his symptoms (both cognitive and psychological) had worsened over time, which is inconsistent with the



7. Malingered Traumatic Brain Injury 139 TABLE 7.3.  Severity Indexing of Selected Scores from Neuropsychological Testing in the Case of FM

T-scores from Volbrecht et al. (2000) Test RAVLT—Total

FM’s T-scores

LOC 29 days

22

40

34

26

19 33

Trails A (Time)

32

43

43

34

Trails B (Time)

40

46

48

32

37

WAIS-IV PRI

35

49

46

36

35

RCFT—Immediate

36

46

42

37

25

RCFT—Delayed

18

44

39

36

23

Note. Data are distilled from the case and Volbrecht et al. (2000). LOC, loss of consciousness; WAIS-IV, Wechsler Adult Intelligence Scale, 4th Edition; PRI, Perceptual Reasoning Index; RAVLT, Rey Auditory Verbal Learning Test; RCFT, Rey Complex Figure Test.

course of recovery from mTBI, and suggests that his responses do not reflect the effects of the mTBI itself. The clinical profile from the MMPI-2-RF was consistent with exaggerated cognitive and somatic problems. FM’s blanket dismissal of preaccident problems is consistent with research suggesting that patients with mTBI often overestimate their premorbid functioning (Greiffenstein, Baker, & JohnsonGreene, 2002), which leads to inflated and unrealistic expectations and subsequent dissatisfaction with cognitive performance following concussion. The patient was educated by his PCP about the expected symptoms of PCS. Questions remain about whether or not providing such information increases or decreases symptomatology. Mittenberg, DiGuilio, Perrin, and Bass (1992) demonstrated that psychoeducation about the course of mTBI can decrease recovery time and increase satisfaction with outcome. On the other hand, inadvertently contributing to a patient’s heightened illness perceptions (e.g., by suggesting that persisting symptoms are due to brain damage and that multiple referrals are needed) likely worsens outcome (Whittaker et al., 2007). Finally, FM voiced themes of injustice regarding his decision to litigate. Perceived injustice has been shown to be associated with poor outcome in various disability and workers’ compensation claims, and litigation involving whiplash, spinal cord, and low back pain (Yakobov, Scott, Thibault, & Sullivan, 2016; Tait & Chibnall, 2016; Trost, Mondon, Buelow, Boals, & Scott, 2016). Some investigators (e.g., Bender & Matusewicz, 2013; Silver, 2012) have theorized an equally potent role of these perceptions in protracted cases of PCS.

CLINICAL APPLICATIONS The critical role of malingering detection in neuropsychological assessment has been promulgated by both major academies in neuropsychology; moreover, the burden of proof lies with the clinician to justify why such testing would not be conducted (National Academy of Neurpsychology [NAN] Position Paper: Bush et al., 2005; AACN Consensus Statement: Heilbronner et al., 2009). Despite this requirement, assessment methods are not standardized. On the contrary, the methods of detection (e.g., observation, multiscale inventories, indices, intra- and intertest performance) vary markedly across practitioners and settings. There are dozens of published “malingering tests,” but in reality there is no such thing as a “malingering test.” These tests are largely tests of effort, and effort can be suboptimal for a number of reasons. It is the task of the forensic neuropsychologist to link poor effort with the intent to fake deficits. Relatedly, the role of bias in the examiner has not been studied nearly as extensively as it has in patients. Preliminary research (e.g., Murrie et al., 2009) strongly suggests that this area warrants further study within the broad context of forensic examination, but perhaps particularly so in malingering detection.

A Word of Caution: Utility Estimates and Research Designs In the assessment of feigned cognitive impairment, the most useful question is typically something akin to “Given this test score, how likely is it that the patient is feigning?” This question is

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addressed by PPP, which is influenced by the base rate of malingering. Greve and Bianchini (2004) have stated that PPP and specificity should be emphasized above other utility estimates in order to reduce the number of false-positive errors. Valid and reliable research data can only be obtained from research employing sound designs. Rogers (Chapter 2, this volume) has cautioned clinicians not to confound litigation status with malingering (i.e., differential prevalence designs); a meta-analysis of the MMPI-2 and malingering (Rogers, Sewell, Martin, & Vitacco, 2003) illustrates this point nicely. Litigation status played a minor role in producing relatively small effect sizes that were overshadowed by diagnostic differences (e.g., genuine profiles of schizophrenia vs. depression). Ross, Putnam, and Adams (2006) made the same case with brain-injured patients; although both incomplete effort and psychological disturbance were good predictors of neuropsychological performance, compensation-seeking status was not. A superior design is the criterion-group design, in which multiple indicators (e.g., effort test failures and presence of external incentive) are used to establish specific group membership and validate tests of feigned cognitive impairment in those groups.

Incremental Validity Studies have shown that using more than one test of feigning (Larrabee, 2012) and multiple detection strategies (Bender & Rogers, 2004) can improve detection. To the extent possible, each feigning-detection test in and of itself should measure largely independent constructs and should have good classification rates. For example, the TOMM (Tombaugh, 1997) is a test of visual recognition memory with very good classification rates that primarily uses the floor effect strategy. Ideally, the forensic neuropsychologist should complement such a feigning measure with an equally accurate feigning test of something other than recognition memory, such as the VIP (Frederick & Foster, 1991). When deciding which tests to combine, neuropsychologists should remember that using an additional test with a lower hit rate might decrease the accuracy of a single, more effective test. The practice of chaining likelihood ratios (LRs; also known as establishing posterior probabilities) may provide incremental validity. LRs have come into favor recently and appear to be a strongly positive step toward improving the transparency

of the statistics behind accuracy rates. In short, the LR considers both the number of findings for poor and good effort. For a review, see Bender and Frederick (Chapter 3, this volume).

Multistep Approach to the Evaluation of Malingered TBI Many of the decisions during an evaluation of mTBI must be rendered on a case-by-case basis. Nevertheless, all cases should involve a determination of the source or sources of the symptoms. At the outset, the clinician must try to answer the fundamental question: Was there a concussion? This determination may be difficult in itself, but probably the most difficult step is deciding whether the symptoms are actually attributable to the reported injury (Ruff & Richardson, 1999). Plus, the determination that the injury did not cause the symptoms is a multistep process that involves both qualitative and quantitative methods (see Table 7.4). The Slick and colleagues (1999) criteria for malingered neurocognitive dysfunction provided a useful heuristic for classifying individuals as malingerers but may have been both too conservative and too liberal in places (see Rogers & Bender, 2013; Rogers, Bender, & Johnson, 2011b). The revised criteria (Slick & Sherman, 2013) have yet to be validated but appear to be a step toward a fuller conceptual understanding of malingering and multifactorial methods for its detection. See Bender and Frederick (Chapter 3, this volume) for a description of how the new criteria differ from the original. Forensic neuropsychologists face major challenges with regard to mTBI, PCS, and malingering. Mild TBI is the most common diagnosis seen in forensic cases, and most of the features of PCS are nonspecific. mTBI is difficult to diagnose, partly because the most easily identified markers of injury (radiological evidence of contusion or hematoma) are exclusionary. Multiple conditions can masquerade as PCS, plus there are many reasons for a patient to put forth questionable effort during testing. Pain, depression, and symptoms of PTSD must be ruled out as potential causes for low scores on testing. Each of these conditions may also be malingered and may require an evaluation of their veracity. Moreover, as I discussed earlier in this chapter, multiple psychological and social factors may contribute to protracted recovery from PCS and be conflated with malingering if not adequately assessed.



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TABLE 7.4.  Sequential Decision Model for Malingering Detection

1. Determine whether there is a brain injury. Evaluate the following: a. Were signs of brain injury present at the scene (e.g., LOC, confusion)? b. Are medical workups (e.g., imaging) suggestive? c. Does the postacute symptom constellation fit with TBI? d. Are the neuropsychological evaluation results suggestive of TBI? e. Are there viable alternative explanations for the symptoms? 2. If Step 1 indicates that a TBI occurred, whether or not the reported symptoms are attributable to the injury must be determined. Evaluate the following: a. History and course/time gradient of the symptom: i. Are patient and collateral reports consistent and congruent with TBI? ii. Does onset of symptoms coincide with the injury? Did their evolution follow the course seen in TBI? b. The impact of preexisting symptoms and comorbid mental disorders such as depression and somatoform disorders. c. Whether any symptoms are being malingered (i.e., exaggeration or prolongation of genuine symptoms) i. Results of validity tests. ii. Determine impact of psychosocial factors, such as litigation, attribution error, and good old days bias on effort and motivation. 3. If Step 1 does not indicate a TBI, then the clinician may conclude that symptom etiology is non-TBI related. a. Therefore, symptoms are associated with another medical condition, psychological disturbance, or malingering. b. However, duration and degree of debilitation are critical here: i. For instance, symptoms of PCS at 72 hours can be clinically debilitating and exist without frank neurological signs. ii. In contrast, PCS symptoms at 12 months posttreatment without evidence of trauma strongly suggest non-neurological mechanisms.

The application of conceptually based strategies to malingering detection (see Rogers, Harrell, & Liff, 1993; Rogers & Correa, 2008; Rogers, Chapter 2, this volume) appears to have improved detection accuracy, but there is still room for further improvement. Whether explicitly stated or not, fairly simple applications of the floor effect strategy still pervade the literature, largely because it is easy to understand and use. More sophisticated applications appear promising. For instance, Backhaus, Fichtenberg, and Hanks (2004) demonstrated the potential in their archival study that used a normative floor effect to detect suboptimal effort. In essence, standards expected in moderate to severe brain injury are applied to the performances of patients with mTBI. This approach (which is similar to severity indexing) resulted in excellent classification rates. Empirical studies have begun to show that hit rates improve when certain feigning detection scales are combined (e.g., Bender & Rogers, 2004; Nelson et al., 2003; Victor, Boone, Serpa, Buehler, & Ziegler, 2009). Larrabee (2014) has argued that the specificity of a finding of malingering increases when two or more tests of feigning are employed within a neuropsychological battery of tests. However, whether the approach should take into account not only the number of tests failed but also the number of tests administered has not been agreed upon. For dissenting views of specificity when using two or more effort tests, see Berthelson, Mulchan, Odland, Miller, and Mittenberg (2013), Bilder, Sugar, and Hellemann (2014), and Odland, Lammy, Martin, Grote, and Mittenberg (2015). Future research should include more sophisticated methods of distinguishing malingered mTBI from genuine neurological and psychiatric conditions, especially when multiple comorbidities are involved. To date, the vast majority of research has compared feigned mTBI with genuine mTBI, whereas less attention has been paid to the performances of patients with other genuine disorders. To achieve maximum clinical utility, future measures must be able to rule out these other conditions as well.

Summary Guidelines for Forensic Neuropsychologists •• Be knowledgeable about the injury characteristics present at the time of the injury. This is important, because durations of LOC and PTA, GCS score, and neuroimaging in the acute period are reasonably good predictors of recovery from

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mTBI (especially when used in combination). In contrast, postacute indicators of severity, including neuropsychological test scores, are less effective predictors in most cases involving mTBI. •• Be familiar with the natural recovery curves of the different severities of TBI and the expected outcomes at various points in time. Some degree of recovery is expected in almost all injuries to the brain, regardless of severity. But complete recovery is expected in almost all cases of single, uncomplicated mTBI. At no time would a worsening of symptoms be expected following mTBI, and when present almost certainly indicates the onset of symptoms with a psychological etiology (in the absence of an evolving bleed). •• Systematically evaluate the role of psychological and litigation factors, including those unrelated to (or only marginally so) external gain: jurisogenic factors, perceptions of injustice, biased recall about preexisting functioning, and attribution errors regarding the cause of their symptoms (Bender & Matusewicz, 2013; Silverberg et al., 2016). •• Be familiar with PCS and its masquerading syndromes, such as anxiety, anxiety sensitivity, PTSD, and depression. •• Use multiple tests of feigning (both embedded and freestanding tests) over the course of the evaluation. •• Only use effort tests that have been validated in the patient population in question. •• Do not rule out a priori the possibility of genuine mTBI and/or PCS. •• Be mindful of the apparent imbalance between the large number of tests available to assess effort and the less impressive knowledge base regarding what “effort” actually means.

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CHAPTER 8

Denial and Misreporting of Substance Abuse Lynda A. R. Stein, PhD Richard Rogers, PhD Sarah Henry, PhD

Substance abuse is often associated with penalties including social sanctions and stigmatization. This association is illustrated by the adoption of penalties for driving under the influence (see Ying, Wu, & Chang, 2013), and by the widespread use of drug-testing policies in work settings (DiThomas, 2012). These policies resulted from substanceabusing employees having higher rates of absenteeism and poor performance (Stein, Smith, Guy, & Bentler, 1993). Research suggests dissimulation on substance use commonly occurs in a variety of settings even when guaranteed confidentiality. As an example, families in urban, low socioeconomic status areas significantly underreport cocaine use, with youth underreporting even more than their parents, presumably due to fear of repercussions (Delaney-Black et al., 2010). In contrast, elderly persons may underreport drug use unintentionally due to memory difficulties (Rockett, Putnam, Jia, & Smith, 2006). As a comparison, treatmentseeking adolescents are more likely to overreport marijuana use to enhance social status among their peers (Williams & Nowatzki, 2005). Moreover, national surveys find greater underreporting of more stigmatizing drugs, such as cocaine or heroin (Magura, 2010). Despite social sanctions and stigma, use of illicit substances is widespread (Substance Abuse and Mental Health Services Administration [SAM-

HSA], 2014). In 2013 alone, the prevalence rate of substance use disorder was 12.2% for adults and 5.2% for persons ages 12–17 years (SAMHSA, 2014). Individuals may withhold reports of their substance use in order to avoid legal consequences (Tourangeau & Yan, 2007) or to enhance social desirability (see Buchan, Dennis, Tims, & Diamond, 2002). Persons are unlikely to be forthcoming when faced with criminal or civil sanctions. Examples of the latter include parenting (e.g., fitness to parent) and employment (e.g., torts arising from drugrelated work injuries). In legal settings (e.g., detention centers), self-reports often underestimate substance use when compared to biological testing results (Mieczkowski, 2002; Knight, Hiller, Simpson, & Broome, 1998). Conversely, although largely underinvestigated, offenders arrested for serious crimes may be motivated to overreport or fabricate drug use to potentially serve as a mitigating factor in sentencing (Rogers & Mitchell, 1991), or to appear more sympathetic in some cases (Ortiz & Spohn, 2014).

OVERVIEW Specific Terminology Specific terms are often used to describe response styles related to substance abuse:

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1. Disacknowledgment: Claiming a lack of knowledge (“I don’t remember”) of either drug usage or behavioral consequences. Disacknowledgment may or may not be sincere. 2. Misappraisal: These distortions are unintentional rather than deliberate attempts to provide false information regarding substance use. 3. Denial: Substance use or related consequences are purposely minimized for specific reasons. Examples of reasons include social desirability, desire to avoid consequences (e.g., unwanted treatment), or unwillingness to accept responsibility for behavior. 4. Exaggeration: Individuals intentionally magnify substance use and its consequences. For example, adolescents may overreport substance use to increase social desirability (Palmer, Dwyer, & Semmer, 1994).

Prevalence of Dissimulation among Substance Abusers

of offenders referred for impaired driving, Lapham, C’de Baca, Chang, Hunt, and Berger (2001) found that nearly 30% of those denying substance abuse during initial screening actually met criteria for a substance use diagnosis when interviewed for a second time. However, the opposite pattern has also been seen in adult offenders. Some detainees are motivated to inflate their use if they believe it could result in mandated substance abuse treatment rather than serving a jail sentence (Hser, Maglione, & Boyle, 1999). Clinical Settings

Among clinical populations, Winters, Stinchfield, Henly, and Schwartz (1991) reported low percentages of adolescents in treatment (5.1–6.3%) who exaggerated drug use. While low, these percentage are nearly double those of nonclinical adolescent populations (i.e., 2.8–3.8% (Meldrum & Piquero, 2015; Petzel, Johnson, & McKillip. 1973).

Forensic Settings

Types of Distortion

Youth involved with the juvenile justice system have higher rates of substance use than their counterparts not involved with the juvenile justice system. As an example, McClelland, Elkington, Teplin, and Abram (2004) found that nearly half of detainees from a juvenile justice sample met criteria for one or more substance use disorders— specifically, alcohol and marijuana use disorders, which are the most commonly seen disorders in juvenile offenders. However, survey data suggest that respondents are likely to minimize their substance abuse even with an assurance of anonymity (Colon, Robles, & Sahai, 2002). On this point, Williams and Nowatzki (2005) studied 367 adolescents referred for substance use assessment; 26% incorrectly denied substance use, as determined by biochemical testing. Additionally, Stein (2016) asked incarcerated adolescents (N = 164), about whether they had under- or overreported amount, frequency, or problems associated with alcohol and marijuana. Inaccurate reports ranged from 13.4% for alcohol problems to 22.6% for marijuana. Despite assurances of confidentiality, 45.1% indicated some concern that the information might not be private. Denial of substance use in adult offender samples is also common, particularly for “harder” drugs such as powder and crack cocaine (Bureau of Justice Statistics, 2010), especially if these drugs could result in additional legal sanctions. In a study

Distortions regarding substance abuse may occur on three dimensions: (1) amount and type of substance abuse, (2) immediate behavioral and psychological effects of substance abuse, and (3) consequent impairment and psychological sequelae from cumulative substance abuse. We focus on the latter two dimensions. Distortions about the immediate behavioral and psychological effects of substance abuse have not been systematically investigated. No standardized measures are available to assess the immediate behavioral consequences of substance use. As a proxy, general guidelines for some substances have been promulgated, such as the relationship of estimated blood alcohol content (BAC) to general level of impairment (National Institute on Alcohol Abuse and Alcoholism [NIAAA], 2015). A respondent may be forthcoming about the amount of drug abuse but inaccurate about impaired judgment and erratic behavior, which often are more relevant to a specific incident than general usage. The Biphasic Alcohol Effects Scale (Martin, Earleywine, Musty, Perrine, & Swift, 1993) was developed to measure intoxicating effects of alcohol according to the client’s report. However, the relationship of BAC to intoxicated behavior continues to be highly variable. The same individual may respond differently with identical BACs depending on mood state and other situational factors (de Wit, Uhlenhuth, Pierri, & Johanson, 1987). Like-



8.  Denial and Misreporting of Substance Abuse 153

wise, the relationship between drug consumption and consequences (i.e., intoxicated behavior and psychological effects) is also variable, and may depend, for example, on tolerance and environmental cues (Siegel, 2005). In contrast, psychometric methods tend to focus on long-term effects and sequelae of substance abuse, including disorders as a result of chronic use (e.g., dependence), cognitive deficits (e.g., memory impairment), and clinical correlates (e.g., personality characteristics). For instance, versions of the Substance Abuse Subtle Screening Inventory (see below) have examined the effects of both reported and unacknowledged substance abuse.

CLINICAL METHODS In this section, brief screens offer rapid and relatively effective means of evaluating self-acknowledged substance use. Given the simplicity of these screens and typical face-valid nature, more elaborate procedures with greater discriminant validity may be particularly useful when misreporting is suspected. Examples of these include structured interviews and multiscale inventories that employ a broad-spectrum approach toward psychological impairment and incorporate syndromes and scales for substance abuse. Finally, specialized methods have been developed specifically for assessment of substance abuse. The validation of substance abuse measures is predicated on the accurate measurement of the external criterion, namely the use/abuse of alcohol and drugs (see Table 8.1). Unfortunately, many studies attempt to satisfy this criterion by simply using uncorroborated self-reporting. Practitioners can quickly recognize the non-independence and paradoxical nature of this simplistic approach. Other research has relied on informants and convergent indicators from other measures to address this concern. While a modest improvement is achieved over “self-report by self-report” criterion, such studies are still largely dependent on secondary sources of self-reporting. In contrast to these approaches, two types of external validity are generally effective: (1) treatment history for establishing drug-related disorders and (2) biochemical methods for confirming the use or nonuse of alcohol and drugs. However, even these methods are limited, because they do not provide a good indication of amounts consumed over a specific time period, or behavioral effects, if any. Research can easily demonstrate the vulnerability of substance

TABLE 8.1.  Types of Test Validation for Substance Abuse Measures and Their Relevance to Honesty and Dissimulation

1. Self-report by self-report. The criterion is acknowledged use/nonuse. Paradoxically, honesty is assumed for the self-described use/nonuse to assess forthrightness on the substance measure. 2. Self-report by informant report. The approach assumes a collateral source, typically a family member, has firsthand knowledge of the respondent’s substancerelated behavior. It is recommended that informants provide (a) a rating of how confident they are in their reports, and (b) a specific description of what they observed. 3. Self-report by treatment history. Treatment history provides an excellent criterion for a longitudinal perspective of substance abuse. Substance abusers are likely to know denial will be detected; therefore, they are likely to be more forthcoming than the target group (i.e., individuals for whom substance abuse has not been established). 4. Self-report by biochemical methods. Biochemical methods can accurately establish current (urine analysis) and long-term (hair analysis) drug use. This is a very effective approach to verify use, although not behavioral effects, of drugs. 5. Self-report by convergent indicators. Degree of association between two or more substance abuse measures is evaluated. To the extent that external validity of measures is based on either item 1 or 2 (above), the convergence of measures also remains vulnerable to response styles. 6. Self-report by simulation design. Users and nonusers are asked simulate on substance abuse measures. Although rarely used, the simulation design provides direct information on the vulnerability of substance abuse measures to distortion. Consideration must be given to type of dissimulation and whether it is general (e.g., social desirability) or substance-specific (e.g., denied drug use).



abuse measures to denial and exaggeration. Therefore, studies should ideally combine treatment history, laboratory methods, and corroborated self-reports (e.g., via significant other) as external criteria with simulation studies of denial and exaggeration. The measures listed below are not intended to be exhaustive. Some measures were included because they contain indicators of response bias, despite relatively little use in the literature. Cutoff scores and classification rates vary depending on factors such as setting (e.g., legal vs. primary care),

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Screen

Items

Coverage

Populations

Face valid

Validity scales

AUDIT

10

Alcohol

Adults/adolescents

Yes

No

MAST

25

Alcohol

Adults

Yes

No

RAPS4

4

Alcohol

Adults

Yes

No

RAPI

23

Alcohol

Adolescents

Yes

No

ASSIST

7–8

Alcohol/drugs

Adults/adolescents

Yes

No

CRAFFT

6

Alcohol/drugs

Adolescents

Yes

No

DUSI

152

Alcohol/drugs

Adults/adolescents

Yes

No

GAIN-SS

20–23

Alcohol/drugs

Adults/adolescents

Yes

No

MAYSI-2

52

Alcohol/drugs

Adolescents

Yes

No

PESQ

40

Alcohol/drugs

Adolescents

Yes

Yes

SIP

10–17

Alcohol/drugs

Adults

Yes

No

TCUDS

15–17

Alcohol/drugs

Adolescents

Yes

No

DAST

10–28

Drugs

Adults/adolescents

Yes

No

Note. See text for full names. Item length varies by version.

variant of the instrument (e.g., Alcohol Use Disorders Identification Test vs. Alcohol Use Disorders Identification Test–Consumption, see below), and criterion (e.g., use, extensive use, or substance diagnosis). Given this variability, an extensive review of cutoff scores and classification rates is not provided (see Table 8.2).

Screens for Substance Abuse Adult Alcohol Screeners MICHIGAN ALCOHOLISM SCREENING TEST

The Michigan Alcoholism Screening Test (MAST; Selzer, 1971) consists of 25 items to determine alcohol abuse via interview or self-administration. Short-MAST (SMAST, 13 items; Selzer, Vinokur, & van Rooijen, 1975) and brief MAST (bMAST, 10 items; Pokorny, Miller, & Kaplan, 1972) are also available. The bMAST has been shown to be effective in assessing self-disclosed problems with drinking severity in some samples (see Connor, Grier, Feeney, & Young, 2007). The MAST is effective in classifying alcohol use disorders among inpatients seeking treatment for self-acknowledged problems (Moore, 1972; Ross, Gavin, & Skinner, 1990). However, because of its high face validity, respondents can easily fake on the MAST by denying alcohol abuse (Otto & Hall, 1988; Nochajski & Wieczorek, 1998).

FOUR-ITEM RAPID ALCOHOL PROBLEMS SCREEN

Items from several screens including AUDIT (see below) and bMAST were evaluated to develop the Four-Item Rapid Alcohol Problems Screen (RAPS4; Cherpitel, 2000). Two quantity–frequency items were added to increase sensitivity for alcohol abuse and harmful drinking (RAPS4QF; Cherpitel, 2002). These screens have been used internationally in emergency departments and may be administered orally or in writing, although some decrement was found for their internal consistency as compared to the use in the United States (Cherpitel, Ye, Moskalewicz, & Swiatkiewicz, 2005; Cremonte, Ledesma, Cherpitel, & Borges, 2010; Geneste et al., 2012). Its use of face-valid items leaves this measure vulnerable to misreporting. Adolescent Alcohol Screeners RUTGERS ALCOHOL PROBLEM INDEX

White and Labouvie (1989) developed the Rutgers Alcohol Problem Index (RAPI) questionnaire for quick screening of alcoholism among adolescents. The RAPI’s 23 face-valid items address potential negative effects of problematic alcohol use, as well as consequences unique to adolescents. The authors have experimented with different time frames (e.g., the last 3 years or “ever”) and different



8.  Denial and Misreporting of Substance Abuse 155

response categories. Frequently applied to college students, the RAPI data indicate three factors: Abuse/Dependence Symptoms, Personal Consequences, and Social Consequences, each with adequate internal consistency and convergent validity (Martens, Neighbors, Dams-O’Connor, Lee, & Larimer, 2007). With face-valid measures, White and Labouvie (1989) have acknowledged the potential for denial or exaggeration on the RAPI, neither of which has been researched. Adult Alcohol and Drug Screeners TEXAS CHRISTIAN UNIVERSITY DRUG SCREEN

The 15-item Texas Christian University Drug Screen–II (TCUDS; Institute of Behavioral Research [IBR], 2007) provides an interview-based, self-report measure of frequency of substance use, treatment history, substance-use diagnoses, and treatment motivation. Its 17-item revision (TCUDS-II) has recently been updated to reflect DSM-5 diagnostic criteria (IBR, 2014). In a study detecting substance use disorders among male inmates (N = 400), sensitivity (.85) and specificity (.78) were good, but the “external” criterion simply involved an earlier version of the instrument (Peters et al., 2000), which may be viewed as criterion contamination. Frequently used in correctional systems (Peters, LeVasseur, & Chandler, 2004), it appears to be susceptible to dissimulation (Richards & Pai, 2003). SHORT INDEX OF PROBLEMS

The Short Index of Problems (SIP; Miller, Tonigan, & Longabaugh, 1995) is a 15-item questionnaire for assessing lifetime and past 3-month alcohol consequences. As a strength, the SIP also has a collateral version for evaluating report consistency. In addition to a total score, five subscales assess alcohol problems (Physical, Social Responsibility, Intrapersonal, Impulse Control, and Interpersonal). Its 2-day test–retest reliability ranges from good to excellent (rs from 71 to .95; Miller et al., 1995). However, the SIP total score only modestly correlated with alcohol dependence criteria (r = .36; Feinn, Tennen, & Kranzler, 2003). Many versions of the SIP share the majority of items, such as SIP modified for Drug Use (SIP-DU; Allensworth-Davles, Cheng, Smith, Samet, & Saitz, 2012) and SIP–Drugs (SIP-D; Alterman, Cacciola, Ivey, Habing, & Lynch, 2009). Given their face validity, all SIP measures appear to be susceptible to

dissimulation, so the collateral version should also be routinely administered. Adolescent Alcohol and Drug Screeners PERSONAL EXPERIENCE SCREENING QUESTIONNAIRE

The 40-item Personal Experience Screening Questionnaire (PESQ; Winters, 1992) has a Likert-type response format addressing drug problem severity, problems often associated with drug use (e.g., physical abuse), and drug use history. Of particular importance, it contains two scales for assessing Defensiveness and Infrequent Responding. The data suggest that only about 15% of protocols were invalid due to compromised self-reporting. However, no formal studies were conducted to determine efficacy of scales to detect response distortion. The PESQ appears to be effective at identifying those with clinical needs for further drug-abuse evaluation (Winters & Kaminer, 2008). CRAFFT

Via interview or written format, yes–no inquiries cover six content items: riding in a Car driven by someone using alcohol/drugs (AD), using substances to Relax or when Alone, dependence problems identified by Friends/family, Forgetting/ regretting actions when using, and getting into Trouble while using. Excellent concurrent validity has been demonstrated in psychiatric (Oesterle, Hitschfeld, Lineberry, & Schneekloth, 2015) and primary care settings (Knight, Sherritt, Shrier, Harris, & Chang, 2002). Obviously, the CRAFFT is vulnerable to drug denial. MASSACHUSETTS YOUTH SCREENING INVENTORY–2

The 52-item Massachusetts Youth Screening Inventory–2 (MAYSI-2; Grisso & Barnum, 2006) has a yes–no response format with seven scales, including Alcohol/Drug Use. As evidence of reliability, adjudicated youth (N = 248) were administered the MAYSI-2 prior to placement decisions, producing a very high correlation (r = .92) between paper and interview-based formats on the Alcohol/Drug Use scale (Hayes, McReynolds, & Wasserman, 2005). When using substance diagnoses, a British study found poor agreement with the Alcohol/Drug Use scale (Lennox, O’Malley, Bell, Shaw, & Dolan, 2015). Important questions have

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been raised about the MAYSI-2 regarding external validity, response styles, and possible cultural influences. Adult and Adolescent Substance Abuse Screeners ALCOHOL USE DISORDERS IDENTIFICATION TEST

The Alcohol Use Disorders Identification Test (AUDIT; Saunders, Aasland, Babor, De La Fuente, & Grant, 1993) consists of 10 questions about alcohol-related symptoms and is administered via interview or written format. Since its inception, various studies (Shields & Caruso, 2003; Reinert & Allen, 2002; Donovan, Kivlahan, Doyle, Longabaugh, & Greenfield, 2006) in hospitals and other settings have demonstrated strong psychometric properties. It has also been well validated for use with youth (Knight, Sherritt, Kim, Gates, & Chang, 2003; Cook, Chung, Kelly, & Clark, 2005). Promoted by the World Health Organization (WHO; Babor, Biddle, Saunders, & Monteiro, 2001), the AUDIT has been studied internationally in nine countries and has demonstrated fairly good results with clinical samples (see Cassidy, Schmitz, & Malla, 2007; Lundin, Hallgren, Balliu, & Forsell, 2015; Pradhan et al., 2012). Higher scores on Impression Management are associated with low AUDIT score, indicating its vulnerability to underreporting (Zaldivar, Molina, Rios, & Montes, 2009). Face-valid items leave the AUDIT vulnerable to dissimulation, which has not been well-studied.

ever, among youth admitted for inpatient evaluations, DAST-A was unrelated to social desirability (Martino et al., 2000). However, it should be noted these correlations indicate “faking good” and not specifically the denial of substance use. Finally, a simulation study found that inpatients could significantly suppress their DAST scores (Wooley, Rogers, Fiduccia, & Kelsey, 2012). Adult and Adolescent Alcohol and Drug Screeners DRUG USE SCREENING INVENTORY

The Drug Use Screening Inventory (DUSI) was developed in the United States for use with both adolescent and adult substance abusers (Tarter & Hegedus, 1991), although most studies focus on adolescents. As the longest screen (152 items covering 10 domains), DUSI items extend beyond substance abuse to address general functioning (school/work, social, mental/physical functioning). International studies have also found that the DUSI can distinguish youth with and without substance problems (e.g., De Micheli & Formigoni, 2002). The revised version (DUSI-R; Kirisci, Hsu, & Tarter, 1994) includes a general Lie scale. Among Brazilian youth from a school setting, the Lie scale classified over half as underreporting, which raises questions about its clinical usefulness (Dalla-Déa, De Micheli, & Formigoni, 2003). A recent simulation study also found that the DUSIR is highly susceptible to complete and partial denial of substance use or related problems in adults (Wooley et al., 2012).

DRUG ABUSE SCREENING TEST

The Drug Abuse Screening Test (DAST; Skinner, 1982) comprises 28 items (yes–no responses) and was developed from the MAST (discussed earlier). The DAST’s content reflects frequency of drug use, and interpersonal, legal, and medical problems associated with use. It has been extensively tested in psychiatric, work, substance treatment, and justice settings (Yudko, Lozhkina, & Fouts, 2007). Shorter versions, as well as a 27-item youth version (DAST-A; Martino, Grilo, & Fehon, 2000) are available. A review of these instruments indicates good reliability (Yudko et al., 2007). Importantly, DAST studies have evaluated the effects of underreporting. Skinner (1982) found DAST with modest negative correlations to denial (r = –.28) and social desirability (r = –.31 to –.38) among treatment-seeking substance users. How-

ALCOHOL, SMOKING AND SUBSTANCE INVOLVEMENT SCREENING TEST

The Alcohol, Smoking and Substance Involvement Screening Test (ASSIST) classifies low, moderate, or high risk for a range of drugs (Humeniuk, Henry-Edwards, Ali, Poznyak, & Monteiro, 2010). The ASSIST was developed by recruiting subjects from medical, addiction treatment, and psychiatric settings across several countries. Although initially validated on adults, ASSIST scores for tobacco, alcohol, and marijuana in an adolescent sample have demonstrated good internal consistency and concurrent validity (Gryczynski et al., 2014). Further work produced a second edition (ASSIST-2), establishing concurrent and construct validity. Its other strengths include discriminant validity (e.g., distinguishing between use, abuse, and de-



8.  Denial and Misreporting of Substance Abuse 157

pendence), and 3-month stability follow-up (Newcombe, Humeniuk, & Ali, 2005). The ASSIST-3 added weighted scoring procedures with validation on samples from primary care and substance abuse treatment settings (Humeniuk et al., 2008). It comprises eight questions. As with all face-valid measures, it is susceptible to misreporting. To partially address this issue, fictitious drug names were included to identify overreporting; no data are available for underreporting or drug denial. THE GLOBAL APPRAISAL OF INDIVIDUAL NEEDS—SHORT SCREENER

The Global Appraisal of Individual Needs—Short Screener (GAIN-SS; Dennis, Chan, & Funk, 2006) originally comprised 20 items addressing lifetime and past-year substance use, crime involvement, and internalizing–externalizing problems for adults and youth. More recent versions include the 23-item GAIN-SS-3 (Dennis, Feeney, & Titus, 2013), which is available in several languages. However, most research pertains to the GAIN-SS version; Dennis et al. (2006) studied almost 8,000 youth and adults from a wide array of treatment settings in the United States and found good to very good alphas and agreement with the full GAIN General Individual Severity scale. As an important note, an independent study of youth recruited from clinical settings found the GAINSS to be highly effective in detecting substance abuse (McDonell, Comtois, Voss, Morgan, & Reiss, 2009). However, another large study of adolescents from outpatient settings raised concern that GAIN-SS may misclassify persons with substance or behavioral disorders (Stucky, Orlando, & Ramchad, 2014). Structured Interviews

An advantage of structured interviews is the standardization of clinical inquiries and responses so that direct comparisons can be made between the respondent and significant others (Rogers, 2001). However, respondent–informant agreement tends to be relatively modest, even when no evidence of dissimulation is found. Therefore, a lack of agreement does not signify deception. In this section, we selectively review (1) diagnostic interviews that contain substance abuse components and (2) targeted substance abuse interviews. Diagnostic interviews for adults and youth include sections on substance disorders covering life-

time or past-year diagnoses. Selected examples for interviews include the following: 1. The Structured Clinical Interview for DSM-5 Disorders (SCID-5; First, Williams, Karg, & Spitzer, 2016) 2. The Diagnostic Interview Schedule for Children–IV (DISC-IV; Shaffer, Fisher, Lucas, Dulcan, & Schwab-Stone, 2000) 3. The Kiddie Schedule for Affective Disorders and Schizophrenia—Present/Lifetime versions (K-SADS-PL; Axelson, Birmaher, Zelazny, Kaufman, & Gill, 2009) 4. The Composite International Diagnostic Interview–3.0 (CIDI-3), validated for youth and adults (Kessler et al., 2009; Haro et al., 2006), is descended from Diagnostic Interview Schedule (Robins, Heizer, Croughan, & Ratcliff, 1981; Kessler & Ustun, 2004). At the time this chapter was completed (January 2017), most validation of structured interviews focused on psychometric characteristics with respect to DSM-IV (American Psychiatric Association, 2000) diagnoses or diagnoses based on earlier versions of the DSM. For DSM-IV, SCID-IV substance use disorders have strong concurrent and discriminant validity among adults in substance treatment (DeMarce, Lash, Parker, Burke, & Grambow, 2013). In terms of changes, DSM-5 (American Psychiatric Association, 2013) combined abuse and dependence criteria into a single disorder; it also adds craving to diagnoses, and increases the diagnostic threshold for substance disorder. Diagnoses specify different levels of severity (i.e., mild, moderate, and severe) classifications. The most recent is the World Mental Health CIDI (WMH-CIDI; Kessler & Ustun, 2004) but sections of the CIDI that continue to be updated and include Medications and Illegal Substance Use (see www.hcp.med.harvard.edu/wmhcidi/instruments_capi.php). As a WHO instrument, the CIDI has been used worldwide and recently in Nepal (Ghimire, Chardoul, Kessler, Axinn, & Adhikari, 2013). In classifying lifetime substance disorder for community youth and adults, utility estimates are generally excellent (Haro et al., 2006; Kessler et al., 2009). Few studies have examined the vulnerability of structured interviews to the denial or exaggeration of substance abuse. Cottler, Robins, and Helzer (1989) conducted a 1-week follow-up on clients, almost one-third of whom were on parole. They

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found that 36% of discrepancies occurred due to forgetfulness and 14% due to misunderstanding. Apparently, none were due to underreporting (e.g., to shorten the interview or avoid interviewer disapproval). For interviews, items that address substance use are easily identified, and respondents should have no difficulty in modifying reports, either through exaggeration or denial. Targeted interviews have been developed for substance use. The following section covers three such interviews. ADDICTION SEVERITY INDEX

The Addiction Severity Index (ASI; McLellan, Luborsky, Woody, & O’Brien, 1980) covers medical, work, alcohol, drugs, family/social, legal and psychiatric domains. Chiefly validated with male Veterans Administration (VA) patients, it emphasizes treatment needs. The ASI has been translated into over 20 languages (McLellan, Cacciola, Alterman, Rikoon, & Carise, 2006). For each ASI section, interviewers rate confidence in the respondent’s answers, although no formal definition of invalidity is provided (University of Pennsylvania VA Center for Studies of Addiction [UPVACSA], 1990). McLellan et al. (1980) reported that only 11 of 750 (1.5%) interviews produced invalid information; however, the manual notes that formal work is needed in response distortion (UPVACSA, 1990). Adults in drug treatment underreported 30-day cocaine and opiate use on the ASI as compared to biological testing (Chermack et al., 2000). Interestingly, however, self-administered ASIs (via the Web or telephone voice-response technology [VRT]) have produced significantly higher drug scores than clinician-administered ASIs, despite respondents reporting greater likelihood of being honest with interviewers (Brodey, Rosen, Brodey, Sheetz, Steinfeld, & Gastfriend, 2004). These findings suggest that some substance abusers may minimize drug use when asked via interviews. The Teen ASI (T-ASI; Kaminer, Bukstein, & Tarter, 1991), modeled after the ASI, has been tested on small samples of adolescent inpatients (see Kaminer, Wagner, Plummer, & Seifer, 1993) and has been translated into nine languages (Kaminer, 2008). In contrast to adults, adolescents did not differ significantly in reported substance use between interviewer or self-administered (via the Web or telephone VRT) methods, although they endorsed being the most honest on the Web-based format (Brodey et al., 2005). The T-ASI-2 (Brodey

et al., 2008) expands coverage of psychological functioning; the authors found good internal consistency for alcohol and drug use in youth recruited from substance use clinics. GLOBAL APPRAISAL OF INDIVIDUAL NEEDS

The Global Appraisal of Individual Needs—5th edition (GAIN-5; Dennis, White, Titus, & Unsicker, 2008) covers a full biopsychosocial assessment, including substance use, and physical and mental health, including scales for DSM-IV disorders. Numerous variations of the instrument are widely used in SAMHSA programming (Dennis et al., 2008). The GAIN-6 is being developed to address DSM-5. For adults and youth in substance treatment, reliability and construct validity of the substance scale are very robust (Dennis et al., 2008). Its unidimensionality and applicability across gender and setting was generally confirmed on adults in substance treatment (Kenaszchuk, Wild, Rush, & Urbanoski, 2013). GAIN-5 has keyed items alerting interviewers to inconsistencies that can be reconciled with respondents. Interviewers rate the quality of responses per section according to no problems (0), respondent appeared to guess (1), misunderstand (2), deny (3), or misrepresent (4). According to Dennis et al. (2008), variations in scores across sections may indicate biased reporting. However, no published data were found to substantiate this scoring.

Focus on the Minnesota Multiphasic Personality Inventory and the Millon Clinical Multiaxial Inventory Many multiscale inventories include scales for the assessment of substance abuse and behavioral correlates. This chapter focuses on two inventories that have considerable substance abuse research: the Minnesota Multiphasic Personality Inventory–2 and the Millon Clinical Multiaxial Inventory. MMPI-2

The MMPI-2 (Butcher, Dahlstrom, Graham, Tellegen, & Kaemmer, 1989) and MMPI-2-RF (Restructured Form; Tellegen & Ben-Porath, 2008) are both used widely. Additional formats have included computerized adaptive versions (see Forbey, Ben-Porath, & Arbisi, 2012). Adolescents were administered the MMPI for Adolescent (MMPI-A; Butcher et al., 1992) until the recent release of the



8.  Denial and Misreporting of Substance Abuse 159

MMPI-A-RF (Archer, Handel, Ben-Porath, & Tellegen, 2016). MMPI-2 substance-abuse indicators have included the MacAndrew Alcoholism scale (MAC; MacAndrew, 1965); Substance Abuse Proclivity scale (SAP; MacAndrew, 1986); the revised MAC (MAC-R; Butcher et al., 1989, 1992); Addiction Acknowledgment and Addiction Potential scales (AAS, APS; Weed, Butcher, McKenna, & BenPorath, 1992); the Substance Abuse scale (SUB; Tellegen & Ben-Porath, 2008); and, for adolescents specifically, the Alcohol/Drug Problem Acknowledgment and Proneness scales (ACK, PRO; Weed, Butcher, & Williams, 1994). Despite initially positive results (MacAndrew, 1981), subsequent MAC research found poor specificity, limiting its clinical usefulness (Gripshover & Dacey, 1994). In addition, Otto, Lang, Megargee, and Rosenblatt (1988) demonstrated that the MAC is susceptible to denied substance use. Adolescent studies have generally failed to demonstrate the effectiveness of the MAC (Gantner, Graham, & Archer, 1992; Stein & Graham, 2001). Mixed results have been found for the effectiveness of the MAC-R in accurately detecting adult substance abuse (Clements & Heintz, 2002; Greene, Weed, Butcher, Arredondo, & Davis, 1992; Stein, Graham, Ben-Porath, & McNulty, 1999). The SAP has produced very similar results to MAC, therefore raising doubt regarding SAP’s incremental validity (Greene, 1991) and subsequently its discriminant validity (Greene et al., 1992). The methodological advantage of the APS involves items of varied content unrelated to substance misuse. Data regarding its discriminant validity have yielded variable results in English (e.g., Weed et al., 1992, 1994) and Spanish (Fantoni-Salvador & Rogers, 1997) versions. Despite its nontransparent content, unfortunately, it has not been tested on samples whose members deny or minimize their substance abuse. MMPI-2-RF no longer contains AAS or APS, but introduces the SUB scale (Tellegen & BenPorath, 2008). It tends to have modest predictive validity of substance-related disorders when combined with RC3 (i.e., Cynicism scale; Haber & Baum, 2014). Of particular relevance to this chapter, the SUB appears markedly susceptible to under- and overreporting in both naturalistic and simulation studies (Burchett & Ben-Porath, 2010; Forbey, Lee, Ben-Porath, Arbisi, & Garland, 2013). Clearly, much more work is needed in validating SUB, and for determinations of SUB-specific denial.

As an important caution, practitioners should definitely avoid using measures of general defensiveness as proxy indicators of denied substance abuse. Some substance abusers’ elevations on these scales would represent an impermissible extrapolation to assume that general defensiveness was masking substance abuse. In doing so, examinees without substance abuse issues would be wrongly categorized as substance abusers and their putative “denial” would possibly being viewed as a barrier to treatment interventions. MCMI

The MCMI-IV, the most recent iteration of this instrument, aligns with DSM-5 (Choca & Grossman, 2015). Understandably most studies focus on previous versions with limited generalizability to the MCMI-IV, including Alcohol Use and Drug Use scales (B and T, respectively). The MCMIIV manual (Millon, Grossman, & Millon, 2015) reported moderate to moderately high utility estimates for these scales based on clinician ratings; however, it does not appear that any standardized interviews or methods were used to establish these diagnoses. In addition, no data are provided on how underreporting or denial would affect the psychometric value of these scales. Clearly, crossvalidation is needed. Focusing on the MCMI-III, a large review by Tiet, Finney, and Moos (2008) of the T scale indicated poor sensitivity. In contrast, a study of substance treatment in Denmark (Hesse, Guldager, & Linneberg, 2012) found that B and T scales correlated strongly with alcohol and drug dependence symptom counts (rs > .60), but provided poor agreement with dependence diagnosis. The Millon Adolescent Clinical Inventory (MACI; Millon, 1993) aligns with DSM-IV and includes a Substance Abuse Proneness (BB) scale that distinguishes between clinical youth with and without a substance abuse diagnosis (Grilo, Fehon, Walker, & Martino, 1996; Pinto & Grilo, 2004) and juvenile offenders (Branson & Cornell, 2008). However, BB may have less clinical utility with African American juvenile offenders (Woodland et al., 2014). The MCMI appears to be vulnerable to faking. Respondents instructed to appear psychologically disturbed produced marked elevations on B and T (Millon, 1983) for the original MCMI. In addition, substance abusers appear to be able to conceal substance disorders if so motivated (Fals-Stewart, 1995). In a study to detect denied drug use, 52%

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of drug abusers were able to successfully deny drug or alcohol abuse, although those unable to elude detection tended evidence a more severe course in their disorder (Craig, Kuncel, & Olson, 1994). Of central importance to this chapter, the MCMI-IV and the MACI have yet to publish research on the vulnerability of substance use scales to detect denied or exaggerated substance abuse.

Specialized Measures The following specialized measures (see Table 8.3) are included. Specialized Alcohol Measure for Adults DRINKER INVENTORY OF CONSEQUENCES

The Drinker Inventory of Consequences (DrInC; Miller et al., 1995), with 50 items (Likert-type or Yes/No, depending on format), was designed to measure drinking consequences, while avoiding the direct measurement of dependence, pathologic drinking, and help seeking. Normative data and acceptable validity information exist on a large sample of alcohol-dependent treatment-seeking adults. Five subscales (see the SIP, discussed earlier) and a total score are derived for not only the individual’s lifetime but also the past 3 months’ problems related to alcohol misuse. Forms for collateral reports aid in detecting potential differences in reporting. DrInC was validated for heavy-drinking, intravenous drug users, with data suggesting a single factor (Anderson, Gogineni, Charuvastra, Longabaugh, & Stein, 2001). Notably, Miller et al. (1995) cautioned that cultural difference may affect scores and their interpretation. As a means to detect possible dissimulation, carelessness or naysaying is assessed with five items; however, no

published studies have examined utility, or careless or defensive responding. Specialized Alcohol and Drug Measure for Adults THE INVENTORY OF DRUG USE CONSEQUENCES

The Inventory of Drug Use Consequences (InDUC; Tonigan & Miller, 2002) was derived from the DrInC using treatment-seeking substance users. It assesses both alcohol and drug consequences for the past 3 months and lifetime. Among substance-involved clients, test–retest reliability was excellent (rs from .89 to .97). Investigators have suggested that the InDUC assesses a single factor, as compared to five (Blanchard, Morgenstern, Morgan, Labouvie, & Bux, 2003; Gillaspy & Campbell, 2006). Like the DrInC, carelessness or naysaying is assessed with five items with unknown validity. Specialized Alcohol and Drug Measures for Adults and Adolescents PERSONAL EXPERIENCE INVENTORY

The Personal Experience Inventory (PEI; Winters & Henly, 1989) includes 33 scales and 300 items that address the severity of drug use and associated psychosocial problems. A PEI—Parent Version (PEI-PV; Winters, Anderson, Bengston, Stinchfield, & Latimer, 2000) demonstrates significant convergence between mother–child reports. The PEI demonstrates good reliability (see Winters & Henly, 1989; Winters, Stinchfield, & Latimer, 2004), and consistent evidence of diagnostic validity (e.g., Winters, Stinchfield, & Henly, 1993; Winters et al., 2004). Similar results are produced across ethnic groups (Winters et al., 2004). The

TABLE 8.3.  Brief Summary of Specialized Measures

Measure

Items

Coverage

Populations

Face valid

Validity scales

DrInC

 50

Alcohol

Adults

Yes

Yes

InDUC

 50

Alcohol/drugs

Adults

Yes

Yes

PEI

300

Alcohol/drugs

Adolescents

Yes

Yes

SASSI

 90a

Alcohol/drugs

Adults/adolescents

Partiallyb

Yes

TLFB

Var.

Alcohol/drugs

Adults/adolescents

Yes

No

Note. See text for full names. Var., variable. a Approximately 90 items, depending on which version is used. b Scales are included that are face valid, whereas other scales are specifically designed to be less face valid.



8.  Denial and Misreporting of Substance Abuse 161

PEI has a Defensiveness scale (11 items), not specific to substance use, and an Infrequency scale (seven items), intended to measure faked drug use by the endorsement of fictitious drugs and improbable statements related to drug use and procurement. Winters (1991) determined cutoff scores for faking bad and found that 5.7% of delinquent youth in drug treatment qualified as faking bad in contrast to 2.6% of high school controls. However, much larger percentages qualified as moderately defensive: 44.5 and 61.6% of drug users and controls, respectively. Subsequently, Winters et al. (1993) adopted a different standard to determine protocol invalidity. Participants who scored in the 90th percentile on either the Infrequency or Defensiveness scales of the normative sample for drug users were classified as invalid. Using this standard, 18.9% were predictably declared invalid for falling beyond 90th percentile. Several later studies, mostly on adolescents in drug treatment, indicated general response distortion rates of about 5–7% (Stinchfield & Winters, 2003; Winters et al., 2004; Winters, Latimer, Stinchfield, & Henly, 1999). No PEI simulation studies have been conducted to determine discriminant validity for response styles. Presently, marked elevations on the Infrequency scale should trigger a more complete evaluation of exaggeration or careless responding. Marked elevations on the Defensiveness scale should not be interpreted as denial or minimization of drug use, since these elevations represent general defensiveness and are more common among nonusers than users. SUBSTANCE ABUSE SUBTLE SCREENING INVENTORY

The adult versions of the Substance Abuse Subtle Screening Inventory (SASSI) include the original SASSI (Miller, 1985), SASSI-2 (Miller, 1994), SASSI-3 (Miller & Lazowski, 1999), and the recently released SASSI-4 (Lazowski & Geary, 2016). Adolescent versions include the SASSI-A (Miller, 1990) and the SASSI-A2 (Miller & Lazowski, 2001). Understandably, most studies focus on the earlier versions. However, the SASSI-3 is no longer available for purchase. The SASSI measures were intended to be effective with both acknowledged and unacknowledged substance abuse. They contain face-valid (FV) and subtle scales for detecting substance problems and validity indicators, and other scales to inform counselors working with respondents.

Feldstein and Miller (2007) have provided a comprehensive critique of the SASSI measures. Internal consistency ranged remarkably from poor to excellent (alphas ranging from 0.27 to 0.95), with the highest values found for the FV scales. Test–retest reliability also evidenced marked variability. Another critique (Miller, Woodson, Howell, & Shields, 2009) noted the poor alphas for subtle scales, and cautioned about the use of SASSI. The effectiveness of the SASSI versions at detecting substance abuse constitutes a matter of some debate. The SASSI-3 manual (Miller & Lazowski, 1999) has claimed outstanding sensitivity (94%), which has not been confirmed by other investigators. The same appears to be true about the SASSI-4, with a claimed sensitivity of 93% when used with a clinical sample (Lazowski & Geary, 2016). However, Feldstein and Miller (2007) reported sensitivity for the SASSI-3 ranging from poor (33%) in a college sample) to excellent (87%) in an offender sample, with overall average equaling 69.8%. The recently released SASSI-4 will need to be investigated by independent researchers. The SASSI may be the preferred choice over other screeners when examining substance-using offenders. Laux, Piazza, Salyer, and Roseman (2011) found the SASSI-3 subtle scales improved overall sensitivity by 10.5–36.4%, above and beyond just the FV scales alone. The SASSI has been studied with response styles. While not usually a clinical concern, participants can overreport substance abuse (Myerholtz & Rosenberg, 1998). With respect with faking good, these same investigators found an alarming 71% of offenders with substance dependence avoided detection on the SASSI-2. College students, attempting to simply fake good rather than specifically deny substance abuse, suppress most SASSI-3 scales and show an elevation the Defensiveness (DEF) scale (Burck, Laux, Harper, & Ritchie, 2010). Refinement of SASSI detection strategies appears to be warranted. Using inpatients, Wooley et al. (2012) studied the SASSI-3 under standard instructions, partial denial, and complete denial of drug use. Only one of the subtle scales, the Subtle Attributes scale (SAT), was modestly helpful in detecting partial but not complete denial. However, the DEF scale was significantly elevated for both denial groups. To differentiate between partial and total denial, these researchers constructed a SAT – DEF index (i.e., subtracting DEF from SAT), which produced moderately robust utility estimates.

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Lazowski and Geary (2016) studied the SASSI4 and reported a sensitivity rate of .79 among substance abusers instructed to “fake good” and further claimed that only four out of 120 successfully denied drug use. However, this claim could be questioned, because 23 of 115 participants in the fake good condition were excluded from the classifications because of inconsistent responding. While not yielding valid test data, inconsistent responding cannot be assumed to represent the denial of substance abuse. TIMELINE FOLLOWBACK

The Timeline Followback (TLFB; Fals-Stewart, O’Farrell, Freitas, McFarlin, & Rutigliano, 2000) uses a calendar method to evaluate daily patterns of alcohol and drug use. It has been used with adults and adolescents (Hjorthoj, Hjorthoj, & Nordengoft, 2012). Good reliability and validity data exist and a collateral form can be used (see Fals-Stewart et al., 2000). Validity has been established with biological testing, record review, and collateral reports (Fals-Stewart et al., 2000; O’Farrell & Langenbucher, 1988). Meta-analytic work demonstrates that the TLFB generally agrees with biological measures, although higher agreement may be found among persons without psychiatric comorbidity (Hjorthoj et al., 2012). It can be used to collect psychometrically sound substance data for up to 12 months (Robinson, Sobell, Sobell, & Leo, 2014). The face valid nature of its interview questions is vulnerable to faking or denial; however, the detailed nature of TLFB may make it hard for respondents to reliably fabricate a convincing pattern of substance use over multiple interviews covering a specified time period. In contrast, denial would be relatively straightforward. No relationship has been observed between TLFB substance use indices and a general measure of social desirability in adults seeking drug treatment (Fals-Stewart et al., 2000). However, as previously underscored, the lack of a relationship does not address the specific denial of substance abuse.

BIOCHEMICAL METHODS Three common methods of detecting substance use are reviewed in this chapter, including breathalyzer, urinalysis, and hair analysis. Eye-scanning, more aptly described as physiological than biochemical, is also noted. Other biochemical methods are not

addressed: blood and nail sampling, skin sensors to detect metabolic processes via sweat, and saliva sampling (see American Society of Addiction Medicine [ASAM], 2013). Biological detection of drug use usually involves screening via immunoassay (IA), which, if positive, is followed by confirmatory testing via gas chromatography/mass spectrometry, liquid chromatography/mass spectrometry, or tandem mass spectrometry (GC-MS, LC-MS, or LC-MS/MS). Alternatively, LC-MS/MS may be used in a single step to identify a far wider range of drugs than is possible with IA (ASAM, 2013). Box 8.1 provides a summary of the methods. IA uses antigen–antibody interactions to compare the specimen with a calibrated quantity of the drug being tested (SAMHSA, 2012). Drug concentrations in various specimens are highly variable and depend on many factors, such as amount of drug consumed, time since use, metabolism rate, body fat, and consumption of liquids (see Jaffee, Trucco, Levy, & Weiss, 2007). The general detection window varies by the sample types. As a general benchmark, the following ranges are presented: 1–36 hours for saliva, 1–3 days for urinalysis, 7–100+ days for hair-analysis, and 1–14 days for sweat via continuous monitoring (Dolan, Rouen, & Kimber, 2004). Detection times for blood and breath are very brief, usually on the order of several hours (ASAM, 2013; SAMHSA, 2012). BOX 8.1.  Laboratory Methods for Detecting Substance Use Screen • Immunoassay (IA). IA is a screener that uses antibodies to detect certain substances in urine. It does not measure amount of sub‑ stance in urine.

Definitive measures • Gas chromatography/mass spectrometry (GCMS). GC-MS provides identification of specific metabolites that are present in different types of drugs. • Liquid chromatography/mass spectrometry (LC-MS). LC-MS scans the breath of subjects to detect specific formations of aerosol pat‑ terns, which, if contaminated by drugs, will be different depending on type of drug ingested. • Tandem mass spectrometry (TMS). TMS allows for identification of specific compounds and metabolites that can differentiate, for example, different medications within a class of opiates.



8.  Denial and Misreporting of Substance Abuse 163

Breathalyzer Although under development for other substances, breath analysis is used primarily for rapid alcohol detection in the field (ASAM, 2013). The provided estimate of BAC is based on metabolism, which may be affected by gender, age, physical condition (especially the liver), and weight (SAMHSA, 2012). Because BAC is employed to define intoxication, clinicians must be careful to distinguish between legal intoxication and clinical intoxication, for which BACs are highly variable. Legal intoxication is generally set at BAC ≥ 80 mg/dL for adults (NIAAA, 2016b). For clinical intoxication, behavioral observations likely include one or more of the following: slurred speech, poor coordination, unsteady gait, nystagmus, attention and memory problems, and stupor/coma (American Psychiatric Association, 2013). At least some degree of tolerance is presumed if BAC ≥ 150 mg/ dL with no signs of intoxication. To put this in perspective, levels at 200 mg/dL render most nontolerant persons extremely intoxicated (American Psychiatric Association, 2013). Body temperature and breathing patterns can affect breath alcohol test results (SAMHSA, 2012). BAC obtained from breath may underestimate actual BAC (Kapur, 1994) by approximately 8.5% (Garriott, 2008), although a substantial minority of results (19–23%) may represent a marked overestimate of BAC (Simpson, 1987). Because of residual alcohol vapor in the mouth, artificially high readings are possible if readings are taken within 20 minutes of alcohol consumption. According to Watson and colleagues (2006), with properly sampled breath, BAC should be highly accurate. Few technical problems occur in breathalyzer administration, particularly with the widely adopted computer-based models. Although concerns have been raised regarding the effective maintenance of breathalyzers (Trichter, McKinney, & Pena, 1995), these concerns can easily be addressed through documentation and service records. False-positive readings may be created by exposure to alcohol-related products. For example, the gas additive, methyl tert-butyl ether (MBTE), may result in false positives on commercial breathalyzers (Buckley, Pleil, Bowyer, & Davis, 2001). This finding may be particularly germane for persons working with gasoline and other fuels oxygenated with MBTE (e.g., auto mechanics, gas station attendants). However, breathalyzer technology employing infrared and electrochemical detec-

tors may mitigate this concern. Alarmingly, use of common alcohol-based hand sanitizers and mouthwashes by persons operating a breathalyzer may cause false-positive readings even when sanitizer is used correctly (Ali et al., 2013).

Urinalysis Urinalysis, the most widely used method (Dolan et al., 2004; SAMHSA, 2012), can detect drugs including amphetamines, barbiturates, benzodiazepines, cocaine metabolites, methadone, phencyclidine (PCP), morphine, ethanol and cannabinoids (Morgan, 1984). It is generally effective, with a detection window of about 1–3 days, except for cannabis, which may be detected for 30 days or more (Musshoff & Madea, 2006; SAMHSA, 2012). The detection window depends on a variety of factors, including drug type, users’ characteristics (e.g., body mass), short or long-term drug use, and urine pH (Moeller, Lee, & Kissack, 2008; ASAM, 2013; SAMHSA, 2012). Of course, sample integrity should be maintained by using proper storage (i.e., refrigeration), if there is lag between collection and testing (ASAM, 2013; SAMHSA, 2012). Because ethanol is rapidly metabolized and eliminated from the body, methods that depend on sampling breath, blood, urine, and saliva are somewhat limited to detection of alcohol consumption, even within the past few hours (Jones, 1993). Therefore, urinalysis is not generally utilized to detect alcohol consumption (Moeller et al., 2008). However, some methods do allow for detection of even moderate amounts of alcohol (roughly 50 g, or a little less than about five bottles of beer) in urine within approximately 24 hours (Helander et al.,1999), and other methods may extend the window to 72 hours (ASAM, 2013). Accuracy of urinalysis may be affected by at least three factors: First, medication (e.g., nasal spray or diet agents) may lead to positive test results for amphetamine (SAMHSA, 2012). Second, foods (e.g., poppy seeds) may produce positive opiate results (Tenore, 2010). Third, certain teas (e.g., teas made from coca leaves) may yield positive cocaine results (SAMHSA, 2012). The accuracy of urinalysis may be substantially affected by methods to avoid detection. The simplest involves dilution by drinking copious amounts of water to produce false negatives. Adulterants can be ingested before urination (e.g., diuretic agents) or posturination (e.g., bleach) to decrease drug detection (Jaffee et al., 2007). Covert urine substitution (“clean” urine substituted

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for a “dirty” specimen) can be very effective if not observed (Jaffee et al., 2007). Certain steps can help to ensure an accurate appraisal of substance abuse results. For example, a detailed history of medications can assist in ferreting out unexpected positive results, as can sophisticated confirmatory tests (Moeller et al., 2008; SAMHSA, 2012). In addition, direct observation of urine collection, while intrusive, can minimize risk of urine substitution and addition of adulterants posturination (Jaffee et al., 2007). Whether ingested or added after voiding, adulterants can be detected via several methods. Specimen validity tests compare characteristics of the specimen with acceptable density and composition ranges for human urine, and test for adulterants (SAMHSA, 2012). During collection, specimen should be checked for color and temperature (SAMHSA, 2012), although color may be altered by food, medication, and other factors (ASAM, 2013).

Hair Analysis Hair follicles absorb drugs and metabolites from blood and sweat glands (Musshoff & Madea, 2006). As hair grows (at about 1 cm per month), it chronicles substance use, so that laboratories can estimate extent of use (Dolan et al., 2004). Hair analysis for illicit drugs began in 1979 with radioimmunoassay (RIA); methodologies expanded through the 1980s to include LC and MS; today, GC-MS, LC-MS and LC-MS/MS are “gold standards,” although LC-MS/MS has demonstrated superiority (Liu, Liu, & Lin, 2015). Steps in hair analysis generally involve collection, decontamination, grinding or cutting the sample, dissolving the hair, extraction, then analysis (Kintz, Villain, & Cirimele, 2006). Most laboratories analyze 3 months of hair growth, but it may be possible to detect substances over a year postuse (SAMHSA, 2012). Hair analysis appears to be the most reliable method for detecting frequent and heavy use of cocaine, opioids, amphetamine, PCP, and ecstasy, but it is understandably less suited for detection of occasional drug use or binge use (SAMHSA, 2012). For occasional use of these drugs, a low cutoff score can improve classification (Gryczynski, Schwartz, Mitchell, O’Grady, & Ondersma, 2014). Hair analysis may also be useful with marijuana (Han, Chung, & Song, 2012; Musshoff & Madea, 2006) but not alcohol use (ASAM, 2013; Cooper, Kronstrand, & Kintz, 2012). Hair analysis for the detection of denied substances has its limitations. Drugs can move down

the hair shaft via sweat, confounding use of hair to formulate chronology of drug use. Additionally, drugs or metabolites may be found in hair from simple environmental exposure (Moosmann, Roth, & Auwarter, 2015; SAMHSA, 2012). Moreover, elements in the environment (e.g., sunlight) may degrade hair, producing false negatives (Pragst & Balikova, 2006; Suwannachom, Thananchai, Junkuy, O’Biren, & Sribanditmongkol, 2015). However, the hair sample undergoes extensive chemical washing that can mitigate contamination or environmental effects. Additional challenges to accurate hair analysis include variations in hair structure, growth rate, melanin, hygiene, and cosmetic hair treatment (e.g., bleach; Dasgupta, 2008). For example, dark pigmented hair tends to bind greater amounts of drug than less pigmented hair (i.e., blonde). A strength of hair analysis involves its ability to detect positive results, even if the individual has abstained for weeks (SAMHSA, 2012). In fact, chronic methamphetamine use appears to be detectable for up to 5 months after abstinence (Suwannchom et al., 2015). In summary, hair analysis is well suited to detection of frequent and heavy illicit drug use. Nonetheless, it can sometimes be used to detect low or single doses, as in drug-facilitated crimes (Cooper et al., 2012; Xiang, Shen, & Drummer, 2015). Newer techniques are expanding the spectrum of drugs that can be tested in hair, even at single or low doses (Montesano, Johansen, & Nielsen, 2014). For measuring treatment effectiveness, it can also be used to indicate periods of abstinence (SAMHSA, 2012). As a laboratory measure, hair analysis cannot be expected to provide information about behavioral responses or psychological sequelae to substance abuse.

Eye Scanning Eye scanning is a relatively new method to assist in drug screens for denied substance use. Equipment can range from binocular size to the size of an automatic teller machine (Tahir & Bakri, 2006). Some require baseline readings when participants are drug-free, whereas others do not (Fazari, 2011; Richman & Noriega, 2002). The primary method used in a pupilometer drug screening is based on reactions to flashing lights: specifically, comparing the individual’s baseline eye reaction to controlled amounts of light to the current reaction. In addition, examination of retinal movement tracking may also be employed (Fazari, 2011). This type of screening can also



8.  Denial and Misreporting of Substance Abuse 165

identify the type of drug used as long as use occurred within the last 48 hours (Fazari, 2011; Tahir & Bakri, 2006). The advantages of pupilometer technology over other laboratory methods include relative ease and brevity. This technique requires only 30 seconds to complete and is a fully automated system. It is noninvasive, and it is presumably difficult to fake or alter results. For probationers, who require frequent substance use monitoring, this method is particularly useful. A further advantage involves cost savings as compared to other methods, which is particularly important for frequent monitoring (Fazari, 2011; Richman & Noriega, 2002). Challenges to accurate assessments via eye scanning may include situational factors (e.g., fatigue or diet), medication, and medical conditions (Fazari, 2011; Hoskins, 2005; Tahir & Bakri, 2006). More work is needed to better understand possible racial, age, and gender differences in detection rates, as this technique is still relatively new and underutilized (Fazari, 2011).

CLINICAL APPLICATIONS Many, if not most, diagnoses of substance use disorders are missed simply because clients are not asked about these disorders (see NIAAA, 2015). In a classic study of 705 patients with alcoholism, Sobell, Sobell, and Vanderspek (1979) found that clinicians were not particularly effective at detecting patients who were under the influence of alcohol. Of those patients denying intoxication, clinicians correctly identified only between 50 and 67%, as subsequently verified by breath analysis. Their false positives ranged across samples from 0 to 17%. Clearly, clinical observation alone is insufficient for the detection of substance abuse. Screens and many specialized substance use measures trustingly assume forthrightness and complete self-disclosure about not only about the type and frequency of substance abuse but also its short- and long-term sequelae. Systematic screening is likely to be helpful in discovering undiagnosed cases of substance abuse. Toward this objective, systematic use of screens, such as AUDIT, DAST, or GAIN-SS, is recommended. Minimized substance use may be evidenced in marked discrepancies among a variety of sources. These include (1) acknowledgment of use with peers but denial with authority figures; (2) minimized history of use in contradiction to treatment/ arrest records; and (3) denial of use in contradic-

tion to performance on face-valid measures of substance use. Because inconsistencies in reported drug use may have many determinants (e.g., confusion secondary to drug use, unawareness of consumed drugs), discrepancies alone should not be considered evidence of dissimulation. Similarly, exaggerated substance use may be evidenced in marked discrepancies across several sources of information, including (1) denial of substance use with peers but exaggeration with authorities; (2) gross exaggeration of past substance use in contradiction of past treatment/arrest records; (3) endorsement of fictitious drugs; and (4) ascribing very atypical behavioral effects to known drug use (e.g., prolonged hallucinatory experiences from the use of marijuana). Careful inquiry into atypical effects may be helpful in distinguishing purely self-serving explanations from more believable descriptions. For instance, use of hallucinogens is unlikely to produce command hallucinations focused solely on execution of a well-planned bank robbery. In particular, caution must be applied in drawing conclusions about the endorsement of fictitious drugs and deliberate exaggeration. Drug users may be motivated to appear knowledgeable about unfamiliar drugs. Alternatively, they may become confused by the myriad street and scientific terms. To identify deliberate exaggerations, clinicians can easily test the limits of credibility via a rare symptom approach. For example, queries could include very uncommon effects (e.g., accelerated hair growth) to different fictitious drugs. Clinical decision models assist in determining whether persons reporting substance use are engaged in dissimulation. For denied or minimized drug use, several sources provide useful information: • Independent witnesses who disconfirm the respondent’s denial or minimization of substance use for a specific event. • Positive alcohol breathalyzer results covering a very limited time period (2–12 hours), which depend on level of intoxication and metabolism rates. • Positive urinalysis results covering a circumscribed period of time (36–72 hours), although marijuana may be detected for more than 30 days. • Positive hair analysis covering an extended period of time (typically from 7 days to 3+ months). • Positive eye scanning for drug use (48 hours). • Observed data (biochemical concentrations or behavioral observations) that disconfirm the

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respondent’s minimized substance use for a specific event or period of time. These are data sources within the clinical decision model. More specifically, many defensiveness scales are not specific to substance use and lack discriminant validity with respect to denied drug use (e.g., PEI DEF scale). Similarly, SASSI scales appear to have limited discriminant validity, although this needs to be further tested with the SASSI-4. Future work might establish utility estimates for promising instruments using a range of cutoff scores for a given setting. For exaggerated drug use, several sources provide useful information in clinical decision making, including the following: • Independent witnesses who disconfirm respondent-reported substance use for a specific event. • Negative alcohol breathalyzer results, covering a very limited time period (2–12 hours), that depend on level of intoxication and metabolism rates. • Negative urinalysis results covering a circumscribed period of time (36–72 hours), although marijuana may be detected for more than 30 days. • Negative hair analysis covering an extended period of time (from 7 days to 3+ months). • Observed data (biochemical concentrations or behavioral observations) that disconfirm the respondent’s exaggerated substance use for a specific event or period of time.

SUMMARY This chapter represents the breadth and diversity of methods that have been applied to the assessment of substance abuse. Sadly, very few advances have been observed in the last decade for psychometric assessment of illicit drugs and nonprescribed use of medications. Most measures appear to be based on completely unwarranted and professionally naive assumption that examinees will be entirely forthcoming about their substance abuse. According to Delaney-Black et al. (2010), as a stark example, parents in a high-risk sample had 650% more use of cocaine than they reported, which was virtually eclipsed by their teenagers (5,200% more use). However, more sophisticated detection strategies hold promise. As illustrated by Wooley et al. (2012) unlikely patterns between substance abuse scales may diminish face validity and improve the detection of denied substance abuse.

Laboratory-based methods of assessing substance use continue to develop and have become more sophisticated. For day-to-day use, their methods are often challenging to administer, vulnerable to countermeasures, and very expensive. The use of eye-scanning technology for drug use—despite being available for more than a decade—has not garnered the systematic research that it clearly deserves. This technology stands out because of its simplicity, low cost, and resistance to countermeasures.

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CHAPTER 9

Psychopathy and Deception Nathan D. Gillard, PhD

Deception has remained a key characteristic of prototypical psychopathy throughout history. Cleckley’s (1941, 1976) influential description of psychopathy included “untruthfulness and insincerity,” while Hare (1985) has included “pathological lying” on the original Psychopathy Checklist (PCL) and Psychopathy Checklist— Revised (PCL-R; Hare, 1991, 2003). Descriptions of psychopathy also include various core features that require deceptive practices, including superficial charm, manipulativeness, and shallowness. Psychopaths are also described as being naturally exploitative of others, with particularly strong skills at conning. Modern conceptualizations of psychopathy often include antisocial and criminal behaviors, which often require deception during the crime itself and the subsequent avoidance of being apprehended. Beyond the fabrications and general deceit of ordinary criminals, psychopaths, almost by definition, use conscious distortions and manipulations across multiple domains of their lives, leaving no relationship unaffected (Cleckley, 1976). This chapter focuses on both the theoretical and actual use of deception in psychopathy. Despite the clear conceptual connection noted by influential authors such as Kraepelin (1915), Schneider (1923), Cleckley (1941, 1976), and Hare (1991, 2003), very few studies have examined the frequency with which psychopaths use deception or their success at doing so. Perhaps more surprising,

the existing studies indicate that psychopaths are not actually any more successful at being deceptive than nonpsychopaths, even though they may do so more frequently (Clark, 1997; Lykken, 1978; Patrick & Iacono, 1989; Raskin & Hare, 1978). This chapter considers psychopathy in relation to malingering, positive impression management, and general deception. The effect of psychopathic traits on the validity of interview-based and selfreport clinical and risk assessment measures is also addressed, with some initial findings on the effectiveness of existing validity scales. Finally, areas for future research are discussed.

EARLY CONCEPTUALIZATIONS OF PSYCHOPATHY Starting in the late 19th and early 20th century, the term psychopathy emerged as representing a “group of persons showing abnormality expressed mainly in the character and intensity of the emotional reactions” (Partridge, 1930, p. 63). This overly inclusive clinical description was commonplace for the time and Partridge pointed out that at least 13 separate terms emerged to describe overlapping conceptualizations of psychopathy. Perhaps the most influential early definitions were those of Kraepelin (1915) and Schneider (1923). Kraepelin (1915) defined psychopathy mostly in behavioral terms, similar to the modern conceptu-

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alization of antisocial personality disorder (American Psychiatric Association, 2013). Accordingly, those with psychopathic traits continuously violated societal rules and the rights of others. They used aggression and deceit frequently and were often destructive. In contrast, Schneider (1923) attributed psychopathy to emotional deficits and characterological disorder; he noted that such individuals either suffer personally or make others suffer due to their abnormal personalities. He believed psychopathic individuals displayed blunted affect and an inability to experience inhibitory emotions.1 Writing near the end of his career, he bluntly stated that they “lack capacity for shame, decency, remorse, and conscience. They are ungracious, cold, surly, and brutal in crime” (Schneider, 1950/1958, p. 126). These contrasting definitions present a conceptual debate that, to some extent, has continued until modern day.

DECEPTION IN THE DEFINITION OF PSYCHOPATHY For many years, the conclusion that “everybody lies” was widely accepted, primarily due to studies that indicated noncriminal participants told an average of two lies per day (DePaulo, Kashy, Kirkendol, Wyer, & Epstein, 1996; see also Vrij, 2000). More recently, challenges to this conclusion arose due to wide individual differences in the frequency of lying. Serota, Levine, and Boster (2010) found that the often replicated mean of “two lies per day” was skewed by a small number of frequent liars. Social psychology studies have examined situations that increase or decrease the willingness to deceive, whereas research in clinical psychology has examined the characteristics of frequent liars. While a discussion of all situational and characterological factors is beyond the scope of this chapter, studies (Kashy & DePaulo, 1996; Halevy, Shalvi, & Verschuere, 2014) have documented psychopathic traits as being particularly salient to deception. Deception represents a common occurrence in many different settings, especially when the incentives are high (Frank & Ekman, 2004). Nowhere is this observation more true than in forensic settings. Likewise, psychopathy has been assumed to be a particularly influential factor in the frequency and success of deception. Despite the large independent literatures on psychopathy and deception, few studies have investigated the association between these two concepts. The following sections

review the theoretical and empirical evidence that does exist. As previously noted, deception plays a pivotal role in defining the core features of psychopathy. Not only have leading theorists and researchers (Cleckley, 1976; Hare, 2003) listed deception directly as a defining feature of psychopathy, but it can also be considered a core component of many other psychopathy features. For example, Cleckley (1976, p. 338) listed “untruthfulness and insincerity” as one of 16 core components of psychopathy. Also included in this list are descriptions of superficial charm, unreliability, and “inadequately motivated antisocial behavior” (p. 338). Each of these characteristics requires purposeful deception. Core features of a Cleckley-defined psychopath stem from broad emotional deficits. Like Schneider (1958), Cleckley (1976) understood psychopathy to consist of primarily innate deficits in emotions, while criminal behavior was a secondary product of these deficits. Cleckley described psychopaths as having a remarkable disregard for truth when recollecting the past, speaking of the current situation, and making promises about the future. They are “at ease and unpretentious” (p. 342) when making promises or lying and can be especially convincing in their portrayal. According to Cleckley, the usual signs that others notice in a clever liar are not evident, which means that the psychopath’s lies are often undetected. Cleckley believed the very concept of honesty has only intellectual meaning to the psychopath, and is devoid of the positive emotion that most nonpsychopaths have attached to honesty and trustworthiness. To Cleckley, a male psychopath would “keep his word” only if doing so was to his benefit. In his description of the psychopath’s antisocial behavior, Cleckley (1976) further expounded on circumstances in which deceit may occur, noting a willingness to lie for “astonishingly small stakes.” Over the last three decades, Robert Hare has pioneered the modern conceptualization of psychopathy. Hare (1991, 2003) covered some of the same basic concepts as Cleckley, though he used different terms and provided somewhat dissimilar descriptions in his PCL-R scoring. First, “pathological lying” is the PCL-R item most directly related to deception. It describes a likelihood of characteristic lying and deceit when interacting with others. The psychopath’s readiness and ease of lying are described as being “remarkable,” and if caught, he or she is not anxious but simply changes the story as needed (Hare, 2003, p. 37).

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Unlike Cleckley, Hare believed psychopaths might freely discuss and even brag about their lies. Hare also included a description of “conning/manipulative” behavior, which shares elements with the previously discussed PCL-R item and Cleckley’s items on untruthfulness and superficial charm. In particular, this item measures deceit used to cheat or manipulate others, whether in extravagant or simple ways. Like Cleckley, Hare describes a willingness to lie in many different circumstances (see Table 9.1). Aside from the interpersonal and affective uses of deceit, Hare (2003) believed psychopathy included antisocial behavioral traits to a greater extent than did Cleckley (1976). These behaviors do not always directly involve lies, but some deceit is necessary to successfully carry out most crimes. Although subsequent models of psychopathy now exist (Harpur, Hakstian, & Hare, 1988; Cooke & Michie, 2001; Hare, 2003), each retains deception as a key element. Forensic experts, community members, and offenders alike recognize the importance of deception to psychopathy, though they differ in their assessment of the centrality of this characteristic. Rogers and colleagues (Rogers, Dion, & Lynett, 1992; Rogers, Duncan, Lynett, & Sewell, 1994; Rogers, Salekin, Sewell, & Cruise, 2000) conducted three studies on the prototypical features of psychopathy and DSM-III antisocial personality disorder (ASPD). Groups of community volunteers, forensic experts, and adult offenders all recognized the importance of deceit in these disorders, but important differences also existed. Community volunteers rated “no regard for the truth” and “pathological lying” as highly prototypical descriptors of adult ASPD, with only “lack of remorse” and “unlawful behavior” ranked higher (see Rogers et al., 1992, Table 1, p. 683). In comparison, forensic experts rated “no regard for the truth” even higher than did laypersons (Rogers et al., 1994, Table 1, p. 478). In sharp contrast, adult offenders ranked untruthfulness as less important and aggression as more important than did experts and members of the community. In both childhood and adulthood, adult offenders placed relatively little emphasis on interpersonal factors as compared to experts and community members. As one possible reason for this difference, inmates may lack insight into the importance of deceit in relation to antisocial behavior more generally. Then again, offenders may see deception as being so common that it does not differentiate psychopaths from nonpsychopaths.

FREQUENCY OF MALINGERING BY PSYCHOPATHS Similar to general deception, there is a relative dearth of studies assessing psychopathy and malingering. Existing studies indicate little or no association between the two (e.g., Edens, Buffington, & Tomicic, 2000; Poythress, Edens, & Lilienfeld, 1998; Poythress, Edens, & Watkins, 2001). For instance, Gacono, Meloy, Sheppard, Speth, and Roske (1995) attempted to examine the presence of psychopathic traits in a small sample of insanity acquittals. They found those with higher levels of psychopathic traits were more likely to admit to previous malingering. They suggested this relationship may indicate that psychopaths have a higher willingness to use deception or a greater proficiency at it. However, this study was flawed in several ways, including (1) the use of self-reported malingering and retrospective data, and (2) providing the incentive to admit feigning to facilitate being released from the hospital (Rogers & Cruise, 2000). Additionally, the study utilized only patients with highly violent and antisocial histories, increasing the likelihood of high PCL-R scores. Kucharski, Duncan, Egan, and Falkenbach (2006) found mixed evidence that criminal defendants with psychopathic traits were able to feign while avoiding detection. While scores on the Minnesota Multiphasic Personality Inventory–2 (MMPI-2) validity scales were generally high to extremely high (means ranging from 83.73 to 95.41 for the F family of scales), scores on PAI indices exhibited less extreme elevations. Furthermore, the high psychopathy group did not differ significantly from the low and moderate psychopathy groups on two of three PAI feigning scales (Rogers Discriminant Function [RDF] and Malingering Index [MAL]). In a known-groups comparison with the Structured Interview of Reported Symptoms (SIRS; Rogers, Bagby, & Dickens, 1992) as the external criterion, it classified roughly twothirds of those in the high psychopath group as feigning, while close to one-fifth of the moderate group (21.6%) was detected. Rogers (1990) has argued that the idea that malingering occurs more frequently in psychopathic and antisocial individuals is a methodological artifact. Most malingering studies are conducted in criminal forensic settings. Similarly, most psychopathy studies are also conducted in correctional or forensic settings. Therefore, both concepts occur at increased rates in the same setting (Rog-



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TABLE 9.1.  Comparison of Hare Facets and Corresponding Cleckley Traits

Hare (1991)

Cleckley (1976) Facet 1: Interpersonal

  1. Glibness/superficial charm

  1. Superficial charm and good “intelligence”

  2. Grandiose self-worth

  9. Pathological egocentricity

  4. Pathological lying **

  5. Untruthfulness and insincerity **

  5. Conning/manipulative ** Facet 2: Affective   6. Lack of remorse or guilt

  6. Lack of remorse or shame

  7. Shallow affect

10. General poverty in major affective reactions

  8. Callous/lack of empathy

  9. Incapacity for love

16. Failure to accept responsibility for own actions *

  4. Unreliability * Facet 3: Lifestyle

  3. Need for stimulation/proneness to boredom

No Cleckley equivalent

  9. Parasitic lifestyle

No Cleckley equivalent

13. Lack of realistic, long-term goals

16. Failure to follow any life plan

14. Impulsivity (no mention of alcohol)

13. Fantastic and uninviting behavior with drink and sometimes without

15. Irresponsibility *

  4. Unreliability * Facet 4: Antisocial

10. Poor behavioral controls

13. Fantastic and uninviting behavior with drink and sometimes without

12. Early behavioral problems

No Cleckley equivalent

18. Juvenile delinquency *

No Cleckley equivalent

19. Revocation of conditional release

No Cleckley equivalent

20. Criminal versatility

No Cleckley equivalent Unloaded items

11. Promiscuous sexual behavior

15. Sex life impersonal, trivial, and poorly integrated *

17. Many short-term marital relationships

No Cleckley equivalent No Hare equivalent   2. Absence of delusions and other signs of irrational thinking   3. Absence of “nervousness” or psychoneurotic manifestations   7. Inadequately motivated antisocial behavior   8. Poor judgment and failure to learn by experience 11. Specific loss of insight 14. Suicide rarely carried out

Note. * denotes items associated with deception; ** denotes items directly tapping deception. From Gillard (2013, p. 22). Reprinted with permission.

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ers, 1990). While few studies exist to resolve this question directly, Rogers found similar portions of ASPD-diagnosed individuals in groups of malingerers (20.8%) and genuine patients (17.7%). Rogers and Robinson (2016) recognized that the problematic relationship between psychopathy and malingering may be troubling to practicing clinicians who must decide whether the former should be used as a determining factor in decisions about the latter. They suggest clinicians weigh the substantial pitfalls of utilizing psychopathy in this regard against the potential benefit. Therefore, the presence of psychopathy might be used as a screen to indicate that increased scrutiny for malingering is needed but it should never be used as evidence of increased likelihood (Rogers & Robinson, 2016). Looked at another way, ASPD and psychopathy may be seen as common factors rather than distinguishing factors in those who malinger.

FREQUENCY OF DECEPTION BY PSYCHOPATHS Conceptual descriptions indicate psychopaths lie frequently, but an interesting question involves psychopaths’ actual (i.e., empirically tested) likelihood of lying compared to nonpsychopaths placed in similar circumstances. Many of the early “studies” of deception in psychopaths were based on clinical case studies and anecdotal reports (e.g., Cleckley, 1941; Hare, 1991). The limited number of empirical studies to date has yielded inconsistent findings on the likelihood that psychopaths lie in a variety of contexts. For self-reports of deception, those with psychopathic traits tend to report higher rates of lying, with at least three studies lending support. Kashy and DePaulo (1996) used a daily diary paradigm (i.e., a daily log of social interactions and the lies told during them) and found such traits were correlated with lying in a normal population.2 Similarly, Halevy et al. (2014) found that in a community sample, psychopathic traits as measured by the Youth Psychopathic Traits Inventory (YPI) were positively correlated with the number of lies told during the 24-hour period preceding the study (r = .31). Seto, Khattar, Lalumiere, and Quinsey (1997) found a moderate connection between PCL-R scores and sexual (r = .36) and nonsexual (.49) deceptive tactics. However, the significance of sexual deceptive practices was erased when accounting for general deceptiveness, suggesting the occurrence of deception is similar across domains.

Observers raise salient concerns about the generalizability of these self-report studies. Given the grandiosity often seen in psychopaths, is it possible they would brag about deception rather than reporting their actual deceptive practices? More generally, should we assume that psychopaths are being honest about their dishonesty, either by raising or lowering its reported frequency? Seto and colleagues (1997) found a moderately large negative correlation between impression management and psychopathy, which suggests these offenders were not concerned with presenting themselves in a positive light, though this does not necessarily equate to honesty. However, their finding may also indicate that those high on impression management suppressed their psychopathy scores. Seeking to fill the void in the literature, Halevy et al. (2014) conducted a second study, offering participants from their earlier self-report study an opportunity to cheat for financial gain. Specifically, they instructed participants to roll dice a number of times, with earnings determined by the number rolled. The number of lies reported in the initial study was correlated with the amount they earned in the dice rolling task (r = .39) when there was zero chance of being detected. The finding was interpreted as evidence that self-reported lies were a relatively accurate measure of the frequency and willingness to lie. As one of the only real-world examinations of lying in psychopaths, Porter, Birt, and Boer (2001) reviewed the correctional records of psychopathic and nonpsychopathic murderers. They found those scoring higher on psychopathic traits were twice as likely to change the details of their version of the crime during incarceration. Cooper and Yuille (2007) hypothesized the primary use of laboratory studies is responsible for the disconnection between empirical findings and the classic description of psychopaths as deceptive. In such studies, participants with psychopathy are research volunteers with poor intrinsic motivation to lie. As Feeley and deTurck (1998) pointed out, laboratory studies use “sanctioned lies” (i.e., experimental instructions asking participants to lie). Sanctioned lies are quite different, cognitively and affectively, from unsanctioned lies. Psychopaths may have no need to lie in laboratory studies, thereby making it appear they lie less frequently than is found in field studies or anecdotal reports. As discussed earlier, clinical case studies that led to the core characteristics of psychopathy indicate frequent use of lying (Cleckley, 1941, 1976; Hare, 1991, 2003). On this point, Cooper and



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Yuille (2007) suggest that although anecdotal evidence is not ideal, it may offer superior evidence to laboratory studies regarding psychopaths’ lying. Regardless of the frequency of deception, classic psychopathic descriptions also include successful deception (i.e., lying without detection), which will now be discussed.

BEYOND LIKELIHOOD: SKILLED DECEPTION AND PSYCHOPATHIC TRAITS Descriptions of psychopaths suggest they are not only more likely to engage in deceptive and manipulative practices but also more skilled at doing so (Hare, 2003). The first part of this assumption has garnered some support based on self-report and laboratory tasks. Regarding psychopaths’ actual success, or skill, at deception, the literature has again been heavy on theory and anecdotal evidence and low on empirical findings. Until recently, the few available studies have focused on malingering. However, new and exciting general deception studies have recently emerged. Despite inconsistent results, a general trend is observed toward poor performance at deception by psychopathic individuals.

Avoiding the Detection of Feigning Researchers have found psychopaths appear to be either no more or only marginally more successful at malingering than nonpsychopaths. For example, Kropp (1994) found psychopaths were no better than other inmates at simulating mental illness in an analogue design study using the SIRS (Rogers et al., 1992). While psychopaths were generally no better at malingering, Kropp (1994) noted a disproportionately high number of psychopaths in the small group of successful feigners that eluded detection on the SIRS. At least two additional studies have supported the early findings of Kropp (1994). First, Edens et al. (2000) found no connection between psychopathic traits and undergraduates’ ability to feign psychosis and psychopathy on the MMPI-2 and Psychopathic Personality Inventory (PPI; Lilienfeld & Andrews, 1996). Specifically, psychopathic traits were not related to the level of feigned symptoms on the two measures or the ability to avoid detection on the Deviant Responding (DR) scale of the PPI, as the DR scale was generally effective at detecting feigning across groups (area under the

curve [AUC] = .98). Using male prison inmates, Poythress, Edens, and Watkins (2001) similarly reported no significant correlations between PPI scores and three feigning measures: the Structured Inventory of Malingered Symptomatology (SIMS; Widows & Smith, 2005), the SIRS (Rogers et al., 1992), and the three commonly used feigning indicators (Positive Impression Management [PIM], MAL, and RDF) on the PAI (Morey, 1991). Methodologically, none of these studies used known-groups design (see Rogers, Chapter 1, this volume) with independently classified malingerers. Therefore, it is possible that psychopaths’ apparent lack of skill for feigning reflects their limited motivation when engaged in laboratory studies. Alternatively, malingering is quite different than other forms of deception and requires skills that may neutralize any advantage psychopaths otherwise have at lying. For instance, a basic understanding of detection strategies and how to avoid elevating them has been found to be helpful to successful malingerers (Rogers, Dolmetsch, & Cavanaugh, 1983). Psychopaths’ use of charm and grandiosity may be useful when deceiving others in interpersonal situations but not be beneficial when dealing with such structured methods of detection. As an example, the PPI DR scale was significantly more effective than the corresponding PIM scale (Unlikely Virtues scale; see Edens Buffington, Tomicic, & Riley, 2001).

Avoiding Detection by Polygraph Two independent research groups have found psychopaths’ deception can be detected by polygraph tests just as effectively as they detect nonpsychopaths. For the first group, Raskin and Hare (1978) conducted a mock crime study with psychopathic and nonpsychopathic offenders. They instructed all participants, guilty and innocent, to deny the crime. Both denial-of-guilt and genuine groups were detected at similarly high rates (> 90% correct classifications). Patrick and Iacono’s (1989) results agreed for the denial-of-guilt group but not the genuine group. Specifically, psychopaths and nonpsychopaths were detected at exactly the same percentage (i.e., 91.7%) but an unacceptably large percentage of genuine responders were misclassified as deniers (41.7%). Their study differed from that of Raskin and Hare (1978) in that a threat (i.e., a list of poor performers would be posted for all to see) was used as the motivation instead of a reward. Both studies generally agree with the

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aforementioned research from malingering studies indicating psychopaths are no more successful than nonpsychopaths when structured or standardized detection methods are used.

Avoiding Detection by Human Judges Classic descriptions of psychopathy describe deception occurring in a wide range of situations, even when there are no obvious advantages. Cleckley (1976), in particular, thought lies by psychopaths were told without the typical signs of lying, making psychpaths more difficult to identify. However, research suggests psychopaths’ skills are not necessarily so clear-cut. Two studies (Cogburn, 1993; Billings, 2004) have used naive judges (i.e., untrained college students) to rate the believability of offender statements. In one of the earliest and more cited studies, Cogburn (1993) found psychopaths, as classified on the PCL-R, exhibited no improvements in “successful” deception when compared to nonpsychopaths. Furthermore, naive judges rated psychopaths as less credible regardless of the actual veracity of their statements. This finding contradicts Cleckley’s (1976) assertion regarding psychopaths’ particular adeptness at appearing honest and avoiding typical signs exhibited by liars. In contrast to Cogburn (1993), Billings (2004) found that individuals with higher psychopathy scores on both the Psychopathy Checklist: Screening Version (PCL:SV; Hart, Cox, & Hare, 1995) and PPI were better able to deceive naive judges when making false statements. Klaver, Lee, Spidel, and Hart (2009) bolstered Cogburn’s (1993) earlier conclusions by studying raters’ observations of psychopaths’ storytelling behavior. Specifically, the undergraduate participants viewed video recordings of psychopathic and nonpsychopathic offenders telling true and false stories. They then rated the credibility of the statements and the nonverbal signs of deception, such as appearing to think hard and nervousness. Psychopaths were not successful at lying. In fact, even worse than in Cogburn (1993), psychopaths’ deceptions were detected more often than those of nonpsychopaths. Moreover, like Cogburn, psychopaths were generally viewed as less credible. In direct contrast, the same researchers (Lee, Klaver, & Hart, 2008) found psychopaths were more successful at avoiding detection when only verbal indicators were rated. Specifically, psychopaths used more appropriate details when lying, suggesting a focused attempt to appear credible. Still, the psy-

chopathic group was far more likely to be judged as noncredible when actually telling the truth. The body of research suggests psychopaths are somewhat proficient at lying in a verbal manner but are unable to successfully control their nonverbal behaviors. This disparity shows their apparent adeptness at using verbal skills, while many behavioral indicators are neglected by research. Given the scripted, analogue design of these studies, an alternative hypothesis is that real-world lying would be easier to accomplish for psychopaths. Additionally, the aforementioned studies suggest psychopaths are often less able to appear credible when telling the truth compared to nonpsychopaths. Although empirically untested, limited emotional expression may account for this observation under the honest condition, while this same characteristic may be beneficial when telling lies. Thus, clinicians must use caution when evaluating the verbal clues of psychopaths that may be useful for detecting deception in the general public. The interpersonal facet of psychopathy in particular may facilitate deception. Lee et al. (2008) found Factor 1 was associated with credibility when discussing false information, while Factor 2 was not. Thus, an arrogant and grandiose behavioral presentation may distract the listener from lies that, contentwise, are not particularly sophisticated. Theoretically, for undetected lying, it would seem advantageous to appear confident and avoid displaying affective arousal related to the deception. On this matter, it has been suggested that psychopaths have very little emotional investment in the words they use. For instance, Williamson, Harpur, and Hare (1991) presented affective and neutral words and nonwords to offenders. Nonpsychopaths were able to distinguish emotional words from nonwords very efficiently; the authors theorized that the affective content facilitated their cognitive processes. In contrast, psychopaths failed to show this same pattern. The lack of emotional investment by psychopaths may free them to deceive without regard for the affective significance of the topic. As Pegay (1943, as cited by Hare, 1991) stated, psychopaths can talk about the deepest of topics and “[pull] the words from their overcoat pocket” (p. 124). Both behavioral and verbal changes occur when psychopaths are placed in experimental situations requiring deception. Regarding verbal behaviors exhibited while lying, Louth, Williamson, Alpert, Pouget, and Hare (1998) found differences in the volume of psychopaths’ voices when compared to



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nonpsychopaths. When discussing deceptive neutral, negative, and positive topics, psychopathic inmates spoke more quietly than other inmates. While not examining the volume of speech, Klaver, Lee, and Hart (2007) found differences in nonverbal behavior while speaking in psychopathic and nonpsychopathic offenders. Besides speaking faster, psychopathic offenders engaged in more blinking and head movements when lying. For male adolescents with behavior problems, Rimé, Bouvy, Leborgne, and Rouillon (1978) concluded that psychopaths appeared more invested during the interview than nonpsychopaths (i.e., they leaned forward more and looked at the interviewer longer). Unfortunately, it is not possible to conclude whether psychopaths intentionally chose these verbal and nonverbal changes.

DECEPTION ON RISK ASSESSMENT AND PSYCHOPATHY MEASURES The assessment of psychopathy frequently occurs as part of forensic evaluations, especially those involving future violent risk. Psychopathy has consistently been found to be one of the strongest single predictors of violent and general risk (Steadman, 2000), and has been included as part of multiple risk assessment measures. They include structured professional judgment (SPJ) guides and actuarial measures. The Historic Clinical Risk–20 (HCR20; Webster, Douglas, Eaves, & Hart, 1997a) and Violence Risk Assessment Guide (VRAG; Harris, Rice, & Quinsey, 1993) even include PCL-R and/ or PCL:SV scores as part of their appraisal. Three large meta-analyses produced encouraging results for actuarial measures when compared to the stand-alone PCL-R. Gendreau, Goggin, and Smith (2002), Yang, Wong, and Coid (2010), and Singh, Grann, and Fazel (2011) found roughly equivalent results for the most frequently used general (i.e., nonsexual) violent risk measures. Singh et al. found slightly higher predictive validity for the Structured Assessment of Violence Risk in Youth (SAVRY; Borum, Bartel, & Forth, 2006) and lower predictive validity for the Level of Service Inventory (LSI; Andrews & Bonta, 1995) and the PCL-R. These meta-analyses continue the long line of studies showing little to no difference among well-validated, published risk assessment measures. Examiners should be careful to assess how well their population matches validation samples, as the latter meta-analysis found higher pre-

dictive validity when the study sample was similar to the initial validation sample.

Intentional Minimization on Risk Assessment Measures There appears to be an implicit assumption held by many clinicians that risk assessment procedures are not greatly affected by deception. However, Gillard and Rogers (2015; summarized later in this chapter), as well as general research on the ease of impression management, strongly question this tacit assumption. Most structured clinical judgments rely on both interviews and records, which have been assumed to protect against falsification (Webster, Douglas, Eaves, & Hart, 1997b). While collateral sources are always encouraged, these records are often unreliable due to missing information and a lack of truly objective information (i.e., records based on previous interviews are similarly susceptible to manipulation). As an illustration, the number and type of previous criminal convictions can now be fairly reliably gleaned from searchable databases due to advances in technology. However, records regarding mental health history, substance abuse, interpersonal relationships, and treatment noncompliance (all included on many risk assessment measures) are often painfully incomplete due to a lack of centralized records and the fact that such records only exist if treatment is sought. When records do exist, practitioners should use caution in assessing their reliability; many of these records are simply based on earlier interviews with the same offender. Reports completed for the court, such as presentence investigations, often contain information primarily based on collateral interviews with family members regarding the offender’s background, education, and personality. These records are often accepted as fact, but they should be seen as susceptible to the same biases as any direct interview. These cautions should not dissuade the use of records, but they should encourage practitioners to ensure the accuracy of clinical data found in past records. Three studies have been conducted using offenders to examine intentional minimization on interview-based and self-report measures, with mixed results. The Self-Appraisal Questionnaire (SAQ; Loza, 2005), a self-report risk measure, was utilized in all three studies. Loza, Loza-Fanous, and Heseltine (2007) compared responses under two conditions: (1) presumably genuine (i.e., confiden-

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tiality-guaranteed instructions) and (2) presumably intentional minimization (i.e., psychological evaluations being used to determine early release). Contrary to expectations, the scores in the real evaluation group were slightly higher on scales measuring substance use and past criminal conduct. However, the results are unreliable due to the use of differential prevalence design, a weak design that assumes motivation may be presumed by the referral issue (see Rogers, Chapter 1, this volume). A second study by the same authors (Loza et al., 2007) used a “simulated” differential prevalence design, with participants first receiving “research only” instructions, then a second condition (i.e., simulated “release evaluation”). High correlations between conditions might indicate that the SAQ is resistant to minimization. However, their results are difficult to interpret due to the previously noted differential prevalence design. Furthermore, the simulation component could not be verified given the absence of a manipulation check. Thus, neither study directly addresses the vulnerability of the SAQ to intentional minimization. Recently, Gillard and Rogers (2015) examined the effects of PIM on both interview-based (HCR-20) and self-report (SAQ) risk assessment measures, as well as the Psychological Inventory of Criminal Thinking Styles (PICTS; Walters, 2001), a measure of offenders’ cognitive styles. Using a jail sample, all measures were administered in a repeated-measures simulation design. Offenders were able to substantially lower both HCR-20 and SAQ scores utilizing PIM. Greater suppression was made to the Historical subscale than to Clinical and Risk items (d = 1.32 vs. 1.06 and 0.60, respectively). Likely due to the ease of changing historical information, as well as their transparency, offenders may easily deny items such as previous violence. Scores on the SAQ were modified in a proportionally similar manner, with effect sizes between 0.96 and 1.34 for all subscales except Associates, a measure of interactions with other antisocial individuals. The presence of psychopathic traits, especially Factor 1 characteristics, was associated with greater change during PIM on the interview-based HCR-20 (Gillard & Rogers, 2015). Interpersonal and affective traits alone were found to predict approximately one-third of the variability in HCR-20 Total scores (R2 = .34) even after Factor 2 traits were accounted for using hierarchical regression. These findings did not hold for the SAQ. Similarly, the PICTS, also a self-report measure, did not exhibit strong changes during the impression

management stage, regardless of the presence of psychopathic traits. As a possible explanation, affective and interpersonal traits were more strongly related to deception during interview than selfreport due to the interaction with the examiner.

Impression Management on Measures of Psychopathy The PCL and PCL-R have been considered the “gold standard” of psychopathy assessment for over two decades (Patrick, 2006). Based on both conceptual grounds and unresolved concerns about deception, researchers such as Ray et al. (2013) have recommended self-report measures not be used in clinical or forensic practice as a proxy for the PCL-R. However, self-report measures of psychopathy have increased in number and popularity over the last 20 years. Such scales provide an alternative approach to the PCL-R and may be advantageous in the following ways: • The allowance of group administration that can be completed in time-limited situations. • An increased sensitivity to changes as a result of time, treatment, and other interventions (Edens et al., 2001) that cannot be accomplished with the relatively trait-based PCL-R. • Elimination of the heavy reliance on collateral data sources. As previously discussed, collateral reports may provide the illusion of reliability but are susceptible to many of the same pitfalls as direct interviews. Despite self-reports’ being prone to manipulation by responders, they have received surprisingly little research attention. On a positive note, the solutions to safeguard self-reports against impression management (i.e., validity scales utilizing well-constructed detection strategies) may be relatively easy to incorporate compared to the solutions for detecting deception during interviews (i.e., training interviewers to spot verbal deception and controlling for unreliable collateral data). To explore the susceptibility of self-report measures to deception, Edens et al. (2001) asked undergraduates to positively manage scores on the PPI and Marlowe–Crowne Social Desirability Scale (MCSDS; Crowne & Marlowe, 1960). The researchers found that regardless of psychopathic traits and the simulation scenario, simulators were able to lower PPI scores (d range from 0.37 to 0.48). Those with higher psychopathic traits had more significant decreases in their PPI scores (mean d



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= 1.22 vs. 0.17). The PPI Unlikely Virtues validity scale was unable to achieve high levels of detection with a false-positive rate of 25%. Readers should note, however, that interpretation of the Edens et al. (2001) results is complicated by the use of a college sample, with the “high” psychopathy group obtaining an honest mean score (M = 396), which is comparable to a youthful prison population (M = 392; Poythress et al., 1998). As a reevaluation of Edens et al. (2001), Edens (2004) found that only certain traits were lowered during PIM. Research has found that the PPI can be separated into two higher-order factors. PPI-I consists of the Social Potency, Fearlessness, and Stress Immunity subscales, while PPI-II consists of the Blame Externalization, Impulsive Nonconformity, Carefree Nonplanfulness, and Machiavellian Egocentricity subscales. The reanalysis found that PPI-II items were lowered significantly more during PIM (d = –0.86), while PPI-I scores actually increased (d = 0.53). As discussed by Edens, these results make conceptual sense, as some PPI-I traits may seem advantageous. Unfortunately, Edens did not evaluate which genuinely occurring psychopathic traits appear to aid PIM the most. Kelsey (2014) provided the only known examination of PIM using a self-report questionnaire with an offender population. As part of a repeatedmeasures simulation design, participants reduced their PPI-R scores well below community levels. Predictably, the finding was most pronounced for those with high psychopathy. Thus, while those with genuinely high levels of psychopathy are able to lower scores to “normal” levels, they are often unable to elude detection. Kelsey (2014) compared PPI-R results to the Self-Report Psychopathy (SRP-4) scale and Levenson Self-Report Psychopathy Scale (LSRP) and found that both were also vulnerable to PIM. Kelsey (2014) also provided initial results for the detection of minimized psychopathy traits. As an initial screen, the PPI-R Virtuous Responding (VR) scale can potentially identify individuals engaging in PIM (cutoff score of > 40) and those with likely genuine responding not engaging in PIM (cutoff score of < 25). These scores currently leave a substantial group of examinees who would require further evaluation. Further work on the PPI-R VR scale cutoff scores is needed before it can be widely used in correctional/forensic settings. Given the vulnerability of self-report psychopathy measures to PIM, Rogers et al. (2002) evaluated whether an interview-based PCL measure

showed similar vulnerability. Using the PCL-YV in a simulation design, adolescent offenders were able to lower scores by 44.2%. Without a record review, the generalizability of these results to realworld forensic evaluations is limited. However, as noted throughout this chapter, whether such a review would decrease the effects of PIM has yet to be studied, likely due to the pervasive belief that such records protect the integrity of results.

Impression Management on Clinical Measures The potential for psychopaths to manipulate assessment results can extend beyond psychopathy and risk to general clinical measures. Unfortunately, only two published studies have examined this important issue. Book, Holden, Starzyk, Wasylkiw, and Edwards (2006) studied undergraduates who successfully portrayed socially desirable personality traits (i.e., were not detected by the validity scale cutoff scores) on the Holden Psychological Screening Inventory (HPSI; Holden, 1996). Successful deceivers had modestly higher primary, secondary, and total psychopathy scores on the LSRP compared to those detected as faking (Cohen’s d of 0.46, 0.36, and 0.50, respectively). In line with previously reported studies, the positive advantage for psychopathy disappeared when participants were asked to malinger “serious psychological problems.” MacNeil and Holden (2006) also analyzed successful PIM on the HPSI. While initial results did not find psychopathy to be advantageous, three particular PPI scales were most relevant for those who avoided detection: Machiavellian Egocentricity, Blame Externalization, and Stress Immunity. Conceptually, these scales—measuring emotional coldness, aggressiveness, and a willingness to engage in selfish acts—could aid in the ability to deceive. Their connection to successfully avoiding formal detection strategies is less obvious. For example, while emotional coldness has been found to aid verbal lying, it does not assist on written multiple-choice assessment measure.

CONCLUSIONS The inclusion of deception in the description of those with psychopathic personality predates the term psychopathy itself. Perhaps because of the centricity of this connection, little research has been devoted to psychopaths’ frequency of and skill at deception. With the increased popularity of selfreport psychopathy and risk assessment measures,

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research on this connection becomes even more imperative. Decisions based on these measures can have far-reaching clinical and forensic consequences. For example, classification as a psychopath is often associated with poor treatment outcomes and sometimes leads to the unwarranted assumption that other symptoms are disingenuous. The significant ramifications of risk assessment findings include extended detention and loss of freedom (or conversely, the avoidance of certain consequences). Thus, the effect that psychopathic traits have on deception, and on the manipulation of assessment results in particular, is a very high priority. Research conducted so far has consistently found the following: 1.  Little, if any, connection between psychopathy and the likelihood of feigning. The most acceptable explanation for malingering is that it is adaptational in nature: It occurs when an individual has (a) substantial investment in (b) an adversarial setting and (c) sees few alternatives (Rogers & Cavanaugh, 1983). Thus, while both malingering and psychopathy occur more commonly in forensic settings, the connection is an artifact, and neither should be used as evidence of the other. 2.  No increased skill at malingering for psychopaths. Avoiding modern detection strategies for feigned mental disorder and cognitive impairment is a skill that requires specific knowledge, unlikely to be aided by psychopathic traits. 3.  Increased likelihood of general deception. This constitutes the most direct support for Cleckley’s (1976) description of “untruthfulness and insincerity” and Hare’s (1985) descriptions of “pathological lying.” In both self-report and experimental designs, psychopathic traits are associated with more frequent deception. However, this likelihood should not be equated with increased skill in avoiding detection. 4.  No increased ability to avoid detection on polygraph tests for psychopaths. Although neurological and autonomic variables are related to psychopathy, current findings indicate no increased ability to avoid polygraph detection. Research into other aspects of the deception– psychopathy connection is less conclusive. The following issues clearly need further investigation: •• The type and effectiveness of verbal, nonverbal, and interpersonal strategies psychopaths use to de-

ceive. Results are mixed but suggest greater fluency with verbal strategies. However, multiple studies found those with higher psychopathic traits were rated as less credible when telling stories. Of particular interest is the finding that psychopaths might be incorrectly perceived to be lying when telling the truth. •• Psychopaths’ skill at using PIM on interviewbased and self-report measures. Initial results indicate psychopaths are able to dramatically decrease scores on psychopathy, risk assessment, and at least one general clinical measure to a greater extent that nonpsychopaths. More research is also needed to establish whether psychopaths lower traits to a believable level that would meet their intended goal without being caught by validity scales. As an initial finding, Kelsey (2014) found that those with high psychopathy traits were no better at avoiding detection that those with lower traits. •• Development of validity scales on risk- and psychopathy-specific measures. Additional research is also needed on effective validity scales included on self-report risk and psychopathy measures. Currently, those that do exist (e.g., Unlikely Virtues on the PPI) often misclassify genuine responders at an unacceptably high rate (i.e., false-positive rate of 25%).

NOTES 1.  Schneider used a wide definition for psychopathy more akin to the modern term personality disorder. Of his 10 typologies, the affectionless psychopath is most similar to the modern conceptualization that is the topic of this chapter (Crowhurst & Coles, 1989). 2. While Kashy and DePaulo (1996) do not reference psychopathy specifically, they measure a number of traits common to psychopaths, including Machiavellianism and social adroitness.

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detection of deception in a prison population. Psychophysiology, 15(2), 126–136. Ray, J. V., Hall, J., Rivera-Hudson, N., Poythress, N. G., Lilienfeld, S. O., & Morano, M. (2013). The relation between self-reported psychopathic traits and distorted response styles: A meta-analytic review. Personality Disorders, 4(1), 1–14. Rimé, B., Bouvy, H., Leborgne, B., & Rouillon, F. (1978). Psychopathy and nonverbal behavior in an interpersonal situation. Journal of Abnormal Psychology, 87(6), 636–643. Rogers, R. (1990). Models of feigned mental illness. Professional Psychology: Research and Practice, 21(3), 182–188. Rogers, R., Bagby, R. M., & Dickens, S. E. (1992). Structured Interview of Reported Symptoms professional manual. Odessa, FL: Psychological Assessment Resources. Rogers, R., & Cavanaugh, J. L. (1983). “Nothing but the truth” . . . a reexamination of malingering. Journal of Psychiatry and Law, 11(4), 443–459. Rogers, R., & Cruise, K. R. (2000). Malingering and deception among psychopaths. In C. B. Gacono (Ed.), The clinical and forensic assessment of psychopathy: A practitioner’s guide (pp. 269–284). Mahwah, NJ: Erlbaum. Rogers, R., Dion, K. L., & Lynett, E. (1992). Diagnostic validity of antisocial personality disorder: A prototypical analysis. Law and Human Behavior, 16(6), 677–689. Rogers, R., Dolmetsch, R., & Cavanaugh, J. L. (1983). Identification of random responders on MMPI protocols. Journal of Personality Assessment, 47(4), 364– 368. Rogers, R., Duncan, J. C., Lynett, E., & Sewell, K. W. (1994). Prototypical analysis of antisocial personality disorder: DSM-IV and beyond. Law and Human Behavior, 18(4), 471–484. Rogers, R., & Robinson, E. V. (2016). Psychopathy and response styles. In C. B. Gacono (Ed.), The clinical and forensic assessment of psychopathy: A practitioner’s guide (2nd ed., pp. 217–230). New York: Routledge/ Taylor & Francis Group. Rogers, R., Salekin, R. T., Sewell, K. W., & Cruise, K. R. (2000). Prototypical analysis of antisocial personality disorder: A study of inmate samples. Criminal Justice and Behavior, 27(2), 234–255. Rogers, R., Vitacco, M. J., Jackson, R. L., Martin, M., Collins, M., & Sewell, K. W. (2002). Faking psychopathy?: An examination of response styles with antisocial youth. Journal of Personality Assessment, 78(1), 31–46. Schneider, K. (1923). Die psychopathischen personlichkeiten [The psychopathic personalities] (M. W. Hamilton, Trans.). Vienna: Deuticke. Schneider, K. (1958). Psychopathic personalities (M. W. Hamilton, Trans.). London: Cassell & Company. (Original work published 1950) Serota, K. B., Levine, T. R., & Boster, F. J. (2010). The



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CH A P TER 10

The Malingering of Posttraumatic Disorders Phillip J. Resnick, MD Sara G. West, MD Chelsea N. Wooley, PhD

EXPLORING THE DISORDERS The History of Posttraumatic Stress Disorder Posttraumatic stress disorder (PTSD) is a relatively new term to psychiatry; however, the concept has existed for over 100 years. In the 1880s, terms such as nervous shock and posttraumatic neurosis were coined to describe the psychological phenomena resulting from exposure to trauma (Adamou & Hale, 2003; Hausotter, 1996; Sparr, 1990). In 1889, Dr. Clevenger proposed the idea that similar diagnoses, including “railroad spine” and “compensation neurosis,” were related to an accidental concussion resulting in abnormalities in the central nervous system (Hall & Chapman, 2005; Thomann & Rauschmann, 2003, 2004). In the 20th century’s many wars, the psychological sequelae of battle were explored. During World War I, this reaction to trauma was called “shell shock,” and in World War II, the term battle fatigue was used. The first DSM used the diagnosis, gross stress reaction (American Psychiatric Association, 1952) and DSM-II (American Psychiatric Association, 1968) used adjustment reaction to adult life. Societal awareness intensified with the return of the veterans from the Vietnam War. The term posttraumatic stress disorder was introduced with the publication of DSM-III (American Psychiatric Association, 1980).

DSM-IV (American Psychiatric Association, 1994) led to an alteration in the criteria used to diagnose PTSD. Criterion A, which describes the traumatic event, was changed from the DSM-III’s objective standard (an event that would be markedly distressing to almost anyone) to a subjective standard (an event that the victim found personally distressing). This broadening of the definition led to a 39% increase in the number of individuals who met diagnostic criteria for PTSD (Breslau & Kessler, 2001). DSM-IV was also responsible for the introduction of the diagnosis “acute stress reaction,” which is a time-limited precursor to PTSD involving dissociative symptoms (American Psychiatric Association, 1994). In 2013, the American Psychiatric Association made major revisions in the criteria for PTSD with the publication of DSM-5. The first notable change was the movement of PTSD from the Anxiety Disorders category to the new category of Trauma- and Stress-Related Disorders in DSM5. Other diagnoses in this new category include reactive attachment disorder, disinhibited social engagement disorder, acute stress disorder, and adjustment disorder. The common link shared by these conditions is that they are all precipitated by stress (Friedman, Resick, Bryant, & Brewin, 2011). DSM-5’s Criterion A redefined the precipitating stressor required for the diagnosis of PTSD. Sexu-

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al violence replaced a vague item about physical integrity. DSM-5 specified actual or threatened death of a loved one, and the event must have been violent or accidental. Finally, DSM-IV’s A2 criterion requiring extreme emotional response was eliminated, because certain groups, such as military personnel or those with a traumatic brain injury, may experience these responses differently yet still develop PTSD (Friedman et al., 2011). The largest change associated with Criterion B (intrusion) is that the dreams are now described as being reflective of the content or the affect related to the trauma. Levin, Kleinman, and Adler (2014) posited that this change is consistent with Res­ nick’s (2004) observation that recurrent dreams with invariable content may serve as evidence for malingering. In DSM-IV, Criterion C consisted of both avoidance and numbing symptoms. These two symptom clusters have been divided into Criteria C and D in DSM-5. Individuals must have at least one symptom in each of these categories to qualify for the diagnosis. Criterion D has also been reworked to include more specific alterations in cognition and mood, in addition to numbing. Criterion E (hyperarousal) was expanded to include reckless or self-destructive behavior, as well as aggression. It reflects the findings of Calhoun et al. (2012), who studied 185 patients with PTSD (one-third of whom were veterans). They demonstrated that 58% of the sample engaged in aggressive behavior and 17% were reckless. Finally, the subtype “with dissociative symptoms” was added to DSM-5, while the acute and chronic specifiers in DSM-IV were eliminated. These significant changes to the diagnosis generated multiple publications speculating on the effects that such alterations would have. McNally (2009) reported that the allowance of additional indirect means of exposures to a trauma would create “a bracket creep, expanding those eligible for the diagnosis.” In 2009, Elhai, Ford, Ruggiero, and Frueh suggested that, though Criterion C would be divided into two more specific symptom clusters, the more stringent requirements would have little impact on the prevalence of the disorder. Following the release of DSM-5, a study of 2,953 participants, Kilpatrick et al. (2013) demonstrated a 4% drop in those who met Criterion A from 93.7% in DSM-IV to 89.7% in DSM-5, as well as a 1.2% decrease in the lifetime prevalence from DSM-IV to DSM-5 criteria. The authors attributed this decrease to the elimination of the indirect exposure to the nonviolent death of a loved one from Criterion A and the failure to have one of

two avoidance symptoms defined by Criterion C in DSM-5. Breslau, Davis, Andreski, and Peterson (1991) described five risk factors for exposure to traumatic events: low education, male gender, early conduct problems, extraversion, and a family history of psychiatric disorders or substance problems. Since only a minority of individuals develops PTSD in response to a trauma, Davidson (1993) identified 11 pretrauma characteristics that cause an individual to be more vulnerable to PTSD (see Table 10.1); these are linked to backgrounds, childhood antecedents, and prior psychiatric issues. A metaanalysis (N = 476) indicated that peritraumatic dissociation is the single best predictor for the development of PTSD (Ozer, Best, Lipsey, & Weiss, 2003). Other factors included prior trauma, prior psychological maladjustment, family history of psychopathology, perceived life threat during the trauma, lack of posttraumatic social support, and peritraumatic emotional responses. After the diagnosis of PTSD was introduced in 1980, the psychiatric community began investigating the disorder in a group of people universally recognized to have been exposed to significant atrocities, specifically, Holocaust survivors. Kuch and Cox (1992) identified 124 Jewish Holocaust survivors deemed to be free of major psychiatric illnesses; 46% met DSM-III criteria for PTSD (American Psychiatric Association, 1980). Thus, although all these survivors experienced significant trauma, less than half of them had symptoms TABLE 10.1.  Pretrauma Characteristics That Increase the Likelihood of Developing PTSD

A. Background 1. Female gender 2. History of psychiatric illness in first-degree relatives B. Childhood antecedents 1. Parental poverty 2. Separation or divorce of parents before the age of 10 3. Trauma in childhood (may be of a sexual nature) 4. Behavioral disorder in childhood or adolescence 5. Poor self-confidence in adolescence C. Prior psychiatric problems 1. Prior psychiatric disorders 2. Introversion 3. Life stress prior to the trauma 4. High neuroticism Note. From Davidson (1993).

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of PTSD at the time of evaluation. Further research on the children of Holocaust survivors indicated that they did not experience more traumatic events than a demographically similar comparison group. However, they still had a greater prevalence of current and lifetime PTSD (Yehuda, Schmeidler, Wainberg, Binder-Brynes, & Duvdevani, 1998). It suggests that a biological component predisposes people to the development of PTSD. In recent years, the neurobiology of PTSD has been more clearly delineated. Dysfunction in both the hippocampus and the amygdala has been linked to PTSD. Increased stress is believed to lead to increased systemic glucocorticoids, which in turn interfere with the biochemical process of long-term potentiation in the hippocampus (McEwen, 1997). This process is hypothesized to be the origin of the memory disturbance noted in PTSD. A number of studies compared the size of hippocampi in those with PTSD to normal controls and the results have been varied (Grossman, Buchsbaum, & Yehuda, 2002). The amygdala is associated with processing fear and the accompanying autonomic responses. It appears that an overactive amygdala may play a role in the inability to extinguish classical fear conditioning and the hyperarousal symptoms that are salient features of PTSD. Diagnosing PTSD may prove difficult due to the high degree of comorbid psychopathology. For instance, in a large study of treatment-seeking outpatients with a primary diagnosis of PTSD, Brown, Campbell, Lehman, Grisham, and Mancill (2001) noted that 92% had another active psychiatric diagnosis. For the clinician, the challenge lies in determining the comorbidities’ presence or absence prior to the trauma, their course following the trauma, and their contribution to the patient’s current symptoms.

Malingering Malingering is defined by the American Psychiatric Association (2013, pp. 726–727) as “the intentional production of false or grossly exaggerated physical or psychological symptoms motivated by external incentives.” This external gain may take the form of financial rewards, relief of responsibilities at a job or at home, avoiding military service, or evading criminal responsibility (American Psychiatric Association, 2013). This definition did not change with the publication of DSM-5. Malingering may be divided into three categories that depict the nature of the symptoms. Pure

malingering is the conscious feigning of a disorder that does not exist at all. Partial malingering is the fraudulent, conscious exaggeration of present symptoms or the allegation that prior genuine symptoms are still present. False imputation refers to the individual’s intentional attribution of actual symptoms to a different cause. For example, individuals who are aware that they developed PTSD due to a prior trauma may falsely ascribe the symptoms to a more recent car accident in order to gain financial compensation. However, it is important recognize that some individuals fail to recognize that consecutive events do not necessarily have a causal relationship (Collie, 1971), and this failure must be differentiated from malingering. Partial malingering is difficult to identify because the individual can, from personal experience, accurately describe the symptoms (Wooley & Rogers, 2015). Differential diagnosis for malingering must also consider factitious disorder and conversion disorder (Table 10.2). Factitious disorders are similar to malingering in that the diagnosis requires the conscious production of false symptoms. They differ, however, in the motivation for the symptom production. Unlike malingering, those with factitious disorder intentionally produce symptoms “even in the absence of obvious external rewards” (American Psychiatric Association, 2013, p. 324). What is notably absent in DSM-5 is reference to the sick role, which served as a criterion in DSM-IV (American Psychiatric Association, 2000). Conversion disorder differs from malingering in that the individual is unaware of the origin of his symptoms. In contrast to individuals who malinger, those with factitious disorder and conversion disorder are ill and may be eligible for compensation. In a forensic setting, malingering should be suspected in all examinees, since external rewards

TABLE 10.2.  A Comparison of Malingering, Factitious Disorder, and Conversion Disorder

Intentional production of symptoms

External reward

Awareness of purpose

Malingering

Yes

Yes

Yes

Factitious disorder

Yes

No

No

Conversion disorder

No

No

No



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are almost always available. Individuals deemed to be disabled may in fact demonstrate no disability even shortly after the determination has been made. While some cases were legitimate, others likely reflected a feigned initial impairment. In a survey of clinicians, Mittenberg, Patton, Canyock, and Condit (2002) found that symptom exaggeration or malingering occurred in about 30% of personal injury and disability cases. Evaluators should be aware that disreputable attorneys may coach their clients about psychological symptoms following trauma, especially in the case of a major accident with multiple plaintiffs (Rosen, 1995). Legally, malingering constitutes fraud, but convictions for perjury requires the trier of fact be convinced beyond a reasonable doubt that a conscious decision to lie under oath occurred. There is frequently not enough evidence to support a conviction. Often, the trier of fact will simply not grant an award to a plaintiff believed to be malingering. Clinicians are hesitant to classify an individual as malingering for several reasons (Burges & McMillan, 2001). First, a wide range of diagnoses must be ruled out prior to classifying someone as a malingerer (Pollack, 1982). Second, a false accusation of malingering may lead to stigmatization and subsequent inability to receive appropriate care (Kropp & Rogers, 1993). Third, the clinician may fear litigation or even physical assault due to labeling someone as a liar. One of us (P. J. R.) has been involved in three cases of clinicians who were sued for defamation of character due to insufficiently supported labels of malingering. It is easier to defend the term feigning, because there is no obligation to prove that the conduct is motivated by an external incentive (see Rogers, Chapter 1, this volume, for the distinction between feigning and malingering). The public’s hostility toward suspected malingerers is understandable given that their undeserved financial gain would be associated with another’s undeserved financial loss (Braverman, 1978). This link may be one of the reasons Trimble (1981) noted that monetary compensation for a posttraumatic disorder is far less than that for physical injury despite the fact that the limitations on an individual’s life caused by psychological symptoms may be greater. There are only two ways that a malingerer may be identified with certainty. The first occurs when an individual participates in activities that he or she has claimed to be incapable of doing. For example, a man involved in an auto accident may

claim that he is unable to drive, yet may be seen driving to the store. The second occurs when the individual confesses to malingering. Given that neither option occurs often, the classification of malingering must involve the integration of multiple pieces of clinical evidence obtained from a thorough investigation and psychological assessment.

MALINGERING OF PTSD PTSD is easy to fake. The diagnosis is based almost entirely on the individual’s subjective report of symptoms, which are difficult to independently verify. Furthermore, in an effort to educate the public, the diagnostic criteria are widely available in print and online, allowing unscrupulous individuals to familiarize themselves with what PTSD symptoms to falsely report. Studies have indicated that participants naive to the criteria of PTSD could achieve the diagnosis on a checklist when asked to do so 86–94% of the time (Burges & McMillan, 2001; Lees-Haley & Dunn, 1994; Slovenko, 1994). The primary motivation for malingering PTSD is financial gain. Once PTSD was included in the DSM, the number of personal injury lawsuits doubled over the next decade (Olson, 1991). Workers’ compensation claims associated with stress-related disorders rose rapidly, and insurance company costs linked to these claims soon outstripped those related to physical injuries (de Carteret, 1994). By the late 1990s, 14% of all workers’ compensation claims were based on stress-related disorders (Guriel & Fremouw, 2003). While financial gain usually serves as the incentive, malingered PTSD to be classified as “disabled” may also serve as a means to “save face” by not admitting to more stigmatizing causes of disability, such as lack of social skills or substance use. The malingerer may gain sympathy and support where none existed before (Keiser, 1968). Finally, malingered PTSD may also be a last-ditch attempt to gain compensation when a physical injury claim is unsuccessful. The adversarial nature of the court system may serve as a breeding ground for symptom exaggeration or partial malingering. Plaintiffs with legitimate disabilities may enter into the process with no intention of lying. However, after exposure to an aggressive deposition by the defense attorney, they may become angry or worry that they will not receive appropriate compensation for their damages (Enelow, 1971). Therefore, plaintiffs may

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exaggerate their symptoms in order to obtain what they believe they rightfully deserve. While prominent in civil litigation, PTSD has a smaller impact in the criminal courts. Appelbaum, Jick, and Grisso (1993) found that only 0.3% of defendants used PTSD as the basis for a not guilty by reason of insanity (NGRI) plea. Berger, McNeil, and Binder (2012) noted that a successful insanity plea based on PTSD is usually related to dissociative phenomena that would cause defendants to misperceive their circumstances and act in a way that would be reasonable in this context. Moskowitz (2004) points out that it may be difficult to determine if the dissociation caused the violence, or conversely, if the violent act spawned the dissociative episode. Individuals involved in the criminal courts and diagnosed with PTSD were more likely to be found competent to stand trial, have a jury trial, and be found guilty than those with other diagnoses (Guriel & Freemouw, 2003). However, a diagnosis of PTSD can result in a reduction of charges or mitigation of penalties (Pitman & Sparr, 1998). Defendants may suggest that PTSD-related aggression or recklessness impacted the process of forming criminal intent (mens rea). Courts have recently been more willing to recognize this, especially in reference to veterans (Porter v. McCollum, 2009), often leading to reduced sentences (Grey, 2012).

Use of Psychological Testing to Detect Feigned PTSD PTSD has been characterized as a relatively easy disorder to feign (Demakis & Elhai, 2011; Koch, O’Neill, & Douglas, 2005; Wetter & Deitsch, 1996). As a result, the use of psychological testing in medicolegal settings has increased (Butcher & Miller, 1999), due to their effectiveness in assessing various forms of response bias (Rogers & Granacher, 2011). Both feigned and genuine PTSD can produce similar elevations on PTSD-relevant scales on psychological tests, which makes it difficult to distinguish between the two presentations (e.g., Elhai, Gold, Sellers, & Dorfman, 2001; Resnick, 1997). Rogers, Payne, Correa, Gillard, and Ross (2009) proposed that individuals with PTSD may experience intensified symptoms and impairment that elevate both clinical and validity scales. In addition, persons with genuine PTSD often present with highly variable symptom profiles, making it difficult to differentiate from feigning profiles (Foa, Riggs, & Gershuny, 1995; Guriel & Fre-

mouw, 2003). As noted, PTSD is also a highly comorbid disorder, especially with major depressive disorder, anxiety disorders, and substance use disorders (Kessler, 2000). Therefore, feigners are difficult to differentiate from their genuine counterparts due to the complex nature of PTSD. To be successful, feigners must elevate the appropriate symptom scales with significant severity, while avoiding detection on validity scales that may alert professionals to their deception. In the next section we briefly discuss the effectiveness of several popular psychological measures used to differentiate between genuine and feigned PTSD. Studies included in this section were used only if they had both a genuine PTSD and feigning group (i.e., simulators or known group). Cohen’s ds are used to assess the magnitude of the differences between genuine and feigned PTSD groups with the effect-size descriptors discussed by Rogers (Chapter 2, this volume). In addition, feigning scale scores are examined to determine their accuracy for feigning and genuine PTSD. Due to the far-reaching consequences of being classified as “malingering,” the minimization of false positives is emphasized to avoid misclassification of genuine responders as feigners. Minnesota Multiphasic Personality Inventory–2

The Minnesota Multiphasic Personality Inventory–2 (MMPI-2; Butcher, 2001) continues to be one of the most commonly used personality inventories for the assessment of feigned PTSD and for some, the most effective test for detecting malingered PTSD (Demakis & Elhai, 2011). Three main detection strategies are used on the MMPI-2 to detect feigned PTSD: Quasi-Rare Symptoms, Rare Symptoms, and Erroneous Stereotypes. Table 10.3 summarizes eight simulation studies that have examined the effectiveness of the MMPI-2 to detect feigned PTSD. Utilizing the quasi-rare detection strategy, prior research has shown that the F scale is often significantly elevated among individuals with PTSD, frequently above established cutoff scores for feigning (Franklin, Repasky, Thompson, Shelton, & Uddo, 2002; Garcia, Franklin, & Chambliss, 2010; Jordon, Nunley, & Cook, 1992; Munley, Bains, Bloem, & Busby, 1995). Specifically, Greene (2000) found the F scale to sometimes be elevated in individuals with complicated histories and numerous symptom complaints, which often occur in those diagnosed with PTSD. As shown in Table 10.3, individuals with genuine PTSD in these eight studies



10. The Malingering of Posttraumatic Disorders 193

TABLE 10.3.  Effect Sizes and Scale Elevations for the MMPI-2 Validity Scales

Quasi-rare symptoms

Rare symptoms

Fb

F-K

Fp

Bury & Bagby (2002) a Eakin et al. (2006) Elhai et al. (2000) Elhai et al. (2001) Elhai et al. (2002) Elhai et al. (2004) Marshall & Bagby (2006) Wetter et al. (1993)

  1.06   0.89   0.93   1.10   0.91   0.53   1.17   1.52

  1.37   1.04

 0.97

  1.38   1.13

  1.24   0.97   1.01   1.41   1.31   1.21   1.53

 1.60

M effect size

  1.01

  1.13

  1.26

  1.24

  1.07

  1.17

  0.13

M PSTD scale elevation M PTSD SD

 84.98  22.33

 82.77  23.83

 5.19 10.53

 66.61  19.75

65.90 15.92

 77.71  14.45

 82.00  14.68

M feigning PTSD scale elevation M feigning PTSD SD

102.35  19.97

107.67  19.08

20.47 14.48

 89.74  23.85

84.85 23.68

 96.64  18.24

 83.30  14.46

PTSD screening cutoff (M + 1 SD) PTSD screening cutoff (M + 1.5 SD) PTSD screening cutoff (M + 2 SD)

107.31 118.48 129.64

106.60 118.52 130.43

15.72 20.99 26.25

 86.36  96.24 106.11

81.82 89.78 97.74

 92.16  99.39 106.61

 96.68 104.02 111.36

 1.10  1.37   0.75

Fptsd

Erroneous stereotypes

F

Ds

FBS

  1.39   0.84   0.87   1.03

  0.02  –0.09   0.47

 0.81  1.32   1.73

Note. All effect sizes are reflected as Cohen’s d values. Total patients with PTSD in review (N = 839); total feigners in review (N = 257). Mean PTSD patient-feigner elevations and SDs based only on samples in this review. aOnly the symptom-coached simulator group was used to provide uniform comparisons with other studies.

demonstrated marked elevations (M = 84.98T) on the F scale. These elevated scores may be related to the development of the scale, as the F scale simply measures divergence from normality but does not necessarily distinguish genuine from feigned presentations (Rogers, Sewell, Martin, & Vitacco, 2003). Developed similarly, the Fb scale also demonstrated marked elevations (M = 82.77T) among patients with genuine PTSD with moderate effect sizes (mean d = 1.13). The F-K scale demonstrated slightly improved effect sizes (mean d = 1.26), but it is important to note that the F scale and F-K index are highly correlated (Nichols, 2011). In fact, Bury and Bagby (2002) found that the F-K does not add incremental predictive capacity over the F family of scales and discouraged its use in assessing feigning psychological symptoms. The Fp and Fptsd scales outperformed the F and Fb scales, with lower clinical elevations for patients with genuine PTSD, with averages around 65T. The Fp scale specifically has gained popularity as a primary scale for the assessment of feigning on the MMPI-2 due to the relative consistency of cutoff scores and low probability of false positives (Rogers et al., 2003). The Fp was specifically de-

signed to assess the differences between genuine and feigned disorders, and appears to be effective with PTSD. As discussed in the previous edition of this book, a cutoff score of Fp > 8 (raw score) is suggested, which produces a relatively small falsepositive rate of 9%. Elhai and colleagues (2004) recognized that the F family scales were not specific to the detection of feigned PTSD, so the Infrequency-Posttraumatic Stress Disorder (Fptsd) scale was developed to differentiate between genuine and feigned combat PTSD. The Fptsd scale was developed empirically with infrequently endorsed items (< 20%) from a sample of male combat veterans with PTSD (N = 940). It includes 20 items from the F scale and 12 more items that reflect family or social problems, antisocial behavior, morally righteous attitudes, and self-injurious behavior. Two evaluative studies have shown its possible potential (mean d = 1.07; Elhai et al., 2004; Marshall & Bagby, 2006). Interestingly, genuine PTSD samples scored below 70T on average. Due to the number of similar items between the Fp and Fptsd, Marshall and Bagby examined but were unable to establish any incremental validity for Pptsd over the Fp. They

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concluded that the standard validity scales may be able to detect malingering across a wide spectrum of psychiatric disorders. The Fake Bad (FBS) and Ds scales both capitalize on the erroneous stereotypes detection strategy, but show markedly different effect sizes. Gough’s Ds scale appears more effective with an average moderate effect size (mean d = 1.17). The FBS scale was designed for only personal injury cases (LeesHaley, 1992) and its effectiveness to detect feigned PTSD outside of that setting has been questioned due to its narrow focus (Arbisi & Butcher, 2004; Rogers et al., 2003). While only three studies examined the FBS, it does not appear to be effective at differentiating between feigned and genuine PTSD (mean d = 0.13) (see Table 10.3). While most studies involve pure malingerers, two studies have examined the effectiveness of the MMPI-2 to detect feigned PTSD in populations that have personal knowledge of PTSD. The MMPI-2 was slightly more effective in the detection of partial malingerers over pure malingerers using all three main detection strategies (Arbisi, Ben-Porath, & McNulty, 2006; Efendov, Sellbom, & Bagby, 2008). For example, the F scale produced very large effect sizes for these two studies (mean d = 1.62). Utilizing the rare symptom strategy, the Fp produced the largest effect sizes (mean d = 2.15), with the genuine PTSD sample averaging below 60T on the Fp in both samples.

MMPI-2 Restructured Form

The MMPI-2 Restructured Form (MMPI-2-RF; BenPorath & Tellegen, 2008; Tellegen & Ben-Porath, 2011) includes revised versions of the MMPI-2 validity scales designed to detect feigning. Although a newly designed measure, the MMPI-2-RF validity scales have already shown utility in several studies of the detection of overreported symptoms even when individuals are coached about the presence and purpose of validity scales (Sellbom & Bagby, 2010). To date, only three studies have examined the ability of these scales to differentiate between genuine and feigned PTSD (see Table 10.4). Similar to the MMPI-2, the revised F Scale (F-r) has been found to be markedly elevated among individuals with PTSD (Arbisi, Polusny, Erbes, Thuras, & Reddy, 2011; Goodwin, Sellbom, & Arbisi, 2013; Marion, Sellbom, & Bagby, 2011). As seen in Table 10.4, the F-r scale for patients with genuine PTSD averaged 80.25T, with a large standard deviation of 24.26, making any PTSD screening cutoff scores extremely high. As a contrast, the Fp-r has demonstrated only slight elevations (M = 63.57T) for genuine PTSD but extreme elevations for feigned PTSD (M = 102.56T). Goodwin et al. (2013) and Marion et al. (2011) also examined the effectiveness of the Fp-r with sophisticated feigners; both studies produced good results (ds = 1.01 and 1.21, respectively). These studies revealed that

TABLE 10.4.  Effect Sizes and Scale Elevations for the MMPI-2 RF Feigning Scales

Quasi-rare symptoms

Rare symptoms

Erroneous stereotypes

F-r

Fp-r

Fs

FBS-r

Goodwin et al. Marion et al. (2011) a Mason et al. (2013)

  1.20   0.79   1.86

1.62 1.02 1.95

1.32 1.09 1.93

  0.74   0.50   1.01

M effect size

  1.28

1.53

1.45

  0.75

M PSTD scale elevation M PTSD SD

 80.25  24.26

63.57 15.47

72.78 21.67

 73.86  15.50

M feigning PTSD scale elevation M feigning PTSD SD

112.28  30.10

102.56 38.01

106.02 27.41

 84.96  14.56

PTSD screening cutoff (M + 1 SD) PTSD screening cutoff (M + 1.5 SD) PTSD screening cutoff (M + 2 SD)

104.51 116.64 128.77

94.45 105.285 116.12

 89.36  97.11 104.86

(2013) a

79.04 86.775 94.51

Note. All effect sizes are reflected as Cohen’s d values. Total patients with PTSD in review (N = 191); total feigners in review (N = 115). Mean PTSD patient-feigner elevations and SDs based only on samples in this review. aOnly the symptom-coached simulator group was used to provide uniform comparisons with other studies.



10. The Malingering of Posttraumatic Disorders 195

there is a 97.3% likelihood that a person is feigning PTSD when scores exceeded a cutoff score of Fp-r ≥ 100, with a false-positive rate of only 2.7%. This cutoff score is recommended by Ben-Porath (2012). However, a cutoff score of Fp-r ≥ 90 was recommended when base rates are expected to be high (i.e., .30 or above), such as PTSD feigning among combat veterans (Goodwin et al., 2013; Marion et al., 2011). The Infrequent Somatic Complaints (Fs), which is a new scale, includes items endorsed by less than 25% of the normative sample and a large sample of medical and chronic pain patients. While the Fs scale has been shown to differentiate between feigned and genuine PTSD groups (mean d = 1.45), this scale was not designed for this purpose. This finding suggests that PTSD feigners may be more likely to endorse symptoms related to somatic and pain complaints than those with genuine PTSD, but that the Fs scale should not be used in isolation for the detection of feigned PTSD. Overall, the MMPI-2-RF validity scales show promise in their ability to detect general feigning, but more research is necessary to understand their effectiveness in identifying feigned PTSD. The rare symptom scales (Fp-r) have demonstrated the

best results thus far, with genuine PTSD samples scoring relatively low on this scale. Personality Assessment Inventory

The Personality Assessment Inventory (PAI; Morey, 2007) has gained wide acceptance in forensic practice (Kucharski, Toomey, Fila, & Duncan, 2007). Evidence suggests it has been useful at assessing feigned PTSD due to its response validity scales and ability to assess a variety of symptoms related to the disorder (Morey, 1996; Mozley, Miller, Weathers, Beckham, & Feldman, 2005). In fact, the Veteran’s Affairs Healthcare System relies on the PAI to obtain valid, clinically relevant patient information (Calhoun, Collie, Clancy, Braxton, & Beckham, 2010). The Negative Impression Management (NIM) validity scale has been shown to be the most effective PAI feigning scale (see Table 10.5) in differentiating genuine and feigned PTSD groups, with average moderate effect size (mean d = 0.98). Importantly, many genuine PTSD responders did not have elevated NIM scores (mean = 63.41T). However, NIM elevations have also been associated with severe psychological impairment (Thomas,

TABLE 10.5.  Effect Sizes and Scale Elevations for the PAI Validity Scales

Rare symptoms NIM

NDSa

Spurious patterns of psychopathology MAL

RDF

 0.04

 0.36

 0.96  1.35

 0.21  0.92

  0.78

  0.50

Eakin et al. (2006) Liljequest et al. (1998) Scragg et al. (2000) Thomas et al. (2012) b

 0.19  1.06  1.17  1.48

M effect size

  0.98

M PSTD scale elevation M PTSD SD

63.41 14.74

57.93 12.86

59.10 14.19

52.74 11.07

M feigning PTSD scale elevation M feigning PTSD SD

84.21 88.82

93.61 25.60

70.74 18.31

59.56 10.74

PTSD screening cutoff (M + 1 SD) PTSD screening cutoff (M + 1.5 SD) PTSD screening cutoff (M + 2 SD)

78.15 85.52 92.89

70.79 77.22 83.65

73.29 80.39 87.48

63.81 69.35 74.88

 1.76

Note. All effect sizes are reflected as Cohen’s d values. Total patients with PTSD in review (N = 117); total feigners in review (N = 158). Mean PTSD patient-feigner elevations and SDs based only on samples in this review. aOnly one study has examined the NDS for feigning PTSD. Therefore, all calculations in this column are based solely on the data from Thomas et al. (2012). bOnly the symptom-coached simulator group was used to provide uniform comparisons with other studies.

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Hopwood, Orlando, Weathers, & McDevitt-Murphy, 2012) and has been found to be elevated in inpatient trauma samples (Rogers, Gillard, Wooley, & Ross, 2012). When evaluating cut scores, Calhoun, Earnst, Tucker, Kirby, and Beckham (2000) found that a cutoff score of NIM ≥ 13 in a sample of combat veterans with PTSD allowed 39% of feigners to go undetected and 35% of genuine PTSD responders to be misclassified. Therefore, the NIM score should be considered in conjunction with other data, as NIM scores may be related to high levels of distress or severe psychopathology. Spurious patterns of psychopathology appear to be less effective than the rare symptoms strategy (i.e., NIM), with the Malingering Index (MAL) producing a barely moderate effect size (mean d = 0.76). The RDF produced even smaller effect sizes (mean d = 0.46), with both genuine PTSD and feigners demonstrating low scores (Table 10.5). A newly created feigning scale, the Negative Distortion Scale (NDS), was designed in an attempt to identify feigners in samples with high levels of psychopathology (Mogge, Lepage, Bell, & Ragatz, 2010). Like the NIM scale, the NDS utilizes the rare symptoms detection strategy. Unlike the NIM, the NDS was created from eight clinical scales by using rarely endorsed items by inpatients. Moreover, 60% of the NDS items are from trauma-relevant scales, which could potentially be problematic for identifying feigned PTSD. Of note, Rogers, Gillard, Wooley, and Kelsey (2013) found the NDS to be highly effective (d = 1.81) compared to the other PAI validity indicators in identifying feigned psychopathology within an inpatient sample simulating distress for disability. In the only NDS study of feigned PTSD, Thomas and colleagues (2012) found impressive effect sizes for simulators: (1) symptom-coached (d = 1.76) and (2) coached on both symptoms and validity indicators (d = 1.46). Thomas et al. concluded that a cutoff score of 85T correctly classified 97% of patients with genuine PTSD, while still correctly classifying 64% of the feigners. Further research is necessary to provide more conclusive evidence that the NDS can accurately differentiate between genuine and feigned PTSD. Trauma Symptom Inventory–2

The Trauma Symptom Inventory (TSI; Briere, 1995) is a popular self-report measure designed to assess psychological symptoms commonly associated with traumatic experiences. However, the

TSI was criticized for its lack of clinical research, as most studies utilized college samples (Edens, Otto, & Dwyer, 1998; Guriel et al., 2004; Rosen et al., 2006). While simulators in these studies did generally score higher than genuine PTSD samples on the Atypical Response (ATR) scale, the classification rates were not impressive (Elhai, Gray, Kashdan, & Franklin, 2005). The Trauma Symptom Inventory–2 (TSI-2; Briere, 2010), an updated version of the TSI with new scales and norms, includes a revised ATR scale. Specifically, the content of the revised ATR scale includes items that are unlikely to be endorsed by genuine PTSD samples, rather than the bizarre or extreme symptomatology found on the original ATR scale (Gray, Elhai, & Briere, 2010). However, only Gray and colleagues have examined the revised ATR scale. Utilizing undergraduate simulators, the recommended cutoff score of ≤ 7 on the revised ATR produced a substantial falsepositive rate (23%), possibly indicating that the revised ATR may be susceptible to general distress or other comorbid symptoms. Significant concerns continue regarding the ATR in identifying feigned PTSD on the TSI-2. The Detailed Assessment of Posttraumatic Stress

The Detailed Assessment of Posttraumatic Stress (DAPS; Briere, 2001) is a 104-item self-report measure designed to evaluate trauma exposure and posttraumatic response. The DAPS includes eight clinical scales that measures trauma-specific symptoms and posttraumatic stress reactions. Additionally, the DAPS examines several associated features of PTSD: trauma-specific dissociation, substance abuse, and suicidality. Of particular importance, the Negative Bias (NB) scale assesses the tendency of respondents to overendorse unusual or unlikely symptoms. Furthermore, the DAPS includes decision rules that combine to create a provisional diagnosis of PTSD (Briere, 2001). The DAPS has only been empirically investigated in one simulation study by Wooley and Rogers (2015) on feigning PTSD. They discouraged the use of the NB scale to detect feigned PTSD based on its disappointing results. The NB scale was found to have remarkable variability for both patients with genuine PTSD (SD = 41.54) and feigners (SD = 69.51). Not surprisingly, the NB scale’s effect sizes suffered as a result of this variability (mean d = 0.71). The DAPS manual (Briere, 2001)



10. The Malingering of Posttraumatic Disorders 197

indicated that only about one-third (32.8%) of the validation sample was from “clinical settings,” and few details were provided about the severity of psychopathology in this subgroup. Therefore, the NB scale may not be effective in clinical samples, and more research is necessary to know whether the scale is sensitive to clinical distress.

Well-Defined Groups The laser accuracy of single-point cutoff scores is the traditional approach to classifying individuals as feigning using validity scales, in which clinicians use a one-point difference to differentiate honest responders from feigners. Rogers and Bender (2013) provided an alternative by identifying scores that are “too close to call” and susceptible to being wrongly classified. To identify an indeterminate range, researchers can exclude scores that occur either within ±1 standard error of measurement (SEM) or ± 5T from the designated cutoff score. Previous studies have found false-positive rates of over 50% in some indeterminate ranges (Rogers, Gillard, Wooley, & Kelsey, 2013; Wooley & Rogers, 2015). The use of well-defined groups could improve the effectiveness of these psychological tests in differentiating between feigned and genuine PTSD, while lowering false-positive rates. A common criticism of using an indeterminate group is the number of individuals excluded from classification. In some samples, establishing welldefined groups with ± 1 SEM can result in a large indeterminate group. However, the far-reaching consequences of being wrongly classified as malingering should be a priority. An advantage of examining this issue is that researchers can evaluate the use of well-defined groups with data already collected (Rogers & Granacher, 2011). Many of the measures used to detect feigned PTSD either have a high false-positive rate or are not specifically designed to detect feigned PTSD. A newly designed measure, the Posttrauma Response Set Measure (PRSM) presented at the American Psychology–Law Society in 2015, was created with these concerns in mind (Weaver & Yano, 2015). This measure includes a diagnostic index to assess genuine PTSD, as well as five malingering indices to detect various forms of feigned PTSD. When compared to the MMPI-2 validity scales, the malingering indices of the new measure (areas under the curve [AUCs] from .69 to .89) were found to be superior in differentiating between the honest responders and feigners (Morson, Davis, & Weav-

er, 2016). Specifically, all of the feigning scales on the brief form were significantly better at detecting feigned PTSD than the F and Fp scales on the MMPI-2. The PRSM has yet to be published or to undergo the peer-review process, but it appears to have potential in the detection of feigned PTSD.

Conclusions about Psychological Tests and Feigned PTSD The presence of malingering is often a major clinical concern when conducting assessments in a medicolegal setting. Psychological assessments can help clinicians reach more accurate conclusions. This review highlights several important findings that clinicians should consider when deciding what psychological tests to administer. • The rare and quasi-rare detection strategy scales appear to be the most successful, with few exceptions, at differentiating feigned and genuine PTSD, as demonstrated with the PAI, MMPI-2, and MMPI-2-RF. • The selection of measures to use should be based on research-based evidence rather than popularity. While newer measures hold promise, more research is necessary to understand the level of their effectiveness across professional settings. • Decisions about which cutoff scores to use can vary widely based on the primary purpose. For example, lower cutoff scores can be used as a way to screen potential feigners and identify which cases need further investigation. In contrast, more conservative cutoff scores can help avoid misclassification by minimizing false positives and provide evidence for possible feigning. In addition, the prudent use of an indeterminate range could further reduce false positives. Psychological testing should not be used alone to make conclusions about feigned PTSD and should be corroborated with other evidence (Burchett & Bagby, 2014; Rogers, 2008). Official military records, hospital records, police reports, and employment records may help to substantiate reported trauma exposure and may reveal previous traumas that might account for the current symptom presentation (Demakis & Elhai, 2011). Clinical interviews may also provide valuable information on whether the patient is inconsistent across his or her presentation or reporting absurd symptoms. In summary, the use of psychological

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testing to detect feigned PTSD is important, but it is only one component in the assessment process.

MALINGERING OF OTHER TRAUMA‑RELATED DISORDERS In addition to PTSD, trauma may lead to other conditions, including chronic pain and neurological sequelae. Otis, Pincus, and Keane (2006) noted that 20–34% of people who are seen for chronic pain also have significant PTSD symptoms. Those with PTSD tend to have more intense pain (Geisser, Roth, Bachman, & Eckert, 1996), and their level of disability is greater (Sherman, Turk, & Okifuji, 2000). When no clinical evidence supports a physiological cause of these neurological symptoms, they may be the by-product of the unintentional (conversion disorder) or intentional (malingering) production of symptoms. Tests to detect malingering in organic disease are invalid in conversion disorder (Lipman, 1962; Smith, 1967). The differential diagnosis is further complicated by the fact that an individual with conversion disorder may also malinger. Clinicians’ ability to distinguish between the two disorders rests on their ability to assess consciousness, an incredibly challenging task. It is worthwhile for the clinician to search for the unconscious gain associated with the individual’s symptoms; patients may unwittingly reveal gains of which they themselves are not aware. Additionally, patients with conversion disorder tend to willingly engage in evaluations because they too are eager for an explanation of their symptoms (Trimble, 1981) and are anxious for a cure (Hofling, 1965). Interestingly, a meta-analysis demonstrated that la belle indifférence (a patient’s lack of concern about his or her symptoms), traditionally associated with conversion disorder, now appears to be more common with organic illness (Stone, Boone, Back-Madruga, & Lesser, 2006). Comorbid personality disorders, most often histrionic and dependent personality disorders, may predispose an individual to conversion symptoms. Other factors include having little formal education, low IQ, low socioeconomic status, and limited psychological/medical knowledge, as well as an existing neurological disorder on which the individual may unconsciously base his or her symptoms (Kaplan & Sadock, 2003; Yutzy, 2002). Depression is quite common after a traumatic accident. It may be related to both physical harm

and emotional loss due to an inability to work or play the same role in one’s family dynamic. Symptoms of depression often accompany PTSD and overlap with PTSD symptoms. Tests evaluating depression rely primarily on self-report, so they can easily be manipulated. PTSD requires a careful differentiation between diagnosis and comorbid diagnoses, in addition to feigning. Compensation neurosis is a term that was first coined in 1879 in response to the injured railroad workers (Rigler, 1879). It was defined in 1979 as a “behavior complex associated specifically with the prospect of recompense and in contradistinction to traumatic neurosis and psychiatric illness . . . precipitated by the stress of illness, accident or injury” (Rickarby, 1979, p. 333). Although not recognized in any version of the DSM, it may be offered in court hearings by defense attorneys. In 1961, Miller stoked the controversy by proposing a similar diagnosis following head injuries entitled “accident neurosis.” The clinical features included the following: 1. Subjects’ unshakable conviction in their lack of fitness for work. 2. Inverse relationship between the severity of the injury and the degree of disability. 3. Absolute failure to respond to treatment until compensation had been awarded. Miller’s study showed that 48 of 50 individuals recovered completely within 2 years of settlement of their personal injury claim. One problem with Miller’s proposal is that he used a broad definition of malingering, suggesting that the motivation behind the production of symptoms (conscious vs. unconscious) is of little consequence. Several studies attempted to replicate his findings but have demonstrated the opposite effect; that is, the removal of the factor of litigation (either because the individual was not eligible for compensation or a settlement was reached) did not cause an improvement in the patient’s symptoms (Kelly & Smith, 1981; Mendelson, 1981; Parker, 1977; Thompson, 1965). Moreover, compensation neurosis is a pejorative term (Modlin, 1960) for a disorder that is not supported by the literature and therefore should not be a valid diagnosis. Compared to PTSD, psychosis is infrequently malingered after personal injury. While fraudulent plaintiffs are willing to invest time and energy into crafting a believable story, few are willing to undergo inpatient hospitalization (Davidson, 1952)



10. The Malingering of Posttraumatic Disorders 199

and treatment with potent medications. It is also quite challenging to pretend to be psychotic over a long period of time, which might be necessary, as litigation may span several months to years.

BOX 10.1.  Clues to Malingered Amnesia 1. Overplaying memory deficits 2. Improbable answers to overlearned data 3. Alleged impairment of procedural memory

ASSESSMENT OF MALINGERED PTSD Conceptual Issues with Malingered PTSD Ali, Jabeen, and Alam (2015) suggest several reasons for the importance of detecting feigned PTSD. First, feigners misdiagnosed with PTSD may receive unwarranted and even harmful therapies. Second, feigning is clearly disruptive to the therapeutic relationship between malingerers and their therapists. It may then create a ripple effect, with clinicians becoming mistrustful of all their patients. Third, malingering negatively impacts the economy. Fourth and finally, malingering creates inaccuracies in the medical database, which can impact research regarding PTSD (Rosen, 2006). Antisocial Characteristics of the Malingerer

The presence of antisocial traits (Hollander & Simeon, 2002) and psychopathic traits (Edens, Buffington, & Tomicic, 2000) may arouse suspicions of malingering. In contrast, persons who are consistently contributing members of society are less likely to malinger (Davidson, 1952). Similar to persons with antisocial traits, malingerers often have poor social and occupational functioning prior to the trauma (Braverman, 1978). These may include sporadic employment with long absences from work or previous incapacitating injuries. Assessing Memory Impairment

Amnesia may play a role in PTSD but it can certainly be feigned as well. It is important to note that some memory distortion may be expected over time in patients with genuine PTSD (Loftus, 1979). In general, malingerers tend to overplay their memory deficits. They may allege an inability to recall overlearned data, including their name, gender, or Social Security number (Brandt, 1992; Levin, Lilly, Papanicolau, & Eisenberg, 1992). Even with a legitimate history of head trauma, procedural memory is rarely impaired. Therefore, malingering should be suspected when examinees states that they can no longer recall how to ride a bike or drive a car. Malingering is also more likely if an individual scores more poorly on those ques-

4. Poor performance on tests labeled “memory testing” 5. Performing worse than chance on memory testing 6. Clear recollection of memory loss examples

tions clearly labeled as “memory testing” or performs worse than chance on forced-choice tests such as the Test of Malingered Memory (TOMM). Some clues to feigned amnesia are presented in Box 10.1.

Behavior during the Evaluation Associated with Malingering Professionals can think of a malingerer as an actor who is playing a role, and the performance will reflect the individual’s preparation (Ossipov, 1944). Thus, a lack of knowledge regarding PTSD will likely result in a poor performance during the evaluation. Malingerers may overact their part by giving an excessively dramatic report of their symptoms. Alternatively, they may adopt a globally evasive posture, hesitating to discuss their return to work or the money they stand to gain from the resolution of their case (Powell, 1991). To avoid being specific, the malingerer may answer questions vaguely, than to clinicians’ focused questions. Conversely, successful malingerers often endorse fewer symptoms and avoid those that are unusual or dramatic (Edens et al., 2001). Malingerers may even “go on the offensive” and attempt to control the interview by trying to intimidate evaluators. They may go so far as to accuse the interviewer of suggesting that they are malingering. During the evaluation, the clinician should take careful note of inconsistencies. Internal inconsistencies occur when individuals report severe symptoms, then contradict themselves (e.g., reports of significant memory impairment, then “remembering” several specific instances when they were unable to recall information). Internal inconsistencies also occur if malingerers provide conflicting accounts of their own story to the same evaluator. External inconsistencies occur when an individual’s symptoms stand in contrast to what is observed or

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learned (e.g., reported poor concentration is contradicted by engaged responding during a 3-hour evaluation). External inconsistencies also occur if individuals report a decreased level of social functioning but friends state that they often engage in social activities. Finally, external inconsistencies occur when there is a discrepancy between the individual’s report and hospital or police records.

Collateral Information and Feigning Collateral information is crucial to validating the examinee’s reported symptoms and the traumatic event. It is helpful to gather collateral data prior to the evaluation, so that evaluators may address any inconsistencies that arise. A review of police reports or witnesses’ accounts of the trauma may provide a more objective view of the events that occurred. Reports written at the time of the incident also help to curtail biased retrospective recall, in which claimants may amplify their memory of the traumatic event (Harvey & Byrant, 2000; Koch et al., 2005). In a similar vein, it is best to insist on seeing the complete set of medical records rather than accept a summary written by the examinee’s treater. Such summaries often favor what the treater perceives to be in the best interest of the patient. Employee files, school records, and tax returns may provide insight into the claimant’s daily functioning prior to the trauma and should be compared to the claimant’s subjective report. Persons most helpful in providing collateral information are those who are close to the claimant but do not stand to gain from the litigation. Information gained from collateral sources helps to establish an individual’s baseline level of functioning prior to the trauma. For example, an individual may claim that the trauma resulted in poor concentration, whereas a coworker may report that the claimant has always been easily distracted. Collateral sources may also be able to provide observational data, such as the claimant’s body movements during dreams, sleep patterns, and the presence of other symptoms consistent with PTSD. Access to these informants often depends on the jurisdiction and whether the expert is employed by the plaintiff or defense attorney. If the evaluator is unable to gain access to relatives, the retaining attorney may sometimes question them in a deposition.

The Interview and Malingered PTSD The interview, when conducted in a careful and thorough manner, is a key component in differen-

tiating between genuine and malingered PTSD. During the interview, the evaluator must take care not to reveal PTSD criteria or convey any bias regarding its diagnosis. If evaluators adopt a confrontational style, examinees may feel compelled to exaggerate symptoms in order to justify their impairment. Interviewers should initially adopt an open-ended questioning style and avoid leading questions that give clues to correct responses. Because PTSD criteria are well-known, evaluators should insist on a detailed account of the specific symptoms the individual is reporting. Falsified symptoms often have a vague or artificial quality (Pitman, Sparr, Saunders, & McFarlane, 1996), lacking the nuances of personally experienced symptoms. Statements such as “I have nightmares” cannot be accepted at face value. Details, including circumstances, degree, frequency, and context, must be explored. For example, malingerers may claim that their nightmares are repetitive and have occurred without variation or decreased frequency over the past several months. Genuine dreams related to trauma often decrease over time. They may, however, increase in frequency when the individual is reminded of the event (e.g., giving a deposition) or experiences new stressors. Fifty percent of nightmares with PTSD show variations on the theme of the traumatic event (Garfield, 1987; Wittman, Schredl, & Kramer, 2007). After a traumatic event, the event may be dreamt almost literally a few times, then gradually include other elements as the event is woven into the rest of the person’s dream life. For instance, a woman who was raped may dream about another situation in which she feels helpless or trapped. A person malingering PTSD is less likely to report variations on the theme of the traumatic event. Posttraumatic nightmares, in contrast to lifetime nightmares unrelated to trauma, are almost always accompanied by considerable body movement (van der Kolk, Blitz, Burr, Sherry, & Hartmann, 1984). Inman, Silver, and Doghramji (1990) found that persons with PTSD-related insomnia, compared to others with insomnia, were more likely to be afraid of the dark and of going to sleep. They were more likely to wake up with the covers torn apart and to talk and shout during their sleep. They more often woke up confused and disoriented, and had difficulty returning to sleep. Nightmares may occur earlier in the night in those with PTSD (Germain & Nielsen, 2003). During an interview with an individual malingering PTSD, the evaluator may note the absence



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of behavioral manifestations of the disorder, such as irritability, inability to focus, or exaggerated startle response. The interviewer may also find that the claimant minimizes other possible causes of his or her symptoms or portrays the pretrauma level of functioning in a overly positive light (Layden, 1966). Careful consideration must be given to the temporal course of symptom development in relationship to the trauma. It may also prove useful to inquire about claimants’ capacity to work versus their ability to enjoy recreational activities. Malingerers may state that they are incapable of remaining employed but acknowledge their active participation in hobbies. Person with genuine PTSD would likely withdraw from both work and recreation. Third parties should not be present during the interview. Informants may alter their stories, consciously or not, based on the claimant’s report. Also, if the evaluator confronts an examinee about suspected malingering, he or she will be far less willing to admit this with others present in the room. In some instances, the evaluator may feel that the situation warrants the use of subterfuge to expose the suspected malingerer. Prior to starting the interview, the evaluator could converse with a colleague about PTSD within earshot of the examinee and make mention of symptoms clearly not associated with the disorder (e.g., grandiosity or rapid speech). The examiner can then see whether the examinee volunteers these symptoms of the disorder.

Psychophysiological Assessment Measurement of the body’s responses to indicators of trauma may serve as one of the few objective methods of distinguishing genuine from malingered PTSD. A physiological reaction to cues related the traumatic event would fulfill Criterion B (Pitman, Saunders, & Orr, 1994). Several studies have evaluated the success of evaluators in diagnosing PTSD based on psychophysiological assessment. Blanchard, Kolb, Pallmeyer, and Gerardi (1982) played an audio recording of combat sounds and measured the heart rate, systolic blood pressure, and muscle tension in the forehead of veterans with PTSD and a control group. The two groups responded differently in terms of heart rate, blood pressure, and forehead electromyography (EMG). The investigators reported that they were able to identify patients with genuine PTSD with 95.5% accuracy using the heart rate response.

Lang (1985) improved on the stimulus used to trigger physiological changes in the previous protocol by using script-driven imagery. This stimulus allowed for a more accurate re-creation of an individual’s unique stressors in a given traumatic event. For example, an army medic might have a strong physiological response to the sound of a helicopter. Pitman and colleagues (1994) requested that 16 combat veterans without PTSD simulate the physiological profile of a patient with PTSD and found that 75% failed in this task. Studies have shown that psychophysiological testing may also be useful in predicting the development of PTSD and estimating treatment response. In 2007, O’Donnell, Creamer, Elliott, and Bryant measured the tonic (resting) and phasic (aroused) heart rate of victims 1 week posttrauma, then evaluated them for symptoms of PTSD 12 months later. They noted that high heart rate reactivity scores (phasic minus the tonic heart rate) predicted the development of PTSD. In 2010, Suendermann, Ehlers, Boellinghaus, Gamer, and Glucksman showed trauma survivors evocative images 1 month after their injuries. They also noted that heart rate reactivity predicted PTSD at 1 month and at 6 months, but skin conductance response was not related. In 2015, Wangelin and Tuerk studied 35 male combat veterans, who received prolonged exposure therapy. Those who completed the therapy showed significantly reduced heart rate and skin conductance reactivity to traumatic imagery. In the largest study of the psychophysiology of PTSD, Keane and colleagues (1998) subjected Vietnam veterans to scripts of traumatic events for three groups: current diagnosis of PTSD (N = 778), past diagnosis of PTSD (181) and no diagnosis of PTSD (369). Many veterans with current PTSD exhibited increased heart rate, skin conductance, and muscle tension compared to those with no diagnosis. The veterans with a past diagnosis fell somewhere in the middle. Unfortunately, however, one-third of those with a current PTSD diagnosis did not respond physiologically, which substantially limits the utility of this assessment method. Another limitation of this method is that persons without PTSD may demonstrate physiological reactivity to upsetting events. In the McNally (2006) study, patients claiming to have been abducted by space aliens (i.e., not a Criterion A diagnosis for PTSD) were exposed to a narrative script similar to their own reported experience. Their psychophysiological responses were at, if not

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above, those of the veterans with PTSD in Keane and colleagues’ (1998) study. Thus, a genuine physiological response cannot confirm the presence of PTSD. In light of the discrepancies in psychophysiological testing in PTSD, Pole (2006) conducted a meta-analysis to determine which variables influence effect sizes between PTSD and control groups. The effect size was greatest between the two groups when (1) the control group has not been exposed to trauma before, (2) the PTSD group had severe symptoms, and (3) members of the PTSD group were diagnosed using stricter DSM-III criteria. The findings are commonsensical. Removing trauma from the control group and increasing the severity of the symptoms in the PTSD group will increase the observed differences. In summary, psychophysiological testing does provide an objective but flawed means of differentiating between genuine and malingered PTSD. A significant minority of those with genuine PTSD do not demonstrate a physiological response, possibly leading to an incorrect classification as a malingerer. Conversely, some individuals without PTSD demonstrate physiological reactivity, possibly leading to an incorrect diagnosis of PTSD. Given this lack of accuracy, this type of psychophysiological evidence should not be admitted as evidence in court.

A MODEL FOR THE DIAGNOSIS OF MALINGERED PTSD No single piece of data is pathognomonic for malingered PTSD. Rather, the diagnosis requires a careful assessment of all the evidence gathered, including a meticulously detailed history of symptoms, past and present, plus social and occupational functioning and careful corroboration of evidence. A clinical decision model for determining malingered PTSD is presented in Table 10.6. The model requires the establishment of (1) the individual’s motivation for feigning his or her symptoms, (2) the presence of at least two of the more common characteristics associated with malingered PTSD, and (3) strong collateral information supporting malingering. An evaluator may choose to confront the examinee after a thorough investigation has led to this conclusion. Direct accusations of lying are rarely successful. A more fruitful approach involves asking the examinee to clarify the inconsistencies. The clinician may also convey sympathy regard-

ing the reasons the individual feels compelled to exaggerate symptoms. This approach creates an environment in which the examinee may feel more comfortable acknowledging his or her malingering. For example (Inabu & Reid, 1967), it is preferable to say, “I am not sure you have told me the whole truth,” instead of “You have been lying to me.” Conversely, causing the examinee to feel ashamed may produce feelings of anger and opposition, which may then lead to a greater desire to continue the charade. In some extreme instances, these feelings may even lead to physical violence directed at the examiner. Finally, once a malingerer is confronted and chooses to deny his or her actions, subsequent acknowledgment is highly unlikely. One federal appellate court ruled that malingering is equivalent to obstructing justice and could result in a harsher penalty (Knoll & Resnick, 1999). If a criminal defendant is malingering, the examiner may therefore choose to disclose this information in the hope of producing a more ­honest response. However, such a notification could have the opposite effect, causing the defendant to cling more fiercely to the malingered symptoms.

TABLE 10.6.  Clinical Decision Model for Establishing Malingered PTSD

A. Establish known motivation for malingering B. Characteristics of the malingerer (two or more of the following criteria): 1. Irregular employment or job dissatisfaction 2. Prior insurance claims 3. Capacity for recreation, but not work 4. Lack of nightmares or nightmares that are inconsistent with presentation 5. Antisocial personality traits 6. Evasiveness or contradictions 7. Unwillingness to cooperate or hostile behavior in the evaluation C. Confirmatory evidence of malingering (one or more of the following criteria): 1. Admission of malingering 2. Incontrovertible proof of malingering (e.g., video recording of a man at a party after claiming that his anxiety did not allow him to leave his home) 3. Unambiguous psychometric evidence of malingering 4. Strong corroborative evidence of malingering (e.g., video recording contradicting alleged symptoms)



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A SPECIAL CASE: PTSD AND MALINGERING IN THE COMBAT VETERAN PTSD in Veterans Over the past five decades, the United States has been involved many military conflicts exposing our military forces to trauma capable of producing PTSD in areas such as Vietnam, Somalia, the Persian Gulf, Iraq, and Afghanistan. In 1980, the Department of Veterans Affairs (VA) began to accept PTSD as a diagnosis for the purposes of assigning service-connected benefits (Bitzer, 1980). Following this, veterans became exposed to the PTSD diagnostic criteria via (1) distributed literature (Atkinson, Henderson, Sparr, & Deale, 1982), (2) contact with veterans diagnosed with PTSD (Lynn & Belza, 1984), and (3) the Internet. Some Internet sites provide suggestions on how to maximize the chances of receiving service-connected benefits (vva.org/what-we-do/outreach-programs/ ptsd) or how to write “stress letters” to submit to the VA ratings board (ptsdsupport.net). In 2007, Sparr and Pitman reported that PTSD is now the most common psychiatric condition for which veterans seek compensation. Following the publication of the DSM-5, Levin et al. (2014) suggested that the VA may be forced to exclude the A3 criterion (learning of the violent or accidental death of a close friend) for compensable injuries or risk a significant increase in PTSD-related claims. In the differential diagnosis of combat PTSD, professionals must also consider malingering, factitious disorder, ASPD, and genuine PTSD secondary to a non-combat-related cause.

Motivation to Malinger Combat-Related PTSD Three main factors can motivate veterans who malinger PTSD: (1) to obtain financial compensation, (2) to reduce criminal culpability, and (3) to obtain admission to a VA hospital. Veterans who successfully malinger PTSD may be well rewarded; those qualifying for a “100% service connection” for PTSD may be eligible to receive up to $40,000 a year for their disability (Burkett, & Whitley, 1998). Once veterans are qualified for PTSD-related disability, there is an ongoing financial incentive to remain disabled regardless of their true status (Mossman, 1994). The Department of Veterans Affairs Office of the Inspector General (2005) surveyed 2,100 veterans with at least 50% service-connected PTSD. It concluded that 25.1% were misdiagnosed and therefore not entitled to their benefits. Extrapolat-

ing to all veterans with service-connected PTSD, an estimated $19.8 billion had been erroneously paid out over a lifetime to those not meeting PTSD diagnostic criteria. In addition, a sample of 92 veterans with alleged PTSD continued to make regular mental health care visits until they received a 100% service connection. Many reported that their symptoms worsened over time despite their treatment. However, when 100% service connection was assigned, 39% of patients’ mental health visits dropped on average by 82%, and some veterans received no mental health care at all (Department of Veterans Affairs Office of the Inspector General, 2005). A diagnosis of PTSD confirmed by the VA may serve as an excusing or mitigating factor in legal cases. Malingered PTSD might play a role in decreasing criminal responsibility for veterans alleging that they experience (1) a dissociative state and resort to survival skills learned in military training (e.g., killing), (2) survivor guilt and want to have themselves killed (e.g., “suicide by cop”) in order to rejoin fallen comrades, and (3) sensation-seeking behavior (e.g., drug offenses) in order relive combat excitement (Wilson, 1981). When assessing the relationship of PTSD to a crime, the evaluator should first consider whether the crime scene recreated a traumatic event, and if so, whether the veteran experienced dissociative symptoms at the time that the crime was committed. Given the easy opportunity for monetary gain, veterans may fraudulently claim that they developed symptoms of PTSD from combat even though they were never exposed to battle (Lynn, & Belza, 1984). Some individuals who never even served in the military have successfully acquired service-connected benefits for PTSD (Burkett & Whitely, 1998). A key step in either supporting or discrediting a veteran’s story involves the collection of collateral data. There are a number of ways that a clinician may attempt to do so.

Collateral Information for Claimed Combat PTSD Military records, including discharge papers (also known as a DD214), often identify stressors to which a veteran has been exposed, but the clinician should be aware that these documents can easily be forged (Burkett & Whitley, 1998). Forgeries may be avoided by obtaining the documents directly from the U.S. Department of Defense (Sparr & Atkinson, 1986). Individuals malingering PTSD may state that their records do not re-

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flect their covert missions or “black ops”; therefore, no evidence of their experiences exist. In such cases, examiners should look for the special training required for these missions in a veteran’s file, and the term classified assignment will appear next to the date of the mission (Burkett & Whitley, 1998). Collaboration with employees at the VA or experienced combat veterans may help to elucidate false claims (Lynn & Belza, 1984). Malingerers may fabricate stories that are as vivid and horrifying as the experiences of true combat veterans (Burkett & Whitley, 1998; Hamilton, 1985). However, they may reveal their dishonesties by incorrectly identifying certain details, including the geography and culture of the area, the military terminology used at the time, and dates related to specific events (Burkett & Whitley, 1998). Family members and friends of combat veterans may be interviewed to determine both the validity of the symptoms and the social functioning of the veteran prior to service in the military. Frueh et al. (2005) performed an archival study of 100 consecutive cases of men who reported combat trauma related to their experiences in Vietnam. Almost all (94%) had received a diagnosis of PTSD. For 41% of the veterans, archival data verified their stories. One-third had served in Vietnam, but the records indicated that they served in positions that would have made exposure to trauma highly unlikely. Five percent had not been in Vietnam or had not served in the military at all. Finally, those with documented combat experience reported twice the rate (28 vs. 12%, respectively) of battlefield atrocities when compared to those with verified combat exposure.

The Interview Evaluators must recognize the possibility of experiencing powerful emotions and potentially biasing their evaluation. The recounting of horrific combat experiences can produce a highly charged, affect-laden environment that can be stressful for both the evaluator and the examinee (McAllister, 1994). Interviewers should also recognize the strong emotions that may be stirred up in examinees while recounting their combat-related experiences; some veterans may therefore be hesitant to discuss their painful experiences. Evaluators should be cognizant of a potential inclination to diagnose PTSD in a veteran out of a sense of moral obligation (Atkinson et al., 1982; Pankratz, 1985). Other veterans may exaggerate their symptoms in

fear of not receiving the compensation that they truly deserve (Fairbank, McCaffrey, & Keane, 1986).

Clinical Indicators of Malingered Combat‑Related PTSD Indicators evident of malingered combat-related PTSD are primarily based on case reports and anecdotal descriptions. Thus, they should be used only in combination with other evidence to support the classification of malingering. Table 10.7 compares genuine and malingered combat-related PTSD. Those with genuine PTSD usually come for an evaluation because of the encouragement of loved ones, have repeated loss of employment and depression, display outbursts of anger, and sometimes have issues with substance use. They may feel guilty about surviving while fellow soldiers died in battle (Burkett & Whitley, 1998). They often attribute blame to themselves and may not directly relate their current state to their combat experience (Melton, 1984). They are more likely to downplay their combat experiences and current symptoms by saying things such as “Lots had it worse than me.” They may also hesitate to express how emotionally traumatizing the experience of war was for them. They are more likely to provide specific, trauma-related examples of avoidant behavior, such as not going out on very hot, dry days that resemble the weather in the Iraq. Conversely, malingerers often present themselves as “victims” and attribute a multitude of problems directly to their experience in the military. They more commonly present with a fear of “losing control” and harming others (Melton, 1984). This fear may even be used as an excuse to gain admission to a psychiatric unit. They might provide tales of their own improbable heroics during wartime, including escape from a prisoner of war (POW) camp or singlehandedly fighting multiple enemies in hand-to-hand combat. Individuals who can recite the DSM criteria or use psychiatric jargon such as “intrusive recollections” should be regarded with suspicion. Malingerers are more likely to thrust forward their symptoms and diagnosis by making broad, conclusory statements such as “I have PTSD.” They may also present with dramatic, attention-seeking behavior and overplay symptoms such as hyperarousal. The nightmares of those with combat-related PTSD often involve a specific traumatic event that occurs repeatedly as it did in reality, without variation (Atkinson et al., 1982). If they discuss



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TABLE 10.7.  A Comparison of Genuine and Malingered PTSD in Combat Veterans

Salient features

Genuine PTSD

Malingered PTSD

Presentation for evaluation

Secondary to encouragement from others

Individual seeks out the opportunity

Attributing blame

To oneself

To those in a position of authority

Combat experience and current symptoms

Downplays them, reluctant to discuss

Emphasizes them

Affect

Withdrawn

Assertive

Theme of nightmares

Feeling helpless

Being in power and having control

Other

Avoids environmental cues reminiscent of the trauma (hot, dry weather)

Describes fear of “losing control” and hurting others

nightmares, malingerers are more likely to describe situations in which they were powerful and in control versus the feelings of overwhelming helplessness that are usual theme of posttraumatic dreams in combat veterans (Atkinson et al., 1982).

CONCLUSIONS Most clinicians would agree that malingering is difficult to identify, no matter what illness is feigned. It requires an assessment of whether false symptoms are intentionally produced and what the individual gains by having those symptoms. The diagnosis of PTSD is primarily based on a subjective report of psychological symptoms, which makes it an easy disorder to simulate successfully. Though it is a complex and lengthy process, meticulous collateral data collection and careful evaluation of the examinee allow for the differentiation between genuine and malingered PTSD. Psychological testing is very important in confirming suspected malingering. Clinicians charged with the task of evaluating PTSD must be well versed in the phenomenology of the disorder and know the crucial differences between genuine and malingered PTSD. In summary, in order to improve the likelihood of recognizing malingered PTSD, the evaluator should do the following: • Establish the individual’s motivation for malingering. • Collect collateral information from multiple sources prior to the evaluation. • Approach the assessment in an unbiased fashion.

• Ask open-ended questions, while being mindful not to reveal the criteria for PTSD. • Assess the veracity of the symptoms based on the phenomenology of legitimate PTSD. • Utilize psychological testing designed to detect malingering. • Clarify inconsistencies in the examinee’s account. • Provide the examinee the opportunity to acknowledge malingering without shame.

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C H A P T E R 11

Factitious Disorders in Medical and Psychiatric Practices Gregory P. Yates, MA Mazheruddin M. Mulla, MA, MPH James C. Hamilton, PhD Marc D. Feldman, MD

The scholarly study of medical and psychiatric deception is rooted in hundreds of carefully documented cases. These reports provide convincing evidence that some patients intentionally exaggerate, lie about, simulate, aggravate, or induce illness, injury, or impairment, either in themselves or in persons under their care (Yates & Feldman, 2016; Sheridan, 2003). The existence of such cases has been widely accepted for over a century, and these clinical phenomena have been codified as psychological disorder since DSM-III (American Psychiatric Association, 1980). Indeed, most clinicians have stories to tell about “Munchausen’s syndrome,” “frequent flyers,” “peregrinating problem patients,” and “hospital hoboes” encountered in their practice. Such cases of intentional feigning are organized into factitious disorder (FD) and malingering. Malingering is not a disorder per se, but rather a condition that may be of clinical concern. Patients with FD deliberately falsify symptoms associated with physical or psychological illness, injury, or impairment in themselves or others (American Psychiatric Association, 2013). The terms factitious disorder imposed on self (FDIOS) Allan Cunnien, MD, now deceased, made valuable contributions to the first two editions; out of respect, his name was continued on the third edition.

and factitious disorder imposed on another (FDIOA) distinguish the two types. Patients with FD may engage in a variety of deceptive behaviors in order to exaggerate or entirely feign the appearance of a medical problem. Common forms of deceptive illness behavior may include exaggerating the severity of a genuine medical problem, falsely reporting symptoms, acting as if symptoms are present when they are not, or interfering with medical tests or test results. For example, individuals may (1) falsify imaging results to indicate the presence of a tumor, (2) describe thoughts of suicidality following a divorce when they have never in fact been married, or (3) tamper with blood or urine samples to simulate evidence of a disease. In some cases, individuals may actually induce illness, injury, or impairment in themselves or another through behaviors such as injection of a harmful substance or mutilation of the skin with sharp instruments or corrosive chemicals. Little empirical research exists on FD despite considerable professional interest in the disorder. The vast majority of published articles are case reports, which are prone to publication bias and should not be taken as a substitute for empirical research (Yates & Feldman, 2016). Nonetheless, case studies are an important source of clinical guidance (Jenicek, 2001). Case studies, in aggregate, have provided the basis for

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11.  Factitious Disorders in Medical and Psychiatric Practices 213

key recommendations for the detection (Kenedi et al., 2011; Kinns, Housley, & Freedman, 2013) and management (Eastwood & Bisson, 2008) of FD, as well as a vital evidence base for this chapter.

DIAGNOSTIC CLASSIFICATION OF FD Modern scientific recognition of FD is thought to have begun with Asher’s (1951) introduction of the term Munchausen’s syndrome to describe a chronic pattern of feigned illness behavior, although selfinflicted dermatoses were acknowledged as early as 1948 in international diagnostic systems (World Health Organization, 1948). Asher (1951) coined the term to describe what is now thought to be a rare, archetypal, and untreatable form of FD. The lifestyles of these patients revolved around hospitalizations, surgeries, and contentious battles with physicians. Subsequently, Meadow (1977) coined the term Munchausen syndrome by proxy to describe a parallel condition in which parents or other caretakers falsify health information or produce factitious disease in children, principally to garner emotional satisfaction. FD was first included as a formal diagnostic category in the third edition of the Diagnostic and Statistical Manual of Mental Disorders (DSMIII; American Psychiatric Association, 1980). It discussed deliberately feigned symptoms (Criterion A) and sick-role motivation (Criterion B) in the absence of external incentives (Criterion C). These criteria were maintained until the release of DSM-5 (American Psychiatric Association, 2013), as were the initial subtypes: predominantly psychological signs and symptoms, predominantly physical signs and symptoms, combined psychological and physical symptoms, and FD not otherwise specified. DSM-5 includes several notable changes in FD classification. FD no longer has its own chapter but is instead grouped under somatic symptom and related disorders (American Psychiatric Association, 2013). The rationale for this change is that somatic symptoms are typically the predominant feature in FD, and the preponderance of cases is encountered in medical rather than psychiatric settings. The subtypes of FD listed previously have been replaced by two new subtypes: “FD imposed on self” and “FD imposed on another” (American Psychiatric Association, 2013). These changes also attempted to address the blanket use of the term Munchausen’s syndrome to describe these cases.

The criteria for FD have also changed in DSM5, whereas the key diagnostic features remain largely the same. In particular, motivation to assume the sick role (in oneself or through another) has been removed as a requirement for DSM-5 diagnosis (American Psychiatric Association, 2013). The revised criteria now include (1) feigning (e.g., symptoms and behaviors) or the covert production of illness; (2) pretending to be ill, impaired, or injured; and (3) deception that occurs without apparent external motivation. It also includes an exclusion criterion regarding other disorders. These changes were intended to shift the focus of diagnostic assessment away from drawing inferences about underlying motivation. Instead, the revised criteria place greater reliance on more objective measures, such as the identification of intentional deception. Evidence of intentional deception is used in DSM-5 as the basis for distinguishing FD from behaviorally similar problems. For example, somatic symptom disorders and functional somatic syndromes are presumed to lack this intentionally deceptive quality. In these cases, medically unexplained illness behavior is regarded as the unintended result of faulty automatic cognitive processes. Rather than being distinct categories, some have argued that all these conditions exist along a continuum of consciousness or voluntariness (Nadelson, 1979). The deceptive illness behavior in FD is thought to be the product of conscious motivation to achieve a particular goal. Cases in which this behavior is attributed to external goals are classified as malingering, whereas the diagnosis of FD is given if the deception is enacted principally for emotional gratification. In practice, most cases of excessive illness behavior involve the presence of both external rewards and emotional gratification. The classification of malingering is addressed extensively by Rogers (Chapter 1) and in subsequent chapters of this volume. Regardless, its conceptual relation to FD is a pervasive theme in this chapter.

FDIOS Factitious Medical Disorders Medical patients with FDIOS may appear in primary care settings, emergency departments, or any number of secondary care specialties. They may even present in palliative care settings (Weis, Gully, & Marks, 2016). The clinical presentation of factitious medical disorders is highly variable.

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By definition, medical falsification can come in multiple forms: (1) exaggerated symptoms or medical history, (2) outright lies about symptoms or medical history, (3) simulations of medical illnesses through the production of compelling signs or symptoms, (4) manipulations to prolong or exacerbate an existing illness, or (5) actual selfinduction of disease (American Psychiatric Association, 2013). Rarely does a factitious medical patient use only one method of deception. Rather, typical patients with FDIOS employ multiple forms of deception, such as a simulated sign of disease (e.g., rashes), feigned symptoms (e.g., joint pain), and a false history (e.g., tick bites) to create the full medical picture of a known disease (e.g., Lyme disease). Medical patients with FDIOS may be encountered, for example, in (1) dermatology (Fliege et al., 2007), with rashes (Levitz & Tan, 1988), burns (Maurice, Rivers, Jones, & Cronin, 1987), infections (Farrier & Mansel, 2002), or nonhealing wounds (McEwen, 1998); (2) endocrinology, with dysregulation of thyroid hormones (Ashawesh, Murthy, & Fiad, 2010), blood sugar (Alinejad & Oettel, 2011), or insulin (Gordon & Sansone, 2013); and (3) neurology (Bauer & Boegner, 1996), with seizures (Romano, Alqahtani, Griffith, & Koubeissi, 2014), weakness (Papa­ dopoulos & Bell, 1999), sensory deficits (Barnett, Vieregge, & Jantschek, 1990), or paralysis (Feldman & Duval, 1997). In general practice, bleeding problems, such as coughing up blood (Kokturk, Ekim, Aslan, Kanbay, & Acar, 2006), blood in the urine (Lazarus & Kozinn, 1991), and anemia (Hirayama et al., 2003) are often encountered, as are infections, including sepsis (Lazarus & Kozinn, 1991). The voluminous case literature on factitious medical disorders suggests that almost any medical problem can be falsified, although patients with FDIOS may particularly favor endocrinology, cardiology, and dermatology services (Yates & Feldman, 2016). In the age of the Internet, patients with FDIOS are now able to research complex diagnoses, learn how to forge laboratory reports, and even order pharmaceutical drugs online. Because the diagnosis of FDIOS relies on conclusive evidence of intentional medical deception (American Psychiatric Association, 2013), the disorder is more likely to be uncovered in cases that include simulation or self-induction of medical signs. Common examples observed by staff members include patients who are caught tampering with blood (Kurtz, Harrington, Matthews, & Nabarro, 1979) or urine samples (Nasser, Israelit, Muhammad, &

Basis, 2009). Other evidence includes possession of syringes (Tausche et al., 2004), drugs (Saiyasombat & Satyanarayan, 2012), or mechanical devices (Martins, Vieira, & de Fátima Ávila, 2005) often used for self-mutilation (Feily et al., 2009). Cases of FDIOS that involve only false histories and feigned or exaggerated symptoms are likely to go undiscovered.

Factitious Psychological Disorders Patients with FDIOS may also feign or produce psychiatric and/or behavioral ailments. Surprisingly, this category is gaining in complexity as developments in neuropsychiatry continue to blur the lines between central nervous system dysfunction and features that are primarily “psychological” or “emotional” in origin. As Parker (1996, p. 38) wrote, “The distinction of psychological from physical is becoming increasingly artificial in the face of neurophysiologic advances pointing to the interplay between psychology and neurobiology.” Thus, on the one hand, psychological disorders are gaining in medical legitimacy; on the other hand, few definitive tests exist to establish the presence or absence of psychological disorders. These features combine to increase the appeal of feigned psychological disorders for those seeking to enact the sick role. Carney and Brown (1983) estimated that psychiatric presentations of FDIOS may account for up to 40% of all factitious presentations, even though many cases are probably missed due to inherent difficulties in objective assessment (Popli, Masand, & Dewan, 1992). Examples of particularly difficult factitious presentations include the following: (1) alcohol abuse (Caradoc-Davies, 1988; Mitchell & Francis, 2003), (2) hallucinations (Bahali & Ipek, 2013; Gregory & Jindal, 2006; Yildiz & Torun, 2003), (3) suicidal or homicidal ideation (Gregory & Jindal, 2006; Thompson & Beckson, 2004), and (4) pain disorders (Mailis-Gagnon, Nicholson, Blumberger, & Zurowski, 2008; Callegari, Bortolaso, & Vender, 2006). Rare or obscure psychological disorders may be simulated, including Stockholm syndrome (Spuijbroek, Blom, Braam, & Kahn, 2012) and dissociative identity disorder (Feldman, Rosenquist, & Bond, 1997; Friedl & Draijer, 2000; Thomas, 1997; for a useful review, see Boysen & VanBergen, 2014); cult brainwashing (Coons & Grier, 1990); and various paraphilias including pedophilia (Porter & Feldman, 2011), zoophilia, and exhibitionism (Hanson, Berlin, Malin, Fedoroff, & McGuire, 1992).



11.  Factitious Disorders in Medical and Psychiatric Practices 215

Factitious psychological symptoms are often more pronounced when the physician and hospital staff members are present. Discrepancies tend to arise between what patients describe and their actual appearance or behavior. The classic findings of a well-defined mental disorder are unlikely in factitious psychological disorders. Typically, patients’ symptoms will represent their stereotyped understanding of mental illness (e.g., exaggerated pangs of sorrow rather than emotional blunting in feigned major depression). Likewise, medications indicated for the factitious condition may appear inexplicably ineffective. Patients with factitious psychological disorders are likely to be unusually receptive to psychiatric hospitalization. This may distinguish them from patients with factitious physical disorders, who are likely to recoil at the suggestion that their problems may be psychological. Parker (1996, p. 41) explained that a “common pattern for the patient with FD and physical symptoms is to leave the hospital against medical advice when referred to the psychiatric unit; the patient with factitious psychological symptoms, however, seeks hospitalization on the psychiatric unit.” She also noted that “if the patient is willing to provide valid psychological data, traits of psychopathic deviation, paranoia, hysteria, depression, and hypochondriasis may be present” (p. 41). However, this conclusion is based on very small numbers of patients who may have had sufficient expertise to avoid detection by the validity indicators on the psychodiagnostic tests. Other authors (e.g., Taskaynatan et al., 2005) have found no comorbid disorders for many patients with FDIOS assessed with psychological evaluations. Misuse of drugs and/or alcohol is common among patients with factitious psychological disorders. They may secretly use psychoactive substances to produce actual signs that suggest a mental disorder. Feldman (2004) warned that stimulants such as amphetamines, cocaine, or caffeine may be used to produce restlessness or insomnia. . . . Drugs such as LSD . . . , mescaline, and marijuana might be used to induce altered levels of consciousness and perception. Heroin and morphine . . . may be employed to induce euphoria. Hypnotics such as barbiturates can be used to create lethargy. Combinations of these substances often produce extraordinarily bizarre presentations. The main difference between factitious and actual drug abusers is that the factitious disorder patient induces an altered state not as an end in itself, but as a way to mislead caregivers and others. (p. 109)

Examples of Factitious Psychological Presentation

Factitious psychological disorders may mimic any genuine disorder or syndrome. However, factitious presentations of psychotic symptoms are particularly common (Limosin, Loze, & Rouillon, 2002). Two subgroups of patients presenting with factitious psychosis may be identified, with very different prognoses. The first is associated with hysterical psychosis, a long recognized condition characterized by brief psychotic symptoms with onset during stress, dramatic personality traits, and rapid return to baseline functioning. In one study, patients with this condition were suggestible and usually hypnotizable (Spiegel & Fink, 1979), which indicates that they may have possessed a degree of voluntary control over symptoms. This raises the possibility of FDIOS. Ritson and Forrest (1970) were the first to evaluate patients with suspected factitious psychosis of this first kind; all presented clear signs of simulation, though several had previous diagnoses of schizophrenia. The second subgroup of patients with factitious psychosis exhibits a more ominous prognosis. Pope, Jonas, and Jones (1982) analyzed the presentations and outcomes for nine patients with voluntary control over psychotic symptoms. Most patients were females with severe personality disorders and no family history of psychosis. Strikingly, long-term follow-up revealed that this group performed more poorly on measures of global assessment and social functioning than did patients with actual manic and schizophrenic disorders. For this characterologically disturbed subgroup, the investigators concluded that “acting crazy may bode more ill than being crazy” (p. 1483). Posttraumatic stress disorder (PTSD) may be a particularly effective vehicle for the enactment of factitious psychological disorder. Persons with trauma may falsely claim PTSD symptoms, or persons may falsify both their traumas and their reports of PTSD symptoms (Resnick, 1999). Sparr and Pankratz (1983) described diagnostic challenges in Vietnam veterans with false claims of combatrelated PTSD; review of military records revealed that none experienced the alleged traumas. Diagnoses included FDIOS with psychological and physical symptoms, and malingering. Diverse diagnoses were also noted by Lynn and Belza (1984) and by Hamilton (1985). Such cases reinforce the need to consider both external motives that are characteristic of malingering and the unique, personalized, psychological motivation to be sick that characterizes FDIOS (Lacoursiere, 1993).

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False presentations of trauma-related symptoms have also been encountered in the form of factitious bereavement. Snowdon, Solomons, and Druce (1978) described 12 such cases in which patients’ chief complaints were the fabricated loss of a loved one with resulting depression or suicidality. Such cases have been observed to differ from genuine bereavement in a number of ways. For instance, Feldman (2004) noted that the allegedly bereaved often divulge especially tragic or gruesome deaths that may involve multiple individuals, including children and adolescents. Moreover, factitious mourners display more pathological moods and behaviors than those typical of the genuinely bereaved. They may present with variable or angry affect, feigned physical illness, suicide attempts, felonious acts, substance abuse, pathological lying, and treatment refusal or resistance (Phillips, Ward, & Ries, 1983). A third type of factitious trauma presentation involves false claims of child sexual abuse. The tragic consequences of missed cases of child abuse need no elaboration. Unfortunately, factitious accounts of abuse do occur, and they require investigators to be aware of motivations behind both false denials and false accusations (Goodwin, Sahd, & Rada, 1978). In a survey of over 1,200 reports of child sexual abuse, Everson and Boat (1989) estimated 4.7 to 7.6% to be false. Only 56% of sexual abuse allegations were substantiated; most of the remaining cases were unresolved. It suggests that the percentage of false claims may be substantially high. Recently, Malloy, Lyon, and Quas (2007) examined 257 cases of alleged child sexual abuse; 23.1% of the children later recanted their accusations, although an unknown number may have felt pressured to do so. Factitious accounts of sexual assault in adulthood have also been described in the professional literature (Dohn, 1986; Feldman et al., 1997; McNamara, McDonald, & Lawrence, 2012). Foreign bodies may be inserted rectally or vaginally by the patient to fabricate “evidence” of acute assault (Gibbon, 1998; Humes & Lobo, 2005; Khan, Davey, Khan, Trigwell, & Chintapatla, 2008). Patients with FDIOS may make similar false claims of stalking (Sheridan & Blaauw, 2004; Pathé, Mullen, & Purcell, 1999) and sexual harassment (Feldman-Schorrig, 1996). It is widely agreed that there are strong negative stigmas attached to mental illness. In light of this, it is reasonable to ask why people would feign mental illness to attract sympathy and compassion from others. The previous examples are noteworthy because they all involve psychological prob-

lems that derive from victimization experiences. The average person feels sympathy and compassion for all people who experience these types of traumatic events, and expects such tragedies to produce psychological scars even in otherwise well-adjusted people. Thus, presenting as traumatized is less likely to invoke negative mental illness stigmas than presenting as schizophrenic, anxious, or depressed.

Challenges to Assessment and Diagnosis The deceitfulness, inherent to FDIOS, constitutes a substantial challenge to its identification in practice. The clinical features of the disorder are also highly variable (Bass & Halligan, 2014). However, the substantial body of published case reports of FDIOS may offer, in aggregate, a useful impression of a “typical” clinical encounter with these patients. Yates and Feldman (2016) conducted a systematic review of 455 such cases in the professional literature. Using a checklist adapted from earlier studies (Bass & Halligan, 2014; Steel, 2009), they identified several common factors leading to a diagnosis of FDIOS. Clinicians were frequently alerted to FDIOS by a discrepancy between the patient’s reported symptoms and the results of objective tests (e.g., normal results on computed tomography [CT] and magnetic resonance imaging [MRI] following acute-onset neurological symptoms). Less commonly, FDIOS was directly implicated by investigations; for example, exogenous insulin administration was generally revealed by an elevated insulin concentration with low Cpeptide concentration. In many cases, deception was confirmed when patients were “caught in the act” (e.g., observed tampering with blood or urine tests) or when a search of their belongings revealed suspicious items, such as syringes or sharp instruments. Many patients were discovered because their “illness” and its response to treatment were atypical, improbable, or outright impossible. This finding was particularly true for dermatological cases of FDIOS, in which the majority of lesions were morphologically bizarre and likely to change or worsen with treatment. When clinicians were able to access patient records, a history of extensive health care encounters (the “thick-file” sign) or a recorded concern for fabricated illness strongly indicated FDIOS. Suspicions were also raised by patients who were “poor historians” (e.g., patients with dramatic, inconsistent, or selective information about themselves and their medical history). These patients were sometimes discovered by con-



11.  Factitious Disorders in Medical and Psychiatric Practices 217

tradictory information gathered from their medical notes, or a collateral history gathered from family and friends. Finally, patients with FDIOS frequently exhibited behaviors highly suggestive of deception, including unusual use of medical terminology and apparent eagerness for even uncomfortable or invasive medical procedures. The diagnosis of FDIOS is further complicated by the difficulty of establishing deceptiveness, as required by DSM-5. This criterion is not well elaborated in DSM-5, and little guidance is provided about what constitutes evidence of deception. As a result, FDIOS is often mistakenly ruled out in cases when the presence of physical illness has been confirmed. Even when physical illness has been ruled out, the process of diagnosis often “defaults” to disorders for which excessive illness behaviors are presumed to be nonintentional (i.e, somatic symptom disorders or functional somatic syndromes)— especially when subjective symptoms predominate. Similarly, DSM-5 criteria do not specify psychological or behavioral parameters that suggest voluntariness of symptom production. Other factors must be considered to distinguish factitious from malingered symptoms. Malingering is more often time-limited and environmentally opportunistic. It should be suspected when a strong temporal association between environmental contingencies and illness complaints is established. In contrast, FDIOS cases are more likely to demonstrate chronicity, history of prior feigned illness, inordinate comfort in the role of patient, and insistence on aggressive pursuit of treatment. Factitious symptoms frequently emerge in the context of psychologically meaningful conflicts with family members or occupational roles. When illness fabrication appears to enable the patient to ignore the psychological ramifications of a recent personal stressor, we may infer that he or she is pursuing the sick role. Malingering, on the other hand, should be considered when the sudden onset of inexplicable symptoms is coupled with attainment of (1) external goals (e.g., drugs, shelter, or financial compensation) or (2) the avoidance of subjectively noxious activities (e.g., prosecution or military duty). In clinical practice, however, it is important to avoid creating a false dichotomy between FDIOS and malingering. Overlap often occurs between these conditions. A given patient may simultaneously demonstrate features of both internal and external motivations or manifest shifts in the primacy of these motivations over time. Diagnosis of FDIOS is unique in that it may involve confronting the individual with the phy-

sician or medical team’s suspicions. This process may be exceptionally challenging. Once a patient’s presenting condition is established to be factitious with a high degree of certainty, health care providers should tactfully convey their conclusions to him or her. They should do so in a manner that does not come across as accusatory or confrontational. Even when approached with empathy, patients with FDIOS most often react defensively, denying that their presenting illness is feigned (Bass & Halligan, 2014). In cases of chronic FDIOS, it is common that patients will react angrily and leave the clinic, only to later seek a new medical setting in which to perpetuate their deception (Sutherland & Rodin, 1990). Since most cases of FDIOS involve physical illness, an important primary consideration involves the medical assessment and treatment of any genuine physical symptoms associated with the feigned illness. In many cases, deception may involve the exaggeration of real, preexisting symptoms, while in other cases, patients may present with genuine symptoms that are the result of previously induced illness. Furthermore, significant comorbid health conditions may be present, independent of the patient’s FDIOS behavior. To our knowledge, FDIOS is not a protective factor against cancer, heart disease, chronic respiratory illness, or any other known malady. Thus, initial medical care of individuals suspected of FDIOS should proceed in the same way as it would for any other patient. Adherence to standard medical treatment protects the health and well-being of both FDIOS patients with genuine medical illness and ill patients who are mistakenly suspected of FDIOS.

Epidemiology Prevalence

FDIOS is considered to be rare in medical settings, but this might be a mistaken assumption. Studies in primary medical care of unexplained medical complaints suggest that 30–64% of all patientinitiated visits are for medical complaints with no identifiable physical cause (Nimnuan, Hotopf, & Wessely, 2001). Complaints of this kind are associated with higher annual costs to health care services due to the increased burden of additional investigations, consultations, and admissions (Barsky, Orav, & Bates, 2005). Functional somatic syndromes (e.g., fibromyalgia and chronic fatigue syndrome) are frequently encountered in primary care (Toft et al., 2005), representing between 1

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and 3% of primary care patients (Kim, Shin, & Won, 2005; Nampiaparampil & Schmerling, 2004; Prins, van der Meer, & Bleijenberg, 2006; Wessely, Chalder, Hirsch, Wallace, & Wright, 1997). Excessive health care service use is common in these disorders and is judged to create distress or problems with adaptive functioning in 15–20% of primary care patients (Arnold, de Waal, Eekhof, & Van Hemert, 2006; Grabe et al., 2003), or even a higher percentage (Fink, Rosendal, & Olesen, 2005). Swanson, Hamilton, and Feldman (2010) surveyed 213 U.S.-based physicians regarding the proportion of patients at their clinics with medically unexplained symptoms and found that 1.5% of patients encountered on a given clinic day met criteria for a somatoform disorder or FDIOS. Reliable epidemiological data concerning the prevalence of FDIOS are difficult to obtain. Population surveys are ineffective for determining the prevalence of disorders like FDIOS in which secretiveness and intentional deceit are central features. Many clinicians are not familiar with FDIOS (Steel, 2009). Clinicians who are familiar with FDIOS may still be reluctant to document the disorder for fear of their patients disengaging from care, taking legal action, or failing to be taken seriously by hospital staff at a future time of genuine medical need. In clinical research, Krahn, Li, and O’Connor (2003) via retrospective chart reviews, identified 93 occurrences of FDIOS over a 20-year period in a general medical inpatient population. They found that FDIOS had been included as an official discharge diagnosis in only 20 of these cases. Table 11.1 show the full extent to which reported prevalence rates for FDIOS vary, with estimates ranging from 0.01 to 33% depending on the patient population and the clinical setting reviewed. Studies that have involved direct assessment of hospitalized patients (Table 11.2) show a similar range; reported rates vary between 0.2 and 13%. Interestingly, irrespective of method, prevalence rates of FDIOS appear to be higher for patients with borderline personality disorder (Cohen & Tohen, 1985; Links, Steiner, & Mitton, 1989), which may be taken as evidence for an association between these two diagnoses (Gordon & Sansone, 2013). FDIOS also appears to be common in inpatient psychiatry services, which is unsurprising, as admission to these services is usually the goal of patients who feign psychiatric disorders (Parker, 1996). An alternative approach is to survey physicians about their experiences with FDIOS. Fliege and

colleagues (2007) surveyed 83 physicians, who observed FDIOS in 1.3% of their patients, though the range of estimates was very wide. In a compelling but seldom cited study, Sansone, Weiderman, Sansone, and Mehnert-Kay (1997) asked primary care patients to complete a confidential questionnaire related to medical self-sabotage; 6.6% of patients admitted to actively and intentionally causing, prolonging, or exacerbating an illness. A key issue in FDIOS is the costs associated with the disorder. We have no data on direct and indirect health care costs or other costs associated with loss of earning capacity. However, frequent hospital admissions and extensive investigations and interventions invariably result in high perpatient costs. Several case reports suggest that direct costs for a single patient could exceed a millions U.S. dollars (Donovan, 1987; Powell & Boast, 1993). It is well known that a large proportion of medical costs are generated by a small number of patients. Identifying and managing FDIOS patients would yield substantial health care cost savings. Patient Characteristics

The majority (66.2%) of patients in Yates and Feldman’s (2016) review were women. Most presented in early adult life, with the mean age of 34 at diagnosis. Significantly represented were persons with medical or laboratory training (57%) such as nurses and laboratory technicians. These demographic findings confirm the available published case series on FDIOS (Carney & Brown, 1983; Goldstein, 1998; Kapfhammer, Dobmeier, Mayer, & Rothenhäusler, 1998; Krahn et al., 2003; O’Reilly & Aggeler, 1976; Petersdorf & Beeson, 1961; Reich & Gottfried, 1983). Most patients with FDIOS are reasonably well-educated, employed, or in school (Krahn et al., 2003). They are at least marginally connected to a network of family and social contacts (Goldstein, 1998), although they generally have few visitors in the hospital (Parker, 1996). Other characteristics may include itinerancy, lawlessness, self-destructiveness, problems with developing and maintaining relationships, open hostility, and pathological lying (Feldman, 2004). All of the larger case series report substantial comorbidity with other mental disorders, but none include comparison rates for others from these populations, so it is difficult to interpret these associations (Goldstein, 1998; Kapfhammer et al., 1998; Krahn et al., 2003). Yates and Feld-



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TABLE 11.1.  Studies Estimating the Prevalence of FDIOS Utilizing Retrospective Chart Review

Nationality

Time period (years)

Aduan et al. (1979)

US

Ballas (1996)

Author(s)

Prevalence of FDIOS

N

Sample

16

343

Adult patients with “fever of undetermined origin” diagnosis admitted to an infectious disease service

9%

US

20

424

Adult patients attending an outpatient sickle-cell anemia service

0.9%

Bhugra (1988)

UK

1

775

Adult patients admitted to an inpatient psychiatry service

0.5%

Cohen & Tohen (1985)

US

3

33

Dahale et al. (2014)

India

10

81,176

Adult patients attending an outpatient neuropsychiatric service

0.01%

Ehrlich et al. (2008)

Germany

11

12,081

Pediatric patients admitted to an inpatient tertiary care service

0.03%

Ehrlich et al. (2008)

Germany

11

1,684

Pediatric patients referred from general medicine to a liaison psychiatry service

0.7%

Ferrara et al. (2013)

Italy

3

751

Patients referred to general pediatric service

1.8%

Fliege et al. (2002)

Germany

1.2

995

Adult patients referred from general medicine to a liaison psychiatry service

3%

Adult patients with BPD diagnoses admitted to an inpatient psychiatry service

33%

Gregory & Jindal (2006)

US

1

100

Adult patients admitted to an inpatient psychiatry service

6%

Kapfhammer et al. (1998)

Germany

4

169

Adult patients referred from general medicine to an inpatient psychiatry service

0.6%

Canada

2

175

Adult patients referred to a pain clinic

0.02%

Pope et al. (1982)

US

3

219

Adult patients admitted to a research ward for psychotic disorders

4%

Rumans & Vosti (1978)

US

10

506

Adult patients with “fever of undetermined origin” diagnoses admitted to a general medical service

2.2%

Canada

3

1,288

Adult patients referred from general medicine to an inpatient psychiatry service

0.8%

Mailis-Gagnon et al. (2008)

Sutherland & Rodin (1990)

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TABLE 11.2.  Studies Estimating the Prevalence of FDIOS Utilizing Direct Assessment of Patients

Nationality

Time period (years)

N

Germany

1

1,538

Adult patients admitted to neurology ward

0.3%

Bhargava et al. (2007)

Oman

7

8,850

Adult patients attending an ENT clinic

0.2%

Links et al. (1989)

Canada

2

88

Author(s) Bauer & Boegner (1996)

Lynn & Belza (1984)

US

0.4

man (2016) identified depression as the most common (41.8%) comorbid psychiatric disorder to be described in FDIOS case reports. Other comorbid psychopathology included personality disorder (16.5%), substance abuse (15.3%), functional neurological symptoms (5.3%), and eating disorders (4.1%). Their findings were, again, consistent with those of several earlier case series of FDIOS (e.g., Fliege, Scholler, Rose, Willenberg, & Klapp. 2002; Goldstein, 1998; Kapfhammer et al., 1998). Because FDIOS can occur in the absence of personality disorder (Nadelson, 1979), the presence of character pathology is not necessary to raise the question of FDIOS. Course

The course of FDIOS is extremely difficult to ascertain. Patients with FDIOS cannot be relied upon to provide an accurate history or access to past medical records. As such, we know little about the onset and development of their factitious illness behavior, or about their long-term outcomes. Large case series indicate that these patients are typically 30–40 years old at the time they are first recognized (Goldstein, 1998; Kapfhammer et al., 1998; Krahn et al., 2003; Reich & Gottfried, 1983), although a substantial number of pediatric cases of FD have been described (Ehrlich, Pfeiffer, Salbach, Lenz, & Lehmkuhl, 2008; Ferrara et al., 2013; Libow, 2000). Also, there is reason to believe that FDIOS in adulthood sometimes results from being the victim of FDIOA in childhood (Bass & Glaser, 2014; Libow, 1995, 2002).

125

Sample

Adult patients with BPD diagnoses admitted to an inpatient psychiatry service Adult patients with PTSD diagnoses admitted to an inpatient psychiatric service for military veterans

Prevalence of FDIOS

13%

5.6%

It seems reasonable to suspect that patients with FDIOS typically progress (1) from less to more extreme modes of medical deception, and (2) from an episodic to a chronic pattern. Data from one study supports this view (Kapfhammer et al., 1998).

Etiology The etiology of FDIOS remains a mystery overall. The diagnosis of FDIOS implies patient knowledge of disease fabrication but not necessarily understanding of the motives for this behavior. FDIOS may offer a way of coping with poor interpersonal attachments and identity problems (Ehlers & Plassman, 1994). This view is supported by the strong associations between FDIOS and an apparent history of abuse (Brown & Scheflin, 1999). The psychopathology of FDIOS remains controversial. Asher (1951) noted motives, such as desire for attention, a grudge against doctors, and desire for drugs and free lodging. FDIOS is also strongly associated with a form of repetitive, quasi-compulsive lying branded pseudologia fantastica (see Weston & Dalby, 1991), which generally involves lies that are grandiose and sometimes even ridiculous. For example, one patient with FDIOS, described by Heym, Sasaki, and Winslow (1977), claimed simultaneously to be a physicist, a counterintelligence agent, and a colleague of the famous French oceanographer, Jacques Cousteau. King and Ford (1988) reviewed 72 cases of pseudologia fantastica and identified a strong comorbidity with imposture, disease simulation, and peregrination, all characteristics associated with FDIOS.



11.  Factitious Disorders in Medical and Psychiatric Practices 221

In contrast to “everyday” lying, which is usually goal-directed (Ford, 1996), the lies frequently observed in FDIOS serve as their own gratification (Deutsch, 1982). The lies seem designed to bolster self-esteem and solicit interest and admiration from others (Green, James, Gilbert, & Byard, 1999).

Prognosis FDIOS is associated with considerable morbidity and mortality. The majority of patients with recognized FDIOS induce illness or injury in themselves (Yates & Feldman, 2016). These selfdestructive behaviors may lead directly to permanent disease or disability (Salvo, Pinna, Milia, & Carta, 2006), either intentionally or as a result of error or miscalculation by the patient. Even mildly deceptive behaviors may cause serious harm, as they can lead to the unnecessary implementation of invasive medical procedures. Often, these procedures are demanded by the patient, which typically places the physician under great pressure to conduct them. This pressure frequently causes clinicians to bypass their regular decisionmaking process, and has led physicians to perform unnecessary surgeries, including mastectomies (Feldman & Hamilton, 2007; Feldman, 2001), hysterectomies (Hamre, Nguyen, Shepard, & Caplan, 2014), adrenalectomies (Cook & Meikle, 1985), amputations (Acarturk, Abdel-Motleb, & Acar, 2014; Oliveira et al., 2013; Shah, Forman, & Friedman, 1982), panceatectomies (Hirshberg et al., 2002), extenteration of the eyes (Lin, Servat, Bernardino, Goldberg, & Levin, 2012), and other permanently disabling procedures. Fliege et al. (2002) report that approximately 50% of patients with apparent FDIOS experienced iatrogenic harm or placed themselves at imminent risk of such harm. Simulations of, or self-induced, disease can be deadly (Minanni et al., 2014; Croft, Racz, Bloch, & Palmer, 2005; Hirayama et al., 2003; Kansagara, Tetrault, Hamill, Moore, & Olson, 2006; Norcliffe-Kaufmann, Gonzalez-Duarte, Martinez, & Kaufmann, 2010; Vaduganathan, McCullough, Fraser, & Stern, 2014), as can initiation of heroic treatment measures based on false information provided by the patient (Eisendrath & McNiel, 2004). There may be cases in which the disease simulation or induction was so crudely or badly miscalculated that the patient’s death was not connected with FDIOS, and was instead regarded as an accidental death or suicide (Croft et al., 2005).

Management Treatment of FDIOS is widely considered impossible due to lack of cooperation, sudden departures, and profound personality disorders. When confronted with the evidence of their deception, patients with FDIOS typically react with denial, aggression, or threats of legal action. They are rarely amenable to treatment by mental health care providers. Consequently, research on effective interventions for FDIOS is very limited. Nevertheless, Eastwood and Bisson (2008) document numerous instances of improvement in their review of 32 case reports and 13 case series in which authors were able to attempt treatment of FDIOS. Firsthand accounts of FDIOS even highlight the possibility of a full recovery (Bass & Taylor, 2013). When FDIOS is suspected, it is of foremost importance that this suspicion be communicated to all individuals involved in the patient’s medical care, consistent with local and national statutes. Clinicians must share their concerns promptly in cases in which patients have already been afforded an opportunity to “escalate” their presentation, such as through interference with surgical wounds, or manipulation of a central venous catheter (Chew, Pace, & Honey, 2002; Klein, du Vall, & Klein, 1995; Mouchantaf, Campagna, & Lamb, 2011; Schmidt, Strutz, Quellhourst, & Müller, 1996). Depending on the constellation of signs and symptoms present, continued medical care in conjunction with psychotherapy may be prescribed. When symptoms are severe and the patient is at imminent risk of reengaging in harmful illness behavior, involuntary hospitalization into a psychiatric hospital may be appropriate (Plassmann, 1994). In both inpatient and outpatient settings, willingness to enter into treatment is fairly low among most individuals with FDIOS due to their refusal to admit their deception (Bass & Halligan, 2014; Savino & Fordtran, 2006). In general, pharmacological interventions for FDIOS have not been shown to be effective (Harding & Fallon, 2014). Psychotherapies such as cognitive-behavioral therapy, though strongly supported for use with other disorders, may be difficult to implement with these patients, because they are often unwilling to form a collaborative partnership with the clinician (Eisendrath & Young, 2005). Still, should patients show some receptiveness to treatment, supportive therapies focused on alliance between the therapist/physician and patient may produce some benefits (Eisendrath & Young, 2005; Plassmann, 1994). Intui-

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tively, patients with isolated or episodic factitious illness behavior should demonstrate better social functioning and better treatment responsiveness (Eisendrath, 1989) than their chronic counterparts. Family therapies may also help to facilitate family members’ understanding of the patient’s pathological need for attention and nurturance (Kozlowska, Foley, & Savage, 2012). This understanding may empower the family to adapt their interpersonal behaviors in such a way as to prevent reinforcement of the factitious illness behavior. Cessation of FDIOS behavior, rather than true insight into psychological causative factors, is probably the best achievable goal. Eisendrath (1989) proposed treatment based on creation of a double bind: for example, telling the patient that failure to respond to standard treatment would confirm self-induced illness. This technique capitalizes on the suggestibility of patients with FDIOS while allowing them to save face. Use of the technique has led to rapid improvement in individual case studies (Teasell & Shapiro, 1994; Shapiro & Teasell, 1997, 2004). Treatment may involve ostensibly portraying the primary target of treatment to be a comorbid condition while surreptitiously aiming to curb the patient’s factitious behaviors (Bass & Halligan, 2014). The vicissitudes of confrontation and treatment are described in Playing Sick?: Untangling the web of Munchausen, Munchausen by Proxy, Malingering, and Factitious Disorder (Feldman, 2004). Though designed primarily for a nonprofessional audience, this text is thorough in its case descriptions and psychological observations. It remains to be determined whether chronic factitious illness behavior can enter remission or whether the associated conflicts are simply channeled into another form of maladaptive behavior, such as conversion, substance abuse, or FDIOA.

FDIOA Overview Perpetrators of FDIOA take on the role of caregiver for a sick person by intentionally feigning or producing signs and symptoms of disease in that person. Terms such as pediatric condition falsification (Ayoub, Schreier, & Keller, 2002), medical child abuse (Roesler & Jenny, 2008), and fabricated and induced illness (Foreman, 2005) are also used in the professional literature. The victim is usually a very young child but may also be a disabled adult, an elder, or even a fetus in utero (Feldman

& Hamilton, 2006; Jones, Delplanche, Davies, & Rose, 2015; Jureidini, 1993). For ease of exposition, we focus on prototypical cases involving a mother and her child. In many cases, a mother either falsely claims that her child is medically or psychiatrically ill, or actually makes the child sick. She then presents the child for treatment while disclaiming knowledge of the origin of the problem. The child may then undergo many diagnostic tests, medication trials, and surgeries. For instance, one of the authors (M. D. F.) is aware of a case in which a 3-year-old had 11 identified physicians who saw him more than 130 times and performed 18 surgical procedures during 15 hospitalizations in five states. Diagnoses included upper respiratory infection, asthma, sinusitis, fever, diarrhea, gastroesophageal reflux, milk intolerance, food allergies, chronic intestinal pseudo-obstruction, and irritable colon. Signs or symptoms commonly induced by the perpetrators of FDIOA abuse include bleeding (Demircioglu, Bekdas, Goksugur, Gunes, & Yildirim, 2014), apnea induced by suffocation (YalndagÖztürk, Erkek, & Sirinoglu, 2015), diarrhea (de Ridder & Hoekstra, 2000), vomiting (Tamay et al., 2007), fever (Tamay et al., 2007; e.g., induced by pathogens: Liston, Levine, & Anderson, 1983; Mantan, Dhingra, Gupta, & Sethi, 2015), seizures (Willis, Roper, & Rabb, 2007), abnormal levels of consciousness (caused by benzodiazepines; e.g., Vennemann et al., 2006), rash (caused by caustic agents; e.g., Peebles, Sabella, Franco, & Goldfarb, 2005), hypernatremia (due to salt poisoning; Su, Shoykhet, & Bell, 2010), and false or exaggerated psychiatric or behavioral abnormalities (e.g., Auxemery, 2014; Ayoub et al., 2002; Jellinek & Schreiber, 2000; Rittner, Pulos, & Lennon, 2005; Schreier, 1997). In many cases, central venous catheters that are eventually placed then become the sources of “medical chaos” (Blyth et al., 2007; Feldman & Hickman, 1998), with mothers contaminating or blowing into them to sicken the child. Acetone added to a urine specimen can create the illusion of uncontrolled diabetes (Verity, Winckworth, Burman, Stevens, & White, 1979), while a panoply of medications or poisons (see Davis, 2013) are used to induce dizziness (diuretics), vomiting (ipecac; e.g., Carter, Izsak, & Marlow, 2006), respiratory distress (opioids), diarrhea (laxatives), hypoglycemia (insulin), or cardiac arrhythmias (tricyclic antidepressants; Holstege & Dobmeier, 2006). As victims of this abuse get older, they may even learn to gratify the mother by feigning symptoms under her tutelage (Croft



11.  Factitious Disorders in Medical and Psychiatric Practices 223

& Jervis, 1989; Ferrara et al., 2014; Stutts, Hickey, & Kasdan, 2003). Sexual abuse may be falsely reported by the mother, although investigators and evaluators should remain aware of the myriad motivations behind false reporting. False accusations may arise out of child custody disputes, by conscious manipulation on the child’s part, or out of the psychological needs of one of the parents (Rand, 1990). Only the last group of complainants could properly be called factitious. Controversy has raged about “whether [FDIOA] should be viewed as a form of abuse, a mental disorder of the caretaker, the resulting emotional consequences to the victim, or a combination” (von Hahn et al., 2001, p. 129). There is no question that the behavior in question constitutes abuse of the person who has been deceptively cast into the sick role. As with FDIOS, there are a variety of potential motives for perpetrating such acts, and these can be mixed in any given case. DSM-5 takes the position that when the motives appear to be primarily psychological in nature, as opposed to material or instrumental, the perpetrator should be diagnosed with FDIOA. This raises concerns that diagnosis of a mental disorder may absolve perpetrators of responsibility for their actions. The questions raised by FDIOA are not unlike those raised by predatory sexual abuse of children, in which the abusive acts are regarded dually as criminal and as a product of psychopathology.

Challenges to Assessment and Diagnosis Clinical methods for the assessment of FDIOA are not well developed (Rogers, 2004). Griffith and Slovik (1989) described clinical features common to FDIOA. These features included a symbiotic mother–child relationship and a laissez-faire attitude on the mother’s part towards the child’s illness. However, there have been concerns that the subjectivity of some of these features, or use of a personal “profile,” heightens risk for a falsepositive diagnosis (Ayoub et al., 2002; Rand & Feldman, 1999). Feldman (2004) proposed criteria that focus on objective observation:   1. Episodes of illness begin when the mother is or has recently been alone with the child, or the child has symptoms that only the mother has observed.   2. Illness abates when the child is separated from the mother.   3. Other children in the family have had unexplained illnesses.

 4. The mother has provided false information about the child.  5. Physiological or laboratory parameters are consistent with induced illness.   6. The suspected disease or disease pattern is extremely rare.   7. Signs and symptoms do not respond to appropriate treatment.   8. The child has been to numerous medical care providers without a cure or even a clear diagnosis.   9. The mother has medical or nursing training or access to illness models. 10. The mother has a personal history of somatic symptom disorder(s). 11. The mother is unresponsive to the child’s needs when unaware of being observed. Greiner, Palusci, Keeshin, Kearns, and Sinal (2013) have since developed a screening instrument for FDIOA abuse on the basis of a retrospective chart review conducted with 19 cases. Many of the features listed earlier are included in this instrument. In addition, several key illness characteristics are included as predictive of FDIOA abuse: bruising of the face and neck, dangerous or inconsistent drug levels, prolonged diarrhea and vomiting, and a history of episodes of apnea or cyanosis (Greiner et al., 2013). However, detection of FDIOA cases on the basis of such characteristics is complicated by the fact that gross evidence of child abuse (e.g., bruises, fractures, burns) is uncommon. Another challenge is to balance general trust toward caregivers with the recognition of factors pointing to FDIOA in a given case. FDIOA is a diagnosis usually established by a meticulous review of clinical information. There is no empirical evidence that psychiatric examinations or psychological tests are helpful for identifying parents with FDIOA. Anecdotally, one of the greatest obstacles to successful prosecution of medical child abuse in these cases is the alleged abuser’s outward appearance of being a dedicated and concerned parent. Covert video surveillance (CVS) can provide proof of FDIOA if the parent continues to abuse the child during hospitalization (e.g., Hall, Eubanks, Meyyazhagan, Kenney, & Johnson, 2000). Every hospital should develop a CVS protocol well in advance of encountering such a case, and several models exist (Byard & Burnell, 1994; Rabbone et al., 2015; Southall et al., 1997, 1987). It is helpful to involve not only medical and psychiatric professionals but also the local child protection agency,

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an attorney, law enforcement, and a professional well versed in FDIOA. Decisions will need to be made as to whether CVS is to be viewed as a clinical, research, or criminal procedure, because the choice has far-reaching implications. For example, this choice will determine whether hospital personnel or police place the cameras (Bauer, 2004). Though legal and ethical concerns about CVS have arisen, it should be pointed out that its presence can help exonerate an innocent parent, as well as impugn a guilty one.

Epidemiology Prevalence

Reports of FDIOA abuse come from both industrialized and developing countries such as Nigeria and Sri Lanka (Feldman & Brown, 2002). In all settings, frequency data for FDIOA are constrained by (1) the elusive nature of the diagnosis, with “successful” cases never being identified, and (2) pediatricians’ lack of familiarity with the diagnosis (Ostfeld & Feldman, 1996). In spite of this, numerous studies have attempted to estimate the prevalence of FDIOA in pediatric care (Table 11.3) with estimates ranging from 0.002 to 13%. Warner and Hathaway’s (1984) finding of a 13% prevalence rate of FDIOA among children attending an outpatient allergy service is striking. Meadow (1984) believed factitious allergy to be the third most common presentation of FDIOA, present in 22 of 71 British cases he had reviewed. Feldman, Stout, and Inglis (2002) noted fabrication or exaggeration of asthma or allergies in 50% of 104 victims of FDIOA in the United States. Warner and Hathaway (1984) attributed their results in part due to the difficulty of objectively establishing the presence of a food allergy or intolerance. The high prevalence rate in Rahilly’s (1991) study may have been due to comparable difficulties applying the diagnostic criteria for an “apparent life-threatening event” in an infant. Unfortunately, no databases are available to systematically collect and analyze FDIOA data in such a way as to confirm these estimates. They may nonetheless be taken as a warning for clinicians to maintain a high index of suspicion when assessing disorders without objective standards for diagnosing (Feldman & Brown, 2002). On a final and chilling note, the high incidence of sibling deaths in cases of confirmed FDIOA cases suggests that these siblings also were potentially victims of medical abuse.

Perpetrator Characteristics

Rosenberg (1987) reviewed 117 published cases of FDIOA and found the perpetrators were biological mothers in 98% of cases. In contrast to Rosenberg, Sheridan’s (2003) later review of 451 FDIOA published cases in the professional literature indicated that only 75% of perpetrators were biological mothers. Other relatives, including fathers and stepmothers, or day care providers or even health professionals, were culpable in the remaining 25%. In a minority of cases, the perpetrators are clinicians who deliberately sicken patients through covert medical interventions (Repper, 1995; Yonge & Haase, 2004; Yorker, 1996) because they desire the excitement and drama of the medical crises they cause. Health care professionals who fail (or refuse) to consider FDIOA in difficult cases have been conceptualized as “professional participants” in FDIOA abuse (Jureidini, Shafer, & Donald, 2003; Zitelli, Seltman, & Shannon, 1987). The fact that perpetrators can co-opt well-trained physicians in this way is testament to their skills of persuasion and manipulation. Importantly, FDIOS and related excessive illness behaviors were present in 24% of mothers in the Rosenberg study (1987). Feldman, Feldman, Grady, Burns, and McDonald (2007) have provided a degree of empirical support for this finding, noting a history of excessive illness behavior in 28 out of 34 cases of urological or renal FDIOA. These histories typically contained evidence of illness deception or a somatization disorder. Researchers (e.g., Adshead & Bluglass, 2005; Gray & Bentovim, 1996) have described a broad range of psychopathological features typical of FDIOA. FDIOA behavior appears to be motivated by a spectrum of unmet psychological needs, often extending to the wider family system. Clinicians may question the statement of Sigal, Gelkopf, and Meadow (1989) that “the parent usually has no intention of killing or maiming the child” (p. 532). Whereas this may be true in most cases, parents may overtly covet bereavement (Firstman & Talan, 1997/2011), or create the appearance of a familial pattern of sudden infant death syndrome in which murder of the child may be intended.

Management Physicians and other mandated reporters of abuse must alert child protection agencies if there is reasonable cause to suspect FDIOA maltreatment. Separation of parent and child is almost always a



11.  Factitious Disorders in Medical and Psychiatric Practices 225

TABLE 11.3.  Studies Estimating the Prevalence of FDIOA

Time period (years)

N

Survey of clinicians

1

N/A

Retrospective chart review

3

751

Belgium

Direct patient assessment

3

US and Canada

Survey of clinicians

UK and Republic of Ireland

Survey of clinicians

Author(s)

Nationality

Denny et al. (2001)

New Zealand

Ferrara et al. (2013)

Italy

Godding & Kruth (1991) Light & Sheridan (1990)

McClure et al. (1996)

Rahilly (1991)

Warner & Hathaway (1984)

Australia

UK

Methodology

Sample

Prevalence of FDIOA

Cases meeting criteria for FDIOA reported by pediatricians

0.002%

Children referred to a pediatric unit

0.53%

1,468

Children presented to a joint paediatric–psychiatric clinic for severely asthmatic children

1%

N/S

20,090

Cases meeting criteria for FDIOA reported by clinicians managing pediatric apnea monitoring services

0.27%

2

N/A

Cases meeting criteria for FDIOA, nonaccidental poisoning, and nonaccidental suffocation in children under 16 years of age reported by pediatricians

0.005%

Cases meeting criteria for FDIOA, nonaccidental poisoning, and nonaccidental suffocation in children in their first year reported by pediatricians

0.003%

1.5%

Direct patient assessment

3

340

Infants admitted to a pediatric unit following an apparent life-threatening event

Retrospective chart review

4

1,600

Cases meeting criteria for FDIOA attending an outpatient pediatric allergy service

necessary step in the detection of FDIOA, even if only temporarily. Often, in a genuine attempt to help the alleged victim, separation from the alleged perpetrator is accompanied by changes in the victim’s medication regimen, therapeutic services, diet, and so on. These additional interventions only serve to obscure the issue of the specific role played by the alleged perpetrator in the development and maintenance of the victim’s health

13%

problems. The child should be safeguarded from further invasive procedures, and siblings must also be protected. Subsequent care should be consolidated at a single medical center, while the doctors who initially diagnosed the FDIOA remain involved to counter the skepticism or disbelief of clinicians new to the case. Relevant personnel must cooperate with the courts, provide education, and dispel the many myths about FDIOA. For exam-

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ple, it is often insisted that FDIOA should be ruled out by the presence of positive laboratory findings, abnormalities on examination, or a history of authentic illness. FDIOA may also be erroneously ruled out in cases in which the perpetrator did not physically fabricate or induce illness in the victim, or when investigations or surgeries were conducted at the direction of physicians. Some hold that FDIOA is so rare that a practitioner who reports it is likely to be wrong. Health professionals involved in a case of FDIOA should also be prepared to discuss the unique aspects of investigation, methodology, criteria for confirmation, intervention, case planning, and legal matters. Decisions about the investigation, diagnosis, and case management are best handled by a multiagency, interdisciplinary team (Holstege & Dobmeier, 2006). Ideally, an FDIOA expert is consulted. In the United States, if clear and compelling evidence is available, the child can be removed from the home on an emergency basis for up to 72 hours. Subsequent court actions include a justification of removal hearing, motions hearings, an evidentiary hearing, and a dispositional hearing, if maltreatment is established. These strategies are described in detail in the excellent practical text by Lasher and Sheridan (2004). Legal systems in other countries offer similar procedures for clinicians who encounter FDIOA and voice reasonable suspicions in good faith (e.g., Every Child Matters; Department for Children, Schools and Families, 2008). Questions inevitably arise as to whether, and when, the child or children can be safely reunified with the FDIOA parent. Longitudinal study of outcomes with and without reunification of parent and child are needed. A general guideline is that the mother and her partner should acknowledge that they have engaged in the medical abuse of the child, and cease to claim any ongoing unexplained problems in the child. Criteria for possible reunification include (1) a child with sufficient cognitive development and language skills to understand and report abuse, (2) only mild abuse, (3) an understanding by the perpetrators as to why the FDIOA took place, (4) identification of trigger situations, and (5) understanding of any partner complicities. Everyone in the extended family should be committed to victim safety. Long-term monitoring must be provided by the court and a child protection agency (Lasher & Sheridan, 2004). FDIOA may be the most lethal form of child maltreatment. Rosenberg (1987) determined in her review that mortality rates for child victims

of FDIOA approached 9%, a figure supported by Sheridan’s (2003) later analysis. Shockingly, Sheridan’s (2003) review also indicated that 25% of the index child’s siblings were known to be dead, suggesting that earlier maltreatment of this kind had been unrecognized. Without intervention, adult survivors of FDIOA may suffer emotional disturbances. Libow (1995) summarized observations from 10 self-identified survivors; she described problems in adulthood, including insecurity, reality-testing issues, avoidance of medical treatment, and posttraumatic stress symptoms. Some survivors expressed considerable residual anger toward the abusing mothers. A surprising number of survivors expressed sympathy for the fathers who passively colluded or failed to protect them. Some of the FDIOA parents have continued fabricating their own medical illnesses or harassing their adult children with fabricated dramas even decades later.

CONCLUSIONS The dearth of empirical research on FD is partly responsible for its historical controversies. As we have described in this chapter, patients with FD typically respond to confrontation with denial and disengagement. They are, generally, not amenable to involvement of psychiatric services, let alone participation in psychiatric research. Consequently, we are unlikely to see large studies or controlled trials involving patients with FD any time soon. This fact should not be cause for pessimism. Several alternative avenues for future research may be usefully explored. The research into FD would benefit from the consolidation of even anonymous case reports to detect patterns, perhaps through a national or international data collection system. A database of this kind would enable authors to submit cases that are unsuitable for journal publication, and without the time constraints imposed by peer review. Researchers would then, for the first time, be able to compare cases among hospitals in a sample uncontaminated by publication bias. Heavily abridged submissions would still enable researchers to formulate a basic demographic profile for these patients. It is possible that a database would also have clinical applications, as clinicians would be able to compare patients suspected of FD to existing entries in the system. However, this use of such data reprises old concerns about the ethics of “blacklists” of such patients.



11.  Factitious Disorders in Medical and Psychiatric Practices 227

It may also be possible to research FD using indirect methods. For example, we have argued in this chapter that most examples of excessive illness behavior reflect the operation of both cognitive and motivational factors. The implications of this view are that (1) illness deception may be a more significant public health issue than it currently appears to be, and more importantly, (2) that motivational influences on excessive illness behavior can be meaningfully studied in patient populations that are generally more agreeable to research participation than are patients with moderate to severe FDIOS. One strategy that may help to improve the detection of FD in clinical settings is increasing the implementation of screening measures for psychosocial correlates in primary care facilities. It is in these settings that cases of FD are most often encountered. As we discussed earlier, a significant amount of research has shown an association between Cluster B personality traits (i.e., rejection sensitivity, neediness) and FD. Although the presence of these traits alone is not be adequate to substantiate FD diagnosis, they may be a useful tool for assessing evidence of psychosocial motivations underlying factitious presentations. Knowledge of such motivations may help to corroborate other clinical information and therefore provide more informed diagnostic conceptualizations of potential FD cases. Several well-supported assessment instruments, such as the Personality Assessment Inventory (PAI; Morey, 1991) and particularly the Minnesota Multiphasic Personality Inventory–2 Restructured Form (MMPI-2-RF; Ben-Porath & Tellegen, 2008), include scales that indicate false or exaggerated reporting. These scales are rarely utilized for the purpose of detecting factitious symptom presentations in health care settings. Due to the evaluative contexts in which these measures are typically given (e.g., assessing job applicants) false or exaggerated reports of health problems are generally interpreted as malingering rather than factitious illness behavior. An equally promising line of inquiry utilizes the experiences of health care professionals who have encountered FD (Fliege et al., 2007; Swanson et al., 2010). In Fliege et al. (2007), physicians were asked about patients under their care, past or present, whom they suspected of exaggerating or fabricating their symptoms. They disclosed a prevalence rate of FD much higher than that in conventional research methods (e.g., retrospective chart review) might suggest. Knowledge of these suspected cases of FD is, arguably, more clinically

relevant than are data on confirmed cases, as most clinicians will not be able to “prove” that a patient under their care is intentionally deceiving staff. Future studies should, when possible, make use of physicians’ own experiences of managing patients suspected of FD. Anonymous data collection may also help us to learn about FD. This approach may involve anonymous questionnaires in health care settings in which investigators may expect to find patients with FD, such as primary care settings (Sansone et al., 1997). The Internet, however, offers more possibilities. In an innovative study, Lawlor and Kirakowski (2014) gathered qualitative data from discussions in two online support groups for individuals with FD. This method took advantage of the fact that discussions within these groups were openly accessible and therefore, essentially, within the public domain. Their findings give unprecedented insight into the motivations of the patients discussed in this chapter, and they are best reviewed in full. It is, of course, difficult to verify online content, or to rule out participants in online groups engaged in “Munchausen by Internet” deception (Feldman, 2000; Pulman & Taylor, 2012). Nonetheless, as patients with FD are increasingly using the Internet to facilitate illness deception (Griffiths, Kampa, Pearce, Sakellariou, & Solan, 2009), it seems fitting that researchers should use online resources to facilitate its detection. Using anonymous data to study FD in the community can provide broad conceptualization. Some individuals with FD behavior do not present to health care services, as their intention is only to deceive family, school, or work communities. Their deceptions usually target friends, family, and coworkers, who lack medical expertise and are therefore more easily deceived than professional medical care providers. Individuals who choose this route may even completely avoid health care professionals. Consequently, little research has been conducted with members of this group, who may nevertheless pose an increased health risk to themselves and others. Although a clinical encounter with FD is a burdensome and often costly process, it at least affords an opportunity to intervene and, in cases of FDIOA, alert child protection services. These opportunities and safeguards do not exist for those who engage in FD behavior only in the community. Accurately establishing a diagnosis of FD requires prolonged and thorough evaluation of presenting symptoms—a process that often is not feasible in U.S. medical facilities, which follow a

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high-volume model of care and afford little time for patient–physician interactions. Thus, the opportunity to identify persons with FD is often limited or fleeting in these settings. One development for reducing the logistical barriers to diagnosis inherent in the health care system is the widespread shift toward the use of electronic medical records (Bass & Halligan, 2014). This format can increase the provider’s access to a new patient’s health information that may have been obtained in different clinical settings. Medical records comprise an important source of information for determining cases of FD, as they may reveal previous patterns of excessive and unnecessary treatment-seeking behavior in support of making a diagnosis (Savino & Fordtran, 2006). Moreover, previous health experiences may also be used to evaluate the legitimacy of presenting symptoms that are suspected of being factitious. Traditionally, such information has not frequently been shared across different providers or facilities, except in cases of referrals. Thus, individuals with FD have easily been able to avoid drawing attention to signs of their deceptive illness behavior by moving to a new setting in which providers do not have access to their health records (Savino & Fordtran, 2006). Conversion to synchronized electronic records will greatly facilitate the physician’s access to such information regardless of where it was obtained. This feature will undermine the FD patient’s ability to shed suspicion of their deception by simply changing facilities. There are, however, two drawbacks to the use of electronic medical records to identify patients with FD. The first applies to the content of electronic medical records (EMRs). Compared to traditional paper charts, there is a decrease in narrative comments that physicians previously used to record clues about their suspicions about the legitimacy of a patient’s signs or symptoms. One of us (J. C. H.) recently reviewed a case in which the first 2 years of the medical record used traditional charting methods and the last two were primarily EMRs. The medical abuse of the pediatric patients would not have been detectable from the EMRs alone. The second drawback applies to traditional medical records as well: Generally, they can only be accessed if patients agree to release them. One of the telltale signs of FD is unwillingness of patients to disclose previous health care encounters or permit the sharing of records. Providers must be alert to patients’ attempts to conceal previous medical contacts, and insist on getting previous medical records as the standard of care. Unlike

many European countries with national health care systems, the potential of EMRs cannot be fully realized in the United States, because it lacks a central health care database. For both FDIOS and FDIOA, there is neglect of the issue of how these cases arise and evolve. Case studies and personal experiences usually involve severe and dramatic cases, audacious deceptions, and prodigious amounts of health care use. It is easy to lapse into the implicit assumption that these cases arise out of thin air, fully formed. Our (M. D. F./J. C. H.) experiences with legal cases surrounding FD suggest instead that these cases have their beginnings in mildly excessive illness behavior. Small inconsistencies, puzzling failures of reliable palliative treatments, and slightly odd attempts to influence clinical decision making are typically seen in the early records of these cases. Through acquiescence and inaction at this stage of the case, providers are essentially participating in patients’ deceptions. Having taken the word of the client that a previous physician diagnosed a patient with asthma, the current provider lists it among a child’s diagnoses. This false diagnosis is assumed by subsequent providers to be accurate and serves as the basis for their clinical decision making. And with every clinician who participates in this way, the case takes on the appearance of greater legitimacy, even as the facts of the case become more and more fantastical. Effective interventions are rare in FD cases that have progressed past the point at which there is no face-saving way for the patient to back away from the ruse and for providers to reconcile themselves to the idea that they have been deceived. The best hope for managing FD is therefore prevention and early identification. Prevention can only be achieved if physicians and other health care providers are trained to (1) resist requests for departures from standards of care, (2) document and act on their uneasy or suspicious feelings, and (3) insist on the release of prior medical records. These simple steps will create an environment that maintains the quality of medical care, while simultaneously limiting opportunities for FD to develop.

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C H A P T E R 12

Conversion Disorder and Illness Deception Richard A. A. Kanaan, MD, PhD

Conversion disorder is an unusual disorder. As the presentation of neurological symptoms without a medical explanation (e.g., fits without epilepsy, or a hemiparesis without a stroke), it may be conceived as simply the neurological member of the broad group of functional disorders (psychiatric disorders that present with primarily physical symptoms, such as cardiac symptoms without cardiac pathology), yet it appears exceptional among them (Kanaan et al., 2010). Its nature appears uniquely mysterious, with a fundamental question over what faculty or system is disordered—or whether it is even a disorder at all. For the first reaction of many on hearing an account of conversion disorder is to ascribe it to deliberate pretence or malingering. Moreover, conversion disorder is unique in requiring that the doctor must prove a patient is not pretending before making the diagnosis (American Psychiatric Association, 1994; World Health Organization, 1992). Because proving someone is not pretending is impossible, the diagnostic criteria have been recently revised in DSM-5 (American Psychiatric Association, 2013) but that the criterion was ever there tells us a great deal about the suspicions of doctors. Why should it be that conversion disorder is so particularly mistrusted? Why should it be so readily taken for malingering?

HYSTERIA, MEET MALINGERING The disparate histories of hysteria (as conversion disorder was previously known) and malingering suggest that they were not always close—they were not so obviously related—but became so only relatively recently. If hysteria was considered gynecological in antiquity, and diabolical in the Dark Ages (Veith, 1993), then it is easy to see these explanations as pejorative but hard to imagine that anyone would choose to feign them. Its association with pretence seems to have occurred as hysteria came to be thought of as an affliction of the brain—a neurological disorder, but one that frustrated neurologists’ attempts to find a brain pathology to explain it (Nicholson, 2012). In the late 19th century, Sigmund Freud described a presumption among his fellow neurologists that all patients with hysteria were malingerers (Freud, 1953a). This presumption was challenged by the greatest neurologist of his time, Jean-Martin Charcot, who proposed that a disorder of brain function, rather than structure, was the underlying cause; but whatever positive view Charcot’s public demonstrations of his “functional” model of hysteria may have had during his life appears to have been swiftly undone by his successors after his death, and particularly by the revelation that at least

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some of his patients were deliberately ”performing” for his grand rounds (Shorter, 1992). Freud himself provided a more enduring challenge, of course, arguing that hysterical patients’ symptoms were subconscious productions in response to repressed emotional trauma (Breuer & Freud, 1953). What neurologists thought of this at the time may have been mixed (Loughran, 2008), but this did not matter to the same extent, since the disorder was no longer considered neurological but, as a result of Freud’s work, increasingly psychiatric. Until—suddenly—it mattered a great deal, with World War I and the advent of “shell shock.” Shell shock was initially thought to be a kind of traumatic brain injury brought about by proximity to exploding shells, but as it became clearer that in most cases it was a “functional” disorder, such as hysteria (Wessely, 2003), it presented doctors with a vexing problem. Simply put, the diagnosis of functional disorders had been strongly gendered (Showalter, 1987) and class based (Leed, 1979) prior to the war: The functional diagnosis for a private soldier, for example, almost by definition, was malingering (Kanaan & Wessely, 2010b). Given the scale of the problem and the starkness of the diagnostic consequences (summary execution for malingering vs. repatriation to a hospital for hysteria), it focused attention on the differential diagnosis for a generation (Kanaan, 2016). It would encourage doctors to find alternatives to this stark choice by pathologizing malingering, and by looking for “mediating diagnoses,” such as factitious disorder (Hyler & Spitzer, 1978), that could still be considered disorders even if they did not offer the full exculpation of hysteria (Kanaan & Wessely, 2010b). If World War I brought the tension between hysteria and malingering to light, what of today, in civilian life? With socialized medicine and the patients’ rights movement in many countries, with the legal risk of an angry patient and the financial rewards of successful malingering never higher, it would seem that the power, and the diagnostic pressures, in the doctor–patient relationship have profoundly shifted. But perhaps more importantly, so has hysteria. Support for Freudian ideas has dropped away dramatically in psychiatry, undercutting the basis on which hysteria was considered a psychiatric rather than a neurological disorder (Kanaan, 2016). Perhaps, consequently, it has become an oddity in general psychiatry in the West, a quaint disorder that is read about but rarely seen; yet in neurology, it presents with unremitting fre-

quency—by one large study, the single commonest condition referred to neurologists (Stone et al., 2012). It would seem we are some way back, toward the conditions prior to Freud, when hysteria was a neurological condition. Have neurologists reverted to their old malingering views now that Freud’s get-out-of-diagnosing card has been withdrawn?

NEUROLOGISTS AND CONVERSION DISORDER While such views can still be detected among neurologists today, there is certainly no rush to endorse feigning. Neurologists avoid making feigning diagnoses (Kanaan & Wessely, 2010a) because of the difficulties they cause (Kanaan, Armstrong, & Wessely, 2009), and are increasingly happy to accept a psychiatric view of conversion disorder as subconscious (Kanaan, Armstrong, & Wessely, 2011). But this acceptance is rarely full-throated, and lingering suspicions of deception remain (Kanaan, Armstrong, Barnes, & Wessely, 2009). Indeed, most neurologists think the two cannot be clearly differentiated, and that a degree of feigning is possible in all cases (Kanaan, Armstrong, Barnes, et al., 2009; Kanaan et al., 2011). Why should this neurological imputation of feigning, though tempered and qualified, be essentially unchanged for 150 years? There are many possible explanations for this, all of them speculative (Kanaan, 2010a, 2010b). At the simplest level, a kind of deception—simulation—is indisputable (Freud, 1953b): Conversion disorders present to neurologists as phenocopies of neurological disorders, for neurologists to sift through and separate from the disorders they resemble. But so do lots of disorders, without causing concerns over feigning. There are several conditions that resemble acute stroke and are described as “stroke mimics,” for example—hemiplegic migraine, Todd’s paresis, and conversion disorder, to name but three—yet only in conversion disorder is feigning considered an issue. More important may be the sense, named “secondary gain” by Freud, that patients with conversion disorder benefit from their illness, or, stronger still, that their illness serves a purpose—even if their gain is not monetary, as in malingering, but psychological, as in factitious disorder (Kanaan & Wessely, 2010b). The benefits that a disabling illness might have had in World War I are much less evident in civilian life, and, some have argued, no different for any illness, yet problems that can be

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solved by becoming ill precede most cases of conversion disorder (Nicholson et al., 2016; Raskin, Talbott, & Meyerson, 1966). That conversion disorder may provide some benefit to its sufferers does not, by itself, argue for any deliberate deception on any patient’s part— Freud’s subconscious model specifically addressed that; but deliberate deception is familiar and common to all but the youngest children, while subconscious models are considerably more complicated, even hermetic. As Freud has declined in influence, there have been impressive attempts to forge more contemporary neuropsychological models (Brown, 2004; Edwards, Adams, Brown, Pareés, & Friston, 2012), but they will have their work cut out for them to achieve the simplicity, and understandability, of the following alternative explanation: Patients with conversion disorder are just pretending to be sick. If such clearly motivated and consciously adopted illness could explain all of the behavior in conversion disorder, why look elsewhere? What, indeed, would be the point, if the behavior of a malingerer and a patient with conversion disorder were truly identical (Quine, 1951)? But, of course, this is equally true of depression, or migraine or back pain, all of which could be explained by feigning; yet while all symptom-based diagnoses share in a degree of suspicion, none is singled out for the wholesale doubt that attends conversion disorder. For that, I believe we have to look at the nature of the conversion disorder symptoms, and the way neurologists elicit these. Here, the parallels with symptom validity testing are clear and, I contend, highly informative.

NEUROLOGICAL SYMPTOM VALIDITY TESTING? Conversion disorder symptoms are inconsistent, both over time and within themselves, which is to say, for example, that a patient with conversion disorder who in some contexts is not able to move his or her legs, such as when the neurologist asks that he or she do so, will be able to move them in other contexts, or at other times, such as when he or she is asleep. Additionally, though his or her legs may be presented as too weak to walk, these same legs may be able to perform other tasks that would seem to require even greater strength, such as running. This “inconsistency” is the single most important diagnostic feature for neurologists (Kanaan, Armstrong, Barnes, et al., 2009; Kanaan et al., 2011), though it is not the only one. It is therefore greatly

prized and sought at length—but it is not always found. Sometimes the neurologist can only find “incongruity” with all other neurological diagnoses—a pattern usually accorded a lower degree of diagnostic certainty (Espay & Lang, 2015). There have long been attempts to frame the inconsistences as “positive” signs rather than “negative” (Slater, 1965)—as diagnostic markers of conversion disorder rather than diagnosis by exclusion. For when the diagnosis is only made on the basis of incongruity with neurological disease, there is always the hypothetical risk that the neurologist is either incompetent (has not considered the whole, long list of rare neurological alternatives) or overconfident (new explanatory neuropathologies may yet be discovered (Sykes, 2010). What exactly a “positive” sign demonstrates is less clear. On the one hand, these positive signs are regularly found in patients with recognized neuropathologies (Eames, 1992; Gould, Miller, Goldberg, & Benson, 1986). This would render the signs futile, except that their doctor can then diagnose comorbidity (a stroke and a conversion disorder), or perhaps argue that conversion disorder is a feature rather than a diagnosis (Slater, 1965). Despite that, neurologists have compiled a long list of such inconsistencies that have been variably validated (Daum et al., 2015; Daum, Hubschmid, & Aybek, 2014) as distinguishing conversion disorder from neurological disease. On the other hand, clinicians may ask whether these inconsistencies may equally be positive features of feigning, for they are clearly analogous to the tests employed in symptom validity testing (SVT). If we look at the clinical tests employed by neurologists (Daum et al., 2015), we can see how these align with detection strategies outlined by Rogers (Chapter 2, this volume). Let’s consider three examples: (1) giveway weakness, (2) Hoover’s sign, and (3) splitting of vibration sense. Giveway weakness is observed when the neurologist tests the patient’s strength by asking the patient to push his or her affected arm, for example, against the neurologist’s arm and the patient’s strength is initially found to be good, but suddenly “gives way.” It is by no means clear from the previous description that this presentation should imply a functional illness rather than neuropathology, and there are several plausible reasons why a subject’s strength could fail (e.g., pain or fatigue). Nevertheless, giveway weakness only rarely occurs in neuropathological populations, and as such would be considered a rare or atypical presentation. Hoover’s sign occurs when the patient, lying on his or her back, cannot press



12.  Conversion Disorder and Illness Deception 239

the affected leg down when asked to do so, but can be surreptitiously felt to do so when asked to raise the other leg (lifting a leg when lying down is easier when the other leg is pressed down to lever against). This represents a failure of consistency— the patient shows power in some circumstances but not others. Finally, splitting of vibration sense is when the patient shows reduced ability to detect vibration on one side of his or her body—but since, unlike other senses, vibration is carried by bone, it should be detectable on both sides, even if the nerves on one side are impaired. This presentation is best conceptualized as improbable, since it would seem to require something fantastical to explain it. Indeed, all of the primary signs fall into these three categories of detection strategies: rare, inconsistent, or improbable. Other, supporting aspects of the neuorological assessment (Kanaan et al., 2011) conform to other detection approaches discussed by Rogers (Chapter 2, this volume) including the following: 1. Obvious symptoms: Symptoms are often dramatic—presentations with gait disturbance, seizures, or limb weakness greatly outnumber presentations with sensory disturbance. 2. Reported versus observed symptoms: Patients’ self-report of their symptoms is typically more severe than what is observed. 3. Significantly below chance performance: Some patients appear systematically to get the answers wrong, for example, in the “yes/no test” (Daum et al., 2015). 4. Intensity of medical complaints: Some patients describe their symptoms as constant, or severe, where observation suggests otherwise (Pareés et al., 2012). 5. Indiscriminate endorsement: Patients may endorse a range of symptoms suggested by an interviewer. This breadth of symptoms endorsement also often includes cognitive impairments (Stone et al., 2015) and when these are tested with SVT, failures are common, though by no means universal (Merten & Merckelbach, 2013). 6. Endorsement of excessive virtue: Patients may present themselves as too psychologically healthy to succumb to mental illness. So, if neurologists’ diagnostic strategies are analogous to SVT, what does that tell us? It tells us that, like SVT, neurologists are using a diagnostic approach for detecting deception. If, like SVT, it

does not exclusively detect deception, then deception is still the most direct inference, the one that must be explained away. It may be unclear that the neurologists’ approach detects deception: On the face of it, many of these tests simply detect rare or unusual features, and neurology is awash with rare and unusual disorders. But in addition to what Rogers (Chapter 2, this volume) refers to as such “unlikely” detection strategies, there is failure in what is referred to as “amplified” strategies: features that suggest the patient does not accurately present his or her symptoms (and in a way that makes them seem greater). The patient’s inconsistency, for example, is not random, but it fails selectively in support of the illness presentation (in the earlier Hoover’s sign example, the “bad” leg is weak when it is overtly tested; when the strength of the “good” leg is overtly tested, the “bad” leg’s strength is covertly revealed). None of these strategies, nor even their combination, is proof of deception—they could equally suggest a “superordinate” pathology (Merten & Merckelbach, 2013) that leaves patients convinced of and enacting an illness, such as that described by Edwards and colleagues (2012); but they are found in deception, and if any superordinate pathology leaves patients convinced by their own deception, it may be deception nonetheless (Kanaan, 2010a, 2010b). Below-chance performance is the closest to proof of deliberate feigning, and it is hard for any superordinate pathology to explain it away, but it is of little practical help in conversion disorder: It is rarely found, though it is also rarely looked for, since the tests for it rely on a sensory deficit, whereas motor deficits are overwhelmingly more common presentations. To be clear, conversion disorder is not rare. As previously stated, it is among the commonest conditions neurologists see. But it is perhaps the only one in which the patient is preferentially diagnosed by a tendency to misrepresent his or her symptoms. Its symptoms are by no means impossible; they are not even implausible per se. But their inconsistency was revealed as neuroanatomy and physiology were developed in the 19th century—the same nerves and musculature extend the hip when pressing the “bad” leg and lifting the “good” leg in Hoover’s sign—and the pattern interpreted as evidence of agency. Any account of conversion disorder must either show how the patient’s agency itself has been disordered (Spence, 2001) or tricked (Edwards et al., 2012), or supplanted by a subagency (Breuer & Freud, 1957).

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IS IT ALL NEUROLOGISTS’ FAULT? Neurologists’ diagnostic approach may explain why conversion disorder is particularly suspected of deception, even compared with the other somatoform disorders. All somatoform presentations come under suspicion, since all show normal anatomy, and all lack a biological test that would prove them a medical disorder as opposed to “merely” a psychiatric disorder; moreover, those who are proven to feign by no means preferentially feign neurological symptoms (Dandachi-Fitzgerald & Merckelbach, 2013; Kanaan & Wessely, 2010a; Lawlor & Kirakowski, 2014). But only conversion disorder is diagnosed by detecting misrepresentation; only neurologists diagnose their somatoform disorder by signs of deception. Even if this explains neurologists’ suspicions, it cannot be the whole story, since concerns about feigning in conversion disorder are far more widespread. After all, the diagnostic manuals that required psychiatrists to exclude feigning before they diagnosed conversion disorder were written by psychiatrists, not neurologists. And the suspicion that patients sense in their neurologists (Nettleton, Watt, O’Malley, & Duffey, 2005), they feel at least as strongly from the general public (Arain et al., 2016)—with good reason, as a substantial proportion of the public does think unexplained weakness suggests that the patient is feigning (Stone et al., 2002). This is harder to explain. It may be that the public has simply picked up on the neurologists’ suspicions—the previously mentioned study by Stone et al. (2002) was conducted in neurologists’ outpatients; however, that result has been replicated in a population without obvious contact with neurologists (Ding & Kanaan, 2016). It may be that the public is now sufficiently attuned to medicine, so that it responds to the same subtle signs of deception as neurologists, yet the account in neither of these studies carried such detailed information, only the idea that the tests had all been negative. It may be that it simply represents one more misogynistic response to this most gendered of psychiatric disorders (Kanaan & Wessely, 2010b), yet the feigning imputation was present irrespective of gender. It may be that the lessons of The Great War and the decline of Freud have continued to resonate with the public, yet the disorder has disappeared from public view and now largely figures as a historical entity, with little association with conversion disorder (Kanaan, 2016).

The story of conversion disorder (hysteria) coming under suspicion because clinical tests produced results resembling malingering once neurology was sophisticated enough to decrypt them seems plausible—but it is only a theory. Equally possible is that neurologists defined hysteria as malingering, perhaps in patients with whom that term would be impolitic, and the diagnostic tests developed were then simply those used to detect malingering. The historical record cannot help us here—as with all symptom-based disorders, the extent of the disease is not defined independently of the way it is diagnosed. Perhaps neurologists did not reveal, with their acumen, the resemblance to malingering; perhaps they inherited it. Is it too great a reach from its earlier incarnation—as the product of witchcraft at least shares with malingering the idea that this is not a natural illness—to a manmade illness? A conclusive answer to the origins of the association of hysteria and malingering may not be possible, but there is no doubting its significance. It is hard, otherwise, to explain the remarkable disdain with which the conversion disorder is held (Ahern, Stone, & Sharpe, 2009). This is evident in its astonishingly low profile given its prevalence (until recently there was not one single conversion disorder support group or charity), and the great difficulty patients have in accepting the diagnosis (Crimlisk et al., 2000). Neurologists clearly play their part in this, even if it is not all their fault. Fortunately, there is a determined attempt under way to rehabilitate the disease, led both by neurologists and psychiatrists: In addition to explicitly repudiating the imputation of malingering (Kanaan et al., 2010; Stone et al., 2011), they are seeking new models to fill the explanatory void (Aybek et al., 2014; Edwards et al., 2012). Only time will tell whether the attempt is successful and perhaps consequently, how deep the association with malingering goes: whether it is in the indivisible “blood” of our conception of the disorder or merely the “marriage” of two unfortunate conditions thrown together.

SUMMARY Conversion disorder has arguably been stigmatized throughout its long history, but its association with illness deception appears relatively new. It was evident in the attitude of 19th-century neurologists but was tempered by Freud’s theory that the illness



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behavior in conversion disorder is subconscious. As Freud’s influence wanes, there is a danger that conversion disorder will once again be considered a form of malingering. The source of the suspicion that conversion disorder is just pretending is unclear, and it is unclear why it should be more pronounced than in other disorders that lack a clear biological explanation, such as depression or other somatoform disorders. One important difference may be the manner of its diagnosis, since neurologists use an approach analogous to SVT. This search for features of deception may create the impression of an association with malingering—but it may also be the case that it simply reflects an association that was already present.

REFERENCES Ahern, L., Stone, J., & Sharpe, M. C. (2009). Attitudes of neuroscience nurses toward patients with conversion symptoms. Psychosomatics, 50(4), 336–339. American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: Author. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington, VA: Author. Arain, A., Tammaa, M., Chaudhary, F., Gill, S., Yousuf, S., Bangalore-Vittal, N., et al. (2016). Communicating the diagnosis of psychogenic nonepileptic seizures: The patient perspective. Journal of Clinical Neuroscience, 28, 67–70. Aybek, S., Nicholson, T. R., Zelaya, F., O’Daly, O. G., Craig, T. J., David, A. S., et al. (2014). Neural correlates of recall of life events in conversion disorder. JAMA Psychiatry, 71(1), 52–60. Breuer, J., & Freud, S. (1957). Studies on hysteria. Oxford, UK: Basic Books. Brown, R. J. (2004). Psychological mechanisms of medically unexplained symptoms: An integrative conceptual model. Psychological Bulletin, 130(5), 793–812. Crimlisk, H. L., Bhatia, K. P., Cope, H., David, A. S., Marsden, D., & Ron, M. A. (2000). Patterns of referral in patients with medically unexplained motor symptoms. Journal of Psychosomatic Research, 49(3), 217–219. Dandachi-Fitzgerald, B., & Merckelbach, H. (2013). Feigning ≠ feigning a memory deficit: The Medical Symptom Validity Test as an example. Journal of Experimental Psychopathology, 4(1), 46–63. Daum, C., Gheorghita, F., Spatola, M., Stojanova, V., Medlin, F., Vingerhoets, F., et al. (2015). Interobserver agreement and validity of bedside “positive signs” for functional weakness, sensory and gait disorders in

conversion disorder: A pilot study. Journal of Neurology, Neurosurgery, and Psychiatry, 86(4), 425–430. Daum, C., Hubschmid, M., & Aybek, S. (2014). The value of “positive” clinical signs for weakness, sensory and gait disorders in conversion disorder: A systematic and narrative review. Journal of Neurology, Neurosurgery, and Psychiatry, 85(2), 180–190. Ding, J. M., & Kanaan, R. A. A. (2016). What should we say to patients with unexplained neurological symptoms?: How explanation affects offence. Journal of Psychosomatic Research, 91, 55–60. Eames, P. (1992). Hysteria following brain injury. Journal of Neurology, Neurosurgery, and Psychiatry, 55(11), 1046–1053. Edwards, M. J., Adams, R. A., Brown, H., Pareés, I., & Friston, K. J. (2012). A Bayesian account of “hysteria.” Brain, 135(11), 3495–3512. Espay, A. J., & Lang, A. E. (2015). Phenotype-specific diagnosis of functional (psychogenic) movement disorders. Current Neurological and Neuroscience Reports, 15(6), 32. Freud, S. (1953a). Charcot. In S. Freud, A. Freud, A. Strachey, J. Strachey, & A. W. Tyson (Eds.), The standard edition of the complete psychological works of Sigmund Freud (Vol. III). London: Hogarth Press. Freud, S. (1953b). Memorandum on the electrical treatment of war neurotics. In S. Freud, A. Freud, A. Strachey, J. Strachey, & A. W. Tyson (Eds.), The standard edition of the complete psychological works of Sigmund Freud (Vol. XVII). London: Hogarth Press. Gould, R., Miller, B. L., Goldberg, M. A., & Benson, D. F. (1986). The validity of hysterical signs and symptoms. Journal of Nervous and Mental Disease, 174(10), 593–597. Hyler, S. E., & Spitzer, R. L. (1978). Hysteria split asunder. American Journal of Psychiatry, 135(12), 1500– 1504. Kanaan, R. A. (2010a). Disbelief and self-deception in conversion disorder. AJOB Neuroscience, 1(1), W1– W2. Kanaan, R. A. (2010b). Hysteria and the varieties of deception. AJOB Neuroscience, 1(1), 55–64. Kanaan, R. A. (2016). Freud’s hysteria and its legacy. In M. Hallett, J. Stone, & A. Carson (Eds.), Handbook of clinical neurology: Functional neurological disorders (Vol. 139, pp. 37–45). New York: Academic Press. Kanaan, R., Armstrong, D., Barnes, P., & Wessely, S. (2009). In the psychiatrist’s chair: How neurologists understand conversion disorder. Brain, 132(10), 2889–2896. Kanaan, R., Armstrong, D., & Wessely, S. (2009). Limits to truth-telling: Neurologists’ communication in conversion disorder. Patient Education and Counseling, 77(2), 296–301. Kanaan, R. A., Armstrong, D., & Wessely, S. C. (2011). Neurologists’ understanding and management of conversion disorder. Journal of Neurology, Neurosurgery, and Psychiatry, 82(9), 961–966.

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Kanaan, R. A., Carson, A., Wessely, S. C., Nicholson, T. R., Aybek, S., & David, A. S. (2010). What’s so special about conversion disorder?: A problem and a proposal for diagnostic classification. British Journal of Psychiatry, 196(6), 427–428. Kanaan, R. A., & Wessely, S. C. (2010a). Factitious disorders in neurology: An analysis of reported cases. Psychosomatics, 51(1), 47–54. Kanaan, R. A., & Wessely, S. C. (2010b). The origins of factitious disorder. History of the Human Sciences, 23(2), 68–85. Lawlor, A., & Kirakowski, J. (2014). When the lie is the truth: Grounded theory analysis of an online support group for factitious disorder. Psychiatry Research, 218(1–2), 209–218. Leed, E. J. (1979). No man’s land: Combat and identity in World War I. New York: Cambridge University Press. Loughran, T. (2008). Hysteria and neurasthenia in pre1914 British medical discourse and in histories of shell-shock. History of Psychiatry, 19(1), 25–46. Merten, T., & Merckelbach, H. (2013). Symptom validity testing in somatoform and dissociative disorders: A critical review. Psychological Injury and Law, 6(2), 122–137. Nettleton, S., Watt, I., O’Malley, L., & Duffey, P. (2005). Understanding the narratives of people who live with medically unexplained illness. Patient Education and Counseling, 56(2), 205–210. Nicholson, T. R. J. (2012). Studies in conversion disorder. Unpublished dissertation, King’s College London, London, UK. Nicholson, T. R., Aybek, S., Craig, T., Harris, T., Wojcik, W., David, A. S., et al. (2016). Life events and escape in conversion disorder. Psychological Medicine, 46(12), 2617–2626. Pareés, I., Saifee, T. A., Kassavetis, P., Kojovic, M., Rubio-Agusti, I., Rothwell, J. C., et al. (2012). Believing is perceiving: Mismatch between self-report and actigraphy in psychogenic tremor. Brain, 135(1), 117–123. Quine, W. V. O. (1951). Two dogmas of empiricism. The Philosophical Review, 60, 20–43. Raskin, M., Talbott, J. A., & Meyerson, A. T. (1966). Diagnosis of conversion reactions: Predictive value of

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C H A P T E R 13

Feigned Medical Presentations Robert P. Granacher, Jr., MD, MBA David T. R. Berry, PhD

The second edition of the Oxford English Dictionary (1989) defines feigned as use of attributes, actions, diseases, etc., as “simulated, counterfeited, pretended, sham.” Practicing physicians and psychologists are confronted daily with various forms of feigned medical presentations, particularly during examinations for impairment or disability. This chapter reviews clinical data on feigning during various medical presentations, categorizes them into their most prominent presentations, and provides data-based information to practitioners regarding the frequency and significance of feigning and malingering by examinees claiming a medical disorder during treatment or evaluation. Also, recommendations for detecting feigning/malingering are provided with case examples. The term feigning is generally used more broadly than is its counterpart malingering, and, in the medical context, includes exaggeration of symptoms, magnification of symptoms, or faking of symptoms. Malingering, in contrast, is a conscious choice to intentionally exaggerate or fabricate a medical or psychological condition for external gain. Since medical presentations are complex in their nature and often involve administrative, civil, or criminal law actions, malingering is defined in this chapter as the intentional production of medical/psychological symptomatology for the sole purpose of achieving some external gain. Feigning, since it can include multiple psychiatric

and psychological issues or comorbidities, is generally more complex to consider and evaluate. While it includes many of the same features as malingering, it is also broader in scope and can include an unawareness of behavior and other emotional drivers for the production of simulated symptoms without clear evidence of intent to gain.

CLASSIFICATIONS As described by Rogers (Chapter 2, this volume) feigned presentations can be classified in three general domains: (1) physical (somatic), (2) cognitive, and (3) psychological (emotional). Beyond these domains, Resnick (1984) has identified three distinct categories of malingering: pure malingering, partial malingering, and false imputation. In pure malingering. the person entirely fabricates a psychological or medical condition that does not exist and has never existed. In partial malingering. the individual is exaggerating symptoms of a condition that actually exist. False imputation refers to an individual ascribing symptoms to an unrelated cause. For example, in personal injury litigation, an individual might claim pain as a result of being struck by a motor vehicle, when in fact, the pain is secondary to an unrelated fall occurring outside the context of the litigation. Most detection methods target either pure or partial malingering,

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as the presumption in false imputation is that the symptoms are genuine and only the source of the symptoms is in question (McDermott & Feldman 2007). As an important development, the Centers for Medicare and Medicaid Services (CMS; www. cms.gov/medicare/coding/icd10) issued a ruling on October 1, 2015, that reimbursement claims for treatment of physical or mental diagnoses shall require the use of 10th edition of the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM; 2016). Any codes used in this chapter utilize the American ICD-10-CM versions (www.cms.gov/medicare/coding/icd10). Other international ICD-10 versions may differ in content and criteria. In the American ICD-10-CM version, malingering (conscious simulation) in 2016 was coded as ICD-10: Z76.5. It was categorized as a treatment billable ICD-10-CM code that could be used to indicate a diagnosis for reimbursement. Malingering, using the medical ICD-10-CM coding system, as applicable to a person feigning illness (with obvious motivation), uses approximate synonyms such as malingering and/or person feigning illness. Two other conditions of feigning are excluded under the malingering ICD-10-CM diagnosis code Z76.5: (1) factitious disorder with predominately psychological symptoms (ICD-10: F68.11), and (2) factitious disorder with predominately physical symptoms (ICD-10: F68.12). DSM-5 diagnostic codes (American Psychiatric Association, 2013) are being replaced with ICD-10-CM codes in medicine and psychology to comply with CMS rules. In DSM-5, malingering is classified as a “V code,” which is not a diagnosis but a description of a problem or condition that may be clinically relevant. However, DSM-5 (American Psychiatric Association, 2013, p. 727) raises certain indices for possible feigning: forensic setting, uncooperativeness, claimed impairment unsubstantiated by objective data, and an antisocial personality disorder (ASPD) diagnosis. It should be noted that Rogers (Chapter 1, this volume) has shown that these screening criteria for malingering have poor accuracy in practice. Because they capitalize on common characteristics (e.g., antisocial behavior and forensic contexts), they are rarely helpful as malingering screens.

the differential diagnosis (McDermott & Feldman, 2007), including (1) undetected physical pathology, (2) somatization disorder, (3) hypochondriasis, (4) pain disorder, and (5) factitious disorder with predominately physical signs and symptoms.

Undetected or Underestimated Physical Illness With the advent of the new ICD-10 diagnostic coding system, there are now 69,823 diagnostic codes in the American form of ICD-10-CM (www.cdc.gov/nchs/icd/icd10cm). Since no more than 1,000 of these diagnostic codes are used for mental disorders, this leaves roughly 68,000 diagnostic illness codes of a physical nature. It seems obvious that each could be malingered theoretically in some fashion. Thus, it is an extraordinary challenge to consider these possibilities. Obviously, any person who presents with unexplained physical complaints may actually have an illness that is not detected during an initial evaluation or even with subsequent testing. This assessment method could erroneously lead to a false-positive conclusion that the person with a genuine physical disorder is malingering. A rule of thumb is for the physician or psychologist to consider seriously and evaluate for malingering before advancing to exploring highly esoteric physical diagnoses in this instance (McDermott & Feldman 2007).

Somatization Disorder In the ICD-10-CM classification system, somatization disorder is listed under the broad heading, Somatoform Disorders. Using the ICD-10 system, somatization disorder is coded as ICD-10: F45.0 (see Table 13.1). Somatization disorder refers to a pattern of recurring polysymptomatic somatic complaints resulting in medical treatment or impaired daily functioning. It usually begins before age 30 and extends over a period of years. Synonyms for this disorder are occasionally used, such as Briquet’s disorder or multiple psychosomatic disorder. McDermott and Feldman (2007) have advised that somatization disorder is included as one of the five conditions from which malingering should be differentiated.

Pain Disorders

DIFFERENTIAL DIAGNOSES If a physician or psychologist raises the issue of malingering as a clinical consideration during examination, five conditions should be considered in

McDermott and Feldman (2007) list pain disorders to be considered in the differential diagnosis of malingering. In ICD-10-CM, the primary heading is ICD-10: F45.5, Pain Disorders Related to Psychological Factors. It is synonymous with pain disor-



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TABLE 13.1.  ICD-10-CM Somatoform Disorders

F45.0

Somatization disorder

F45.1

Undifferentiated somatoform disorder

F45.2

Hypochondriacal disorders •• F45.20: hypochondriacal disorder unspecified •• F45.21: hypochondriasis •• F45.22: body dysmorphic disorder •• F45.29: other hypochondriacal disorders

F45.5

Pain disorders related to psychological factors •• F45.41: pain disorder exclusively related to psychological factors •• F45.42: pain disorder with related psychological factors

F45.8

Other somatoform disorders

F45.9

Somatoform disorder, unspecified

der. As seen in Table 13.1, two conditions are subsumed: F45.41, Pain Disorder Exclusively Related to Psychological Factors, and F45.42, Pain Disorder with Related Psychological Factors. The practitioner should identify which of these two disorders is of primary concern during the evaluation or defer solely to ICD-10: F45.5, Pain Disorders Related to Psychological Factors. The latter may be the best the practitioner can do in terms of diagnosis in an evaluation for feigned or malingered pain.

Hypochondriasis Similar to the aforementioned conditions, hypochondriacal disorders also comprise a subset of somatoform disorders. The ICD-10-CM codes for hypochondriacal disorders include (1) F45.20, Hypochondriacal Disorder Unspecified, (2) F45.21, Hypochondriasis, (3) F45.22, Body Dysmorphic Disorder, and (4) F45.29, Other Hypochondriacal Disorders (see Table 13.1). Hypochondriasis is a potentially billable ICD10-CM code as a treatment diagnosis. Obviously in independent medical evaluations and/or other forms of examination for testimony, professionals cannot bill medical insurance for services rendered. Hypochondriasis focuses on preoccupation with the fear of having, or the idea that a person has a serious disease based on the person’s misinterpretation of bodily symptoms. Generally, persons with hypochondriasis are eager to undergo diagnostic evaluations using multiple tests, and they

usually cooperate for most forms of examination. In contrast, most malingerers are often uncooperative with the diagnostic process as a potential indicator for malingering under DSM-5 (American Psychiatric Association, 2013). Unlike those with hypochondriasis, malingerers are unlikely to show any relief or pleasure in response to negative test results. As a further distinction, hypochondriacal patients also simulate or self-induce illness. When self-induction does occur, the deceptions generally reflect a desperate need to convince physicians or psychologists to perform further tests (Rogers, 2008).

Factitious Disorder with Predominantly Physical Symptoms Factitious disorder, coded ICD-10: F68.1, is not a billable ICD-10-CM diagnosis code under any circumstance. It represents a category of psychiatric disorders characterized by physical or psychological symptoms that are intentionally produced in order to assume the sick role. Thus, there is no external incentive for the behavior, such as that seen in malingering (e.g., unwarranted economic gain or avoidance of legal responsibility). Generally the person is unaware of any self-motivating factors. Factitious disorder is also characterized by a plausible presentation of physical symptoms or an acute illness that is under the individual’s control, and often these fabrications result in multiple unnecessary hospitalizations, such as those seen in Munchausen syndrome (Goldberg et al., 2014). A somatic factitious disorder is characterized by habitual presentations for treatment of an apparent acute illness, with the patient giving a plausible and dramatic history, all of which are false. It can include quite ingenious methods to induce a real illness, such as autovenipuncture and blood letting to produce chronic anemia (Granacher, 1982). A clinical indicator is that the patient/examinee with factitious disorder welcomes the chance to undergo medical and surgical procedures—including those that most people would seek to avoid— because he or she finds the sick role intrinsically gratifying. Conversely, malingerers generally seek to minimize medical contacts through which their deceptions might be uncovered (McDermott & Feldman, 2007).

EPIDEMIOLOGY (BASE RATES) Mittenberg, Patton, Canyock, and Condit (2002) and Larrabee (2003) have reported base rates for

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malingering of 40% for litigants with mild traumatic brain injury. Mittenberg et al. (2002) also reported a base rate of 33.5% for pain or somatoform disorders. These percentages were consistent with the previous work of Carroll, Abrahamse, and Vaiana (1995), who investigated the cost of excessive medical claims for automobile personal injuries and analyzed soft tissue injury claims as a function of the compensation system per state. They found that 35–42% of all medical costs submitted in support of an auto injury claim were excessive. Overall, studies have provided further support for a base rate of malingering of 40% or more in settings with external incentive. For instance, Ardolf, Denney, and Houston (2007) found that of those examinees referred for pretrial presentencing neuropsychological evaluation, 22% met Slick, Sherman, and Iverson’s (1999) criteria for definite malingering, and 32% met criteria for probable malingering, for a combined definite/probable rate of 54% malingering. It should be noted that the Slick et al. model for classifying malingered neurocognitive deficit has been strongly criticized by Rogers, Gillard, Berry, & Granacher, 2011). However, it remains the most common framework used in the published literature in this area. Van Hout, Schmand, Wekking, and Deelman (2006) found that 57% of a sample with suspected neurotoxic injury failed at least one of three symptom validity tests. Greve et al. (2006) reported a malingering prevalence of 40% (33% probable, 7% definite, using the Slick et al. [1999] criteria) for examinees claiming exposure to occupational and environmental substances. The consistency of these base rates has led to a “magical number” of 40% ± 10 as representative of the base rate of invalid neuropsychological testing in settings with an external incentive (Larrabee, Millis, & Meyers, 2009). Almost any medical illness can be feigned/ malingered. However, certain types of medical problems are more likely to be feigned/malingered than others (McDermott & Feldman, 2007). The largest study to date giving base rates of malingering and symptom exaggeration was published by Mittenberg et al. (2002). Their meta-analysis consisted of cases referred to 144 neuropsychologists. Estimates were based on 33,531 annual cases involved in personal injury (n = 6,371), disability (n = 3,688), criminal (n = 1,341), or medical matters (n = 22,131). Base rates were not very different among geographic regions or practice settings, but were related to the proportion of plaintiff versus defense referrals. Twenty-nine percent of personal injury, 30% of disability, 19% of criminal, and 8%

of medical cases involved probable malingering and symptom exaggeration. The percentages of malingering for specific diagnoses were as follows: 39% mild head injury, 35% fibromyalgia/chronic fatigue, 31% chronic pain cases, 27% neurotoxic cases, and 22% electrical injury claims. Malingering was supported by evidence in the following percentage of cases, including severity (65%) or pattern of cognitive impairment inconsistent with the condition (64%). Other sources of evidence include scores below empirical cutoff scores on forced-choice tests (57%); discrepancies among records, self-­ report, and observed behavior (56%); and implausible self-reported symptoms in interview (46%). Last, other evidence includes implausible changes of test scores across repeated examinations (45%), and validity scales on objective personality tests (38%).

Physical Feigning/Malingering The difficulty with evaluating the multiple possible presentations of physical feigning/malingering is that often studies have very few participants. This fact may result in substantial challenges in adhering to the requirements of Daubert v. Merrell Dow Pharmaceuticals (1993). Daubert requires those testifying with regard to malingering of physical, cognitive, or psychological injury/impairment also to report known error rates for the particular feigning/malingering diagnosis in question. It will likely be necessary in many cases for a practitioner to admit that error rates of malingering or feigning for a particular physical entity are just not known. Moreover, the variance of feigning/malingering rates is extremely wide. For instance Leavitt and Sweet (1986) surveyed 105 board-certified orthopedic surgeons and neurosurgeons from six states. The estimates of patients with low back pain who were malingering varied widely, from a low of 1% to a high of 74%. Interestingly, the majority of surgeons (78%) made low estimates, indicating that 10% or fewer of their patients malingered their pain. Their estimates were generally not related to primary or secondary gain, but were more closely associated with inconsistencies the surgeons found during their physical examinations. The two discrepancies in self-presentation most frequently cited as suggestive of malingering involved (1) muscular weakness in the examinations not seen in other personal activities, and (2) claimed disablement disproportionate to the objective physical findings.



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When estimates of malingering are based on direct psychological measurement of feigning/malingering, however, the incidence of malingered pain is significantly higher than that detected by physical examination alone. Moreover, 25–30% of fibromyalgia cases were found to be feigning/ malingering in a study by Gervais, Russell, et al. (2001). When psychological effort measures are added to findings from physical examination of patients claiming chronic pain, similar results of higher estimates of malingering are found (Gervais, Green, Allen, & Iverson, 2001). These data (Gervais, Russell, et al., 2001; Gervais, Green, et al., 2001) suggest that multiple factors are involved in the determination of base rates for physical medical malingering. They include, for instance, the clinical setting, the individual practitioner, the practitioner’s specialty, whether psychological measurement of malingering is added to estimates of malingering by physical examination, and clinical patients versus those examinees seeking compensation. Using mild head injury as a paradigm of claimed physical injury, Larrabee (2003), in a meta-analysis of 11 studies, found a 40% prevalence rate of malingering in 1,363 examinees who were seeking compensation for mild head injury.

Cognitive Feigning/Malingering Traumatic brain injury (TBI) is the most likely alleged neurocognitive impairment in those who present for medical examination in a litigation setting. Probable feigned cognitive impairment conservatively approximates a 40% base rate in mild TBI examinations undertaken in compensationseeking circumstances (Berry & Schipper, 2008). Professionals who evaluate cases of civilians and veterans with persistent TBI complaints face formidable challenges. Factors should invariably be given careful scrutiny, including premorbid and postinjury psychological status and the potential confounding influence of insufficient effort and symptom exaggeration (Gfeller & Roskos, 2013). Medical examinations of cognitive status in criminal defendants require even higher levels of suspicion for malingered neurocognitive dysfunction. Ardolf et al. (2007) presented data to determine the prevalence of negative response bias from 105 criminal defendants referred on a consecutive basis for neuropsychological assessment. Based on the Slick et al. (1999) criteria, classification for malingered neurocognitive dysfunction revealed the following: 19% of cases were valid; 27% of cases

were possible malingered neurocognitive dysfunction (MND); 32% were probable MND; and 22% were definite MND. The combined rate of probable and definite MND was 54%. Other base rates of malingered cognitive dysfunction were noted earlier. For methods to identify definite cognitive feigning, and to also identify probable cognitive feigning, the reader is referred to Berry and Schipper (2008), as well as Bender (Chapter 7), Frederick (Chapter 17), and Garcia-­Willingham, Bosch, Walls, and Berry (Chapter 18) in this volume.

Psychological Feigning/Malingering This topic is covered extensively in other chapters in this volume, which include malingered psychosis (Resnick & Knoll, Chapter 6) and malingered PTSD (Resnick, West, & Wooley, Chapter 10). Feigned psychological impairment (Nelson & Sweet, 2009) may represent a distinct construct from feigned cognitive impairment. Therefore, different feigning measures and detection strategies should be used to for each different domain of feigning (see Rogers, Chapter 2, this volume).

DETECTION OF MEDICAL FEIGNING/MALINGERING Granacher (2015) provided a systematic method for evaluating the physical, cognitive, and psychological aftereffects of TBI. He believes that all cases of medical examination that include a significant incentive should include the services of either a psychologist or neuropsychologist to provide feigning/malingering assessment of the examinee and consultation to the physician. Guides to the Evaluation of Permanent Impairment (American Medical Association, 2008), stresses the need for malingering screens and detection in the chapter “Mental and Behavioral Disorders,” but it provides little information or guidance for dealing with feigned/malingered physical disorders. But the Guides do give considerable guidance on the importance of evaluating motivation and also malingering in general, pointing out that malingerers may present with complaints suggesting a mental and behavioral disorder, a physical disorder, or both. In particular, the Guides (American Medical Association, 2008, p. 353) admonish practitioners: Examiners should always be aware of this possibility when evaluating impairments. The possibility of avoiding responsibility and/or obtaining monetary

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awards increases the likelihood of exaggeration and/or malingering. Nonspecific symptoms, which are difficult to verify, tend to be overrepresented, including headache, low back pain, peripheral neuralgia, and vertigo. Malingering occurs along a spectrum—from embellishment to exaggeration, to outright fabrication.

In applying the Guides. the addition of response bias measures to the medical laboratory and physical examination can enhance the accuracy of medical evaluations in which incentives may lead to poor effort. Neuropsychologists have published an important policy statement on symptom validity assessment (Bush et al., 2005). This statement from the National Academy of Neurpsychology (NAN) Policy and Planning Committee advises that adequate assessment of response validity is essential in order to maximize confidence in the results of neurocognitive and personality measures, and in the diagnoses and recommendations that are based on the results. Assessment of response validity is deemed necessary, as a component of any medically required examination. When determined by the neuropsychologist to be necessary for the assessment of response validity, administration of specific symptom validity tests are also medically necessary. The statement pointed out that when interpreting the data from symptom validity testing, they should generally be given substantially greater weight than subjective indicators of suboptimal effort. Subjective indicators, such as examinee statements and examiner observations,

should be afforded less weight due to the lack of scientific evidence supporting their validity.

Detection of Physical Feigning/Malingering With the aforementioned models of medical malingering consisting of physical (somatic), cognitive, and psychological (emotional) forms, it should be noted that there are no direct measurements of physical malingering available within psychological test formats. However, some examiners use the Fs scale of the MMPI-2-RF to identify the reporting of uncommon somatic symptoms (Wygant, Ben-Porath, & Arbisi, 2004). The 16 items of the Fs scale were endorsed by 25% or fewer of men and women in several large samples of medical patients. Physicians, while performing a physical examination, can often detect symptom magnification/malingering, but this is usually not part of all physician’s traditional medical training. Table 13.2 provides a number of fairly simple tests with supporting references that physicians commonly use to detect symptom magnification or feigning/ malingering while performing an independent medical examination (Martelli, Zasler, Nicholson, Pickett, & May, 2002).

Detection of Cognitive Feigning/Malingering Larrabee (2012) draws a distinction between symptom validity tests (SVTs) and performance validity tests (PVTs). SVTs assess the accuracy

TABLE 13.2.  Physical Tests for Malingering

Test

Reference

To test for . . .

Axial Loading Test

Waddell et al. (1980)

Low back symptoms

Burns’s Bench Test

Evanski et al. (1979)

Low back pain

Cervical Motion Test

Sobel et al. (2000)

Whiplash pain

Gordon–Welberry Toe Test

Roby-Brami et al. (1989)

Low back pain

Hoover Test

Hoover (1908)

Limb paresis

Magnuson’s Test

Kiester & Duke (1999)

Pain localization

Mankopf’s Maneuver

Greer et al. (2005)

Response to pain

Proprioception Test

Incesu & Sobaci (2011)

Functional sensory loss

Stenger’s Test

Hanley & Tiffany (1954)

Hearing loss

Teal’s Test

Teal (1918)

Unilateral deafness



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and honesty of self-reports of symptoms and problems, and tend to be most appropriate for detection of psychological feigning. In contrast, PVTs index the credibility of functional impairment using actual tests of performance rather than self-reports about deficits. PVTs have received significant attention in the neuropsychological literature since the early 1990s, and the publication rate in this area remains quite strong. This trend suggests that practitioners should closely monitor this evolving research base. Other, more comprehensive expositions of PVTs may be found in excellent edited volumes on detecting malingered neuropsychological deficits by Boone (2007), Larrabee (2007), as well as Denney and Sullivan (2008). A recent meta-analysis by Sollman and Berry (2011) summarized results for the most frequently studied PVTs in terms of effect sizes as well as classification accuracy. This information may be useful when determining which PVTs to include in a battery for a compensation-seeking evaluation. They found that the most commonly studied tests that met stringent inclusion criteria were the Test of Memory Malingering (TOMM; Tombaugh, 1997), the Victoria Symptom Validity Test (VSVT; Slick, Hopp, Strauss, & Thompson, 1997), the Medical Symptom Validity Test (MSVT; Green, 2004), the Word Memory Test (WMT; Green, 2003), and the Letter Memory Test (LMT; Inman et al., 1998). Sollman and Berry (2011) indicated that results reflected large effect sizes for all measures, with an overall mean d of 1.55 (95% confidence interval [CI] of 1.48–1.63). Mean specificity was 0.90 (95% CI of 0.85–0.94). Mean sensitivity was 0.69 (95% CI of 0.63–0.75). Although some variability occurred across the five tests reviewed, it is clear that practitioners have multiple, well-validated procedures suitable for detection of feigned cognitive/neuropsychological deficits. It is also worth noting that although all of these tests employ forced-choice methodologies and the “floor effect strategy,” each also includes manipulations of face difficulty level that create a dilemma for the feigning test-taker. For example, the LMT systematically increases the length of the stimulus to be recalled, as well as the number of response foils from which the stimulus must be chosen. Thus, because the items appear to be increasingly more difficult on the basis of face validity, examinees must decide when to begin giving incorrect answers that demonstrate impairment, yet not present an implausible level of deficit. This provides a theoretical basis for believing that feigning patients will produce implausible performances on these tests.

As noted earlier, PVTs are particularly imperative when an examinees is claiming cognitive/neuropsychological deficits in a compensation-seeking context.

Detection of Psychological Feigning/Malingering Procedures for identifying feigning of psychological disorders may include symptom validity indices that are embedded in multiscale inventories, as well as stand-alone instruments dedicated to documenting invalid symptom reports. Considering the first category, current Minnesota Multiphasic Personality Inventory (MMPI) measures (see Wygant, Walls, Brothers, & Berry, Chapter 14, this volume) include the second edition of the MMPI (MMPI-2; Butcher, Dahlstrom, Graham, Tellegen, & Kaemmer, 1989), and the MMPI-2 Restructured Format (MMPI-2-RF; Tellegen & BenPorath, 2008). In addition, the Personality Assessment Inventory (PAI; Morey, 2007; see Boccaccini & Hart, Chapter 15, this volume) is also an excellent measure. These three multiscale inventories of psychopathology have multiple indices to detect random responding, faking bad, and faking good. Additionally, published meta-analytic reviews support the accuracy of their validity scales, including (1) the MMPI-2 (Rogers, Sewell, Martin, &Vitacco, 2003), (2) the MMPI-2-RF (Ingram & Ternes, 2016; Sharf, Rogers, Williams, & Henry, 2017), and (3) the PAI (Hawes & Boccaccini, 2009). It should be noted that some researchers in this area (Berry, Sollman, Schipper, Clark, & Shandera, 2009) see these embedded validity scales as having generally more robust negative predictive power (NPP; i.e., nonfeigners having unelevated fake bad scales) than positive predictive power (PPP; i.e., feigners having elevated fake bad scales). This classification profile suggests that built-in validity scales should be used as screens that trigger administration of a stand-alone feigning scale, which generally has a more robust PPP. The two major stand-alone measures for feigned mental disorders consist of the Miller Forensic Assessment of Symptoms Test (M-FAST; Miller, 2001; see Smith, Chapter 23, this volume) and the second edition of the Structured Interview of Reported Symptoms (SIRS-2; Rogers, Sewell, & Gillard, 2010; see Rogers, Chapter 22, this volume). Of the two, the SIRS-2 has the most published information as well as a meta-analytic review. Green and Rosenfeld (2011) reported a mean d for the original SIRS Total Score of 2.02 and moder-

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ate to strong support for the eight SIRS-2 primary scales. In terms of classification accuracy, these authors reported a weighted mean sensitivity of .74 and weighted mean specificity of .89. Given the time required to administer the SIRS-2, it probably makes most sense to use the fake bad validity indices from the PAI or MMPI-2/MMPI-2-RF as screens, with elevations on these instruments followed by administration of the SIRS-2.

CASE STUDIES OF FEIGNING/MALINGERING Ms. D: A Case of Feigned Cognitive Deficits Secondary to Mild TBI Ms. D, a 45-year-old white, married woman, had completed a few years of college but had not earned a degree or certificate. Approximately 16 months prior to evaluation for disability, she had been involved in a collision between the golf cart she was driving and another vehicle at her place of employment. She was seen by Emergency Medical Services (EMS) shortly after the accident, who twice recorded Glasgow Coma Scale Scores of 15 following the accident. At the Emergency Department, she stated that she had hit her head in the accident but had not lost consciousness. She had minor lacerations on her body, including a hematoma on her left parietal scalp. Computed tomography (CT) of her head at the time of the accident failed to reveal evidence for brain injury secondary to her accident. Additionally, magnetic resonance imaging (MRI) at the time of her forensic neuropsychiatric examination was also unremarkable. She complained of headache and was placed off work for 1 week. She continued to seek treatment for head pain and was eventually referred for neuropsychological testing. She ultimately underwent two complete neuropsychological evaluations. During both examinations, she performed below the cutoff scores on one or more PVTs. However, her prominent deficits on neuropsychological testing were interpreted as consistent with a TBI affecting the “left anterior language areas.” She was referred to one of the authors (R. P. G.) for evaluation in regard to a workers’ compensation award. Upon interview, Ms. D complained of nervousness, memory loss, and widespread pain. She appeared to be feigning expressive language deficits, with very sparse output. However, the examiner noted the following: “She cannot maintain the ruse and she breaks through with fluent speech for periods of time . . . then she becomes dysfluent again . . . [and] also attempts to demonstrate wordfinding difficulty in a crude and poorly formulated

manner.” Despite her claimed memory deficits she related her history well and was noted to have maintained an extremely detailed diary as well as descriptions of every clinician’s findings and could quickly retrieve requested information from the records. Considering test results, examiners characterized her subjective effort as “extraordinarily poor.” In terms of objective data on effort, Ms. D was administered three PVTs: the TOMM, the VSVT, and the LMT. She performed below the cutoff score for poor effort on all three measures, and on the VSVT was significantly below chance, which suggests that she knew the correct answer and chose the incorrect one. Ms. D also completed the MMPI-2-RF. Four Fake Bad Scales (FBS) were significantly elevated: Fs (115T), F-r (88T), FBS-r (86T), and Response Bias Scale (RBS; 88T). This pattern was interpreted as indicating possible overreporting of psychological dysfunction of somatic and/or cognitive symptoms, as well as of noncredible memory complaints. The clinician concluded that Ms. D was “malingering a traumatic brain injury for financial gain.”

Mr. T: A Case of Feigned Mood Disorder Mr. T, a 40-year-old white male, was unemployed following a back injury said to have occurred when a wooden pallet fell on him at his workplace. He reported quitting school after the eighth grade, had no general equivalency degree (GED), and had been working as a clerk at the time of his injury. Surgery on his spine to repair the injury was reportedly unsuccessful, and Mr. T complained of chronic pain that “is driving me insane.” He was referred for evaluation to determine whether he experienced psychiatric impairment as a result of his injury. Mr. T was noted to be both morbidly obese and a diabetic. He had been diagnosed as depressed by his family physician, who placed him on antidepressants, which were said to be ineffective in controlling his mood disorder. He had strong signs of vegetative depression with sleep disturbance, social withdrawal, and anhedonia. Mr. T was intimidating to test administrators, often angry, and at one point threatened to tear up the MMPI2 and throw it in the trash. He only cooperated with the psychometrists after severe chastisement by the clinician. Mental status examination was unremarkable, with the exception of yelling at the examiner. On formal testing, Mr. T performed below the cutoff score on both the TOMM and the VSVT,



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with both scores statistically significantly below chance. When he completed the MMPI-2, his FBS and Fb scores were markedly elevated (106T and 120T, respectively), indicating overreporting of psychological and physical symptoms. Interestingly, his L scale was also significantly elevated at 70T. This pattern is not unusual in patients pursuing psychological damage claims, as they often claim great personal virtue and deny the minor personal flaws—features addressed by the L scale. The clinician diagnosed probable mood disorder associated with chronic pain, while noting that it was not possible to be certain of the diagnosis given the poor cooperation by the patient. Thus, the level of psychiatric impairment could not be determined. This case showed an interesting mixture of probably genuine pain complaints but clearly exaggerated mood symptoms.

Ms. B: A Case of Back Injury and Chronic Pain with Claimed Secondary Psychiatric Disturbance Ms. B, a 46-year-old white female, claimed workrelated back injury with chronic pain, as well as an associated mental disorder. Ms. B had completed high school but attended special classes for reading. She was working in a nursing home when she reportedly injured her back lifting an extremely obese resident. Her medical presentation was complicated by chronic obstructive pulmonary disease (COPD) and chronic smoking. She also had a prior history of a Workers’ Compensation claim for another alleged back injury several years before the present claim. Upon interview, Ms. B was noted to be a moderately obese woman. She was observed to be drawing attention to her experience of pain by displaying great difficulty rising from a chair, moving extremely slowly, as well as moaning and wincing. She reported that her pain prevented her from sleeping more than 3 hours a night as well as dressing or bathing herself. Her presentation was extremely histrionic and dramatic. Her level of impairment was judged to be inconsistent with objective neurological testing in the medical records. She had multiple psychiatric complaints, including depression, sadness, nervousness, panic, and suicidal ideation. Ms. B also had many cognitive concerns involving memory loss, confusion, disorientation, and so forth. Ms. B claimed that her psychiatric symptoms arose secondary to her back injury at the nursing home. However, record reviews indicated that she was previously injured in a motor vehicle accident

and later fractured numerous ribs while coughing due to pneumonia. Following her second divorce, and prior to the nursing home injury, she had overdosed on an antidepressant medication in a suicide attempt, which required resuscitation and hospitalization. Additionally, she also had a long history of treatment for anxiety disorders documented in her medical records. Her mental status examination was unremarkable except for expression of anxiety, depression, and the earlier noted histrionic qualities. On formal testing, Ms. B performed below the cutoff scores on the TOMM, the LMT, and the VSVT. She also scored statistically below chance on one index from the VSVT. On the MMPI-2-RF, her Random Responding and Fake Good scales were within normal limits. However, her FBS had marked elevations including F-r (120T), Fp-r (85T), Fs (115T) and FBS-r (99T). Her SIRS-2 profile indicated a high probability of feigned mental disorders, with two primary scales in the “definite feigning” range, as well as four in the “probable feigning” range. The report noted invalid effort on all three PVTs, which precluded establishing any cognitive deficits. She also was judged to have an anxiety disorder that predated her back injury in question. Finally, she was judged to be severely malingering to obtain a Workers’ Compensation award.

SUMMARY Feigned medical presentations can be combined with feigning from other domains, such as feigned mental disorders and simulated cognitive deficits. This chapter underscores the need—particularly with complex medical and somatic presentations—to systematically evaluate multiple avenues for the assessment of feigning/malingering. Careful record reviews, extensive interviews, and psychological testing provide the foundation for the systematic evaluation of response styles.

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dants referred for neuropsychological evaluation. Clinical Neuropsychologist, 20, 145–159. Berry, D. T. R., & Schipper, L. J. (2008). Assessment of feigned cognitive impairment using standard neuropsychological tests. In R. Rogers (Ed.), Clinical assessment of malingering and deception (3rd ed., pp. 237– 254). New York: Guilford Press. Berry, D. T. R., Sollman, M. J., Schipper, L. J., Clark, J. A., & Shandera, A. L. (2009). Assessment of feigned psychological symptoms. In J. N. Butcher (Ed.) Oxford handbook of personality. New York: Oxford University Press. Boone, K. B. (2007). Assessment of feigned cognitive impairment: A neuropsychological perspective. New York: Guilford Press. Bush, S. S., Ruff, R. M., Tröster, A. I., Barth, J. T., Koffler, S. P., Pliskin, N. H., et al. (2005). Symptom validity assessment: Practice issues in medical necessity: NAN Policy and Planning Committee. Archives of Clinical Neuropsychology, 20, 419–426. Butcher, J. N., Dahlstrom, W. G., Graham, J. R., Tellegen, A., & Kaemmer, B. (1989). MMPI-2: Manual for administration and scoring. Minneapolis: University of Minnesota Press. Carroll, S., Abrahamse, A., & Vaiana, M. (1995). The costs of excessive medical claims for automobile personal injuries. Santa Monica, CA: RAND Corporation. Daubert v. Merrell Dow Pharmaceuticals, 509 (U.S., 579, 1993). Denny, R. L., & Sullivan, J. P. (Eds.). (2008). Clinical neuropsychology in the criminal forensic setting. New York: Guilford Press. Evanski, P. M., Carber, D., Nehemkis, A., Waugh, T. R. (1979). The Burns’ test in low back pain: correlation with the hysterical personality. Clinical Orthopedic Related Research, 140, 42–44. Gervais, R. O., Green, P., Allen, L. M., Iverson, G. L. (2001). Effects of coaching on symptom validity testing in chronic pain patients presenting for disability assessments. Journal of Forensic Neuropsychology, 2, 1–19. Gervais, R. O., Russell, A. S., Green, P., Allen, L. M., Ferrari, R., & Pieschl, S. D. (2001). Effort testing in patients in patients with fibromyalgia and disability incentives. Journal of Rheumatology, 28(8), 1892– 1899. Gfeller, J. D., & Roskos, P. T. (2013). A comparison of insufficient effort rates, neuropsychological functioning, and neuropsychiatric symptom reporting in military veterans and civilians with chronic traumatic brain injury. Behavioral Sciences and the Law, 31, 833–849. Goldberg, S. A., Yezemytski, Z., Skorochod, Y., BenChetrit, E., Yonnon, A. M., & Munter, G. R. (2014). Munchausen’s syndrome: Multiple episodes of polymicrobial bacteremia. American Journal of Medicine, 127(12), e1–e2. Granacher, R. P. (1982). Single case study: Autovenipuncture as a cause of factitial anemia. Journal of Nervous and Mental Disease, 170(5), 308–310.

Granacher, R. P. (2015). Traumatic brain injury: Methods for clinical and forensic neuropsychiatric assessment (3rd ed.). Boca Raton, FL: CRC Press. Green, D., & Rosenfeld, B. (2011). Evaluating the gold standard: A review and meta-analysis of the Structured Interview of Reported Symptoms. Psychological Assessment, 23, 95–107. Green, P. (2003). Green’s Word Memory Test for Windows user’s manual. Edmonton, Alberta, Canada: Green’s. Green, P. (2004). Medical Symptom Validity Test for Windows: User’s manual and program. Edmonton, Alberta, Canada: Green’s. Greer, S., Chambliss, M. L., Mackler, L. (2005). What physical exam techniques are useful to detect malingering? Journal of Family Practice, 54(8), 719–722. Greve, K. W., Bianchini, K. J., Black, F. W., Heinly, M. T., Love, J. M., Swift, D. A., et al. (2006). Prevalence of cognitive malingering in examinee reporting exposure to occupational and environmental substances. Neurotoxicology, 26(6), 940–950. Hanley, C. N., & Tiffany, W. R. (1954). Auditory malingering and psychogenic deafness: Comments on a new test and some case reports. Archives of Otolaryngology, 60, 197–201. Hawes, S. W., & Boccaccini, M. T. (2009). Detection of overreporting of psychopathology on the PAI: A meta-analytic review. Psychological Assessment, 21, 112–124. Hoover, C. F. (1908). A new sign for the detecting of malingering and functional paresis of the lower extremities. Journal of the American Medical Association, LI(9), 746–747. Incesu, A. I., & Sobaci, G. (2011). Malingering or simulation in ophthalmology–visual acuity. International Journal of Ophthalmology, 4(5), 558–566. Ingram, P. B., & Ternes, M. S. (2016). The detection of content-based invalid responding: A meta-analysis of the MMPI-2-Restructured Form’s (MMPI-2-RF) overreporting validity scales. Clinical Neuropsychologist, 30(4), 473–496. Inman, T. H., Vickery, C. D., Berry, D. T. R., Lamb, D. G., Edwards, C. L., & Smith, G. T. (1998). Development and initial validation of a new procedure for evaluating adequacy of effort given during neuropsychological testing: The Letter Memory Test. Psychological Assessment, 10, 128–149. International classification of diseases, ICD-10-CM. (2016). Reno, NV: Channel. Kiester, P. D., & Duke, A. D. (1999). Is it malingering, or is it real? Postgraduate Medicine, 106(7), 77–84. Larrabee, G. J. (2003). Detection of malingering using atypical performance patterns on standard neuropsychological tests. Clinical Neuropsychologist, 17, 410–425. Larrabee, G. J. (2007). Assessment of malingered neuropsychological deficits. New York: Oxford University Press. Larrabee, G. J. (2012). Performance validity and symptom validity in neuropsychological assessment. Jour-



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nal of the International Neuropsychological Society, 18, 625–631. Larrabee, G. J., Millis, S. R., & Meyers, J. E. (2009). 40 ± 10, a new magical number: Reply to Russell. Clinical Neuropsychologist, 23, 746–753. Leavitt, F., & Sweet, J. J. (1986). Characteristics and frequency of malingering among patients with low back pain. Pain, 25, 357–364. Martelli, M. F., Zasler, N. D., Nicholson, K., Pickett, T. C., & May, V. R. (2002). Assessing the veracity of pain complaints in associated disability. In R. S. Weiner (Ed.), Pain management: A practical guide for clinicians (6th ed., pp. 789–805). Boca Raton, FL: CRC Press. McDermott, B. E., & Feldman, M. D. (2007). Malingering in the medical setting. Psychiatric Clinics of North America, 30, 645–662. Miller, H. A. (2001). Miller-Forensic Assessment of Symptoms Test (M-FAST): Professional manual. Odessa, FL: Psychological Assessment Resources. Mittenberg, W., Patton, C., Canyock, E. M., & Condit, D. C. (2002). Base rates of malingering and symptom exaggeration. Journal of Clinical and Experimental Neuropsychology, 24, 1094–1102. Morey, L. C. (2007). The Personality Assessment Inventory professional manual. Lutz, FL: Psychological Assessment Resources. Nelson, N. W., & Sweet, J. J. (2009). Malingering of psychiatric disorders in neuropsychological evaluations: Divergence of cognitive effort measures and psychological validity measures. In J. E. Morgan & J. J. Sweet (Eds.), Neuropsychology of malingering casebook (pp. 195–203). New York: Psychology Press. Oxford English dictionary (2nd ed.). (1989). Oxford, UK: Clarendon Press. Resnick, P. J. (1984). The detection of malingered mental illness. Behavioral Sciences and the Law, 2(1), 20–38. Roby-Brami, A., Ghenassia, J. R., & Bussel, B. (1989). Electrophysiological study of the Babinski sign in paraplegic patients. Journal of Neurology, Neurosurgery, and Psychiatry, 52, 1390–1397. Rogers, R. (Ed.). (2008). Clinical assessment of malingering and deception (3rd ed.). New York: Guilford Press. Rogers, R., Gillard, N. D., Berry, D. T. R., & Granacher, R. P. (2011). Effectiveness of the MMPI-2-RF validity scales for feigned mental disorders and cognitive impairment: A known-groups study. Journal of Psychopathology and Behavioral Assessment, 33, 355–367. Rogers, R., Sewell, K. W., & Gillard, N. D. (2010). Structured Interview of Reported Symptoms 2nd Edition

(SIRS-2), professional manual. Lutz, FL: Psychological Assessment Resources. Rogers, R., Sewell, K. W., Martin, M. A., & Vitacco, M. J. (2003). Detection of feigned mental disorders: A meta-analysis of the MMPI-2 and malingering. Assessment, 10, 160–177. Sharf, A. J., Rogers, R., Williams, M. M., & Henry, S. A. (2017). The effectiveness of the MMPI-2-RF in detecting feigned mental disorders and cognitive deficits: A meta-analysis. Journal of Psychopathology and Behavioral Assessment 39, 441–445. Slick, D., Hopp, G., Strauss, E., & Thompson, G. (1997). The Victoria Symptom Validity Test. Odessa, FL: Psychological Assessment Resources. Slick, D. J., Sherman, E. M. S., & Iverson, G. L. (1999). Diagnostic criteria for malingered neurocognitive dysfunction: Proposed standards for clinical practice and research. Clinical Neuropsychologist, 13, 545–561. Sobel, J. B., Sollenberger, P., Robinson, R., Polatin, P. B., & Gatchel, R. J. (2000). Cervical nonorganic signs: a new clinical tool to assess abnormal illness behavior in neck pain patients: A pilot study. Archives of Physical Medicine and Rehabilitation, 81(2), 170–175. Sollman, M. J., & Berry, D. T. R. (2011). Detection of inadequate effort on neuropsychological testing: A meta-analytic update and extension. Archives of Clinical Neuropsychology, 26, 774–789. Teal, F. F. (1918). A new ear test for malingering. Laryngoscope, 28(8), 613. Tellegen, A., & Ben-Porath, Y. S. (2008). MMPI-2-RF: Technical manual. Minneapolis: University of Minnesota Press. Tombaugh, T. N. (1997). The Test of Memory Malingering (TOMM): Normative data from cognitively intact and cognitively impaired individuals. Psychological Assessment, 9, 260–268. Van Hout, M. S. E., Schmand, B., Wekking, E. M., & Deelman, B. G. (2006). Cognitive functioning in patients with suspected chronic toxic encephalopathy: Evidence for neuropsychological disturbances after controlling for insufficient effort. Journal of Neurology, Neurosurgery, and Psychiatry, 77, 296–303. Waddell, G., McCulloch, J. A., Kummel, E., & Venner, R. M. (1980). Nonorganic physical signs and low back pain. Spine, 5(2), 117–125. Wygant, D. B., Ben-Porath, Y. S., & Arbisi, P. A. (May 2004). Development and initial validation of a scale to detect somatic over-reporting. Paper presented at the 39th Annual Symposium on Recent Developments in the Use of the MMPI-2 and MMPI-A, Minneapolis, MN.

PA R T I I I

PSYCHOMETRIC METHODS

C H A P T E R 14

Assessment of Malingering and Defensiveness on the MMPI-2 and MMPI‑2‑RF Dustin B. Wygant, PhD Brittany D. Walls, MS Stacey L. Brothers, BA David T. R. Berry, PhD

OVERVIEW We begin this chapter with a general description of the Minnesota Multiphasic Personality Inventory–2 (MMPI-2) and the MMPI-2—Restructured Form (MMPI-2-RF) validity scales, focusing on their basic properties, development, and the strategies they utilize to detect response bias. These strategies are explained within the framework offered by Rogers (Chapter 2, this volume). We primarily cover the standard validity scales included in the official scoring of the MMPI-2 and MMPI-2-RF, although we also note some of the research scales that were later developed for the tests. Also, it is worth noting that the MMPI-2-RF was released in 2008, the same year as the previous edition of Clinical Assessment of Malingering and Deception was published. Since that time, response style research on the MMPI-2-RF has far outnumbered that on the MMPI-2. Consequently, the chapter focuses more on the MMPI-2-RF, which was not covered by Greene (2008). Much of clinical assessment in psychology and psychiatry is based on self-report, whether in the context of an interview or standardized psychological testing. As such, it is always subject to potential distortion (Wygant & Granacher, 2015).

Adequate clinical assessment therefore requires consideration of various threats to the accuracy of self-reported symptoms and functioning. The issue is particularly important in forensic settings, in which criminal defendants and civil litigants often possess significant motivation to present in a particular manner. Moreover, the adversarial nature of the legal setting frequently requires forensic clinicians to establish and defend the validity of their conclusions and opinions (Wygant & Lareau, 2015). Since its inception in the 1940s, the Minnesota Multiphasic Personality Inventory (MMPI; Hathaway & McKinley, 1943) and its subsequent revisions, the MMPI-2 (Butcher, Dahlstrom, Graham, Tellegen, & Kaemmer, 2001) and MMPI-2-RF (Ben-Porath & Tellegen, 2008/2011), have enjoyed widespread use in clinical and forensic settings (e.g., Archer, Buffington-Vollum, Stredny, & Handel, 2006; Camara, Nathan, & Puente, 2000; Martin, Schroeder, & Odland, 2015; Schroeder, Martin, & Odland, 2016). Much of this popularity is due to the wide array of psychological constructs captured by the tests, which are described quite well in various clinical texts (Ben-Porath, 2012; Graham, 2012; Greene, 2011). However, another crucial factor, and the focus of this chapter, is their ability, via validity

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scales, to detect invalid responding during an assessment (i.e., protocol validity). Although McGrath, Mitchell, Kim, and Hough (2010) questioned the need to assess response validity in self-report measures, two recent studies clearly show that invalid responding (particularly overreporting) can markedly weaken the correlations between clinically substantive scales of the MMPI-2-RF scales and relevant criteria (Burchett & Ben-Porath, 2010; Wiggins, Wygant, Hoelzle, & Gervais, 2012). Moreover, it is often evident in evaluations in high-stakes settings (e.g., forensic cases) that overreporting on selfreport tests is associated with dramatic presentation of symptoms during clinical interviews (Burchett & Bagby, 2014).

Threats to Protocol Validity A major attraction of the original MMPI was the availability of multiple scales to detect nonveridical responding (Greene, 2008). Researchers and clinicians using the MMPI wished to have objective indices for the detection of invalid responding, so that interpretation of obtained results was

based only on valid data. Ben-Porath (2013) conceptualized invalid responding as falling into two major categories: 1. Non-content-based invalid responding: responses do not have a meaningful relationship to test items (e.g., random or fixed responding). 2. Content-based invalid responding: responses to self-report items misattribute actual level of psychological functioning or impairment (e.g., overreporting/faking bad, underreporting/defensive or guarded responding). The MMPI-2 and MMPI-2-RF include indices designed to assess both of these types of invalidity. Table 14.1 provides the detection strategy utilized by each scale in the framework outlined by Rogers (Chapter 2, this volume).

Detection Strategies Employed by MMPI-2 and MMPI-2-RF Validity Scales Before reviewing specific validity scales, it is worth noting the general detection strategies employed

TABLE 14.1.  Content-Based Detection Strategies Employed by MMPI-2 and MMPI-2-RF Validity Scales

Scale

Detection strategy

MMPI-2 F (Infrequency)

Quasi-rare symptoms—infrequent in normal individuals

Fb (Back Infrequency)

Quasi-rare symptoms—infrequent in normal individuals

Fp (Infrequency Psychopathology)

Rare symptoms—infrequent in psychiatric inpatients

FBS (Symptom Validity)

Erroneous stereotypes—less common in cooperating disability/litigant examinees

L (Lie)

Social desirability: denial of personal shortcomings and common faults

K (Correction)

Defensiveness: Denial of psychological problems

S (Superlative Self-Presentation)

Mixed strategy: claims of virtuous behavior and denial of personal faults

MMPI-2-RF F-r (Infrequent Psychopathology Responses)

Quasi-rare symptoms—infrequent in normal individuals

Fp-r (Infrequent

Rare symptoms—infrequent in psychiatric inpatients

Fs (Infrequent Somatic Responses)

Rare symptoms—infrequent in medical/pain patients

FBS-r (Symptom Validity)

Erroneous stereotypes—less common in cooperating disability/litigant examinees

RBS (Response Bias Scale)

Empirical—items associated with poor effort on cognitive performance validity tests

L-r (Uncommon Virtues)

Social desirability: denial of personal shortcomings and common faults

K-r (Adjustment Validity)

Defensiveness: denial of psychological problems



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by the MMPI-2 and MMPI-2-RF. With respect to the overreporting indicators embedded in the MMPI-2 and MMPI-2-RF, most fall under the unlikely detection strategy (see Rogers, Chapter 2, this volume) and are uncommon in individuals with bona fide pathology. Two specific strategies that fall under the unlikely detection heading include quasi-rare symptoms and rare symptoms. These empirical strategies utilize low frequencies from either nonclinical (quasi-rare) or clinical (rare) samples to identify items that occur infrequently. The idea behind this well-used strategy is that individuals feigning such problems do not recognize them as infrequent or as rare among genuine patients. The infrequency scales on the MMPI-2 (F, Fb, Fp) and MMPI-2-RF (F-r, Fp-r, Fs) utilize both quasi-rare and rare strategies. The premise of another strategy discussed by Rogers, erroneous stereotypes, is based on the notion that feigning individuals endorse many misconceptions about illness or psychopathology. The Symptom Validity scale previously referred to as the Fake Bad Scale (FBS/FBS-r), generally falls into this category; it includes items rationally selected by the scale’s primary authors (Lees-Haley, English, & Glenn, 1991) that were thought to be reflective of feigned presentations in medicolegal and civil litigation settings. Although not included as an official validity scale for the MMPI-2, the Dissimulation Scale (Ds; Gough, 1954), is another good example of the erroneous stereotypes strategy. Rogers, Sewell, Martin, and Vitacco (2003) expressed support for Ds as an effective validity scale on the MMPI-2. Moreover, Rogers, Gillard, Berry, and Granacher (2011) examined the 39 Ds items retained in the MMPI-2-RF in a criterion-groups study of individuals feigning mental disorders and cognitive impairment. These authors found that the MMPI-2-RF version of Ds (labeled Ds-rf) exhibited good classification accuracy at cut scores of 25 or higher. Not all of the scales fit neatly into these detection strategies. For example, the MMPI-2-RF Infrequent Somatic Responses (Fs) scale not only utilizes a quasi-rare symptoms approach in identifying items in medical settings but also incorporates a rational selection of items with somatic content. The Response Bias Scale (RBS), a standard validity scale on the MMPI-2-RF, used an empirical keying approach to identified items discriminated between individuals passing and failing cognitive performance validity tests, irrespective of the item content.

MMPI-2 VALIDITY SCALES MMPI It is helpful to trace the development of the validity scales across the three versions of the test (MMPI, MMPI-2, and MMPI-2-RF). In terms of content nonresponsiveness, the MMPI Cannot Say Scale (CNS), a raw count of the number of items either omitted or answered both True and False was the sole indicator. Indexing CNS was seen as important, as these items are essentially removed from the scorable item pool and effectively serve to suppress the clinical profile. The other two types of content nonresponsive answers, random and fixed responding (yea- and naysaying) were not addressed in the standard MMPI validity scales. With respect to content-responsive faking bad, the F (Infrequency) scale included 64 items selected because they were rarely endorsed in the MMPI normative group. Scale F is sensitive to both faking bad and random responding—and problematically—severe psychopathology. The L (Lie) scale included 15 items that offer an opportunity to deny common shortcomings and index blatant faking good. Finally, the K (Correction) scale, added later in the MMPI, included 30 items largely selected because they better identified individuals with significant psychopathology who fail to produce elevations on the MMPI basic clinical scales. Scores from this scale were used to index faking good, as well as add a “correction” for defensiveness to selected MMPI basic scales. In the following decades, a plethora of additional validity scales were developed, and meta-analytic reviews of faking good and faking bad indices were published by Baer, Wetter, and Berry (1992) and Berry, Baer, and Harris (1991), respectively.

MMPI-2 The developers of the MMPI-2 (Butcher, Dahlstrom, Graham, Tellegen, & Kaemmer, 1989) clearly recognized the importance of validity scales to the success of the original instrument, as indicated by their retention from the MMPI in addition to several new validity indices. The CNS, L, and K scales were retained unchanged. The F scale lost four problematic items. Because the F items appear primarily in the first part of the test, the Fb (Back Infrequency) was added to the MMPI-2. It includes 40 items infrequently endorsed by the MMPI-2 normative sample on the latter part of the test. Two new scales, VRIN (Variable Response

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Inconsistency) and TRIN (True Response Inconsistency), were also added. They consist of item pairs to assess generic random responding and fixed random responding, respectively (yea- and naysaying). Additionally, many supplementary validity scales appeared before and after publication of the MMPI-2. In order to focus the present review, we discuss only those supplementary scales that were adapted to the MMPI-2-RF. Feigning researchers have long postulated the existence of multiple types of malingering. For example, Lees-Haley et al. (1991) argued that the F scale is sensitive to feigned psychotic symptoms but not the false cognitive or somatic complaints of those involved in personal injury lawsuits. Based on his experience in this type of evaluation, Lees-Haley et al. rationally selected 43 items for the FBS, later termed the Symptom Validity scale (which was still referred to with its original abbreviation, FBS). FBS has been shown in two meta-analytic reviews of forensic studies to be effective in identifying cognitive/somatic feigning in forensic settings (Nelson, Hoelzle, Sweet, Arbisi, & Demakis, 2010; Nelson, Sweet, & Demakis, 2006). In order to decrease false positives on F and Fb scales, Arbisi and Ben-Porath (1995) developed a new infrequency scale (Infrequency Psychopathology, Fp) that consisted of items rarely endorsed in large samples of psychiatric inpatients. As a true rare symptom strategy, the Fp scale has substantially lower false-positive rates than F and Fb in samples with severe psychopathology (Rogers et al., 2003).

Review of Research on MMPI-2 Validity Scales Soon after the publication of the MMPI-2, it became clear that research on validity scales, particularly the fake bad indices, was extremely popular. A Web of Science search in mid-July 2016, using the keywords MMPI-2 + Validity Scales, returned 290 citations. Given the vast amount of research, this section relies primarily on meta-analyses and other summary works (e.g., Hoelzle, Nelson, & Arbisi, 2012; Sleep, Petty, & Wygant, 2015). The revised MMPI-2 manual lays out a step-bystep approach to evaluating profile validity. Figure 14.1 graphically presents this process using recommended cut scores for inpatient settings. The number of omitted or double-marked items (CNS) is checked first. Next, the possibility of inconsistent or contradictory responding is examined through inspection of the VRIN and TRIN scales. Presum-

ing that the results are valid through this point, feigning is evaluated through the F and Fp scales. Next, F and Fb scales are compared to determine whether a change in response set from the first to second part of the test is likely (F – Fb > 30T). Supplementary fake bad scales (e.g., FBS) may also be checked. Finally, guarded and defensive responding is addressed through inspection of the scores on L and K. Only if the results have passed all of these hurdles is it appropriate to interpret the clinically substantive scales from the MMPI-2. As noted, several meta-analytic reviews address the accuracy of MMPI-2 validity scales in detecting dissimulation. Considering faking-bad first, Rogers et al. (2003) published an extensive meta-analysis of the literature, with large effect sizes discriminating feigning groups from genuine patients for several validity scales. The F scale had the strongest effect size (Cohen’s d = 2.21), with Fp exhibiting a similarly large effect size (d = 1.90). A significant limitation in this literature noted by Rogers et al. was the wide range of optimal cut scores reported for most faking bad scales. However, Rogers et al. recommended use of the Fp scale, as it had considerably lower mean scores in genuine patients than the F scale and a tighter convergence of reported optimal cut scores. Use of the Fb scale was not recommended, as it tended to be elevated in genuine patient groups. Nelson et al. (2010) evaluated the MMPI-2 FBS scale in forensic clinical settings. They reported that the FBS had moderately higher effect sizes in this setting (d = 0.95) than the traditional scales (F = 0.71; Fb = 0.68; Fp = 0.51) and worked particularly well in traumatic brain injury (TBI) populations. They noted gender-related differences on FBS and recommended use of separate cut scores for men and women. The concern about FBS overclassifying feigning among women has been noted by others as well (e.g., Butcher, Arbisi, Atlis, & McNulty, 2003; Butcher, Gass, Cumella, Kally, & Williams, 2008). However, Lee, Graham, Sellbom, and Gervais (2012) examined over 1,000 disability claimants and found no evidence of predictive bias between men and women in the relationship between FBS and cognitive performance validity test failure. Thus, well-validated validity scales are available for detecting exaggeration and fabrication of psychological problems on the MMPI-2 in both general and forensic clinical practice. Assessing defensive and guarded responding on the MMPI-2 is important in a number of contexts, such as screening for psychological stability in preemployment evaluations or child custody

261

CNS ≥ 30

NO

YES

VRIN ≥ 80T

Invalid Profile

TRIN ≥ 80T

NO

YES

Random Responding Likely

F≥ 100T OR Fb ≥ 110T OR Fp ≥ 100T NO

YES

L ≥ 80T OR K ≥ 65T

Faking Bad Likely

Acquiescent/Nonacquiescent Responding Likely

FIGURE 14.1.  Algorithm for evaluating MMPI-2 validity scales.

NO

YES

NO

YES

Valid Profile

Faking Good Likely

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proceedings. Unfortunately, only two English language studies since 2010 have examined the denial of psychopathology on the MMPI-2. Earlier, Baer and Miller (2002) reported on a meta-analysis of the MMPI-2 faking good scales. Interestingly, they found that two of the nonstandard faking good scales had the highest mean d scores (Wiggins’s Social Desirability [Wsd; Wiggins, 1959] = 1.56; Positive Malingering [Mp; Cofer, Chance, & Judson, 1949] = 1.36). In contrast, L had a mean d = 1.19, and for K, d = 1.13. These authors also noted that the grand mean effect size for the faking good scales was 1.25, which they pointed out was roughly half that of the faking bad scales (mean d = 2.07) reported by Berry et al. (1991). Despite the mean superiority of the nonstandard faking good scales, Baer and Miller (2002) still recommended relying most heavily on L and K scales, because they were the best validated; we agree with this for current practice.

Recommendations for Interpreting the MMPI-2 Validity Scales Figure 14.1 presents the stepwise approach to evaluating MMPI-2 validity scales recommended in the manual for the test. We support this approach, as it systematically addresses multiple threats to protocol accuracy. One refinement we would recommend is to consider the nature of any suspected feigning and focus scale knowns to address that issue. Thus, feigned psychosis may be best addressed by the Fp scale, whereas feigned somatic and cognitive complaints, with FBS. Indeed, Bianchini, Etherton, Greve, Heinly, and Meyers (2008) found that FBS had the highest effect size in differentiating between TBI claimants meeting criteria for malingered pain-related disability (MPRD; Bianchini, Greve, & Glynn, 2005) and those in a compensation-seeking evaluations without any evidence of malingering. Greve, Bianchini, Love, Brennan, and Heinly (2006) found similar support for FBS between claimants with malingered neurocognitive dysfunction (MND; Slick, Sherman, & Iverson, 1999) and those who did not exhibit any evidence of malingering. Regarding whether FBS overclassifies feigning for those with medical issues (e.g., Butcher et al., 2003, 2008), Greiffenstein (2010) conducted a comprehensive analysis in a medical sample and concluded that FBS scores were not significantly influenced by medical impairment. Nevertheless, it is recommended that FBS be restricted to use as a measure of feigned somatic and neurocogni-

tive complaints in forensic neuropsychological and medico-legal settings. Should any overreporting scales be elevated at or beyond the cut scores shown in Figure 14.1, we recommend follow-up testing with a structured interview such as the Structured Interview of Reported Symptoms–2 (SIRS-2; Rogers, Sewell, & Gillard, 2010; see Rogers, Chapter 22, this volume) to obtain converging evidence. Similarly, if the faking good indicators are elevated, additional testing with the Paulhus Deception Scales (Paulhus, 1998; see Smith, Chapter 23, this volume) is suggested.

MMPI-2-RF VALIDITY SCALES The Restructured Form of the MMPI-2 was developed by Ben-Porath and Tellegen (2008/2011) to capture the clinical substance of the MMPI-2 item pool in line with contemporary models of personality and psychopathology. Moreover, the MMPI-2-RF emphasized more sophisticated scale construction techniques than used for some of the earlier MMPI scales to enhance the overall psychometric properties of the test. The MMPI-2-RF includes nine standard validity indicators. Many of its validity scales are similar to their MMPI2 counterparts, making interpretation of these scales relatively straightforward given familiarity with the MMPI-2 validity scales. Some scales include minor revisions, while two new scales were added. The first step in assessing protocol validity of the MMPI-2-RF is to determine the number of responses that were either left blank by the respondent or were marked true and false. Similar to the MMPI-2, this number of nonresponses is tabulated as “Cannot Say.” The MMPI-2-RF score report provides the percentage of items responded to for each scale. The interpretative manual indicates that scales with at least 90% items responses can be interpreted without problem. When the respondent leaves more than 10% of the items in a scale unanswered, psychologists run the risk of attributing a nonelevated score to a lack of psychopathology, when it might simply be due to a lack of item responses. This issue was explored by Dragon, Ben-Porath, and Handel (2012), who simulated nonresponses by randomly inserting between 10 and 90% nonresponses in two archival samples that had both MMPI-2-RF and criterion data. They found that clinical elevations on the Restructured Clinical (RC) scales were impacted



14. Assessment on the MMPI‑2 and MMPI‑2‑RF 263

with only 10% of nonresponses inserted into the results. However, their criterion validity remained robust with upwards of 50% unscorable responses. The authors tentatively suggested that prorated scores might be used to correct for nonresponding, but they recommended more research to determine the feasibility of this approach. Similar to the MMPI-2, the MMPI-2-RF includes two validity scales to capture non-contentbased invalid responding. The Variable Response Inconsistency (VRIN-r) and True Response Inconsistency (TRIN-r) function like their MMPI-2 counterparts in capturing random and fixed (acquiescent or nonacquiescent) responding. Handel, Ben-Porath, Tellegen, and Archer (2010) examined the ability of VRIN-r and TRIN-r to capture various levels of inconsistent responding. Their results supported the interpretative guidelines from the MMPI-2-RF manual (Ben-Porath & Tellegen, 2008/2011) that a T score ≥ 80 on either scale invalidates the protocol. Evaluating an MMPI-2-RF protocol for contentbased invalid responding includes a review of the measure’s over- and underreporting indicators, which are presented in Table 14.1. There are five overreporting validity scales on the MMPI-2-RF. Similar in development to the MMPI and MMPI2 Infrequency (F) scale, the Infrequent Responses scale (F-r) on the MMPI-2-RF comprises 32 items rarely endorsed (≤ 10%) in the MMPI-2-RF normative sample (Ben-Porath, 2012). Elevations on the F-r scale may reflect overreporting, inconsistent responding, or serious psychopathology. After accounting for non-content-based invalid responding with VRIN-r/TRIN-r, elevations on F-r should be interpreted along with the Infrequent Psychopathological Responses (Fp-r) scale to assess potential over-reporting of psychopathology. The MMPI-2 Fp scale was revised slightly for inclusion as Fp-r (Infrequent Psychopathology Responses). Most of the Fp-r items (14) come from the MMPI-2 Fp scale. Four of the MMPI-2 Fp scale items that were also scored on the L scale and 3 items that overlapped with the MMPI-2-RF Fs scale were not included in Fp-r. Additional item-refinement techniques removed and added several Fp items to improve its effectiveness (Ben-Porath, 2012). The Fp-r scale includes 21 items rarely endorsed among psychiatric patients with bona fide psychopathology. It is aligned with Rogers’s description as a rare symptoms approach to identifying feigning. Wygant, Ben-Porath, and Arbisi (2004) developed the Infrequent Somatic Responses (Fs) scale using a hybrid approach of identifying rare

items and rational selection of items with somatic content. These authors identified 120 items that were endorsed by less than 25% of patients in three large (over 60,000) archival samples of patients with various medical problems and chronic pain. From these 120 items, 16 were selected that included content describing physical maladies and malfunction. Thus, Fs items describe physical problems that are rarely endorsed by patients with genuine medical problems and chronic pain. The MMPI-2-RF includes a revised version of the Symptom Validity Scale (FBS-r). The scale functions similarly to its MMPI-2 counterpart as a measure designed to detect noncredible somatic and cognitive complaints, and it includes 30 of the 43 original items. Finally, the Response Bias Scale (RBS; Gervais, Ben-Porath, Wygant, & Green, 2007) includes 28 items that were empirically identified (regardless of item content) in a large sample of disability litigants, who were administered a battery of cognitive performance validity tests (PVTs; Gervais et al., 2007). Items on the RBS statistically discriminated between those passing and failing cognitive PVTs. It is similar to the empirical keying approach used in the development of the original MMPI Clinical Scales. RBS identifies negative response bias in forensic neuropsychological or disability settings and is correlated significantly with performance on cognitive PVTs. Underreporting on the MMPI-2-RF is assessed through two revised MMPI/MMPI-2 scales, L-r and K-r, and function much like their MMPI-2 counterparts. Ben-Porath (2012) described their development. It began by performing an itemlevel factor analysis that comprised items from the MMPI-2 underreporting scales (L, K, S) and two experimental underreporting scales, the Wsd (Wiggins, 1959) and the Mp (Cofer et al., 1949). The latter two were identified in a meta-analysis of underreporting scales by Baer and Miller (2002) as outperforming the L, K, and S scales. Ben-Porath and Tellegen (2008/2011) performed a factor analysis that identified two independent factor scales. The first factor shared 11 items from the MMPI-2 L scale, two items from the Wsd scale, and one from the Mp scale. Given the large overlap with the original L scale, they identified this scale as L-r with the name “Uncommon Virtues” to avoid the pejorative label, “Lie Scale,” used in the MMPI and MMPI-2. The second factor scale included 14 items from the K scale and five from the S (Superlative) scale. Ben-Porath and Tellegen called this scale K-r, which they labeled “Adjustment Valid-

264

II I .   P s y c h o m e t r i c M e t h o d s

ity,” to indicate that this scale identifies assertions of good psychological adjustment (Ben-Porath, 2012).

Research on MMPI-2-RF Validity Scales and Interpretative Recommendations As of July 2016, there have been over 300 peerreviewed publications related to the MMPI-2-RF since its release in 2008. Of those, over 60 have directly examined the validity scales, and several others used these scales as external criteria but were not the focus of the study (e.g., Crighton, Wygant, Applegate, Umlauf, & Granacher, 2014; Crighton, Wygant, Holt, & Granacher, 2015; Crighton, Tarescavage, Gervais, & Ben-Porath, 2017). Much of the research on the MMPI-2-RF validity scales has focused on the overreporting scales in varied settings, utilizing different research methods. For instance, the scales have been examined among civil forensic litigants (e.g., Crighton et al., 2017; McBride, Crighton, Wygant, & Granacher, 2013; Youngjohn, Weshba, Stevenson, Sturgeon, & Thomas, 2011), criminal defendants (e.g., Gu, Reddy, Green, Belfi, & Einzig, 2017; Sellbom, Toomey Wygant, Kucharski, & Duncan, 2010; Wygant et al., 2010), veterans (Goodwin, Sellbom, & Arbisi, 2013; Whitney, 2013), psychiatric inpatients (Glassmire, Jhawar, Burchett, & Tarescavage, 2017; Purdon, Purser, & Goddard, 2011), and correctional inmates (Wall, Wygant, & Gallagher, 2015). Studies have applied both analogue (e.g., Marion, Sellbom, & Bagby, 2011; Wygant et al., 2009) and criterion groups (e.g., Rogers et al., 2011; Sullivan, Elliott, Lange, & Anderson, 2013; Tarescavage, Wygant, Gervais, & Ben-Porath, 2013; Wygant et al., 2011) designs. A recent meta-analysis by Ingram and Ternes (2016) examined effect sizes of the MMPI-2-RF overreporting scales from 25 studies. Overall, they concluded that the MMPI-2-RF overreporting scales are effective response bias indicators, with most studies showing that feigning individuals produce a validity scale profile one standard deviation above the control group, regardless of the various moderating factors. After accounting for moderating effects such as diagnostic group and methodology (simulation vs. known group), among others, the MMPI-2-RF overreporting validity scales showed consistently large effects. They point out, however, that the magnitude of effects is often smaller than expected or desired given the interpretative guidelines from the test manual. For example, they note that the MMPI-2-RF

Interpretative Manual (Ben-Porath & Tellegen, 2008/2011) indicates that feigning be considered when F-r reaches a T score of 70, which reflects 2 standard deviations above the normative comparison group. However, Ingram and Ternes note that most feigning studies show a 1.0–1.5 standard deviation difference between feigning and control groups. Consequently, they recommend examining sample/context-specific data to ensure that an individual’s profile is examined in relation to “typical” scores in that setting. Fortunately, the MMPI-2-RF Technical Manual (Ben-Porath & Tellegen, 2008/2011) provides mean scores for each scale across numerous clinical and forensic groups (Table D-1). For example, see Box 14.1 as an illustration of how mean scores on F-r vary across comparison groups. Data from these comparison groups (among others) can be easily viewed on the MMPI-2-RF Score Report available from Pearson Assessment. Ingram and Ternes (2016) recommend higher cut scores to ensure specificity for overreporting. They indicated that F-r, Fp-r, and Fs, while all producing large effects, are influenced by various moderating effects, particularly diagnostic comparison and referral group, although their analyses do not provide specific recommendations with respect to these issues (e.g., using the validity scales to detect specific diagnoses such as posttraumatic stress disorder [PTSD]). FBS-r and RBS were noted as having robust findings regardless of various contextual factors. Fp-r was described as having the strongest overall capacity to detect feigning, which is similar to the findings for Fp on the MMPI-2 meta-analysis conducted by Rogers and colleagues (2003). Non-Content-Based Invalid Responding

After checking Cannot Say, the next step in assessing protocol validity in an MMPI-2-RF profile is to assess non-content-based invalid responding.

BOX 14.1.  Mean Scores on F-r across Several Comparison Groups • MMPI-2-RF normative group (male/female): M = 50, SD = 10/M = 50, SD = 10 • Psychiatric inpatients (male/female): M = 76, SD = 28/M = 78, SD = 27 • Forensic disability litigants (male/female): M = 78, SD = 22/M = 73, SD = 21 • Pretrial criminal defendants (male/female): M = 75, SD = 28/M = 72, SD = 23



14. Assessment on the MMPI‑2 and MMPI‑2‑RF 265

As noted earlier, the MMPI-2-RF includes revised versions of VRIN and TRIN (VRIN-r/TRIN-r) that function in the same manner as their MMPI2 counterparts (Handel et al., 2010). In a recent study, Burchett and colleagues (2016) examined the impact of non-content-based invalid responding on the content-based validity scales (both over- and underreporting) of the MMPI-2-RF. With the exception of FBS-r and K-r, the remaining content-based invalid responding scales (F-r, Fp-r, Fs, RBS, and L-r) were significantly impacted by non-content-based invalid responding. They cautioned that a small but significant percentage of individuals with random and fixed responses inserted into their results were missed by VRIN-r and TRIN-r and could be misclassified as overreporting on Fp-r and Fs. Consequently, these authors warned against overinterpreting Fp-r and Fs when the profile includes moderate levels of non-content-based invalid responding. They note that these two scales are particularly impacted because they are short in length, with infrequently endorsed items that only require a small raw score to reach clinically significant T scores. A T score above 80 on either VRIN-r or TRIN-r invalidates the remainder of the MMPI-2-RF profile (see Box 14.2). No interpretation, including indications of overreporting, should be made at that point. As noted in the test manual, T scores of 70–79 on VRIN-r or TRIN-r indicate the need to interpret the remaining profile with caution. Feigned Psychopathology

Once non-content-based invalid responding has been assessed, the overreporting validity scales should be reviewed. A number of studies have examined the ability of the MMPI-2-RF overreporting scales to detect feigned psychopathology, some of which are presented in Table 14.2. As summarized by Rogers (Chapter 1, this volume), these studies include both simulation designs (i.e., experimental groups instructed to feign psychopathology being typically compared to patient

BOX 14.2.  Caution: Marked Inconsistencies (i.e., ≥ 80T on VRIN-r or TRIN-r) Cannot Be Interpreted for Two Reasons 1. Clinically substantive scales are invalid. 2. Overreporting scales may wrongly classify inconsistent responders as feigning.

groups), and criterion group designs (i.e., grouping of actual examinees based on their performance on a response bias indicator, such as the SIRS-2; Rogers et al., 2010). Rogers (Chapter 1, this volume) provides a detailed review of malingering research designs. What is evident from Table 14.2 are the moderate to very large effect sizes for each scale across all of the studies. Consistent with the meta-analytic findings from Ingram and Ternes (2016), the overreporting scales all yielded median effect sizes over 1.0 for the criterion group and simulation studies, with the exception of FBS-r (median simulation = Cohen’s d of 0.74). These effects are in the moderate to large range based on Rogers’s characterization of effect sizes in malingering research (see Rogers, Chapter 2, Box 2.2, this volume). However, FBS-r fared well in the detection of feigned neurocognitive impairment (see next section), which is much more in line with its focus and use. Three studies presented in Table 14.2 illustrate effect sizes for the validity scales in relation to criterion groups studies. Two of those studies, Sellbom et al. (2010) and Rogers et al. (2011) employed the SIRS and the SIRS-2, respectively. Both found large effects across the infrequency scales (F-r, Fp-r, Fs). The effects were slightly larger in the Sellbom et al. study, which examined criminal defendants undergoing forensic evaluations, whereas the Rogers et al. study included civil litigants evaluated for disability. This difference may be due somewhat to an artifact of demand characteristics of the evaluation, in which feigning criminal forensic defendants often “fake crazy” in competency and criminal responsibility/insanity evaluations, whereas feigning civil litigants often “fake hurt,” an issue that was supported empirically by Wygant and colleagues (2007). Nevertheless, effect sizes were large across the five scales in the Rogers et al. sample and were consistent across comparisons between the feigning group (failed SIRS-2) and various diagnostic groups. The effect sizes for F-r and Fp-r were smaller in the Chmielewski, Zhu, Burchett, Bury, and Bagby (2017) study, which compared disability litigants identified as feigning psychopathology with the Miller Forensic Assessment of Symptoms Test (M-FAST; a score ≥ 6) with those who scored below 6 on the M-FAST. As noted earlier, most individuals assessed in disability settings are not prone to reporting psychotic symptoms, and are instead more apt to report symptoms of mood and anxiety disorders, somatic symptoms, and neurocognitive impairment (Larrabee, 2003). Thus, use of the M-FAST, which was designed to

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II I .  Ps y c h o m e t r ic M e t h o ds

TABLE 14.2.  MMPI-2-RF Assessment of Feigned Psychopathology

Study authors

Effect sizes (Cohen’s d) Experimental group

Comparison group

F-r

Fp-r

Fs

FBS-r

RBS

Criterion groups designs Chmielewski et al. (2017)

Litigants: M-FAST ≥ 6

Litigants: M-FAST < 6

1.27

0.86

0.96

0.83

NR

Rogers et al. (2011)

Litigants failed SIRS-2

Litigants: Genuine PTSD

2.09

1.29

1.76

1.11

1.92

Litigants failed SIRS-2

Litigants: Genuine MDD

2.04

1.82

1.70

0.99

1.71

Litigants failed SIRS-2

Litigants: Genuine other Axis I

1.90

1.71

1.72

1.29

1.92

Defendant failed SIRS

Defendant passed SIRS

2.11

2.07

1.19

1.59

NR

2.04

1.71

1.70

1.11

1.92

Sellbom et al. (2010)

Median d (criterion groups) Simulation designs Goodwin et al. (2013)

Veterans feign PTSD

Veterans PTSD

1.20

1.62

1.34

0.74

0.81

MH prof. feign PTSD

Veterans PTSD

1.19

1.06

0.43

0.82

0.92

Sophisticated simulators

Patients PTSD

1.15

1.21

1.07

0.66

0.94

Naive simulators

Patients PTSD

0.79

1.02

1.09

1.07

1.08

Sophisticated simulators

Patients MDD

1.02

1.22

0.75

0.66

0.22

Naive simulators

Patients MDD

0.99

2.04

1.06

0.15

0.82

Sophisticated simulators

Patients SCZ

1.49

1.67

0.71

0.50

1.64

Naive simulators

Patients SCZ

2.05

2.82

1.89

1.22

1.74

Mason et al. (2013)

Feign PTSD

Veterans PTSD

1.86

1.95

1.93

1.01

1.21

Sellbom & Bagby (2010)

Uncoached simulators

Psychiatric inpatients

1.37

2.30

1.32

0.69

1.31

Coached simulators

Psychiatric inpatients

0.44

1.36

0.42

0.10

0.51

Wall et al. (2015)

Feigning offenders

Offenders with mental illness

1.18

1.68

1.39

0.85

1.14

Feigning offenders

Offenders without mental illness

1.68

1.75

1.63

1.55

1.80

1.19

1.67

1.09

0.74

1.08

Marion et al. (2011)

Median d (simulation studies)

Note. NR, not reported; F-r, infrequent responses; Fp-r, infrequent psychopathology responses; Fs, infrequent somatic responses; FBS-r, symptom validity; RBS, Response Bias Scale; M-FAST, Miller Forensic Assessment of Symptoms Test; PTSD, posttraumatic stress disorder; MH prof, mental health professionals; MDD, major depressive disorder; SCZ, schizophrenia; SIRS, Structured Interview of Reported Symptoms.



14. Assessment on the MMPI‑2 and MMPI‑2‑RF 267

capture feigned severe psychopathology, may not yield results as strong in this particular setting in which feigned psychosis is unlikely to be exhibited by litigants. The remaining five studies presented in Table 14.2 include simulation designs, whereby participants were instructed to feign symptoms of specific disorders, such as depression, PTSD, and schizophrenia (Goodwin et al., 2013; Marion et al., 2011; Mason et al., 2013) or general symptoms of mental disorders (Sellbom & Bagby, 2010; Wall et al., 2015). Interestingly, the effect sizes were relatively similar across the scales for simulation and criterion groups designs. Moreover, across the various simulation studies, the infrequency scales (F-r, Fp-r, Fs) show relative consistency regardless of specific disorder and feigning instructions (i.e., fake specific disorder or general psychiatric symptoms). The study by Marion and colleagues (2011) compared both naive and sophisticated simulators of major depression, PTSD, and schizophrenia to bona fide patients with these disorders. Interestingly, while the effect sizes for Fp-r are generally lower in the sophisticated simulators versus patient comparisons (vs. the naive simulators and patient comparisons), they nevertheless remain in the moderate to large range. Four studies in particular examined the ability of the MMPI-2-RF validity scales to detect feigned symptoms of PTSD, which is a particularly challenging disorder to differentiate from feigning. F-r and Fp-r exhibited similar effects across these studies that compared litigants with PTSD classified as genuine on the SIRS-2 (Rogers et al., 2011) or simulators that included sophisticated and naive students, Department of Veterans Affairs (VA) patients with PTSD, and mental health professionals. This consistency suggests that F-r and Fp-r should be given strong weight in assessing PTSD feigning, although the average effects across these studies were also strong for Fs and RBS. Table 14.3 presents classification statistics for the various overreporting validity scales at different cut scores. Summarizing these results is challenging in light of differences in base rate, study design, and cut scores examined in the study. It is apparent from these results, however, that the MMPI-2-RF validity scales evidence higher levels of specificity than sensitivity. Based on these results, as well as the Ingram and Ternes (2016) meta-analysis, we can offer some general guidelines about assessing feigned psychopathology with the MMPI-2-RF validity scales. First, however, it is important to remember

that firm conclusions about malingering should never be based on the MMPI-2-RF validity scales alone. Professionals may infer the presence of feigning from validity scales scores, with the caveat that malingering and feigning are not mutually exclusive with genuine impairment. Moreover, it is worth mentioning that Rogers (Chapter 2, this volume) points out the difficulty of assigning exact cut scores in identifying feigning. Thus, while very elevated scores on the overreporting scales appear to be quite specific to malingering and feigning, lower scores (but still elevated) may be suggestive of feigning but not conclusive. There is a gray zone (sometime referred to as an indeterminate range) of elevated scores (usually around a T score of 90) that suggests the possibility of feigning, which will need to be confirmed or disconfirmed by other data, such as additional measures like the SIRS-2, and records reflective of genuine impairment. Our recommendations are generally consistent with the guidelines offered by Ben-Porath and Tellegen (2008/2011) in the MMPI-2-RF interpretive manual. After assessing noncontent invalid responding with VRIN-r and TRIN-r, scores should be examined on F-r. Interpretation of F-r needs to be placed within the context of the individual’s purported history of psychopathology. While the interpretive manual indicates that T scores above 80 should begin to raise suspicion about possible overreporting, this threshold is prone to many false positives. We would recommend that scores above 90T would begin to raise concern if the examinee has no corroborated history of mental illness. Even scores of 100T on F-r are not uncommon among individuals with significant psychopathology, such as schizophrenia or PTSD. We agree with the interpretative manual that T scores of 120 on F-r invalidate the profile and tend to result in a 10% or less false-positive rate in most feigning studies. Fp-r should be considered the primary validity scale in determining feigned psychopathology on the MMPI-2-RF, particularly with severe psychopathology such as psychotic disorders (see Box 14.3). As reported by Ingram and Ternes (2016) and evident in Tables 14.2 and 14.3, T-scores above 90 on Fp-r should raise concern about feigning, and scores above 100 appear to be quite specific to feigning. Similarly, Fs, developed to capture overreported somatic symptoms, also appears to be quite effective as an indicator of feigned psychopathology, particularly PTSD. In a similar vein, RBS, developed as an adjunct measure of neurocognitive response bias, performed quite well as a measure of feigned psychopathology, exhibiting the

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TABLE 14.3.  Summary of Classification Statistics for Assessment of Feigned Psychopathology with the MMPI-2-RF

Study authors

Experimental group

Comparison group

Chmielewski et al. (2017)

M-FAST ≥ 6

Goodwin et al. (2013)

Cut score

BR

OCC

SN

SP

PPP

NPP

M-FAST < 6

F-r/100 F-r/110 F-r/120 Fp-r/100 Fs/100 FBS-r/90 FBS-r/100

.07 .07 .07 .07 .07 .07 .07

NR NR NR NR NR NR NR

.63 .48 .44 .09 .31 .54 .20

.82 .88 .91 .99 .91 .78 .91

.22 .24 .29 .36 .22 .16 .16

.97 .96 .95 .93 .94 .96 .94

Veterans feign PTSD

Veterans PTSD

F-r/100 F-r/110 F-r/120 Fp-r/90 Fp-r/100

.30 .30 .30 .30 .30

NR NR NR NR NR

.72 .62 .48 .66 .45

.74 .81 .85 .98 1.00

.54 .59 .58 .94 1.00

.86 .83 .79 .87 .84

MH prof. feign PTSD

Veterans PTSD

F-r/100 F-r/110 F-r/120 Fp-r/90 Fp-r/100

.30 .30 .30 .30 .30

NR NR NR NR NR

.73 .60 .48 .20 .10

.74 .81 .85 .98 1.00

.55 .58 .58 .82 1.00

.87 .83 .79 .74 .72

Soph. simulators

Patients PTSD

Fp-r/90 Fp-r/100

.30 .30

.75 .75

.56 .47

.89 .95

.68 .87

.82 .80

Naive simulators

Patients PTSD

Fp-r/90 Fp-r/100

.30 .30

.74 .71

.48 .37

.89 .95

.65 .77

.80 .78

Soph. simulators

Patients MDD

Fp-r/90 Fp-r/100

.30 .30

.75 .76

.30 .23

.93 .97

.66 .79

.76 .75

Naive simulators

Patients MDD

Fp-r/90 Fp-r/100

.30 .30

.77 .76

.58 .52

.93 .97

.79 .89

.84 .83

Soph. simulators

Patients SCZ

Fp-r/90 Fp-r/100

.30 .30

.84 .81

.81 .67

.88 .93

.73 .80

.91 .87

Naive simulators

Patients SCZ

Fp-r/90 Fp-r/100

.30 .30

.87 .88

.87 .80

.88 .93

.75 .83

.94 .92

Mason et al. (2013)

Feigned PTSD

Veterans PTSD

F-r/100 F-r/120 Fp-r/100 Fs/100 FBS-r/90 RBS/100

.54 .54 .54 .54 .54 .54

NR NR NR NR NR NR

.88 .65 .54 .65 .35 .78

.82 .94 .97 .94 .97 .68

NR NR NR NR NR NR

NR NR NR NR NR NR

Rogers et al. (2011)

Feigned MI

Genuine MI

F-r/100 F-r/120 Fp-r/100 Fs/100 FBS-r/80 FBS-r/100 RBS/80 RBS/100

.20 .20 .20 .20 .20 .20 .20 .20

.81 .92 .92 .90 .64 .90 .64 .87

.91 .66 .13 .28 .91 .38 .97 .78

.80 .95 .99 .98 .62 .95 .61 .88

.53 .77 .77 .78 .38 .66 .39 .62

.97 .92 .82 .84 .96 .86 .99 .94

Marion et al. (2011)

(continued)



14. Assessment on the MMPI‑2 and MMPI‑2‑RF 269

TABLE 14.3. (continued)

Study authors

Experimental group

Comparison group

Sellbom & Bagby (2010)

Coached simulators

Cut score

BR

OCC

SN

SP

PPP

NPP

Psych. inpatients

F-r/100 F-r/120 Fp-r/90 Fp-r/100

.30 .30 .30 .30

.61 .69 .76 .77

.40 .27 .49 .43

.72 .92 .92 .97

.38 .60 .73 .85

.74 .75 .81 .80

Uncoached simulators

Psych. inpatients

F-r/100 F-r/120 Fp-r/90 Fp-r/100

.30 .30 .30 .30

.75 .79 .86 .85

.78 .64 .79 .72

.72 .92 .92 .97

.55 .78 .81 .91

.88 .86 .91 .89

Sellbom et al. (2010)

Failed SIRS

Passed SIRS

F-r/100 F-r/120 Fp-r/90 Fp-r/100

.30 .30 .30 .30

.78 .88 .81 .86

.96 .89 .74 .74

.72 .88 .85 .90

.60 .76 .67 .76

.98 .95 .88 .89

Wall et al. (2015)

Feigning offenders

Offenders MI

F-r/100 F-r/120 Fp-r/90 Fp-r/100 Fs/100 RBS/90

.30 .30 .30 .30 .30 .30

.73 .78 .78 .78 .85 .75

.67 .61 .69 .67 .75 .58

.77 .89 .84 .86 .91 .86

.56 .70 .65 .67 .78 .64

.84 .84 .86 .86 .89 .83

Note. NR, not reported; F-r, infrequent responses; Fp-r, infrequent psychopathology responses; Fs, infrequent somatic responses; FBS-r, symptom validity; RBS, Response Bias Scale; M-FAST, Miller Forensic Assessment of Symptoms Test; PTSD, posttraumatic stress disorder; MH prof, mental health professionals; MDD, major depressive disorder; SCZ, schizophrenia; SIRS, Structured Interview of Reported Symptoms; def., defendants; MI, mental illness; Psych, psychiatric; soph., sophisticated.

second largest median effect size in criterion group studies and the largest median effect size in simulation studies. However, in light of its underlying detection strategy, which was premised on identifying items associated with poor performance on cognitive PVTs, more research is needed to fully understand how this scale may function as a measure of feigned psychopathology. One possibility is BOX 14.3.  Steps in Identifying Feigned Psychopathology with the MMPI-2-RF 1. Establish that the profile does not have ex‑ cessive non-content-based invalid respond‑ ing with CNS, VRIN-r, & TRIN-r. 2. Examine possible psychiatric overreporting with F-r and Fp-r. Bear in mind that F-r will be possibly confounded by genuine psychopa‑ thology. 3. If the examinee has a significant history of mental illness, Fp-r should be prioritized over F-r in establishing symptom feigning.

that most of the studies we reviewed generally fell into two categories: 1. Those that examined feigned mood and anxiety disorders (particularly DSM-5 trauma-related disorders such as PTSD), which are quite heterogeneous in presentation. 2. Civil forensic settings in which, again, individuals may feign emotional, somatic, and cognitive symptoms. For both categories, the individuals examined in those studies appeared to have overreported general symptoms of mental disorders (captured well by F-r), severe psychopathology, and somatic symptoms (captured by Fp-r and Fs), as well as neurocognitive dysfunction (captured by RBS). Feigned Cognitive Complaints

Given the complexity of neurocognitive dysfunction, the MMPI-2-RF is aptly positioned to capture the somatic and emotional sequelae of brain dys-

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function and impairment. Moreover, the validity scales on the test have been found to be useful in capturing various threats to protocol validity in neuropsychological evaluations. According to two recent surveys of clinical neuropsychologists, the MMPI-2-RF is the most widely used measure of symptom validity in neuropsychological evaluations (Martin et al., 2015; Schroeder et al., 2016). Moreover, Martin, Schroeder, and Odland (2015) noted that RBS and FBS-r are the most widely cited sources in the test for capturing invalid selfreport in neuropsychological evaluations. Tables 14.4 and 14.5 present effect size and classification data, respectively, in studies that examined the MMPI-2-RF validity scales in relation to (1) cognitive PVTs, (2) malingering criteria for MND (Slick & Sherman, 2013; Slick et al., 1999) or MPRD (Bianchini et al., 2005), or (3) simulation studies with feigned symptoms of a head injury or attention-deficit/hyperactivity disorder (ADHD). It is apparent that effect sizes are generally smaller in these comparisons than in the ones pertaining to the assessment of feigned psychopathology, which were presented in Table 14.2. This should not be surprising, however, as the MMPI-2-RF is a self-report measure of psychopathology with validity scales that are more aligned with assessing feigned psychopathology than cognitive functioning (and feigning). Thus, there is a mismatch between the methodology in which the two domains of functioning (and feigning) are assessed, which may result in attenuated associations. Nevertheless, some individuals feigning neurocognitive dysfunction also exhibit evidence of feigned emotional symptoms; thus, all five validity scales still warrant attention in neuropsychological evaluations, particularly those conducted in a forensic context. As noted earlier in the chapter, the RBS (Gervais et al., 2007) was developed by identifying items (regardless of content) that differentiated between individuals who failed or passed cognitive performance validity measures. Since its development, studies have found that RBS is significantly associated with cognitive PVT scores across various settings (e.g., Cohen’s d comparing those who passed and failed PVTs ranged from 0.78 to 1.87). As noted in Table 14.4, RBS has the largest median effect size (i.e., d = 1.36) across all of the studies that employed a PVT criterion groups design. It is important that validity measures not be too impacted by genuine pathology. This issue was directly examined in two studies pertaining to RBS and FBS-r. McBride et al. (2013) exam-

ined a sample of 92 litigants claiming head injury who underwent neuroimaging with computerized tomography (CT) and magnetic resonance imaging (MRI). The presence (or absence) of lesions was documented by neuroimaging and examined in relation to performance on cognitive PVTs and the RBS. Similar to PVTs, scores on the RBS were not significantly related to presence or location of brain lesion. In addition, Martin, Schroeder, Heinrichs, and Baade (2015) examined the relationship between RBS and FBS-r in a sample of neuropsychological examinees. After controlling for effort captured by performance validity tests (PVTs), they found that RBS and FBS-r were not significantly associated with results from neuropsychological testing. Several studies examined the performance of the overreporting scales in relation to the MND or MPRD criteria, which have generated much research since their development in the late 1990s. As noted in Table 14.4, F-r and RBS have the largest effect sizes in relation to litigants with no evidence of feigning when compared to litigants meeting these criteria. The MND and MPRD criteria are multifaceted and require consideration of multiple methods of assessment. Both highlight the importance of incorporating self-report validity scales in the assessment of malingering. We would recommend that the MMPI-2-RF validity scales, particularly Fs, FBS-r, and RBS, be considered when assessing for MND and MPRD. In the assessment of feigned neurocognitive dysfunction, three scales appear to be particularly useful: RBS, FBS-r, and Fs. As noted earlier, RBS is often associated with poor performance on cognitive PVTs. Thus, elevated scores, particularly above 80T, should definitely raise suspicion about feigned self-reported cognitive complaints, which are directly captured by the MMPI-2-RF Cognitive Complaints (COG) scale. This point was illustrated empirically by Gervais, Ben-Porath, and Wygant (2009), who showed that high COG scores likely represent feigned cognitive dysfunction when associated with elevated scores on the RBS. The RBS appears to be a better measure of feigned neurocognitive complaints than the FBSr. Furthermore, Fs was associated with feigned symptoms of a head injury by Wygant and colleagues (2009). It is important to clarify that we do not advocate that clinicians replace cognitive PVTs with MMPI-2-RF validity scales. It might be tempting in light of the findings that those failing cognitive PVTs tend to show elevations on the RBS, but it is



14. Assessment on the MMPI‑2 and MMPI‑2‑RF 271

TABLE 14.4.  MMPI-2-RF Assessment of Feigned Neurocognitive Dysfunction

Experimental group

Comparison group

Study authors

Study design

Gervais et al. (2010)

WMT, TOMM, CARB

Passed all PVTs

Harp et al. (2011)

Simulation of ADHD

Effect sizes (Cohen’s d) F-r

Fp-r

Fs

FBS-r

RBS

Failed any PVT

 0.57

 0.40

 0.43

 0.52

0.78

Feigned ADHD

Genuine ADHD

 0.66

 0.65

 0.88

 0.34

NR

Exaggerated ADHD

Genuine ADHD

 0.56

–0.06

 0.33

 0.36

NR

Jones & Ingram (2011)

TOMM, VSVT

Failed PVTs

Passed PVTs

NR

NR

 0.80

 1.06

1.05

Jones et al. (2012)

TOMM, WMT, VSVT

Passed all PVTs

Failed three PVTs

 1.10

 0.92

 1.06

 1.34

1.69

Nguyen et al. (2015)

MND criteria

Valid neurological Sx

MND: Failed Slick

 0.46

NR

 0.12

 0.59

0.54

Valid medical Sx

MND: Failed Slick

–0.08

NR

–0.05

–0.16

0.23

Valid psychiatric Sx

MND: Failed Slick

 1.07

NR

 0.75

 0.98

0.75

Rogers et al. (2011)

VSVT, TOMM

BC: VSVT/ TOMM

Passed PVT/ Cog.

 1.10

 0.64

 1.08

 1.86

1.87

Schroeder et al. (2012)

MND criteria

TBI failed Slick

TBI passed Slick

 1.07

 0.57

 1.09

 1.44

1.61

Tarescavage et al. (2013)

MND criteria

Prob./def. MND

Incentive Only

 0.94

 0.60

 0.62

 0.80

1.17

Wygant et al. (2009)

Simulation: HI

HI simulation

HI controls

 0.78

 0.74

 0.90

 0.42

0.62

Wygant et al. (2010)

TOMM, WMT

Litigant failed PVTs

Litigants passed PVTs

 1.05

 0.63

 1.14

 1.13

1.24

Criminal failed PVTs

Criminal passed PVTs

 1.48

 1.46

 1.30

 1.11

1.48

Wygant et al. (2011)

MND/MPRD

Prob./def. MND/MPRD

Incentive only

 1.63

 1.02

 1.27

 1.20

1.55

Youngjohn et al. (2011)

WMT, DCT

Failed PVTs

Passed PVTs

 0.68

 0.44

 0.71

 0.86

NR

Median d (PVT pass/fail studies)

 1.08

 0.63

 1.06

 1.11

1.36

Median d (MND/MPRD studies)

 1.00

 0.60

 0.69

 0.70

0.96

Note. NR, not reported; F-r, infrequent responses; Fp-r, infrequent psychopathology responses; Fs, infrequent somatic responses; FBS-r, symptom validity; RBS, Response Bias Scale; WMT, Word Memory Test; TOMM, Test of Memory Malingering; CARB, Computerized Assessment of Response Bias; PVT, Performance Validity Test; ADHD, attention-deficit/ hyperactivity disorder; VSVT, Victoria Symptom Validity Test; MND, malingered neurocognitive dysfunction; BC, below chance; Cog., cognitive disorder; HI, head injury; TBI, traumatic brain injury; Sx, symptoms; DCT, Dot Counting Test; MPRD, malingered pain-related disability; Prob./def., probable/definite.

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TABLE 14.5.  Summary of Classification Statistics for Assessment of Feigned Neurocognitive Impairment with the MMPI-2-RF

Study authors

Experimental group

Comparison group

Harp et al. (2011)

Feigned ADHD

Cut score

BR

OCC

SN

SP

PPP

NPP

Genuine ADHD

F-r/100 Fs/91 Fs/100

.52 .50 .52

NR NR NR

.18 .36 .05

.95 .95 1.00

NR .78 NR

NR .58 NR

Exaggerated ADHD

Genuine ADHD

F-r/100 Fs/91 Fs/100

.47 .47 .47

NR NR NR

.06 .33 .11

.95 .95 1.00

NR NR NR

NR NR NR

Jones & Ingram (2011)

Failed PVTs

Passed PVTs

Fs/70 FBS-r/62 RBS/73

.41 .41 .41

.67 .69 .72

.55 .79 .75

.76 .58 .70

.61 .57 .63

.71 .81 .80

Nguyen et al. (2015)

Valid neurological Sx

MND: Failed Slick

F-r/101 Fs/99 FBS-r/96 RBS/105

.42 .42 .42 .42

.64 .60 .64 .57

.27 .20 .30 .13

.93 .90 .90 .90

.73 .60 .69 .50

.63 .60 .63 .58

Valid medical Sx

MND: Failed Slick

F-r/101 Fs/91 FBS-r/96 RBS/101

.40 .40 .40 .40

.61 .57 .59 .63

.16 .16 .11 .16

.90 .83 .90 .93

.50 .38 .40 .60

.63 .61 .61 .64

Valid psychiatric Sx

MND: Failed Slick

F-r/120 Fs/115 FBS-r/105 RBS/114

.17 .17 .17 .17

.77 .85 .79 .82

.27 .36 .18 .18

.87 .94 .91 .94

.30 .57 .27 .40

.86 .88 .85 .85

Rogers et al. (2011)

Below chance: VSVT/TOMM

Passed PVT/Cog.

F-r/100 F-r/120 Fp-r/70 Fs/100 FBS-r/80 FBS-r/100 RBS/80 RBS/100

.20 .20 .20 .20 .20 .20 .20 .20

.78 .86 .87 .85 .65 .87 .65 .86

.62 .17 .19 .21 .88 .17 .98 .67

.80 .95 .96 .93 .62 .95 .61 .88

.44 .46 .55 .43 .37 .46 .39 .59

.89 .82 .82 .82 .95 .82 .99 .91

Schroeder et al. (2012)

TBI failed Slick

TBI passed Slick

F-r/88 Fs/83 FBS-r/89 RBS/92

.44 .44 .44 .44

NR NR NR NR

.38 .43 .48 .43

.93 .96 .96 .92

NR NR NR NR

NR NR NR NR

Tarescavage et al. (2013)

MND

Incentive only

F-r/100 F-r/110 Fs/90 Fs/100 FBS-r/90 FBS-r/100 RBS/90 RBS/100

.30 .30 .30 .30 .30 .30 .30 .30

NR NR NR NR NR NR NR NR

.43 .34 .41 .20 .35 .10 .52 .34

.87 .93 .82 .92 .87 .97 .84 .92

.58 .66 .49 .52 .53 .58 .58 .65

.78 .77 .76 .73 .76 .71 .80 .76

(continued)



14. Assessment on the MMPI‑2 and MMPI‑2‑RF 273

TABLE 14.5. (continued)

Study authors

Experimental group

Comparison group

Wygant et al. (2009)

HI simulation

Wygant et al. (2010)

Wygant et al. (2011)

Cut score

BR

OCC

SN

SP

PPP

NPP

HI control patients

F-r/100 F-r/120 Fp-r/80 Fs/90 Fs/100 FBS-r/80 FBS-r/90

.50 .50 .50 .50 .50 .50 .50

NR NR NR NR NR NR NR

.44 .26 .44 .57 .44 .44 .26

.96 1.00 1.00 .83 .96 .83 .91

NR NR NR NR NR NR NR

NR NR NR NR NR NR NR

Litigant failed PVTs

Litigants passed PVTs

RBS/90

.30

.75

.38

.91

.64

.77

Criminal failed PVTs

Criminal passed PVTs

RBS/100 RBS/110

.30 .30

.80 .80

.59 .44

.89 .95

.69 .79

.84 .80

MND/MPRD

Incentive only

F-r/100 F-r/110 Fs/90 Fs/100 FBS-r/90 RBS/80 RBS/100

.30 .30 .30 .30 .30 .30 .30

NR NR NR NR NR NR NR

.56 .45 .45 .24 .44 .91 .45

.86 .93 .86 .97 .81 .49 .89

.63 .73 .58 .77 .50 .43 .64

.82 .80 .78 .75 .77 .93 .79

Note. NR, not reported; F-r, infrequent responses; Fp-r, infrequent psychopathology responses; Fs, infrequent somatic responses; FBS-r, symptom validity; RBS, Response Bias Scale; WMT, Word Memory Test; TOMM, Test of Memory Malingering; CARB, Computerized Assessment of Response Bias; PVT, Performance Validity Test; ADHD, attention-deficit/ hyperactivity disorder; VSVT, Victoria Symptom Validity Test; MND, malingered neurocognitive dysfunction; TBI, traumatic brain injury; Sx, symptoms; DCT, Dot Counting Test; MPRD, malingered pain-related disability.

still important to gather that information through performance measures. As noted by Gervais, BenPorath, Wygant, and Green (2008), T scores above 80 on the RBS suggest a strong possibility that the individual would fail PVTs. Accompanied by PVT failure at that level on the RBS provides strong corroborating evidence of MND. Feigned Somatic Symptoms

Assessing feigned medical presentations is quite difficult and often requires close consultation with medical practitioners. The assessment of feigned somatic and medical presentations is particularly challenging for most psychologists, since they lack the training and ability to conduct physical evaluations required to substantiate feigning in relation to bona fide impairment. Further complicating matters, some psychological disorders (e.g., somatic symptom disorder) include noncredible somatic presentations, thus requiring inference into the underlying psychological factors that influence the presentation (e.g., secondary gain issues).

Generally speaking, there are no “gold standard” indications of feigned medical and somatic presentations. Some medical practitioners have relied on measures of nonorganic pain (e.g., Waddell signs; Waddell, McCullough, & Kummel, 1980) to corroborate feigned medical presentations. Fishbain, Cutler, Rosomoff, and Rosomoff (2004) raised concerns about their use as an indicator of response bias, but recent data may link Waddell signs and somatic overreporting (Wygant, Arbisi, Bianchini, & Umlauf, 2017). Self-report, however, plays a significant role in the assessment of somatic and medical feigning. The MMPI-2-RF is unique compared to other instruments (including the MMPI-2) in that it contains a validity scale specifically designed to detect overreported somatic complaints using a quasi-rare symptoms approach, the Fs. Studies have directly examined the ability of the MMPI-2-RF validity scales to detect feigned medical and somatic presentations. Wygant et al. (2009) presented three studies related to the MMPI-2-RF validity scales in civil forensic settings.

274

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One study compared 44 male VA medical (nonpsychiatric) patients who completed the MMPI-2 (from which the MMPI-2-RF validity scales were rescored) with 32 medical (nonpsychiatric) VA patients who completed the test with instructions to exaggerate their physical symptoms, as well as the emotional symptoms associated with their physical ailments. The effect sizes were impressive for all five validity scales between the two groups, ranging from 1.73 for Fp-r to 2.31 for FBS-r.1 Classification analysis revealed that participants with Fs scores at or above 90T were 29.9 times more likely to be in the feigning group versus the control group. Indeed, only 2.3% of the medical controls scored above 90T on Fs, whereas 68.8% of the feigning group was above this cut score. Similar results were found for FBS-r cut scores of 80T. As noted earlier, Wygant and colleagues (2011) examined a medicolegal sample of 251 litigants evaluated by a forensic neuropsychiatrist. Sixtynine percent of these patients claimed disability due to pain impairment and were classified using MPRD (Bianchini et al., 2005) criteria. Those claimants in the probable/definite feigning group (n = 91) scored significantly higher than the incentive-only group (n = 103) on all five overreporting validity scales, with effect sizes ranging from a Cohen’s d of 1.02 (Fp-r) to 1.63 (F-r). Both of the somatic overreporting scales, Fs and FBS-r, yielded large effect sizes in this comparison, d = 1.27 and 1.20, respectively. Moreover, classification analyses found a good range of sensitivity and specificity for the various scales at different cut scores, which is important depending on the goal of the evaluation (screening vs. definitive conclusions). Overall, the authors suggested that cut scores of 100T on F-r, 70T on Fp-r, 90T on Fs, 100T on FBS-r, and 100T on RBS yielded the best mix of sensitivity and specificity in the classification of MPRD. Sellbom, Wygant, and Bagby (2012) conducted a simulation design in which undergraduate simulators of physical problems in the context of a disability evaluation were compared to nonmalingering psychiatric patients diagnosed with a somatoform disorder and to male medical (nonpsychiatric) patients treated at a VA medical center. Fs yielded a large effect size (d = 1.42) between the medical patients and somatic malingering group. The effect size was smaller between the malingering group and somatoform patients (d = 0.73). FBS-r did a good job of differentiating the malingering and medical groups (d = 1.91), but fared much worse in the comparison of malingering and somatoform patients (d = –0.58). Indeed, the so-

matoform patients had significantly higher scores on FBS-r than the malingering group. Sellbom and colleagues concluded that while Fs and FBS-r were effective in identifying noncredible somatic symptoms, Fs was more specific to malingering, whereas FBS-r was elevated in both malingering and somatoform patients. Most recently, Wygant et al. (2017) examined a sample of 230 outpatients with chronic pain undergoing evaluations for back and spinal injuries. Every patient underwent extensive evaluation, which included the MMPI-2-RF and Waddell signs. Even though MMPI-2-RF validity scales and Waddell signs utilize different methodologies (self-report vs. medical examination), with the exception of Fp-r, the remaining four overreporting MMPI-2-RF validity scales were significantly associated with total Waddell signs score. The Fs scale produced the largest effect size (d = 1.31) comparing those patients with Waddell sign scores above 2 from those with scores of 0. In terms of interpretive recommendations, we suggest that Fs and FBS-r be used as the primary means of assessing feigned medical and somatic presentations. Scores above 80T on either scale should raise suspicion about the possibility of somatic overreporting, although as noted by Sellbom and colleagues (2012), this elevated responding can also be found among patients with somatoform disorders. Thus, while the presentation may reflect somatic amplification, the underlying cause for the overreporting must be determined independent of the MMPI-2-RF. The reviewed studies, however, collectively suggest that scores of 100T on either Fs or FBS-r appear to be fairly specific to feigned presentations (false positives ranging from 0 to 5%). It is also important to note that scores on the other overreporting scales (F-r, Fp-r, and RBS) may also be elevated in cases involving somatic overreporting and should be interpreted as well. It is crucial to remember that feigned medical and somatic presentations can occur independently or along with feigned psychopathology and neurocognitive impairment. Ultimately, profiles with evidence of somatic overreporting should result in caution in interpreting scores on the somatic scales of the MMPI-2-RF (RC1 and Somatic/Cognitive Specific Problems scales). Rogers (Chapter 2, this volume) discusses the difficulty of assigning exact cut scores in identifying feigning. His points are well taken, particularly with respect to the assessment of feigned somatic and medical presentations with the MMPI-2-RF. As noted earlier, the task of differentiating feigned



14. Assessment on the MMPI‑2 and MMPI‑2‑RF 275

and genuine medical impairment is difficult for psychological practitioners to do with self-report alone. While the studies reviewed in this section suggest some utility for the MMPI-2-RF (particularly Fs and FBS-r) to detect feigned medical presentations, psychologists should not make definitive conclusions about physical feigning from the MMPI-2-RF alone. While sensitivity of the MMPI2-RF validity scales is often lower than mental health professionals might wish, research does suggest they may be quite specific to overreporting. In this regard, Greene (2008) provided a useful analysis of the frequency of MMPI-2-RF scores in normative, clinical, personal injury, and medical samples. Fs scores over 100T occurred in less than 2% of these various groups, whereas FBS-r scores over 100T occurred in less than 1% of these groups. Thus, extreme scores on these two scales (while rare even in forensic settings) are likely to be quite specific to overreporting.

Assessing Underreporting with the MMPI‑2-RF The assessment of underreporting represents a particularly challenging endeavor and is much more difficult than assessment of overreporting. As noted earlier, positive impression management conceptually manifests as defensiveness (denial of psychopathology) and social desirability. The MMPI-2-RF includes scales that tap both dimensions of underreporting. Only a few studies, however, have examined the underreporting validity scales on the MMPI-2-RF. Sellbom and Bagby (2008) evaluated L-r and K-r in two studies employing a simulation design. In the first, L-r and K-r both successfully differentiated between psychiatric patients and undergraduates who completed the MMPI-2 under either standard or faking good instructions. The second study examined undergraduates instructed to fake good in relation to a sample of child custody litigants, a setting in which underreporting is quite common (see Archer, Hagan, Mason, Handel, & Archer, 2012). Across both studies, L-r and K-r generally exhibited large effect sizes over 1.0. Jiménez-Gómez, Sánchez-Crespo, and Ampudia-Rueda (2013) examined whether the items remaining on the MMPI-2-RF from previous MMPI/ MMPI-2 underreporting scales (Butcher & Han, 1995; Edwards; 1957; Nichols & Greene, 1991; Wiggins, 1959) could augment L-r and K-r in the assessment of social desirability. Using a simulation design, they instructed individuals free of psychopathology to complete the MMPI-2 with

standard or socially desirable instructions. The restructured versions of the earlier experimental underreporting scales showed mixed results that were equivalent but generally not stronger than L-r and K-r. L-r exhibited a large effect size of 1.05, whereas K-r had a lower effect size of 0.74. Finally, Crighton, Marek, Dragon, and Ben-Porath (2017) used a simulation design similar to that of Sellbom and Bagby (2008), but they utilized a manipulation check to assess for compliance with underreporting instructions. Participants compliant with the dissimulation instructions exhibited a larger effect sizes on L-r (1.50) and K-r (1.34). Classification accuracy between these two groups revealed good sensitivity (.63) and specificity (.88) for L-r at a cut score of 65T. When the cut score was raised to 70T, sensitivity dropped to .57, but specificity rose to .97. Classification for K-r revealed lower sensitivity, but good specificity. At a cut score of 60T, K-r had a sensitivity of .51 and specificity of .86. When raised to 66T, specificity increased to .92, but sensitivity dropped to .35. It is important to consider that traditional values, in line with certain Christian beliefs, may impact scores on the L-r scale. Bridges and Baum (2013) examined the items in L-r in outpatient clients from a Christian, university-based psychological services center. Several items on L-r may be endorsed at higher levels by individuals with strong Christian beliefs than the MMPI-2-RF normative sample. Consequently, accounting for traditional moral values, often associated with religious affiliation, should be considered in light of elevations on L-r. We recommend that the guidelines from the test manual (Ben-Porath & Tellegen, 2008/2011) be used in interpreting L-r and K-r. T scores above 65 on L-r suggest that the individual may be underreporting by exhibiting an unlikely virtuous presentation. As T scores approach 80, the likelihood of significant under-reporting should be considered. In these situations, it is probable that the clinically substantive scales on the test will be greatly suppressed. As such, it is not possible to rule out the presence of psychopathology with low scores on the clinically substantive scales. As far as interpreting scores on the K-r scale, cut scores need to be lowered to 60T (instead of 65T) as indicated in the interpretative manual. This reduction is due to K-r having a much lower ceiling than L-r. The maximum T score for K-r (at a raw score of 14) is 72, whereas the maximum T score for L-r (also at a raw score of 14) is 105. As scores on K-r approach 70T, the presence of underreporting is strongly suggested.

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SUMMARY As noted at the beginning of this chapter, much of our clinical assessment relies on self-report, and as such, is subject to distortion. The MMPI has a long history of use and empirical examination in the area of assessing response bias. The MMPI-2 and MMPI-2-RF reflect this tradition and have expanded on the approaches to detecting feigned and defensive presentations. Future research needs to continue to examine the utility of the MMPI-2 and MMPI-2-RF validity scales in a variety of settings. One area of study that is particularly needed is continuing to develop cut scores for the scales in relation to particular diagnostic groups in different clinical contexts (e.g., civil vs. criminal forensic, neuropsychological, fitness-for-duty), a point made by Ingram and Ternes (2016) in their meta-analysis of the MMPI-2-RF validity scales. We need to continue to incorporate these contextual factors into the assessment of protocol validity.

NOTE 1.  Although not reported in the original study, the effect size for RBS (which was not part of the MMPI2-RF at the time of the publication) was 2.09 in relation to the medical control and feigning groups.

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C H A P T E R 15

Response Style on the Personality Assessment Inventory and Other Multiscale Inventories Marcus T. Boccaccini, PhD Jessica R. Hart, MA

Evaluators rely on multiscale inventories for assessing response style and malingering nearly as often as they rely on forensically relevant instruments (FRIs) specially designed to measure malingering. For example, in a recent survey (McLaughlin & Kan, 2014), forensic evaluators reported using feigning-specific measures—for example, the Structured Interview of Reported Symptoms (SIRS) and the Miller Forensic Assessment of Symptoms Test (M-FAST)—in about 66% of cases involving response style concerns, in contrast to about 52% for multiscale inventories—for example, the Minnesota Multiphasic Personality Inventory–2 (MMPI-2) and the Personality Assessment Inventory (PAI)—in the same types of cases. The evaluators’ preference for feigning-specific measures makes sense, with meta-analyses supporting scores from these measures as strong predictors of feigned responding (e.g., d = 2.02 for the SIRS total score; Green & Rosenfeld, 2011). But their use of multiscale inventories also makes sense, with metaanalyses reporting similar-size effects for the most predictive validity scales from these measures (d ≈ 1.50–2.00; Hawes & Boccaccini, 2009; Rogers, Sewell, Martin, & Vitacco, 2003). The popularity of multiscale inventories for assessing response style is almost certainly attribut-

able to their versatility. Whereas response-stylespecific measures focus solely on response styles, multiscale inventories provide information about both symptomatology and response styles. Indeed, multiscale inventories provide valuable information about symptoms and traits that are relevant to a broad range of forensic assessment issues, including those in which examinees may be motivated to either overreport impairment (e.g., criminal responsibility or personal injury) or underreport impairment (e.g., child custody or risk assessment). And while response-style-specific measures tend to focus on overreporting (negative distortion) or underreporting of impairment (positive distortion), multiscale inventories typically provide measures of both response styles. The MMPI-2 (Butcher, Dahlstrom, Graham, Tellegen, & Kraemmer, 1989; Butcher et al., 2001), the PAI (Morey, 1991, 2007), and the Millon Clinical Multiaxial Inventory–III (MCMI-III; Millon, Millon, Davis, & Grossman, 2006) are—in this order—the three most commonly used multiscale inventories in forensic practice (Archer, Buffington-Vollum, Stredny, & Handel, 2006; Baumgart, 2010; Lally, 2003; Neal & Grisso, 2014). The presence of validity scales on each of these multiscale inventories appears to play a key role in their pop-

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ularity, as evaluators rate these three measures as the most relevant multiscale inventories for assessing response styles (Baumgart, 2010; Lally, 2003). With Wygant, Walls, Brothers, and Berry devoting Chapter 14 (this volume) to the MMPI-2/MMPI2-RF (Restructured Form), we focus primarily on the PAI and the recently released MCMI-IV (Millon, Grossman, & Millon, 2015). In addition, we review response styles on two emerging multiscale inventories: the Personality Inventory for DSM-5 (PID-5; Krueger, Derringer, Markon, Watson, & Skodol, 2013) and the Personal Problems Questionnaire (PPQ; van den Broek & Monaci, 2011).

EFFECT SIZE AND UTILITY ESTIMATES This review provides information about effect size and utility estimates for response style measure scores (see Rogers, Chapter 2, this volume). Because overall effect size estimates do not provide information about how to interpret specific validity measure scores, we focus on utility estimates for our recommendations, because they provide more specific information for decision making. The four most common utility estimates are summarized in Box 15.1. BOX 15.1.  Overview of Utility Estimates in the Context of Response Distortion 1. Sensitivity: the proportion of distorted responders detected by a specific cutoff score. For example, the proportion of feigners who score at or above the cutoff score on a negative distortion scale. 2. Specificity: the proportion of valid responders who are accurately classified as valid by the cutoff score. For example, the proportion of nonfeigners who score below the cutoff score on a negative distortion scale. 3. Positive predictive power (PPP): the proportion of responders who scored at or above the cutoff score who were actually distorting their response. For example, the proportion of those scoring at or above the cutoff score on a negative distortion scale who were actually feigning. 4. Negative predictive power (NPP): the proportion of responders who scored below the cutoff score who were valid responders. For example, the proportion of those scoring below the cutoff score on a negative distortion scale who were valid responders.

Predictive Power and Clinical Practice Positive (PPP) and negative (NPP) predictive power values are especially useful statistics for practitioners, because interpretation is based on the instrument score, which is the piece of information that practitioners have in the field (i.e., they do not know the responder’s true status). But PPP and NPP values vary depending on the base rate of response distortion in the sample, so values from studies with atypically high base rates of distortion (e.g., simulation studies) or atypically low base rates of distortion do not apply to many real-world settings. Fortunately, NPP and PPP can be estimated for any base rate situation using sensitivity, specificity, and base rate values (see Baldessarini, Finklestein, & Arana, 1983). We provide PPP and NPP values for base rates of 15 and 25%, but anyone can use available online calculators to estimate PPP and NPP for other base rates (see, e.g., http://vassarstats.net/clin2. html).

Screening In and Screening Out No single utility estimate or value is optimal for all types of decision making. In a screening context, evaluators focus on sensitivity and NPP, making sure that they correctly identified all of the possible inaccurate responders with the cutoff score (high sensitivity) and that all of those scoring below the cutoff score (i.e., not selected for an indepth evaluation) are truly valid responders (high NPP). Focusing on these two statistics may lead to incorrectly identifying some valid responders as distorted responders, but these errors are tolerable in a screening context, because these “false positives” should be accurately identified through the subsequent in-depth evaluation process. In other instances, the evaluator may want to use a validity scale score that is very unlikely to classify a valid responder as a distorted responder. For example, given the potentially negative consequences of labeling someone as malingering (e.g., denial of treatment), evaluators may choose to conclude that a responder is malingering only when there is a very low likelihood that an honest responder would obtain a score at or above the cutoff score. In these instances, evaluators would focus on a cutoff score with high specificity and PPP, making sure that they correctly identified honest responders as honest (specificity) and that those scoring at or above the cutoff score are truly feigning (high PPP).

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Classifying Genuine Responders In many clinical contexts, evaluators may want to know whether a multiscale inventory profile is especially likely to be the result of genuine responding. Unfortunately, many response distortion studies focus on identifying scores that indicate a high likelihood of distorted responding, as opposed to honest responding, and they rarely report utility estimates for the low cutoff scores that are especially uncommon among feigners. One method for identifying honest responding is to look for cutoff scores with very high levels of sensitivity. When a cutoff score has a sensitivity value of 1.0, it means that all of the nongenuine responders scored at or above the cutoff score, and that any score below the cutoff must have been produced by a genuine responder.

THE PAI The PAI (Morey, 1991, 2007) is a 344-item multiscale inventory designed to aid in the process

of clinical diagnosis, treatment planning, and screening for psychopathology. Respondents rate each item from 0 = false, not at all true to 3 = very true. The PAI provides scores for 22 nonoverlapping scales, including four validity scales, 11 clinical scales, five treatment consideration scales, and two interpersonal scales. Several different approaches are available for assessing response bias on the PAI, including validity scales, indices based on combinations of profile characteristics, and discriminant function scores based on mathematically weighted combinations of scale and subscale scores. Most of these scales and supplemental validity indicators are described in the PAI manual (Morey, 2007) and may be calculated through the hand- or computerscoring options available through the PAI publisher. Researchers have developed two relatively new feigning indicators: the Negative Distortion Scale (NDS; Mogge, Lepage, Bell, & Ragatz, 2010) and the Multiscale Feigning Index (MFI; Gaines, Giles, & Morgan, 2012). Although neither of these

TABLE 15.1.  Morey’s Recommended Cutoff Score Interpretation for PAI Validity Scale and Supplemental Validity Indicator Scores

Response bias Response bias/PAI scale

Unlikely

Possible

Likely

Inconsistency (ICN)

< 64T

64T to 72T

≥ 73T

Infrequency (INF)

< 60T

60T to 74T

75T to 85T

Very likely

Random/Careless

Short minus full form T (ALC, SUI)

One >

|5T|a

≥ 86T

Both > |5T|

Negative Distortion Negative Impression (NIM)

< 73T

Malingering Index

73T to 91T

92T to 109T

≥ 111T

84T to 110T

Rogers Discriminant Function (RDF)

≥ 110T

> 59T

Negative Distortion Scale (NDS)

13b

Multiscale Feigning Index (MFI)

77Tb

Positive Distortion Positive Impression (PIM)

.99), but lower sensitivity (.43). In other words, genuine patients almost never have scores this high across both INC and INF, although only 43% of random responders score this high. If the goal is to ensure that protocols are not a product of random responding, there should be a high likelihood of a random responder scoring at or above the cutoff score (see Box 15.2). In these instances, it appears most useful to consider whether the respondent scores at or above the cutoff score on either INC or INF. More than 99% of randomly generated protocols have either an ICN score of 64T or higher or an INF score of 71T or higher. But 21% of the clinical standardiza-

BOX 15.2.  Assessment Goal of Detecting Random Responding on the PAI • If the goal is to significantly reduce the likeli‑ hood of random protocols, use the combined cutoff score pattern of INC ≥ 73T and INF ≥ 75T. It is very uncommon (< 1%) for a patient responding honestly to have both INC and INF scores this high. • If the goal is to eliminate almost all random protocols, use the combined cutoff score pattern of INC ≥ 64T or INF ≥ 71T. Almost all randomly generated PAI protocols (94%) have either an INC or INF score this high.

tion sample had an ICN or INF score this high (i.e., specificity = .79). Using slightly higher cutoff scores improves specificity. Only 7% of the clinical standardization sample had an INC ≥ 73T or INF ≥ 75T. In other words, genuine patients rarely have scores this high. But 94% of random responders will score this high on at least one of the scales. Detection of Back Random Responding

Understandably, random response indicators become less effective with fewer items (Clark, Gironda, & Young, 2003). ICN and INF may work well when there is random responding across the PAI, but not as well when there is honest responding followed by random responding (i.e., back random responding). Morey and Hopwood (2004) developed a procedure using scores on the ALC and SUI scales to detect back random responding. The SUI and ALC scores for the PAI short form are based on the first four SUI and ALC items. For these two scales, the likelihood of back random responding increases as the difference between the short form T score and the full form T score (absolute value) increases. Morey and Hopwood (2004) tested back random responding by replacing the last 150 PAI item responses with random responses for patients from the clinical standardization sample (see Box 15.3). As a result, about 70% of the profiles had ALC and SUI difference scores of greater than 5T (sensitivity = .70). But only 9% of the genuine protocols from the clinical standardization sample had difference scores this large on both scales (specificity = .91). In a follow-up study with psychiatric inpatients, researchers found that sensitivity for a cutoff score value > 5T decreased as the number of randomly responded items decreased from the last 200 items (sensitivity = .77) to the last 50 items (sensitivity = .32; Siefert, Kehl-Fie, Blais, & Chriki, 2007).

BOX 15.3.  Assessment Goal of Detecting Back Random Responding on the PAI Suspect back random responding when the absolute value of both the Suicidal Ideation (SUI) and Alcohol Problems (ALC) difference scores are greater than 5 points. This combined pattern of profile characteristics occurs among only about 10% of genuine responders. But back random responding becomes difficult to detect when there is random responding for 100 or fewer items.



15. Response Style in Multiscale Inventories 285

Negative Distortion PAI Measures

The PAI provides multiple validity scales and supplemental validity indicators for detecting negative distortion (i.e., feigning). Validity scales simply sum item scores. Supplemental validity indicators are based on combinations of PAI validity, scale, and/or subscale scores. The PAI provides two main types of supplemental scores: index scores and discriminant function scores. Index scores are based on specific PAI scale cutoff score combinations, whereas discriminant function scores are based on a mathematically weighted combination of scale scores. •• Negative Impression (NIM). NIM is a nineitem validity scale designed to identify respondents attempting to create an overly negative impression. NIM was developed using both rare and improbable symptoms detection strategies (see Rogers, Chapter 2, this volume) to identify negative distortion. Morey (2007) describes two types of NIM items: (1) bizarre and unlikely symptoms, and (2) exaggerated or distorted self-impression. NIM items only contribute to the NIM scale score; they do not contribute to clinical or other scale scores. Although higher NIM scores indicate a higher likelihood of exaggeration or effortful negative distortion, Morey warns that NIM is “not a malingering scale per se” (p. 29). Indeed, NIM items are endorsed more frequently by patients with genuine and more severe impairment than by patients with no or less severe impairment, which suggests that NIM elevations may to some extent be a sign of genuine psychopathology. •• Negative Distortion Scale (NDS). The NDS is a 15-item, researcher-designed validity scale for differentiating between genuine psychiatric patients and those exaggerating symptoms (Mogge et al., 2010). NDS, like NIM, uses a rare symptoms detection strategy. Unlike NIM items, each NDS item is also included on one of the PAI clinical scales. The NDS developers selected the 15 items based on low endorsement among samples of male and female psychiatric inpatients. They argue that NDS, which focuses on the actual endorsement of rare but genuine symptoms, may be more effective at identifying exaggerated severe psychopathology than NIM, which focuses on distortions and bizarre symptoms. The 15 NDS items are 73, 108, 130, 134, 167, 183, 206, 212, 266, 283, 303, 305, 306 (reverse scored), 309, 315. Although most NDS studies have reported effects for raw scores (i.e., sum of the

item scores), one used T scores based on data from the PAI community normative sample (Thomas, Hopwood, Orlando, Weathers, & McDevitt-Murphy, 2012). To calculate an NDS T score from a raw score: (1) subtract 4.2343 from the raw score, (2) divide that result by 4.17302 to get a Z score, (3) multiple the Z score by 10, (4) then add 50 to the result from step 3 (Christopher Hopwood, personal communication, April 19, 2016). Those who want to use NDS scores for decision making may find it helpful to calculate T scores, for all the reasons that T scores are useful (e.g., provides information about the extent to which a score is above or below the normative sample mean). •• Malingering Index (MAL). Morey (2007) developed the MAL index to be relatively independent of genuine psychopathology and a more specific indicator of malingering than NIM. The MAL index score is based on eight different PAI profile configurations or characteristics. The MAL index score increases by one point for each profile characteristic that is present, with higher scores indicating a higher likelihood of malingering. Some of the profile characteristics are based on a single PAI scale, such as NIM ≥ 110T. Others are based on multiple scales, such as one point being scored when the Depression Index (DEP) is greater than 84T and the Treatment Rejection Index (RXR) is greater than 44T. MAL is based, to a large extent, on a spurious pattern of psychopathology detection strategy, although the inclusion of NIM and INF validity scale scores means that MAL also considers the endorsement of improbable symptoms. •• Rogers Discriminant Function (RDF). The RDF uses a spurious pattern of psychopathology detection strategy to identify feigning. The RDF score is calculated using a weighted combination of 20 PAI scores—including ICN and INF—and a constant. Twelve of the scales are positively weighted (ICN, INF, RXR, Somatic: Health Concerns [SOM-H], Anxiety: Physiological [ANX-P], Anxiety-Related Disorders: Phobias [ARD-P], Depression: Cognitive [DEP-C], Paranoia: Hypervigilance [PAR-H], Paranoia: Resentment [PAR-R], Schizophrenia: Psychotic Experiences [SCZ-P], Borderline Features: Negative Relationships [BOR-N], Antisocial Features: Egocentricity [ANT-E]), while eight are negatively weighted (STR-T, Conversion [SOM-C], Anxiety: Affective [ANX-A], Anxiety-Related Disorders: Obsessive– Compulsive [ARD-O], Anxiety-Related Disorders: Traumatic Stress [ARD-T], Schizophrenia: Thought Disorder [SCZ-T], Borderline Features: Identifty Disorder [BOR-I], Borderline Features:

286

I II .  Ps y c h o m e t r ic M e t h o ds

Self-Harm [BOR-S]). The weights are based on the results of a discriminant function equation that optimally differentiated between feigning and honestly responding individuals in a response bias study (Rogers, Sewell, Morey, & Ustad, 1996). RDF equation results are transformed into T scores for interpretation, with higher scores indicating a higher likelihood of feigning. One potentially useful property of RDF scores is that they appear to be independent of impairment and psychopathology (Morey, 2007). •• Multiscale Feigning Index (MFI). The MFI is based on the idea that those feigning impairment endorse a wide range of symptoms at a high level of severity, as opposed to reporting symptoms specific to a particular disorder or narrow range of disorders (Gaines et al., 2012). Thus, MFI uses components of both symptom severity and indiscriminant symptom severity detection strategies. The MFI score is the average T score across seven PAI clinical scales: Somatic (SOM), Anxiety (ANX), Anxiety-Related Disorders (ARD), Depression (DEP), Mania (MAN), Paranoia (PAR), and Schizophrenia (SCZ). Higher scores indicate a higher likelihood of feigning. Detection of Negative Distortion

More than 40 studies have examined the ability of PAI measures to detect negative distortion. The most thorough meta-analysis of findings from these studies reported large effects for NIM (d = 1.48), MAL (d = 1.15), and RDF (d = 1.13; Hawes & Boccaccini, 2009). In other words, higher scores are associated with a higher likelihood of negative distortion on all three of these PAI negative distortion measures, with NIM appearing to have the largest predictive effect. Since then, more than 15 negative distortion studies have been published, greatly extending the knowledge base concerning the detection of negative distortion on the PAI. This review focuses on utility estimates for PAI validity measure cutoff scores. Of course, utility estimates are only useful to the extent that those engaging in negative distortion score higher on the validity measure than genuine responders (i.e., there is a large overall effect size). Unfortunately, researchers often report utility estimates for only one cutoff score value (i.e., the score that they concluded was most useful) and rarely report them for scales that do not perform well (i.e., when the effect size is small). Because researchers tend to report utility estimates for only the cutoff scores they perceive to be useful, any review of utility es-

timates has the potential to provide an overly favorable view of cutoff score performance. Readers should be mindful of these shortcomings in utility estimate reporting when using any findings from this review to make decisions for clinical practice. Table 15.2 summarizes sensitivity and specificity values for the most commonly studied PAI negative distortion measure cutoff scores across all available negative distortion studies. In other words, these findings come from studies using different designs (i.e., simulation, known groups) and different disorders/referral questions (e.g., psychosis, forensic evaluation, posttraumatic stress disorder [PTSD], depression). The tables provide the median sensitivity and specificity value across studies, as well TABLE 15.2.  Summary of Sensitivity and Specificity Values for PAI Negative Distortion Indicators

Sensitivity Scale/ cutoff score mdn Range

Specificity mdn

Range

k

NIM ≥ 70T ≥ 73T ≥ 77T ≥ 81T ≥ 84T ≥ 92T ≥ 110T

.74 .74 .78 .64 .58 .50 .33

.53–.89 .33–.96 .40–.91  .21–1.00 .32–.89 .05–.82 .10–.54

.74 .85 .79 .95 .89 .91 .99

.48–.95 .66–.95 .50–.95  .79–1.00  .67–1.00  .79–1.00  .93–1.00

 5  7 12  9  8 20 10

.74 .51 .27 .17

 .25–1.00  .17–1.00 .07–.55 .07–.60

.76 .13–.99 .93  .81–1.00 1.00  .96–1.00 1.00  .73–1.00

10 23  9 11

.61 .71 .43

.57–.89 .47–.84 .24–.90

.74 .70 .91

.62–.85 .53–.90  .73–1.00

 5  7 11

.88 .85 — — — —

.78–.98 .82–.89 .71–.89 .64–.89 .39–.55 .33–.51

.62 .71 — — — —

.56–.77 .61–.96 .77–.90 .77–.97 .94–.96 .96–.96

 4  4  3  2  2  2

MAL ≥ 2 (71T) ≥ 3 (84T) ≥ 4 (98T) ≥ 5 (110T) RDF ≥ 59T ≥ 60T ≥ 70T NDS ≥ 11 ≥ 13 ≥ 15 ≥ 19 ≥ 25 ≥ 27

Note. mdn, median; k, number of samples/comparisons. Some studies provided data for more than one comparison (e.g., honest vs. simulating psychosis, honest vs. simulating depression).



15. Response Style in Multiscale Inventories 287

as the range of sensitivity and specificity values across studies. The best cutoff scores should have strong specificity or sensitivity values, with little variation across studies. Table 15.3 provides PPP and NPP values based on the median sensitivity and specificity values from Table 15.2, for plausible estimates of the base rate of malingering in clinical contexts (15%, 25%). Overall, the PAI negative distortion scales seem to be better at ruling out the possibility of honest responding (high specificity) than ruling in the possibility of feigning (high sensitivity). For example, it is very rare for a genuine responder to obtain a MAL score of 4 (98T) or higher (median specificity = 1.00), or an NIM score of 110T or higher (median specificity = .99). It is highly likely that anyone scoring at or above these cut scores is feigning, irrespective of study design, referral question, or disorder. PPP values suggest that 85–100% TABLE 15.3.  PPP and NPP Estimates for Commonly Reported PAI Cutoff Scores Using Median Sensitivity and Specificity Values

15% base rate Scale/cutoff score

25% base rate

PPP

NPP

PPP

NPP

 .33  .47  .40  .69  .48  .50  .85

.94 .95 .95 .94 .92 .91 .89

 .49  .62  .55  .81  .64  .65  .92

.51 .38 .92 .89 .86 .85 .82

 .35  .56 1.00 1.00

.94 .92 .89 .87

 .51  .71 1.00 1.00

.90 .85 .80 .78

 .29  .29  .46

.91 .93 .90

 .44  .44  .61

.85 .88 .83

 .29  .34

.97 .96

 .44  .49

.94 .93

NIM ≥ 70T ≥ 73T ≥ 77T ≥ 81T ≥ 84T ≥ 92T ≥ 110T MAL ≥ 2 (71T) ≥ 3 (84T) ≥ 4 (98T) ≥ 5 (110T) RDF ≥ 59T ≥ 60T ≥ 70T NDS ≥ 11 ≥ 13

Note. PPP, positive predictive power; NPP, negative predictive power; NIM, Negative Impression; MAL, Malingering Index; RDF, Rogers Discriminant Function; NDS, Negative Distortion Scale.

of scores in this range are produced by feigners. Slightly lower scores on MAL (≥ 3, 84T) and NIM (≥ 92T) indicate that the issue of feigning may be indeterminate or too close to call. PPP values for these cutoff scores range from .50 to .71 (Table 15.3), suggesting that those scoring in this range are somewhat more likely to be feigning than not. But this also means we can expect that 30–50% of those scoring in this range are not feigning. The wide range of sensitivity values for all of the well-studied PAI cutoff scores means that no cutoff score will consistently “catch” all of those who are attempting to feign. As described in the next section, some of this variability is attributable to cutoff scores tending to perform better for detecting more severe forms of feigned impairment (e.g., psychosis) than others (e.g., anxiety, depression). The most promising sensitivity findings are for the NDS cutoff scores of ≥ 11 (median = .89) and ≥ 13 (median = .85). Although only five studies have reported NDS cutoff score findings, they are based on a relatively wide range of feigning disorders/contexts, including inpatient psychiatric evaluations (Mogge et al., 2010), psychosis (Lehr, 2014), disability evaluations (Correa, 2013; Rogers, Gillard, Wooley, & Kelsey, 2013), and PTSD (Thomas et al., 2012; Wooley & Rogers, 2015). Thus far, there is little evidence of significant variability in NDS cutoff score performance due to the evaluation context or disorder being feigned. Existing studies provide evidence of strong specificity for higher NDS cutoff scores (e.g., ≥ 25 or ≥ 27), suggesting that NDS may emerge to be an especially useful measure of negative distortion (Rogers et al., 2012; see Box 15.4). Disorder‑ and Evaluation‑Specific Negative Distortion

In their meta-analysis, Hawes and Boccaccini (2009) reported significantly stronger effect size values for PAI negative distortion measures when participants were likely feigning severe impairment (e.g., forensic evaluation, impatient psychiatric) rather than less severe impairment (e.g., mood or anxiety disorders). Effect size estimates for severe impairment were very large: d = 2.32 for NIM, d = 1.89 for MAL, and d = 2.03 for RDF. Effect size estimates for mood/anxiety disorders were smaller than those for more severe impairment but still moderate to large in size: d = 1.25 for NIM, d = 0.90 for MAL, and d = 1.23 for RDF. These findings of larger effects for severe impairment make sense, because these measures—especially MAL

288

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BOX 15.4.  PAI Assessment Goal of Detecting Negative Distortion • Assessment goal of identifying a high likeli‑ hood of negative distortion: Malingering Index (MAL) scores ≥ 4 or Negative Impression (NIM) scores ≥ 110T indicate a high likeli‑ hood of negative distortion, irrespective of the disorder being feigned or evaluation context. To achieve this high specificity, however, many feigners will not obtain scores this high. • Assessment goal of identifying possible feigning: MAL scores ≥ 3 or NIM scores ≥ 92T indicate that negative distortion is more likely than not, but many honest responders may still obtain scores in this range. • Assessment goal of identifying profiles where negative distortion is unlikely: Scores of MAL < 2 and NIM < 70 appear to be the best indicators of a low likelihood of negative distortion. • Assessment goal of using the recently de‑ veloped Negative Distortion Scale (NDS) for detecting negative distortion: Most feigners score at or above 11 on NDS, and a cutoff score of 13 or higher appears to provide both strong sensitivity and acceptable specificity for screening purposes.

and RDF—were designed to detect more severe forms of impairment. Indeed, MAL and RDF were developed using responses from research participants feigning severe mental disorders. For NIM, items reflecting bizarre and unlikely symptoms are probably more likely to be endorsed by those feigning psychosis rather than other forms of impairment. Nevertheless, the variability in effect size estimates highlights the need to take a closer examination of the performance of PAI negative distortion measures for different types of commonly studied disorders and evaluation contexts (see Box 15.5 on p. 291). SEVERE IMPAIRMENT

Many PAI validity scale studies focus on detecting negative distortion in contexts likely to encourage the feigning of severe impairment. For example, researchers have asked participants to feign impairment so that they can stay in the hospital (Baity, Siefert, Chambers, & Blais, 2007) or be found not guilty by reason of insanity (Blanchard, McGrath, Pogge, & Khadivi, 2003; Fernandez, Boccaccini, & Noland, 2008). Others have asked simulators to

feign “serious mental disorder” (Morey & Lanier, 1998), schizophrenia (e.g., Rogers, Ornduff, & Sewell, 1993; Rogers et al., 1996), or psychosis (Atkins, 1999). Moreover, most known-groups studies examine PAI scores in forensic contexts, in which feigners often report severe symptoms for the purposes of being found incompetent to stand trial or not guilty by reason of insanity. Nearly all of these studies have examined the ability of PAI scores to detect offenders classified as feigning by the SIRS (e.g., Boccaccini et al., 2006; Gaines et al., 2012; Kucharski, Toomey, Fila, & Duncan, 2007; Rogers, Sewell, Wang, & Cruise, 1998). NIM and MAL have produced the most consistent effect size and utility estimate findings across studies. For example, a NIM cutoff score of ≥ 73T has correctly identified between 74 and and 90% of feigners in these studies (sensitivity = .74 to .90; Baity et al., 2007; Kucharski et al., 2007; Rogers et al., 1993; Sullivan & King, 2010). The more frequently studied cutoff score of NIM ≥ 77T has correctly identified between 56 and 90% of feigners across 10 studies, with all but two of these sensitivity values falling between .74 and .91 (see, e.g., Boccaccini, Murrie, & Duncan, 2006; Kucharski et al., 2007; Morey & Lanier, 1998; Rogers et al., 1998). The cutoff score of NIM ≥ 81T or higher tends to lead to specificity values in the .80–1.0 range in these studies, while the cutoff score of NIM ≥ 92T tends to lead to specificity values in the .85–1.00 range (see, e.g., Boccaccini et al., 2006; Gaines et al., 2012; Sullivan & King, 2010). For MAL, focusing on studies of feigned severe impairment leads to sensitivity values ranging from .81 to 1.00 for a cutoff score ≥ 2, although specificity can be low for this cutoff score (Boccaccini et al., 2006; Gaines et al., 2012; Morey & Lanier, 1998). Thus, a cutoff score of MAL ≥ 2 may be useful as a screening criterion for feigning severe impairment, catching most feigners but requiring further evaluation to remove honest responders who may be incorrectly classified. The cutoff score of MAL ≥ 3 is associated with more varied sensitivity levels (e.g., .32 to .81; Morey & Lanier, 1998; Rogers et al., 1996), but strong specificity values (see Table 15.2). As described earlier, MAL cutoff scores ≥ 3 do a good job of screening out genuine responders in all feigning contexts, including severe impairment. For RDF, predictive effects appear to depend on study design. RDF scores perform well in simulation studies (d = 1.69) but poorly in known-groups studies (d = .31; Hawes & Boccaccini, 2009). Specifically, RDF scores do a poor job of classifying



15. Response Style in Multiscale Inventories 289

offenders and patients in real-world settings who have been identified as feigning either entirely or based in part on the SIRS (Atkins, 1999; Boccaccini et al., 2006; Edens, Poythress, & WatkinsClay, 2007; Gaines et al., 2012; Kucharski et al., 2007; Rogers et al., 1998; Wang et al., 1997). This is such a consistent finding that it is difficult to make recommendations for the use of RDF scores for detecting the feigning of severe impairment in real-world forensic evaluations. Indeed, Rogers has called for extensive cross-validation of RDF before it is recommended for use in clinical forensic practice (see Wang et al., 1997). It may be that RDF is useful for detecting feigning that is relatively independent of the SIRS. Or, it may be that RDF just does not work as well in real-world settings, where the motivations to feign differ from those in simulation studies. Because most of these known-groups or criterion-groups studies do not report utility estimates for RDF scores, the utility estimates in Tables 15.2 and 15.3 do not account for the poor performance of RDF in these studies and likely present an especially optimistic view of the detection of feigning in forensic evaluation contexts. Both of the most recently developed PAI validity measures—NDS and MFI—were designed to detect feigning of severe impairment. Researchers developed the NDS using data from patients at an inpatient psychiatric facility (Mogge et al., 2010) and the MFI using data from patients in a correctional facility (Gaines et al., 2012). Both of these development studies reported large effects for identifying responders classified as feigners on the SIRS (d = 1.60 for MFI; d = 1.38 for NDS). In each of these studies, regression analyses indicated that the scores from the new measure outperformed NIM, MAL, and RDF for detecting feigning. A cutoff score of MFI > 74 correctly identified 76% of feigners and 85% of nonfeigners, suggesting that this score can be used to identify possible feigners (Gaines et al., 2012). A cutoff score of MFI > 84 correctly identified 31% of feigners and only identified 2% of genuine responders as feigning (specificity = .98), suggesting that scores in this range indicate a high likelihood of feigning. For NDS, a cutoff score ≥ 11 correctly identified 88% of feigners, suggesting that this score can be used to identify possible feigning (Mogge et al., 2010). DISABILITY

A series of relatively recent studies has focused on negative distortion in the context of disability

evaluations, with feigners ostensibly motivated to feign for financial gain and/or access to treatment and services (Correa, 2013; Hopwood, Orlando, & Clark, 2010; Rogers, Gillard, Wooley, & Ross, 2012; Rogers et al., 2013). Each of these studies reported strong predictive effects for at least one PAI negative distortion measure, although effects for specific measures seem to vary from sample to sample. For example, in the earliest disability context study, RDF was the only measure to produce a large predictive effect for differentiating between simulators and genuine patients (d = 1.55; Bagby, Nicholson, Bacchiochi, Ryder, & Bury, 2002). In a more recent study comparing PAI scores of genuine chronic pain patients to those of persons simulating chronic pain, NIM (d = 1.29), MAL (d = 1.09), and RDF (d = 1.25) were each predictive of feigning (Hopwood et al., 2010). Specificity was strong for cutoff scores of NIM ≥ 92T (.97), MAL ≥ 2 (.99), and RDF ≥ 70 (.97), but no cutoff score led to a high level of sensitivity. The highest level of sensitivity was observed for an RDF cutoff score > 60T, which correctly identified 57% of the feigners (sensitivity = .57). Although NIM, MAL, and RDF have performed well in recent disability feigning studies, NDS scores have performed even better (d = 1.35– 1.81), at least in terms of absolute value (Correa, 2013; Rogers et al., 2013). In the largest of these NDS studies, an NDS cutoff ≥ 11 correctly identified 98% of the feigners, while a cutoff score ≥ 25 identified 98% of the genuine responders (Rogers et al., 2013). Thus, a cutoff score ≥ 11 appears to indicate the possibility of negative distortion, whereas a score ≥ 25 indicates that negative distortion is highly likely. PTSD

More PAI studies have examined the feigning of PTSD than any other specific disorder. Scores on each of the PAI validity indicators tend to be elevated among those feigning PTSD, but effects are larger for NDS, NIM, and MAL (d = 1.00–1.80) than for RDF (d < 0.92; Lange, Sullivan, & Scott, 2010; Scragg, Bor, & Mendham, 2000; Thomas et al., 2012; Wooley & Rogers, 2015). Both of the PTSD simulation studies that examined NDS scores found that NDS had the strongest predictive effect, with d values of 1.52 and 1.76 for comparisons between feigners and genuine patients (Thomas et al., 2012; Wooley & Rogers, 2015). Unfortunately, these two studies examined somewhat different NDS cutoff scores. One reported utility

290

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estimates for only very high cutoff scores (e.g., ≥ 25, ≥ 27, ≥ 28), all of which led to high specificity (< .95) but low sensitivity (< .40). The other reported utility estimates for T scores (> 65, > 75, > 85), corresponding with raw score cutoffs of approximately ≥ 11, ≥ 15, and ≥ 19. Sensitivity was .78 for the ≥ 11 cutoff score (specificity = .77). Although strong, this sensitivity value is somewhat lower than the sensitivity value for NDS in non-PTSD studies, suggesting that a lower cutoff score may be useful for screening in the possibility of feigned PTSD. Several studies have also suggested a somewhat lower NIM cutoff score (≥ 70) for identifying possible feigning of PTSD than that for other disorders, although sensitivity values are still somewhat low from a screening-in perspective (e.g., sensitivity = .53 in Lange et al., 2010; sensitivity = .75 in Thomas et al., 2012). For a NIM cutoff score ≥ 92T, sensitivity values tend to fall in the .30 to .60 range, with specificity values in the .70 to 1.0 range (Lange et al., 2010; Thomas et al., 2012; Wooley & Rogers, 2015). In terms of ruling out the possibility of feigned PTSD, a MAL cutoff score < 3 appears to be effective, with specificity values ranging from .83 (Bowen & Bryant, 2006) to 1.00 (Lange et al., 2010). In the most sophisticated PTSD simulation study, Wooley and Rogers (2015) asked patients from a trauma unit to completely fabricate PTSD, exaggerate their symptoms (partial malingering), or attribute current symptoms to a past trauma. They found that NIM, MAL, and NDS were useful for differentiating between honest responding and completely feigned PTSD (d = 1.07–1.52) and PTSD attributed to a past trauma (d = 0.96–1.34). But these measures were less useful for differentiating between honest responders and those exaggerating symptoms (d = 0.65–0.84). DEPRESSION AND ANXIETY

Relatively few studies have examined the ability of PAI negative distortion measures to detect feigned depression or non-PTSD-related anxiety disorders. Several of those that do exist have compared the ability of PAI scores to detect feigning of these disorders and more severe disorders (Greene, 2004; Rogers et al., 1993, 1996; Veltri & Williams, 2012). The overall pattern of findings from these studies is that PAI is less effective for identifying feigned depression and anxiety than for identifying other disorders (see, e.g., Greene, 2004; Rogers et al., 1993; Veltri & Williams, 2012). Unfortunately, few studies have reported utility estimates for identify-

ing feigned depression or anxiety, which means it is difficult to make recommendations for practice. For detecting feigned depression, Lange et al. (2010) reported a sensitivity value of .50 and specificity value of 1.00 for a MAL cutoff score ≥ 3, but Greene (2004) reported a sensitivity value of only .22 for this same cutoff score (specificity not reported). Both of these studies also reported perfect specificity values (1.00) for NIM cutoff scores of 81T. COGNITIVE IMPAIRMENT

Finally, five relatively recent studies have examined the association between PAI negative distortion measures and feigning of cognitive impairment or the sequelae of head injury. One study reported small (d ≈ .40), but nonsignificant effects (Armistead-Jehle, 2010). The other four reported moderate to large statistically significant effects for at least one validity measure (Keiski, Shore, Hamilton, & Malec, 2015; Mooney et al., 2014; Whiteside, Dundar-Mayer, & Waters, 2009; Zakzanis, Gammada, & Jeffay, 2012). For example, researchers have found that NIM scores were predictive (d = 0.86) of failure rates on Trial 2 of Test of Memory Malingering, as well as clinical opinions of cognitive malingering (d = 1.25) based on the results of symptom validity tests and the reporting of improbable neurological symptoms (see Whiteside et al., 2009; Zakzanis et al., 2012). The only study in this area to report utility estimates was a simulation study comparing PAI scores from a clinical sample of patients with traumatic brain injury (TBI) to those of college students simulating TBI symptoms after a motor vehicle collision (Keiski et al., 2015; see Box 15.5). Simulators produced higher scores than did genuine TBI patients on NIM (d = 1.41), MAL (d = 1.08), and RDF (d = 1.16). The most effective cutoff scores for identifying the possibility of feigning were an NIM cutoff score of 73T (sensitivity = .67, specificity = .83) and an RDF cutoff score of 50T (sensitivity = .81, specificity = .61). As with most PAI negative distortion studies, scores of NIM ≥ 92T and MAL ≥ 4 were uncommon among genuine responders (specificity = .99 and 1.00, respectively).

Positive Distortion PAI Measures

The three measures for detecting positive distortion on the PAI consist of a validity scale, an index, and a discriminant function.



15. Response Style in Multiscale Inventories 291

BOX 15.5.  Disorder-Specific PAI Recommendations 1. Screening out honest responders: scores of NIM ≥ 92T and MAL ≥ 3 are useful for screen‑ ing out honest responders, regardless of the disorder being feigned. 2. Identifying the possibility of feigned severe impairment (e.g., psychosis): cutoff scores of NIM ≥ 73 and MAL ≥ 2 appear to be the most useful for identifying the possibility of feigning, although some feigners will go undetected. 3. Detecting feigning in disability evaluations: NDS appears to be the most useful measure for detecting feigning in disability evalua‑ tions. A cutoff score of NDS ≥ 11 indicates the possibility of negative distortion, whereas a score of ≥ 25 indicates that negative distor‑ tion is highly likely. 4. Detecting feigned PTSD: NDS, NIM, and MAL have performed better than RDF for detect‑ ing feigned PTSD. The cutoff score of NIM ≥ 70T appears to be most useful for identify‑ ing the possibility of feigning. At this point in time, there are too few studies of NDS cutoff scores to allow for PTSD-specific interpretive guidelines. 5. Detecting feigned cognitive impairment: Although recent studies suggest that several PAI negative distortion measures may be useful for detecting feigning of cognitive im‑ pairment, the absence of utility estimates in these studies does not allow for the develop‑ ment of interpretative guidelines.

•• Positive Impression (PIM). PIM is a nine-item validity scale designed using a denial of personal faults detection strategy. Morey (2007) created the PIM to identify respondents who are attempting to create an unrealistically favorable impression. PIM items were rarely endorsed in the PAI normative samples, although they were somewhat more likely to be endorsed by nonclinical adults than by clinical patients (Morey, 2007). Higher PIM scores indicate a higher likelihood of the respondent attempting to portray him- or herself as relatively free of shortcomings. •• Defensiveness Index (DEF). The DEF index is a PAI profile configuration measure based on the spurious pattern psychological adjustment detection strategy. The DEF score increases by one point for each of nine PAI profile characteristics

that is present, with higher scores indicating a higher level of defensiveness. Some of the profile characteristics are based on a single PAI scale, such as PIM ≥ 50T. Others are based on multiple scales, such as a Dominance (DOM) T score 15 or more points larger than the Verbal Aggression (AGG-V) T score. These profile characteristics are more common among individuals asked to present a positive impression than among those responding honestly (Morey, 2007). •• Cashel Discriminant Function (CDF). The CDF uses a spurious pattern of psychological adjustment detection strategy to identify socially desirable responding. The CDF score is based on a weighted combination of six PAI scale scores, including PIM. Some of the scales are positively weighted (PIM, MAN, BOR, RXR), while others are negatively weighted (Alcohol Problems [ALC], Stress [STR]). The weights are based on the results of a discriminant function equation that optimally differentiated between defensive and honestly responding individuals in a response bias study (Cashel, Rogers, Sewell, & Martin-Cannici, 1995). CDF equation results are transformed into T scores for interpretation, with higher scores indicating a higher likelihood of socially desirable responding. One potentially useful property of CDF scores is that they appear to be independent of genuine impairment and psychopathology (Morey, 2007). Detection of Positive Distortion

Ten simulation studies have examined the ability of at least one PAI validity measure to identify positive distortion. Most studies have asked simulators to appear well-adjusted or as ideal candidates for the purpose of hiring or employment (e.g., Cashel et al., 1995; Fernandez et al., 2008; Kurtz, Henk, Bupp, & Dresler, 2015, Morey & Lanier, 1998). Others have focused on attempting to appear pathology free to gain access to benefits (Correa, 2013) or as part of a criminal forensic evaluation (e.g., relating to substance use; Fals-Stewart, 1996; Fals-Stewart & Lucente, 1997). A prior review of findings from positive distortion simulation studies reported strong mean effect sizes values for PIM (d = 1.83) and DEF (d = 1.17), and a somewhat weaker mean effect for CDF (d = 0.63; Sellbom & Bagby, 2008). The three simulation studies published since that review continue this trend, with very strong effects for PIM (d = 1.91, 1.41, and 1.47) and DEF (d = 1.88 and 1.68),

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and somewhat weaker effects for CDF (d = 0.94 and 0.65; Correa, 2013; Fernandez et al., 2008; Kurtz et al., 2015). Table 15.4 summarizes sensitivity and specificity values for PIM and DEF cutoff scores. There were too few studies of CDF cutoff scores to include in the table. The most commonly studied PIM cutoff score has been the manual recommended ≥ 57T, which falls at about the 75th percentile. In other words, about 25% of examinees obtain a score in this range. The median sensitivity and specificity values of this cutoff score are .81. The specificity value is consistent with Morey’s (2007) recommendation (see Table 15.1) that scores this high are likely a product of positive distortion, and the median specificity value of .98 for the PIM cutoff score of ≥ 68T is consistent with his recommendation that this score indicates a high likelihood of distortion. The NPP estimates for the cutoff score of ≥ 68T are .90 for a 15% base rate of distortion and .83 for a 25% base rate. Findings from forensic field settings indicate that a PIM cutoff score ≥ 57T is useful for facilitating interpretation of other PAI scale scores. For example, in one recent study, defensive responders diagnosed with antisocial personality disorder had lower Antisocial Features (ANT) scores (M = 55.15) than nondefensive responders without

TABLE 15.4.  Summary of Sensitivity and Specificity Values for PAI Positive Distortion Indicators

Scale/cutoff score

Sensitivity

Specificity

mdn

Range

mdn

Range

k

— .81 — — .41 —

.54–.90 .43–.93 .68–.82 .54–.78 .17–.49 .29

— .81 — — .98 —

.71–.73 .78–.89 .79–.93 .86–.92 .95–1.0 1.00

2 9 2 2 3 1

— — — .81 .67

1.00 .57–.73 .54–.89 .71–.79 .57–.70

— — — .86 .94

.39 .55–.71 .62–.76 .79–.87 .89–.96

1 2 2 5 3

PIM ≥ 50T ≥ 57T ≥ 61T ≥ 64T ≥ 68T ≥ 72T DEF ≥ 2 (44T) ≥ 3 (51T) ≥ 4 (57T) ≥ 5 (63T) ≥ 6 (70T)

Note. mdn, median; k, number of samples/comparisons. Some studies provided data for more than one comparison.

BOX 15.6.  Assessment Goal of Detecting Positive Distortion on the PAI • Positive Impression (PIM) scores ≥ 68T or Defensiveness Index (DEF) scores ≥ 6 are rare among genuine responders and indicate a high likelihood of positive distortion. • A PIM cutoff score ≥ 57 identifies about 80% of those engaging in positive distortion. Scores this high are not common among genuine responders, but the median specificity value of .81 suggests that about 20% of genuine responders score in this range.

the diagnosis (M = 57.50), suggesting that even moderate ANT elevations among defensive responders may signal a clinically significant level of antisocial traits (Boccaccini, Harris, Schrantz, & Varela, 2017). Three studies found that PAI ANT scores were somewhat stronger predictors of institutional misconduct among those scoring below the PIM 57T cutoff score than those scoring above it (Edens & Ruiz, 2006; 2009; Reidy, Sorenson, & Davidson, 2016). In other words, ANT scores were more useful for prediction among those responding openly. As an important caution, this finding has not generalized to the prediction of postrelease criminal recidivism (Boccaccini, Murrie, Hawes, Simpler, & Johnson, 2010; Boccaccini, Rufino, Jackson, & Murrie, 2013). Nonetheless, more research is needed to examine the role of PIM scores in PAI profile interpretation, including the possible role of accounting for PIM in hiring contexts (see Kurtz et al., 2015). Also consistent with the PAI manual (see Table 15.1), DEF scores of 6 or higher appear to be uncommon among genuine responders, with a median specificity value of .94 (see Table 15.4). The NPP estimates for the cutoff score of DEF ≥ 6 are .96 for a 15% base rate of distortion and .93 for a 25% base rate. The DEF cutoff score ≥ 5 leads to both strong sensitivity (.81) and specificity (.86), suggesting that it may be useful as a conservative estimate for screening in purposes (see Box 15.6).

THE MCMI‑IV The Millon Clinical Multiaxial Inventory (MCMI; Millon, 1983) is a broadband, multiscale measure of adult personality and psychopathology. Millon developed the MCMI for use among patients seek-



15. Response Style in Multiscale Inventories 293

ing or receiving mental health treatment, and it is not intended for use within nonclinical populations. The MCMI has undergone several revisions, and the MCMI-IV is the most recent version (Millon et al., 2015). Because the MCMI-IV is relatively new, the validity research in this chapter is based on administrations of earlier versions of the MCMI, typically the MCMI-III (Millon et al., 2006). The MCMI-IV consists of 195 true–false items written at about a fifth-grade reading level. The items are organized into 12 clinical personality pattern scales, three severe personality pathology scales, seven clinical syndrome scales, and three severe clinical syndrome scales. The MCMI-IV also includes validity measures for detecting random responding, negative distortion, and positive distortion. Table 15.5 lists these MCMI-IV response distortion measures, along with developer recommended cutoff scores for identifying response bias. A distinguishing feature of the MCMI is the use of base rate (BR) scores rather than standard (T) scores. BR scores are scaled to account for prevalence rates of each disorder being assessed. For the MCMI validity scales, the BR scores were scaled so that scores of 85 and above identified the highest 10% of patients, scores of 75–84 identified the next 15%, scores of 35–74 identified the middle 60%, and scores of 34 and below identified the lowest 15% (Millon et al., 2006).

TABLE 15.5.  Millon et al.’s Recommended Cutoff Score Interpretation for MCMI-IV Validity and Modifying Scale Scores

Response bias/ MCMI-IV scale

Response bias Unlikely

Possible

Likely

Random/Careless Validity (V)

0

1

2–3

≤8

9–19

20–25

BR 21–60

BR 61–114

BR ≥ 115

BR < 75

BR ≥ 75

Disclosure (X)

BR 21–60

BR 7–20

Desirability (Y)

BR < 75

BR ≥ 75

Inconsistency (W)

Concerns about Forensic Use of the MCMI‑IV for Assessing Response Style Before reviewing the MCMI-IV validity scales and support for their use, it is important to note that others have questioned the ability of the MCMIIII validity indices to detect response distortion and have cautioned against their use in forensic evaluations (Berry & Schipper, 2007; Melton, Petrila, Poythress, & Slobogin, 2007; Morgan, Schoenberg, Dorr, & Burke, 2002; Rubenzer, 2009). In fact, due to the lack of sufficient support for MCMI-III validity and modifying indices, Sellbom and Bagby (2008) wrote, “Under no circumstances should practitioners use this instrument in forensic evaluations to determine response styles” (p. 205). Their primary concerns were the small number of validity scale studies and underwhelming effect sizes. Even those recommending the use of the MCMI in forensic evaluations have noted limitations of the validity scales: “The expert . . . should be aware that the validity scales of the MCMI remain the least researched and least validated of all MCMI scales” (Craig, 1999, p. 295). Even the MCMI-IV manual warns users about the limitations of the measure for any case involving a nonclinical population (e.g., child custody evaluations; Millon et al., 2015). We were able to find only three relevant MCMI validity scale studies published since the Sellbom and Bagby (2008) review (Aguerrevere, Greve, Bianchini, & Ord, 2011; Lenny & Dear, 2009; Ruocco et al., 2008). None of these studies examined the MCMI-IV, and only one used a known-groups design that provides information about detection of response bias when the measure is administered in real-world forensic evaluation contexts (Aguerrevere et al., 2011). Given these continued shortcomings of the MCMI validity scale research literature, it seems that the Sellbom and Bagby (2008) conclusion still applies and that practitioners should avoid using the MCMI-IV to differentiate between honest and dishonest responders in actual forensic cases.

Negative Distortion Disclosure (X) Debasement (Z)

Random or Careless Responding MCMI‑IV Measures

Positive Distortion BR ≤ 6

Note. Recommended cutoff scores are from the MCMI-IV professional manual (Millon, Grossman, & Millon, 2015).

The MCMI-IV includes two scales designed to detect random or careless responding. The manual also provides recommended cutoff scores to classify an examinee’s protocol as invalid (response bias likely), questionable (response bias possible), or acceptable (response bias unlikely) based on concerns

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of random or inconsistent responding (see Table 15.5). When a protocol falls within the questionable range, the MCMI-IV authors recommend considering reading ability, attention difficulties, fatigue, carelessness, language barriers, cognitive impairment, or uncooperativeness as possible explanations. •• Validity (V). Scale V consists of three items for which true responses are highly improbable. It is unlikely that respondents properly attending to item content would endorse any of these items (e.g., “I was on the front cover of several magazines last year”). •• Inconsistency (W). Scale W consists of 25 pairs of items containing similar content. Most people respond similarly to each item in the pair. An elevated Scale W score indicates that the examinee gave different responses to a pair of items with similar content. Detection of Random Responding

Few studies have investigated random or inconsistent responding on the MCMI-III or MCMI-IV. Those studies that do exist raise significant concerns about the utility of Scale V (see Box 15.7). In a study of random responding on the MCMIIII, researchers used a computer program to produce 40,000 random response protocols (Charter & Lopez, 2002). Of these protocols, 12% had a raw score of 0 on Scale V, and 38% received a score of 1. In other words, only half of the completely random profiles were detected by the recommended MCMI-III Scale V cutoff score (i.e., ≥ 2). In another study, a Scale V cutoff score of ≥ 1 detected only 505 out of 1,000 completely random protocols, but correctly identified 336 of 336 clinical protocols as valid (Briddick, 2004). Thus,

BOX 15.7.  Assessment Goal of Detecting Random Responding on the MCMI • Neither Scale V nor Scale W appears to be sufficiently sensitive to detect randomly gener‑ ated protocols. • Because Scale V has only three items, random responders may go undetected simply due to chance. However, genuine patients attending to item content rarely endorse Scale V items and a raw score of 1 appears to be unlikely among clinical patients, suggesting a high likelihood of random responding.

although the specificity of Scale V was 1.00, sensitivity was only .50. These findings for Scale V are almost certainly attributable to there being only three items on the scale. By chance alone, it is possible for a random responder to endorse none or only one of these items. Scale W has also been largely unstudied due to its addition to the MCMI during the final MCMI-III revision. In the MCMI-IV normative data sample, Scale W correctly identified only 63% of randomly generated datasets using the cutoff score for classification as questionable (raw score ≥ 9; Millon et al., 2015).

Negative Distortion MCMI‑IV Measures

The MCMI-IV includes two validity scales designed to detect a response style characterized by negative distortion, or overreporting of impairment. A third scale, designed for positive distortion, may also have some utility with low scores as indicators of negative distortion. Table 15.6 provides a summary of sensitivity and specificity val-

TABLE 15.6.  Summary of Sensitivity and Specificity Values for MCMI-III Negative Distortion Indicators

Scale/cutoff score

Sensitivity

Specificity

mdn

Range

mdn

Range

k

.70 .57 .38 .30

.63–.76 .52–.61 .35–.41 .00–.50

.67 .73 .88 .95

.62–.71 .69–.81 .88–.88  .93–1.00

2 3 2 3

.38 .62 .58 .64

.33–.43 .57–.67 — —

.86 .71 .76 .74

.85–.87 .70–.71 — —

2 2 1 1

.64 .60 .58

— .59–.61 .55–.86

.78 .64 .79

— .62–.66 .72–.96

1 2 3

Disclosure (X) ≥ BR 80 ≥ BR 85 ≥ BR 90 ≥ RS 178 Desirability (Y) ≤ BR 20 ≤ BR 25 ≤ BR 35 ≤ BR 39 Debasement (Z) ≥ BR 81 ≥ BR 83 ≥ BR 85

Note. mdn, median; k, number of samples/comparisons; BR, base rate score; RS, raw score. Please note that utility estimates refer to MCMI-III and not the most recent MCMI-IV, noted in Table 15.5. Some studies provided data for more than one comparison.



15.  Response Style in Multiscale Inventories 295

ues for the MCMI-III negative distortion measures cutoff scores. •• Disclosure (X). Scale X classifies a respondent’s overall response style on a continuum from overly open and self-revealing to secretive and defensive. The former suggests possible exaggeration of symptoms, while the latter suggests defensiveness. Both extremely high and extremely low scores are clinically interpretable on this scale. The Scale X raw score is calculated by summing endorsements to 121 items from the clinical personality patterns scales. According to the MCMI-IV manual, BR scores on Scale X between 21 and 60 are considered acceptable. Overreporting should be considered for scores between 61 and 114. In such situations, the MCMIIV scoring system adjusts personality and psychopathology BR scores to account for this response style. Scale X BR scores of 6 or less, or 115 or more, suggest that the profile is highly likely to be invalid (due to positive distortion or negative distortion, respectively). •• Debasement (Z). Scale Z assesses the examinee’s tendency to portray him- or herself in an overly negative or pathological manner. High scores may indicate either a tendency to respond in a self-deprecating manner or a cry for help from an examinee experiencing extreme psychological distress. This response style may make the examinee’s overall protocol appear more psychologically impaired than would be objectively discovered. Higher scores on this scale suggest possible overreporting of psychological or interpersonal difficulties, and should be interpreted with caution. •• Desirability (Y). Scale Y assesses the degree to which the examinee responds in a manner that portrays him- or herself as socially attractive, virtuous, stable, and without significant problems. The MCMI developers recommended its use solely for detection of positive distortion; however, some studies with the MCMI-III have suggested the potential utility of low scores to indicate negative distortion (Daubert & Metzler, 2000; Ruocco et al., 2008; Schoenberg, Dorr, & Morgan, 2003a, 2003b).

TABLE 15.7.  PPP and NPP Estimates for Commonly Reported MCMI-III Negative Distortion Cutoff Scores Using Median Sensitivity and Specificity Values

Scale/cutoff score

15% base rate

25% base rate

PPP

NPP

PPP

NPP

.27 .27 .36 .51

.93 .91 .89 .88

.41 .41 .51 .67

.87 .84 .81 .80

.32 .27

.89 .91

.48 .41

.81 .85

.23 .33

.90 .91

.36 .48

.83 .85

Disclosure (X) ≥ BR 80 ≥ BR 85 ≥ BR 90 ≥ RS 178 Desirability (Y) ≤ BR 20 ≤ BR 25 Debasement (Z) ≥ BR 83 ≥ BR 85

Note. PPP, positive predictive power; NPP, negative predictive power.

al., 2011) and dissociative disorders (Webb, 1997). Tables 15.6 and 15.7 summarize utility estimates from these studies, but the values in these tables should be interpreted with caution due to the small number of studies (see Box 15.8). In a simulation study, 160 psychiatric outpatients completed the MCMI-III twice, once honestly and again with fake-good or fake-bad instructions (Daubert & Metzler, 2000). Standard cutoff scores for Scales X (BR ≥ 85), Y (BR ≥ 35), and Z (BR ≥ 85) were moderately effective in identifying those who were faking bad, with sensitivity ranging from .55 to .61 and specificity ranging from .76 to .81. However, the use of lower cutoff scores (X ≥ 80, Y ≤ 39, and Z ≥ 81) improved classification accuracy (sensitivity = .64–.76; specificity = .71– .78). Nonetheless, another simulation study found that only Scale X differentiated between students feigning dissociative disorders and patients diagnosed with dissociative disorders (Webb, 1997).

Detection of Negative Distortion

BOX 15.8.  Assessment Goal of Identifying Feigners Using the MCMI-IV

Studies using simulation designs with clinical comparison samples and known-groups designs have found some support for the use of the MCMIIII X, Y, and Z scales in detection of feigned psychopathology (Daubert & Metzler, 2000), as well as feigned traumatic brain injury (Aguerrevere et

Because there are very few studies reporting MCMI cutoff scores (see Table 15.6) and none reporting findings for MCMI-IV cutoff scores, evaluators should not use the MCMI-IV to identify feigning in real-world forensic cases.

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BOX 15.9.  Assessment Goal of Detecting Positive Distortion on the MCMI-IV A Scale Y cutoff score ≥ 75 appears to be un‑ common among honest respondents, although many who are attempting to appear problem free will not obtain Scale Y scores this high.

Detection of Positive Distortion MCMI‑IV Measures and Detection of Positive Distortion

The MCMI-IV includes two scales that were designed to provide information about positive distortion: X and Y (described earlier; see Box 15.9). In the Daubert and Metzler (2000) simulation study, cutoff scores for Scales X, Y, and Z (BR ≤ 35, BR ≥ 85, and BR ≤ 35, respectively) were, at best, only moderately effective for identification of fake-good response styles. Sensitivity ranged from .10 to .41, and specificity ranged from .93 to .98. However, the use of alternative cutoff scores (X ≤ 59, Y ≥ 74, and Z ≤ 38) improved classification accuracy. With these optimal cut scores, sensitivity ranged from .51 to .64, and specificity ranged from .75 to .96. Other studies have also reported relatively strong specificity values for the Scale Y cutoff score of > 75 (.98), but very low sensitivity (.19; Fals-Stewart, 1995). In a more recent simulation study, parents were instructed to respond honestly to the MCMI-III or to respond in a manner that made them look like good parents (Lenny & Dear, 2009). Parents in the fake-good condition scored significantly higher than the honest group on Scale Y (d = 1.71) and significantly lower on Scales Z (d = 1.37) and X (d = 1.17). Although the d = 1.71 effect size is promising and in line with effects from positive distortion scales from other measures, the authors did not provide utility estimates to help in translating these findings into practice.

THE PID‑5 The PID-5 (Krueger, Derringer, Markon, Watson, & Skodol, 2013) is a multiscale measure of adult personality based on the Emerging Models and Measures section of the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5; American Psychiatric Association, 2013). It is presently offered by the American Psychiatric Association as one of several emerging measures for research and clinical evaluation.

The PID-5 consists of 220 items, grouped into 25 personality trait facets, as well as five indices of broad trait domains, including Negative Affect, Detachment, Antagonism, Disinhibition, and Psychoticism. Examinees respond to items using a 4-point scale ranging from 0 (very false or often false) to 3 (very true or often true). The PID-5 authors did not include validity scales or any other methods of assessing response style, an omission that has the potential to limit its use in both clinical and forensic settings (Al-Dajani, Gralnick, & Bagby, 2016; Hopwood & Sellbom, 2013; Krueger, Derringer, Markon, Watson, & Skodol, 2012; McGee Ng et al., 2016).

Detection of Response Distortion Researchers recently evaluated the extent to which response bias affected PID-5 scale scores, using MMPI-2-RF validity scales to classify university students and psychiatric patients as overreporting, underreporting, or credibly responding (McGee Ng et al., 2016). As expected, overreporters (i.e., feigners) scored significantly higher on many of the PID-5 facet, domain, and personality disorder scales compared to credible responders (d ranging from 0.04 to 1.84), and credible responders scored significantly higher than underreporters (d ranging from 0.02 to 1.06). In both patient and student samples, the effect sizes for overreporting versus credible responding were large, and the effect sizes for credible responding versus underreporting were medium. Based on these results, the authors concluded that there was “a clear and unequivocal effect of response bias on the PID-5” (p. 58). Thus, there appears to be a clear need for validity scale development for the PID-5.

Development of PID‑5 Validity Scales In a recent dissertation study, Fiduccia (2015) began developing validity scales for the PID-5. Participants in Study 1 were instructed to provide desirability estimates for response options on all PID-5 items, in the context of a mental health evaluation. She used the Study 1 findings to create three validity scales: Weighted Desirability (WD), Extreme Desirability (ED), and Balance Desirability (BD). Participants in Study 2, a simulation design, were randomly assigned to honest, defensive, or feigning conditions. Based on these results, Fiduccia developed two additional validity scales based on the range of symptom endorsements (Selectivity) and the range of symptom severity (Severity). Generally, the symptom-based scales (i.e., Selectivity and Severity) were better at discriminating among



15. Response Style in Multiscale Inventories 297

honest, feigned, and defensive responding than desirability-based scales (i.e., WD, ED, and BD). For example, Fiduccia identified Selectivity and Severity cutoff scores with sensitivity values greater than .70 and specificity values of .98. The performance of these PID-5 validity scales suggest potential usefulness in discrimination between honest and feigned responding, but require additional validation before they can be used in clinical or forensic settings.

THE PPQ The PPQ (van den Broek & Monaci, 2011) is an emerging screening measure of cognitive, emotional, and physical disabilities following physical or neurological injury. Examinees are instructed to respond to items using a 3-point scale, rating the extent to which they have experienced each problem. The PPQ consists of 156 items organized into three clinical scales and 12 clinical subscales. These subscales represent common complaints following newly acquired disabilities, including emotional, neurocognitive, and physical complaints (van den Broek, Monaci, & Smith, 2012). The PPQ also contains three validity scales designed to detect noncredible responding, one for each of the three total clinical scales (vCog, vEmot, and vPhys). Items on the validity scales utilize detection strategies to include implausible symptoms, overly severe complaints, overly specified symptoms, and unusual symptom combinations (van den Broek et al., 2012).

Detection of Response Distortion Only one available study examines the PPQ’s validity scales (van den Broek et al., 2012). This simulation design study compared scores from patients with genuine brain injuries, healthy participants instructed to respond honestly, and healthy participants instructed to feign mild TBI. Simulators scored significantly higher than genuine patients and control subjects on all three validity scales. Optimal cutoff scores for detecting feigning response styles were 5 for vCog, 3 for vEmot, and 4 for vPhys. Sensitivity for these scales ranged from .70 to .90, and specificity ranged from .95 to .98. The PPQ appears to show promise as a brief measure to detect response bias in neurological injury populations. However, only one study has evaluated the ability of the PPQ’s validity scales to detect feigning. Additional research is needed to examine the validity of these scales and to assess cutoff scores in additional samples.

SUMMARY AND CONCLUSIONS The appeal of multiscale inventories is that they provide information about response styles and at the same time provide information about personality traits, psychopathology, and distress. Evaluator surveys and trends in the research literature both suggest that the MMPI-2 (see Wygant et al., Chapter 14, this volume) and the PAI are the most appropriate multiscale measures for assessing response style. The PAI research literature continues to evolve, examining response distortion in specific evaluation contexts and for specific disorders. At this point in time, the PAI validity scales seem to be more useful in screening out honest responders than screening in dishonest responders. Scores of MAL ≥ 4, NIM ≥ 110, and PIM ≥ 68 are very rare among genuine responders (median specificity > .95), and MAL ≥ 3, NIM ≥ 81, and PIM ≥ 68 are uncommon (median specificity > .80). Recently, the most promising PAI findings have been for the newly developed NDS scale. Although there are only a handful of NDS studies, sensitivity and specificity values have been relatively consistent across studies, irrespective of the disorder being feigned or the evaluation context. We have provided an equation for transforming NDS raw scores to T scores, which should help with NDS interpretation. For the MCMI, the small number of validity scale studies simply makes it difficult, if not impossible, to argue for its use as a measure of response style in real-world forensic evaluations. Response bias researchers seem to focus their efforts on measures other than the MCMI. Research findings on the PID-5 and PPQ are promising but more research clearly is needed.

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CH A P TE R 16

Dissimulation on Projective Measures An Updated Appraisal of a Very Old Question Kenneth W. Sewell, PhD Ashley C. Helle, MS

Little in the field of psychological assessment has generated as much controversy, professional debate, and impassioned stances as the use of projective measures. Although some of these controversies are mentioned by way of context, we focus in this chapter specifically on dissimulation in projective measures—both the potential for its occurrence and the likelihood that it can be detected. Many contemporary users of projective measures eschew the psychoanalytic framework that led to their development, and even to the “projective” moniker. Nonetheless, a brief description of that origin is useful in understanding the structure and typical use of this category of assessment tools. Freud and his followers viewed projection as an ego defense mechanism. From this vantage point, unacceptable impulses, thoughts, and wishes experienced by an individual at an unconscious level are projected onto someone else. Thus, projection allows the individual to process those unacceptable impulses consciously (albeit incompletely). Given this theoretical proposition, it seems logical that the psychoanalyst faced a difficult challenge in sorting out, through a patient’s free association, whether and when projection was occurring, versus a more or less faithful account of the patient’s view of some other person. So, rather than relying solely on the patient’s description of other persons as the source of potentially projected ma-

terial, psychoanalytic clinicians began devising tasks that were thought to leave the patient little choice but to project. The basic idea was to present the patient with a stimulus that is inherently incompletely structured (e.g., an inkblot, a decontextualized drawing, or an incomplete sentence), then demand a structured response: “What might that be?”; “What is happening in the drawing?”; or “How does the sentence end?” It was theorized that the construction of the response would necessarily invoke projection. If there is no right or true response, then whatever response is generated must be a projection of the patient’s inner world. A few of these early projective tasks gained wide enough acceptance to remain in use today. Hermann Rorschach (1921) published Psychodiagnostik, which detailed the Rorschach Inkblot Test (or more simply, the Rorschach). Morgan and Murray (1935) introduced the Thematic Apperception Test (TAT). A number of incomplete sentence tests (e.g., Rotter Incomplete Sentence Blank; Rotter & Rafferty, 1950), projective drawing tasks (e.g., Kinetic Family Drawing: Burns & Kaufman, 1970; Draw-A-Person: Goodenough, 1926; Harris, 1963; House-Tree-Person: Buck, 1948, 1969), and even alternative inkblot tests (most notably, the Holtzman Inkblot Technique; Holtzman, Thorpe, Swartz, & Herron, 1961) have been developed and widely used over the years.

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In part owing to the nature of projective techniques and their original underlying theoretical bases, interpreting their results is thought to require substantial expertise and training. Langdon and Zillmer (2009), who wrote Open to Interpretation . . . the Art and Science of Inkblots, state in the book’s title directly how psychologists (including the first author of this chapter, K. W. S.) have frequently been trained to view and utilize projectives. Often, in a given clinical setting, an “expert” on projective measures (or, more commonly, solely on the Rorschach) would emerge, then function as the local guru for the other psychologists and trainees, holding regular workshops and case conferences to interpret projective test protocols. The possibilities seemed endless, and the guru often seemed able to “see” things in the protocols that the other practitioners were unable to perceive on their own. This same complexity seems related to one of the biggest controversies associated with projective methods; specifically, they lack the precision necessary to function as psychological “tests” in legal contexts. It leads to an alternative view that projective measures function best as material for clinical judgment, and to the often-repeated belief that projective tests—because of their complexity—are difficult (if not impossible) to fake. We address the former controversy in a later section. We turn now to a general discussion of dissimulation (faking good or faking bad) on projective measures. First, we begin with the question of whether projective measures can be faked. Is dissimulation possible, even for a person who is highly motivated to fake? In responding to a projective measure, patients encounter a substantial degree of response ambiguity. In other words, without substantial training with the particular measures, it can be difficult for an examinee to anticipate the specific meaning that the examiner is likely to attach to a particular response. Thus, projective measures lack obvious face validity, an attribute often viewed as a protection against dissimulation. It is certainly the case that the obverse may be true: Pathology-related measures with high face validity have been frequently criticized for how easy they are to fake (see Rogers & Kelly, 1997). But beyond the rather obvious problem with high face validity, the relation between the level of ambiguity and modifiability of responses remains largely unexplored. For example, persons motivated to “fake bad” on the Rorschach might not be able to know how color responses impact the overall interpretation, but they might well self-edit their initial perceptions and offer responses that they themselves

perceive as “crazy” or disconnected from reality. Such a response style could then impact interpretation. We later review the existing literature on dissimulating on projective measures, and attempt to establish a scientifically-informed answer to the question of whether or not projective measures can be faked. Next, we turn to the detection of dissimulation. When persons attempt to fake on projective measures, can their pattern of responding give them away? The open-ended nature of responding to a projective measure may complicate the assessment of response styles. With highly structured response formats (e.g., the true–false responses to the Minnesota Multiphasic Personality Inventory–2 [MMPI-2]), specific items and scales can be devised (e.g., the MMPI-2 validity scales) for the detection of dissimulation. For projective measures, it is possible that specific patterns of responses might be discovered that indicate dissimulation, but the sheer number of response possibilities limits the likely precision of such an effort unless an alternative response format (e.g., forced choice; see Pettigrew, Tuma, Pickering, & Whelton, 1983) is adopted to reduce response possibilities. Below, we review the available research on detecting dissimulation using projective measures, and attempt to characterize the level of precision that can be attained. This chapter’s broad focus includes dissimulation (both malingering and defensiveness) on projective measures in general. However, the vast majority of empirical studies relevant to this topic utilize only the Rorschach. Research utilizing other projective measures is included, but the sheer scarcity of relevant studies does impede offering broad generalizations.

MALINGERING Can Projectives Be Feigned to Indicate Psychopathology? Can feigners alter their projective test results in a manner that reflects pathology? This question must precede the question of whether we can detect feigning psychopathology, and if so, then how? Whereas it may be a popular belief that the Rorschach, or other projective measures, cannot be faked due to their ambiguous nature, the literature suggests that projectives may well be vulnerable to dissimulation. Studies of feigning psychopathology on projective tests exhibit diverse methodology and mixed



16.  Dissimulation on Projective Measures 303

findings on how to detect feigning. Several early simulation studies (Fosberg, 1938, 1941, 1943) examined the alterability of Rorschach scoring using the Klopfer system on mixed samples of patients and nonpatients. Fosberg concluded that there were no differences between protocols generated under standard and feigning instructions; thus, he claimed that the Rorschach was not susceptible to feigning. Fosberg utilized test–retest correlations as his analytic approach, which has been criticized (see Cronbach, 1949) as being unable to detect important mean differences, as long as the rankorders remain comparable. Fosberg also utilized chi-squared analyses to compare multiple protocols generated by individual participants. This approach was similarly criticized by Cronbach as being potentially confounded by extreme frequency differences; various Rorschach scores exhibit just such extreme frequency differences when the values counts or ratios are analyzed directly. Specifically, chi-square tests examine frequencies and essentially ignore scale units, disregarding the weight or contribution of each score when determining significance (Cronbach, 1949). Carp and Shavzin (1950) conducted a study with similar methodology using the Beck scoring system. They detected differences between protocols based on feigning status. Specifically, they found that F+ was lower in those feigning; nonetheless, they concluded that large individual differences in both directions “balanced out in the analyses” (p. 232). In other words, the group means did not differ greatly even though many individual simulators altered their responses in the expected directions. Additional Rorschach studies detected major differences between genuine and faked profiles. Feldman and Graley (1954) conducted a simulation study in which feigning produced significantly higher scores on several determinants (m, CF+C, FC) and content scores (Sexual Anatomy and Dramatic). In contrast to profiles obtained under standard instruction, feigned protocols were lower on R and P (Feldman & Graley, 1954). Fewer popular responses (P) are likely attributable to malingerers’ efforts to appear abnormal via the denial of obvious responses. On this point, Easton and Feigenbaum (1967) replicated the finding of lower R and P scores when attempting to feign psychopathology; however, no results were reported for form quality or dramatic content. Table 16.1 summarizes results for indicators that were reported in at least four group comparisons (e.g., simulators vs. controls) of malingering on the

Rorschach, organized by study and methodology/ samples types. Most early research was conducted with general rather than clinical samples. However, an early examination of simulated feigning with forensic inpatients yielded some similar results. Using a within-subjects design, inpatients were instructed to respond to the test under two conditions: malingering and defensive (Seamons, Howell, Carlisle, & Roe, 1981). The malingering condition resulted in fewer popular responses (Cohen’s d = 0.53) and the dramatic content was considerably higher (Cohen’s d = 1.11) in the feigning group as compared to the defensive group. Across these early studies, methodological concerns became evident that limit the generalizability of their results. Most researchers used nonclinical samples (often college students with presumably no significant psychopathology) and straightforward within-subjects designs. Participants are administered the Rorschach twice, once under standard instructions and again under some type of simulation instructions. The absence of a group tested twice under standard (honest) instructions is problematic; some of the observed findings (or lack thereof) may be artifacts of Rorschach readministration at such short intervals. Also, without clinical comparison samples, we cannot ascertain whether faked Rorschach protocols are similar to or distinct from those produced by genuine patients. Similar methodology concerns are also present within non-Rorschach malingering studies (e.g., Timmons, Lanyon, Almer, & Curran, 1993). Among these non-Rorschach studies, none were found utilizing a known-groups design, which, when properly applied, is generally considered the strongest design on which to draw conclusions about a measure’s sensitivity to dissimulation (Rogers, 2008). Finally, the use of certain criterion measures may impact the conclusions of the study. Specifically, the use of another measure of malingering as a criterion (what Rogers & Bender, 2013, term “partial criterion design”), such as the MMPI/MMPI-2 (e.g., Ganellen, Wasyliw, Haywood, & Grossman, 1996; Wasyliw, Benn, Grossman, & Haywood, 1998) can be problematic given that the MMPI itself has limited accuracy at individual classification of malingering cases. The association between the number of responses provided for the Rorschach and the Rorschach scoring is another primary concern with Rorschach-based malingering studies, because a low number of responses (malingered or not) can substantially influence scoring. Malingered Rorschach profiles tend to have fewer responses over-

304

II I .  Ps y c h o m e t r ic M e t h o ds

 3

A

N

A

L

 5a

A

L

 6

A

L

 6

A

H

 7

A

H

 2

B B

 1

B

 8

C

DEPI

m

CF+ C

N

H

N

N

N

N

H

N

 4a

 3

N

Lambda

N

WSum6

N

X–%

H

SCZI

MOR

N

S-Con

F+%

L

Bl

Dramatic

A

R

A

 2

X+%

 1

P

Article

Method

TABLE 16.1.  Patterns of Findings among Studies of Malingering and Faking Bad on the Rorschach

L

L

H

H

C

N Lb

H

H

N

N

N H

N

N

H

H N

L

C

N N

H

H

N

N

 9

N

H

L

L

10

N

N N

H N

N N

N N

N

H

N

L

N

N N

N

N

N

N

N

H

H Na

N

N Na

Na

Note. H, higher score in simulators; L, lower; N, no difference. Article: (1) Netter and Vigilone (1994); (2) Frueh and Kinder (1994); (3) Caine et al. (1995); (4) Meisner (1988); (5) Perry and Kinder (1992); (6) Feldman and Graley (1954); (7) Easton and Feigenbaum (1967); (8) Seamons et al. (1981); (9) Ganellen et al. (1996); (10) Batchelder (1995). Method: A, nonclinical simulators versus nonclinical controls; B, nonclinical simulators versus clinical controls; C, clinical simulators versus clinical controls. a Analyses controlled for R. bNo longer significant when controlling for R.

all; therefore, this artifact of the Rorschach may significantly impact the conclusions of malingering research. Perry and Kinder (1992) attempted to equalize the number of responses by using only the first and second response to each card. They compared undergraduates under standard conditions and undergraduates instructed to feign schizophrenia. Simulators evidenced substantially higher scores on Wsum6, SCZI, X–%, and M–%, and lower P and X+%. This pattern suggests that prior findings were not fully accounted for by a constricted number of responses. The Rorschach Performance Assessment System (R-PAS; Meyer, Vigilone, Mihura, Erard, & Erdberg, 2011), described more fully in the next section, is an alternative Rorschach administration and scoring system that also potentially addresses problems associated with the number of responses. However, we found no dissimulation studies using the R-PAS, so any amelioration of this concern cannot be evaluated. Research regarding the ability to feign projective measures other than the Rorschach has been limited, but what is available demonstrates mixed

findings. Attempts to feign sentence completion tests designed for use in disability evaluations have shown that students instructed to malinger were able to simulate elevated scores (Timmons et al., 1993). The TAT (Morgan & Murray, 1935) has also shown susceptibility to feigning, yielding higher levels of hostile and aggressive impulses (Kaplan & Eron, 1965). More recently, when asked to “fake bad” on the TAT, graduate students were able to produce significantly lower scores on the cards assessing the expression of achievement, affiliation, aggression, autonomy, and abasement needs (Annalakshmi, 2006). In addition, the Draw-APerson test (DAP) has demonstrated susceptibility to malingering (Carmody & Crossman, 2011) in a sample of young adults and adolescents instructed to feign distress after a motor vehicle accident. Overall, the combined results of the literature indicate that the Rorschach and other projectives are susceptible to malingering/feigning. However, considering diverse methodology and mixed findings, a clear set of guidelines for which scores can identify feigning cannot be established. This



16.  Dissimulation on Projective Measures 305

conclusion is consistent with previous reviews of the literature (e.g., Elhai, Kinder, & Frueh, 2004; Ganellen, 2008; Perry & Kinder, 1990; Schretlen, 1997; Stermac, 1988). Also consistent with prior reviews, we note widely mixed results and lack of replicability of the findings. However, two themes emerged on the Rorschach. First, persons attempting to feign pathology tend to produce responses that do not conform to those seen as “typical” of unimpaired persons. On the Rorschach this is manifested by fewer popular responses (lower P). Second, more frequent responses that do not conform well to the shapes represented on the inkblots (as reflected in form quality indices, such as X+%, X–%, M–%).

Does Feigning Psychopathology Impact Clinical Interpretation? As demonstrated earlier, people can and often do alter their projective test results when attempting to feign psychopathology. However, it is certainly conceivable that these alterations have little or no impact on the resulting clinical interpretation. Do these alterations by feigners to represent a psychological disorder have the intended impact on clinical interpretation? The answer to this question is not straightforward. It assumes that projective measures are used for the purpose of determining the presence or absence of diagnosable pathology. Although this is often the case, projective measures are also commonly used to generate personality descriptions, traits, and tendencies rather than diagnoses per se. The feigning of psychopathology (and the attempt to detect such feigning) in psychological assessments assumes that the examiner will ultimately utilize the test to render conclusions about pathology, which may or may not be the case. But if used primarily for diagnosis, projective techniques often show marginal diagnostic efficiency (e.g., Hunsley & Bailey, 1999, 2001), even when the concept of diagnosis is broadened to include the identification of problem behaviors. The Rorschach has been criticized for producing a high number of false positives in the realm of clinical diagnoses (e.g., Wood, Nezworki, Garb, & Lilienfeld, 2001), which can be highly problematic when considering malingering. Feigning, at its core, is an attempt to engineer a false positive. Thus, caution is warranted in the interpretation of high Rorschach scores as feigned, because they may represent false positives in genuine responders.

The detection of feigning is further complicated by diverse outcomes when attempting to feign a specific disorder, such as depression. Meisner (1988) found simulators of depression showed lower R (consistent with other studies; e.g., Easton & Feigenbaum, 1967) and more content containing Blood (Bl) or other Morbid (MOR) elements. However, Caine, Kinder, and Frueh (1995) found no significant differences for feigned depression on Rorschach protocols for a number of variables, suggesting that the feigned and genuine depression protocols looked similar. Both depression groups scored higher than the control group on the MOR scale, but were not significantly different from one another. Overall, Caine et al. demonstrated that the Rorschach is susceptible to feigned depression. Similar patterns have been examined for the simulation of PTSD. Simulators had higher X–%, higher SumC, more Dramatic content, and lower X+% than a group of actual patients with posttraumatic stress disorder (PTSD; despite finding no differences on 17 other indices; Frueh & Kinder, 1994). Divergent from these studies, Labott and Wallach (2002) found that women simulating dissociative identity disorder did not differ significantly from honest controls on Rorschach indices. Additional studies have demonstrated that feigned psychopathology on Rorschach profiles does not look identical to profiles of persons with a true mental illness diagnosis (Bash & Alpert, 1980). Specifically, feigners had more rejected cards, lower P (a somewhat common finding; see Table 16.1), and lower F%. When attempting to provide diagnostic conclusions for psychotic disorders, experts in Rorschach interpretation misidentified feigned schizophrenia as genuine at the same or higher rates as they identified genuine schizophrenia (Albert, Fox, & Kahn, 1980). On this same issue, Netter and Viglione (1994) also found a false-positive rate of 45% for feigned schizophrenia being misclassified as genuine, using the SCZI indicator. However, the false-negative rate for genuine patients was also high (30%), suggesting this indicator of schizophrenia is not accurate. Despite scoring improvements over the years, Exner (1991) cautioned more generally that “nonpatient subjects may be able to approximate serious disturbance if set to do so” (p. 495). Comparison across studies of attempts to feign psychopathology is limited given that many focus exclusively on indices their authors consider relevant to the particular disorder (e.g., Meisner, 1988). Other large differences in methodology

306

I II .  Ps y c h o m e t r ic M e t h o ds

complicate these comparisons even more, such as the presence or absence of a control/comparison group, or a group with the actual diagnosis (e.g., Frueh & Kinder, 1994). Overall, some studies have demonstrated differences between feigning profiles and true profiles. For example, dramatic content has consistently been shown to be higher in those simulating pathology, and P has been lower among simulators in most (but not all) of the studies that reported it. However, as a general conclusion, simulators can feign pathology on a projective measure.

Can Malingering on Projectives Be Detected? Several studies have directly examined the ability of the Rorschach or other projective tests to detect malingering. However, their research methods and analyses that do not adequately answer the question. For example, Rorschach simulation research rarely utilized cutoff scores to classify groups and quantify error rates. Likewise, formal efforts to develop detection strategies (as per Rogers, 1984; Rogers & Bender, 2013) are largely missing. Still, the research that has attempted to answer this question is reviewed. Very low popular scores (P) on the Rorschach can be characterized as a detection strategy referred to as erroneous stereotypes (see Rogers, Chapter 2, this volume), when used to detect feigned psychosis. Some feigners wrongly assume that genuine patients with psychotic disorders use virtually no typical (i.e., popular) responses. Batchelder (1995) found that 74% of feigners have very low P responses. However, a large number of psychotic patients (37%) and nonpsychotic patients (50%) were misidentified as malingerers using low P responses. These findings illustrate a critically important point. Feigning scales must be developed and validated based on a proven detection strategy. It is quite possible that a subset of P items is erroneously mistaken as being “too normal” by most feigners. Simply using all P items without investigating each item’s effectiveness should and did result in an ineffective feigning indicator. In a more comprehensive approach, McDougall (1996) identified indicators of malingering (11 Rorschach variables and other extratest behaviors) and indicators of nonmalingering (three Rorschach variables) that were able to correctly classify a sample of patients with schizophrenia and simulators. These indicators correctly classified 78.3% as malingerers or nonmalingerers. However, the false-positive rate was high at 42.5%. McDou-

gall’s approach suffered from its overcomplexity and lack of defined detection strategies. Thus, taken together with Batchelder (1995), these studies suggest these indicators may be useful as an initial screening for possible malingering; however, they should not be relied on for final determination of malingering, because the false-positive rate is simply too high. In summary, projective measures often incorrectly indicate pathology in feigners, with no reliable methods for its detection. The conscientious clinician, who desires to employ the Rorschach in contexts vulnerable to malingered presentations, must ensure that other tests or procedures are administered to evaluate for malingering. Then, if malingering is suspected on the basis of these other assessment tools, the interpretability of the Rorschach findings should be treated with extreme caution. Studies of malingering on projectives have several limitations; some are due to the nature of projectives, and others are due to common methodological problems. As mentioned earlier, the diagnostic inefficiency of projectives intrinsically limits their expected accuracy in detecting feigning. Furthermore, the multitude of indices that are derivable from projectives (particularly from the Rorschach) has resulted in the various studies using different (and usually multiple) indices. This variability in indicators makes review and comparison across studies difficult and introduces familywise error rate, which is often ignored. Therefore, despite single studies with promising results, the methodological issues raised here preclude the current development of reliable detection rules. Thus, the current state of the literature offers little basis for employing projective measures as an effective means to identify feigned psychopathology.

DEFENSIVENESS Can Projectives Be Manipulated to Conceal Psychopathology? Defensiveness (see Rogers, Chapter 2, this volume), refers to an individual’s efforts to keep pathology from being revealed on tests, such as projective measures. Although defensiveness is not as prominent as malingering in the empirical literature, defensiveness has important implications (e.g., detection of pathology in child custody cases) for projectives. As with malingering, we address the following questions: Can people alter their responses in a defensive manner on projective measures? If so, can this be detected?



16.  Dissimulation on Projective Measures 307

Table 16.2 summarizes results for indicators that were reported in at least four group comparisons (e.g., simulators vs. controls) of defensiveness on the Rorschach, organized by study and methodology/samples types. Although there is a general tendency for defensive simulators to exhibit higher F+% values, most indicators show very varied results across studies. Although it makes generalizations difficult, this pattern indicates that attempts to conceal pathology on the Rorschach often lead to results that differ from a straightforward response set. Early research on the TAT concluded that the TAT is susceptible to “guarded” responses. However, the methodology was weak given that members of a normal sample were instructed to appear “guarded,” rather than asking persons with pathology to conceal it (Hamsher & Farina, 1967). More recently, Annalakshmi (2006) instructed participants to “fake bad” (previously described in the section on malingering) and “fake good” on the TAT. When instructed to “fake good” or respond in a defensive manner, the scores for several positive indicators (expression of achievement, affilia-

tion, aggression, autonomy, and abasement needs) were significantly higher, suggesting the participants were able to successfully produce defensive profiles. Beyond the TAT, participants can produce more “adjusted” profiles on other projectives. For instance, the Holtzman Inkblot Technique (Holtzman et al., 1961) has also been shown to be susceptible to more guarded responses, as identified with significantly fewer pathology indicators, fewer Pathognomic Verbalizations, better Form Appropriateness, better Integration, more Rejections, and more Movement (Krieger & Levin, 1976). Similar results were found on the sentence completion test study discussed earlier, specifically that students asked to simulate full recovery from an accident produced their lowest levels of angry negativity and disability exaggeration (Timmons et al., 1993). Overall, the research indicates that a defensive approach on a projective measure can alter the scores in the direction of concealing pathology. However, whether the alterations are sufficient to impact the clinical interpretation remains an entirely separate question.

3

B

N

N

4

B

N

5

B

2b

C

2b

N

SumShd

N

SumC’

N

N

SumC

N

B

m

B

3

TCI%

2b

Cop

N

Movement

N

F%

N

AgPast

B

AgPot

L

2b

AgC

Ha

AG

H

WSum6

X+%

A

X–%

P

1

R

Article

Method

TABLE 16.2.  Patterns of Findings among Studies of Minimizing and Faking Good on the Rorschach

N

Nc

N

N

N

N

N

N

N

N

N

N

N

N

H

Nc

N

L

L

L

H

L

N

L

L

L

L

L

N

d

L

L

L

L

N

L

N

L

L

L

d

d

N

d

L

L

L

L

N

L

N

L

L

L

d

d

N N

N

N

N

N

L

N

H

Hc

N

H

H

H

N

N

N

H

H

N

H

H

C

N

L

N

H

Hc

N

N

N

N

H

N

N

N

N

L

N

L

3

C

L

L

H

d

L

N

N

N

H

L

N

N

L

L

d

d

3

C

L

L

H

d

L

N

N

N

H

L

L

N

L

L

d

d

Note. H, higher score in simulators; L, lower; N, no difference. Article: (1) Schultz and Brabender (2013); (2) Hartmann and Hartmann (2014); (3) Norbech et al. (2016); (4) Wasyliw et al. (1998); (5) Grossman et al. (2002). Method: A, nonclinical simulators versus nonclinical controls; B, clinical simulators versus clinical controls; C, clinical simulators versus nonclinical controls. a Analyses controlled for P. bAnalyses controlled for R. cWSum6-Lv2. dNo post hoc comparisons were available due to lack of significant main effects.

308

II I .  Ps y c h o m e t r ic M e t h o ds

Does Defensiveness Impact Clinical Interpretation?

CURRENT CONTROVERSIES AND NEW DEVELOPMENTS

Grossman, Wasyliw, Benn, and Gyoerkoe (2002) examined the attempt to minimize psychopathology on the Rorschach, using MMPI indicators of minimizing as a group identification variable. Based on this partial-criterion design, they concluded that minimizers of pathology on the MMPI did not show increased minimization/defensiveness on the Rorschach when compared to nonminimizers. Specifically, the Rorschach pathology indicators (D, Adjusted D [AdjD], Wsum6, X+%, X–%, Intellectualization, Schizophrenic Index [SCZI], Suicide Constellation [Scon], Coping Deficit Index [CDI], and Depression Index [DEPI]) appeared approximately as frequently for MMPI minimizers as nonminimizers. This study suggests that defensiveness on the Rorschach might not effectively mask pathology. Overall, a critical concern and major limitation of the research in this area involves the lack of differentiation between (1) simulators attempting to mask actual psychopathology and (2) genuine responders without psychopathology. It severely limits the interpretability of the findings, and the clinical implications that may be derived from this research. To underscore this concern, Grossman et al. (2002) showed that very few (0–12%) patients attempting to minimize pathology on the Rorschach showed elevations on MMPI clinical scales, compared to 19–45% of nonminimizing patient controls.

The Rorschach as a Test in Forensic Contexts

Can Defensiveness on Projectives Be Detected? Given the research reviewed earlier, much of which is summarized in Table 16.2, conclusions regarding the ability of projectives to detect defensiveness cannot be offered without considerably more empirical research. Even though defensiveness often needs to be considered, the presence or absence of pathology indicators on projectives should not be used to rule in or to rule out a defensive response style; instead, other assessment tools should be utilized. Regrettably, the literature on feigning and defensiveness on projectives can be described as in a nonevolving state, such that little, if any, new literature is being published. Without continuing to advance the empirical examination and publication of these questions regarding detection, only a few conclusions and recommendations for practice can be made.

As discussed, a traditional argument suggests that projective tests such as the Rorschach are inherently suited for use in contexts subject to attempted dissimulation (such as forensic settings) because they are invulnerable to conscious manipulation. Despite the empirical studies reviewed earlier that refute this concept, as well as other authors (e.g., Wood et al., 2001) warning that such invulnerability is a myth, it persists in contemporary literature. For instance, Gray and Acklin (2008) stated, “There are a number of situations in which the Rorschach test is potentially valuable and may even be the only instrument that can adequately elucidate requisite information, particularly information concerning thought organization and pathology malingering” (p. 149). Likewise, Goldstein (2016) asserts that “the Rorschach is more difficult to fake, because litigants typically do not know how to respond” (p. 88). In recent years, considerable debate has ensued about whether projective measures, and especially the Rorschach, have any place in forensic assessment at all, let alone in specifically assessing for dissimulation in such settings. Much of this controversy stems from arguments about whether the Rorschach is a “test” or not; and if it is a test, just what kind of test it is. The usual dichotomy places projective measures in contrast to objective tests. However, most meanings of the term objective do not constitute a grammatical opposite to the concept of projective, as described earlier. Nonetheless, in assessment parlance, objective does not mean true or accurate; instead, it merely indicates a high degree of response clarity. In other words, on a rating scale, true– false, or multiple-choice test, the meaning of particular response (how it will be scored, how it will feed into interpretive schemes, etc.) is predefined. Although objective tests offer some conveniences for researchers interested in finding ways to detect dissimulation, utilizing an objective measure is by no means a safeguard against undetected dissimulation. Perhaps to avoid psychoanalytic frameworks, some scholars prefer to characterize the Rorschach as a performance-based test rather than a projective measure (McGrath, 2008). This designation allows measures previously considered to be projective tests to be aligned with other personality and even neuropsychology assessment tools that



16.  Dissimulation on Projective Measures 309

have high response ambiguity. In these areas, the manifest response is given meaning not by its alignment with theoretical conceptions but rather by its demonstrated correlation with clinical variables and outcomes. Even neuropsychological tools with high response ambiguity (e.g., some are used to assess mild traumatic brain injury; see Bender, Chapter 7, this volume) are potentially susceptible to malingered pathology. Forensic assessment experts (e.g., Mossman et al., 2007) specifically argue that all test results in forensic settings must never be interpreted in a vacuum, but must always consider other sources of information. In this way of thinking, perhaps Rorschach results could constitute a part of such other information. However, there are important differences between the findings from an assessment tool such as the Rorschach and behavioral observation or historical/biographical data. The former require extensive training to score and interpret, whereas the latter are more open to scrutiny by judges, jurors, and even opposing counsel. Thus, results of projective measures, almost by definition, rely on trusting the expertise of the expert witness in ways that other sources of nontest information do not. Thus, it is our opinion that the Daubert standard should be met for the Rorschach and other projective measure results to be utilized in forensic testimony. The most critical component of Daubert on which the Rorschach and other projective measures fall short is that of a known and acceptable error rate. Also, as discussed below, Internet proliferation of Rorschach images and interpretation information may indicate that the Rorschach lacks sufficient maintenance of standards and controls to ensure the integrity of the procedure.

Impact of the Internet on Coaching and Self‑Preparation Some examinees who intend to dissimulate might do so “off the cuff”‘ with little or no preparation. Alternatively, as is likely the case in many highstakes forensic contexts, other examinees might engage in some form of preparation and/or obtain advice (or “coaching”) from someone with more expertise and knowledge (e.g., an attorney). For that reason, many simulation studies attempt to mimic this preparation potential by coaching the assigned simulators either on the phenomena they are to simulate, or even on the strategies to avoid detection. The Internet has substantially impacted the nature and quantity of materials that would-be

dissimulators can access and use to prepare themselves or to coach others. A few published studies examine the outcome of Internet coaching and self-preparation on the Rorschach specifically. Schultz and Loving (2012) examined the presence of available Internet-based Rorschach information and concluded that 59% of the sites with Rorschach information were classified as no threat or minimal threat to test security. However, that still leaves a large proportion of sites with information that could be a threat to test security if individuals were to seek out the information. It should also be noted that some of the sites provide inaccurate information. Correct or incorrect, access to this information may render some test results unusable (Schultz & Loving, 2012). Schultz and Brabender (2013) tested defensiveness on the Rorschach in two groups: One had access to Wikipedia printouts of Rorschach cards and common responses, whereas the other read an irrelevant Wikipedia article. Participants with Wikipedia Rorschach information had lower R and higher P, X+%, XA%, and WDA%; however, when controlling for higher P, the other differences between groups were no longer statistically significant (X+%, XA%, WDA%). It suggests that having knowledge about the Rorschach beforehand (e.g., Internet information) does influence the resulting interpretations and conclusions, primarily through high P. But including a higher number of P responses tends to influence other indicators that have additional implications, making the impact of prior knowledge even greater. A follow-up study (Hartmann & Hartmann, 2014) examined the Rorschach and MMPI-2 results in psychiatric outpatients and nonpatients asked to simulate defensiveness. Using a clinical comparison sample, they contrasted two simulation groups instructed to “fake healthy,” uncoached outpatients, and Internet-coached outpatients. In contrast to Schultz and Brabender (2013), these findings suggested that both simulation groups were unable to produce healthy profiles; instead, their profiles were similar to the patient group under standardized instructions. The Internetcoached outpatients had higher F% scores; however, they did not have other scores that would typically suggest defensiveness when paired with F% (e.g., high P). Finally, Norbech, Fodstad, Kuisma, Lunde, and Hartmann (2016) sought to answer a similar question; however, they expanded their sample to incarcerated offenders. The results suggested

310

I II .  Ps y c h o m e t r ic M e t h o ds

that when compared to standard-instruction nonoffender controls, defensive offenders (Internetcoached and uncoached), produced higher F%, higher X-%, and lower M, m, SumC, X+%, P, AG, and COP (Norbech et al., 2016). Consistent with Hartmann and Hartmann (2014), Internetprepared examinees were not successful in producing “healthy” profiles. Additionally, in both studies, the authors stated that for individuals without cognitive or perceptual difficulties, Internet information may be useful for self-preparation intentions. However, for individuals with perceptual difficulties (Hartmann and Hartmann had a psychiatric sample), utilizing information provided about the Rorschach on defensiveness may be more challenging. Interestingly, both studies also noted that although instructed to use movement and color (from Internet sources), their simulators did not appear to utilize this advice. In conclusion, the new literature in the area of coaching and self-preparation to feign on the Rorschach suggests that this type of self-preparation may lead to different profiles (e.g., higher F%); however, individuals who prepare in this manner are often unable to produce results comparable to “healthy” profiles. The existing literature thus far demonstrates that the while Internet information may not necessarily assist individuals in successful impression management, exposure to it may still substantially alter their responses. Such changes may compromise the interpretability of a minimizer’s profile, despite being unable to produce a defensive profile as intended. This area of inquiry is new, and additional research is necessary.

The R‑PAS The R-PAS (Meyer et al., 2011) has recently been introduced as the putative successor to the Exner Comprehensive System (CS). The R-PAS was developed with several purposes in mind, but with the overarching goal of minimizing the variability in the number of responses per protocol (Erard, 2012; Meyer et al., 2011). The potential for the number of responses (R) to vary dramatically from protocol to protocol is a feature of the CS that has often been criticized as limiting its utility in deriving valid clinical and forensic decision rules (see Meyer, 1992). In addition to altering the administration rules to constrain R variability, the R-PAS developers wanted to maximize the new system’s reliance on Rorschach variables with empirical support (Meyer et al., 2011; Mihura, Meyer, Dumitrascu, & Bombel, 2013).

Concerns have already arisen regarding the use of the R-PAS in forensic settings (Gurley, Sheehan, Piechowski, & Gray, 2014; Kivisto, Gacono, & Medoff, 2013). These concerns focus on the normative data, and questionable reliability and validity. One of the main limitations is the bootstrapping of the R-PAS’s psychometric characteristics via reanalyses of data that were collected from CS administrations. Some R-PAS promoters, comfortable that such analyses demonstrate acceptable reliability and validity, claim that admissibility standards for using the R-PAS in court are largely met (Erard, 2012; Erard & Viglione, 2014). Focusing on this chapter, no new empirical studies directly evaluate Rorschach dissimulation using the R-PAS. Mihura (2012) argues that feigned and genuine psychosis should be distinguishable on the Dramatic Contents scale; however, she does so simply on the basis of its differential correlations with validity indicators within the normative data rather than via a separate empirical investigation. So whether forensic clinicians utilize the R-PAS in expert testimony or not, our current review cannot specifically evaluate whether its dissimulation vulnerability or detection ability differs from the (mostly CS-based) studies reviewed earlier.

A DEARTH OF NEW RESEARCH In updating this chapter beyond the version in the previous edition (Sewell, 2008), we noticed (1) few new peer-reviewed studies on dissimulation with projectives, but (2) an increasing proportion of unpublished dissertations. We decided (as in 2008) to exclude most dissertation research from the chapter. Many of the dissertations we identified were methodologically questionable and/or were patently unclear. To present such work without substantial commentary would risk equating these works with those in the published literature. Thus, dissertations were included (i.e., Batchelder, 1995; McDougall, 1996) only when the approach or the results were dramatically divergent from the published literature. The lack of sound empirical research on this topic—especially peer-reviewed articles—represents a serious concern. In the absence of new and evolving literature, very few additional conclusions can be rendered regarding the ability of projective tests to detect feigning and defensiveness. Additionally, and particularly relevant for this chapter, is the influence of the dearth of research on specific clinical recommendations that can be made.



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For example, we cannot provide specific utility estimates for particular cutoff scores, or characterize certain detection strategies most useful with projectives. Clinical guidelines for the detection of dissimulation on projective tests cannot be put forth without consistency of findings across strong empirical studies and cross-validation.

clearly applicable and do not believe that the existing research literature allows that standard to be met with any projective measure currently in use. Primarily, based on the peer-reviewed literature, we conclude that the Rorschach and other projective measures fall short on the scientific need to have known and acceptable error rates.

CONCLUSIONS

REFERENCES

Projective measures continue to be utilized in a variety of clinical settings, including forensic contexts. The Rorschach, as the most commonly studied of all projective measures, also draws the most strident proponents and critics. Based on the studies reviewed and the literature more broadly, we offer the following fours conclusions. First, all tested projective measures are susceptible to dissimulation. Although response ambiguity may make the task of dissimulation more difficult than for highly face valid tests, determined dissimulators can and do alter their responding in accordance with their motivations. Second, dissimulation appears capable of leading to inaccurate diagnostic conclusions in many, although certainly not all, cases. This conclusion is more cautiously proffered for defensiveness than for malingering. For malingering, the evidence is strong that simulated pathological protocols are often indistinguishable from those produced by patients with genuine psychopathology. For the concealment of pathology, the results are more mixed but still indicate that defensive persons do often alter their presentation. Thus, test interpretations are confounded even when simulators do not achieve their goal of appearing well-adjusted, but produce a misleading clinical picture. Third, no indicators or strategies for detecting dissimulation on projective measures have emerged that have sufficient positive and negative predictive power for use in clinical or forensic decision making. Researchers appear to remain content to use existing clinical indicators rather than to develop scales designed specifically for particular response styles. As a result, projective measures should not be relied on to detect and identify malingering or defensiveness in individual cases, regardless of professional context. Fourth, and finally, we cannot recommend using projectives in forensic contexts or in other clinical contexts that have a high likelihood of involvement in the legal system. Having reviewed the literature, we see the Daubert standard as

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C H A P T E R 17

Feigned Amnesia and Memory Problems Richard Frederick, PhD

Complaints of memory problems are common in forensic assessment. Studies concerning how to identify memory complaints as feigned saturate the forensic psychological and neuropsychological literature. This chapter concerns the validity and usefulness of specific detection strategies for different clinical presentations of amnesia and memory loss. Training in neuropsychology leads to advanced knowledge of how memory works, how memory problems develop, and how memory problems present clinically. For use by a broader audience of mental health professionals, this chapter instead concerns test-based evidence of unreasonable and suspicious complaints of memory problems.

DIRECT EVIDENCE OF FEIGNED AMNESIA In criminal forensic settings, it is not unusual to encounter defendants who claim to have profound amnesia—who say they are unable to remember anything at all about their behavior at a certain time or for an extended period (Cochrane, Grisso, & Frederick, 2001; Frederick, Carter, & Powel, 1995). Neurologists and neuropsychologists are skilled and able to comment on the believability of the explanation for the amnesia—for example, head trauma or intoxication. Psychiatrists and psychologists recognize that certain psychotic or manic episodes might produce bona fide amnesia.

In competency-to-proceed evaluations, defendants occasionally claim that they cannot recall anything at all about the crimes with which they are charged. Defendants who actually cannot recall the events of the alleged crimes might not be able to provide effective assistance to their counsel. Laws vary among jurisdictions, but the general rule is that amnesia, despite the apparent serious challenge to assist counsel, does not automatically result in a conclusion by the court that the defendant is incompetent (Frederick, DeMier, Smith, & Towers, 2014). Some clinicians decide to believe defendants who tell them that they cannot recall the circumstances of an alleged crime. If a defendant says, “I can’t remember that,” some clinicians might write, “Defendant cannot recall that.” However, the goal of good forensic assessment is to determine what is actually true about memory ability and capacity, and not merely to provide a conduit for false claims. A strategy to investigate claims of amnesia is to assess directly whether memories exist. This procedure is often referred to as forced-choice testing (FCT) of amnesia (Denney, 1996; Frederick et al., 1995; Frederick & Denney, 1998).

Preparing Questions for FCT The first goal of FCT is to identify the domain of information the defendant claims to have forgotten. A good way to do this is to review the evidence

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file prepared by the prosecutor or defense attorney carefully. This file is often referred to as the facts of the case. It is important for forensic evaluators to keep in mind that the facts of the case are usually considered facts only by the prosecution—they are unproven allegations that must be submitted as evidence at trial. It is not the goal of forensic assessment to attempt to determine whether the facts of the case are accurate or whether the defendant is guilty of the crimes he or she claims not to remember. The legitimate goal of the forensic evaluator is to assess whether the defendant can factually and rationally appreciate the facts of the case. FCT of amnesia in a criminal case requires that the evaluator overtly state an assumption that the defendant has been made aware of the facts of the case in discussions with his or her attorney, then to proceed with interviews; that is, the evaluator should never create a situation in which he or she provides evidence of “guilty knowledge”—that the defendant has memory of events because he or she participated in a crime. Evaluators should overtly state in their forensic reports that they have evaluated only the defendant’s knowledge of the facts of the case as claimed by the prosecution. In interviews preceding FCT, the evaluator asks the defendant what he or she knows about the case but does not provide answers—for example, “What are you charged with? What do the police say you did? What is the evidence against you?” Through these interviews, the evaluator identifies the domain of information the defendant claims not to recall. Once that domain is established, the evaluator prepares two alternative forced-choice questions. For example, suppose that in the interview, the defendant claims not to know what the charge is. A legitimate question for FCT is, for example:

“What are you charged with?” a. “You are charged with bank robbery.” b. “You are charged with drug distribution.”

Once the question is asked and responded to in FCT, the examiner identifies the correct answers. Obviously, the questions should be arranged so that learning the correct response to a previous question does not predispose one to get future questions correct. For example, a possible direction of questioning proceeds like this: 1. “What are you charged with?” a. “You are charged with bank robbery.” b. “You are charged with drug distribution.”

In this example, the examinee incorrectly chooses (b). The examiner responds, “The correct answer was (a). You are charged with bank robbery.” 2. How many banks have you been charged with robbing? a. Two b. Four Let’s say the examinee incorrectly chooses (a). The examiner responds, “The correct answer was (b). You have been charged with robbing four banks.” Questions that follow might be ordered like this: 3. “What is the name of the first bank you are charged with robbing?” 4. “In what month do the police say you robbed the First National Bank?” 5. “How much money do the police say you obtained from the First National Bank in March?” Test construction should follow several sequential steps. When making questions, identify the correct answer (a), then generate a fully plausible alternative (b). Prepare all questions and alternatives in advance. Once the questions are prepared, use a random number generator to assign the 1’s and 2’s to the (a) and (b) positions. Many websites are available for this task. To compare the number of correct responses to the normal distribution of random outcomes, it is necessary to generate at least 25 questions (Frederick et al., 1995). Once the set of questions has been generated, it is useful to ask a colleague without knowledge of the case to take the constructed test to see how the questions might be improved, so that they are as fair as possible and do not result in a person with bona fide amnesia selecting one answer over another. The following is an example of an unfair question: 6. “What do the police say you [a male defendant] wore when you robbed the bank?” a. “A dress.” b. “Pants and a sweatshirt” The question is unfair, because a male defendant with true amnesia would be expected to give the answer (b). Questions that are biased in favor of correct responses diminish the sensitivity of the task. Questions that are biased in favor of incorrect responses unfairly place the defendant as risk for failure of FCT. The questions should be unbi-

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ased for individuals with amnesia. Here is a much better question: 7. “The police say you were dressed as a woman when you robbed the bank. What do they say you wore?” a. “A dress” b. “A skirt and a blouse”

Administering and Interpreting FCT When evaluating the defendant, ask the first question and wait for a response before presenting the alternatives. If the defendant responds with the correct answer, discard the question and proceed to the next question. If the defendant answers incorrectly or says, “I don’t know,” present the two preselected alternatives. Resistance to the task is often diagnostic, but it is better to work through the resistance and generate data. If the defendant chooses the right answer, say, “That’s right. You are charged with bank robbery. Let’s proceed.” If the defendant chooses incorrectly, say, “That’s wrong. The correct answer was (a). You are charged with bank robbery.” Present questions in this way until list of questions is completed. For interpreting FCT, the score to be evaluated is the number of correct responses. This score is then evaluated in light of the null hypothesis that existed prior to assessment (see Frederick & Speed, 2007): H0 : p ≥ .5 HA: p < .5 Mental health professionals are only interested in deviations below the range expected for random responding—an interval symmetrically distributed about 50% response accuracy. In other words, guessing will result in a certain range of scores around 50%, and a score within that range is ambiguous and unhelpful. When test scores reject the null hypothesis and support the alternative hypothesis, the appropriate conclusion is that memories exist and that the claim of amnesia is false. Determining the decision point is a simple matter of statistical hypothesis testing, and the intricacies of such statistics for this method are thoroughly reviewed in Frederick and Speed (2007). Denney (1996) and Frederick and Denney (1998) reviewed potential problems and solutions for this method. Denney (1996) found normal distributions of score (symmetrical about 50% accuracy) for normal adults when given question-

naires that had been used to evaluate criminal defendants. Using a replication of Denney (1996), Frederick and Denney (1998) showed that great variability can exist among individual item accuracy for naive test-takers without violating the underlying expectation of overall scores. In fact, when substantial variability occurs in individual item accuracy, the test of the overall score is actually made significantly more conservative, as long as the mean accuracy for naive guessing remains at .50. As Frederick and Denny demonstrated, the only significant threat to test interpretation is when the mean accuracy for guessing substantially deviates from 50%—when items are systematically biased toward certain responses.

INDIRECT EVIDENCE THAT MEMORY COMPLAINTS ARE FEIGNED OR EXAGGERATED Victoria Symptom Validity Test Forced-choice methodologies were originally developed for direct assessment of suspicious memory complaints (Binder, 1990; Hiscock & Hiscock, 1989) and based on many studies of noncredible perceptual complaints, including the early work of Pankratz, Fausti, and Peed (1975). With these techniques, an examinee is given information to remember and then tested for recognition of that information. The psychologist is no longer directly evaluating whether memories exist but is considering whether performance on a structured, standardized test is believably consistent with the clinical presentation of memory complaints. One of the first commercially available standardized forced-choice tests was the Portland Digit Recognition Test (PDRT; Binder, 1993). The essential strategy of the PDRT is that examinees are presented orally with five-digit numbers to remember. After variable delays across 72 trials of 5 seconds, 15 seconds, or 30 seconds, examinees are presented with and choose between printed numbers. The PRDT is a long, exhausting procedure; it was altered to an abbreviated form (Binder, 1993), which was essentially the PRDT with options for early discontinuation. More recently, the Victoria Symptom Validity Test (VSVT; Slick, Hopp, Strauss, & Thompson, 1997) was constructed in a computer-administered form. The VSVT has only 48 trials, the longest of which is 15 seconds. Both tests promote the idea of “easy” and “hard” stimulus items. The VSVT promotes this notion by having some “easy” stimulus items, with no digits in common with the foil, and “hard” items,



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with some digital transpositions of the foil. In the VSVT, easy and hard items are distributed equally among all three time intervals (5 seconds, 10 seconds, and 15 seconds). From Binomial Theory to Empirical Cutoff Scores

What is often unrecognized about the PDRT and the VSVT is that both tests eventually abandoned comparisons to chance as a primary basis for making decisions about test performance. For example, Thompson (2002) reported, “It should be noted that . . . the VSVT cut-off scores are based on binomial probability theory and are not norm-referenced” (p. 49). However, this is incorrect. Slick et al. (1997) derived interpretive statements for commercial use from their Appendix B, in which they tabulate “probability of feigning for assumed base rate (p’)” based on any score, whether below chance or not. These values are based on this calculation (as shown on p. 69 of the manual): p′ * LR X LR X + 1

LRX is defined in the VSVT manual as “likelihood ratio of score X (ordinate for score X from a sample of persons known to be A divided by the ordinate for score X from a distribution of a sample known not to be A)” (p. 69). A likelihood ratio is derived from test score sensitivity (Se) and specificity (Sp). But the sensitivities or specificities for these potential cutoff scores for the VSVT are not reported in the manual. Furthermore, no overt discussion is presented in the VSVT manual of “persons known to be A” and “known not to be A.” The validation sample contained (1) 95 undergraduate “controls,” (2) 43 undergraduate simulators asked to “feign cognitive impairment in as convincing manner as possible,” (3) 205 individuals with possible head injury who were seeking compensation, and (4) 32 patients (20 had a seizure disorder and 12 had a head injury or “other brain dysfunction”). The authors did little to demonstrate whether participants were “persons known to be A” or “known not to be A.” They openly conceded that the VSVT itself could not even accurately classify 70% of the simulators. The 205 patients were essentially ignored in deriving classification accuracy. So it is not clear at all how the LRs in Appendix B were generated, yet these LRs established the basis for interpretation of countless examinees evaluated with this internationally distributed commercial test.

Elements of Diagnostic Studies

The VSVT manual serves as a useful instructive example of the serious problems with most tests or procedures designed to assess whether presented memory complaints are valid. According to Guyatt, Sackett, and Haynes (2006, p. 275), a valid diagnostic study consists of four steps: 1. assembles an appropriate spectrum of patients, 2. applies both the diagnostic test and the reference standard to all of them, 3. interprets each masked to the other, and 4. replicates its findings in a second, independent, “test” set of patients.

The use of simulators to evaluate malingering has long been recognized as a necessary element to evaluate the promise of techniques to identify feigned memory impairment, but the use of simulators is no substitute for validating the test on an appropriate spectrum of patients that includes individuals who intend to feign impairment or exaggerate memory problems (Rogers & Cruise, 1998; Rogers, Sewell, & Salekin, 1994). The inclusion of 205 compensation-seeking claimants would seem to provide an appropriate spectrum of patients for VSVT validation. However, Slick et al. (1997) did nothing to evaluate individual characteristics of these claimants prior to administration of the VSVT (no reference standard), so these claimants provided no useful information about the behavior of the VSVT with respect to the effect of feigning. Without a reference standard, there is no basis on which to determine whether patients were “persons were known to be A” (known to be feigning) or “known not to be A” (not feigning) reported in the VSVT manual. After application of the diagnostic test, the authors attempted to evaluate whether 23 of the 205 might be properly construed as faking (pp. 56–57). Unfortunately, no cross-validation was reported in the VSVT manual. Meyer (2003) strongly encouraged psychologists to adhere to the Standards for Reporting of Diagnostic Accuracy (STARD), which identifies 25 essential elements of valid diagnostic studies, including (1) descriptions of inclusion and exclusion criteria for the study and descriptions of how participants were sampled from populations (if not consecutive patients, what criteria were applied to select), (2) clarification of whether individuals who made judgments about final classification were masked to the assigned groups (eliminate bias in classification), (3) the use of confidence intervals for classification accuracy, and (4) discussions

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and explorations of reproducibility. The VSVT manual does not describe any of these elements in its reporting diagnostic testing and cannot be considered a report on a valid diagnostic study. The Necessity of Cross‑Validation

Other problems in VSVT test validation include failure to attempt to reproduce test results with a different sample of patients. The lack of cross-validation studies is endemic in research on tests and techniques devised to evaluate whether memory complaints are real, feigned, or exaggerated. The use of single samples to generate values of test sensitivity and specificity does not provide the degree of heterogeneity that will be encountered in clinical use of tests and techniques (Moons et al., 2012). Straus, Glasziou, Richardson, and Haynes (2011) noted that initial validations cannot distinguish between real diagnostic accuracies for the target disorder and chance associations due to idiosyncrasies in the initial validation sample. A cross-validation must occur in a second, independent group of patients. Cross-validation is essential to develop faith in a diagnostic test. A test is considered useful only if the classifications are accurate for patients from the same populations as the development sample—and this must be accomplished by subsequent samples of patients (Justice, Covinsky, & Berlin, 1999). The VSVT manual did not provide any cross-validation studies to evaluate the accuracy of the “probabilities of feigning” reported in Appendix B. Evaluating Effectiveness of Indicator Cutoff Scores

The Youden index (Straus et al., 2011; Youden, 1950) is the maximum value of (Se + Sp – 1) in a range of cutoff scores. The Youden index identifies the optimum cutoff score with respect to best overall classification accuracy—equal weight is given to sensitivity and specificity. The Youden index does not identify the cutoff score with greatest utility (Smits, Smit, Cuijpers, & de Graaf, 2007)—utility is determined by context, and the most useful cutoff score might be the one with the lowest false-positive rate (FPR), despite having a high false-negative rate. The Youden index must be greater than 0 for a test to have any meaning, but a Youden index must be greater than .50 for a test to be useful. For example, a test with Se = .90 and Sp = .90 has a Youden index of .80 (.90 + .90 – 1.00). A test with Se = .50 and Sp = .50

has no diagnostic value, and the Youden index is 0.00 (.50 + .50 – 1.00). Alternatively, Grimes and Peipert (2010) noted that simply adding Se and Sp (referred to hereafter as “Se + Sp”) renders a benchmark equivalent to the Youden index, and they characterized the usefulness of cutoff scores in this way (“mediocre” for Se + Sp less than 1.50, “good” for Se + Sp that ranges from 1.50 to 1.80, “very good” for Se + Sp > 1.80, and “perfect” for Se + Sp = 2.00). Characteristics of the VSVT

The authors of the VSVT manual described the test as having “poor” sensitivity (Se = .30) and excellent specificity (Sp = 1.00, p. 55) when restricted to significantly below-chance responding, and the authors validated no other detection strategy. These values (.30 + 1.00 = 1.30) indicate that the VSVT is a mediocre classification instrument. Nevertheless, Jones (2013b) reported quite promising results for the VSVT when evaluated in an active duty military sample primarily of patients with traumatic brain injury (TBI), who were consecutive referrals (70% of these individuals were eligible for disability compensation or potentially medically excludable from deployment). Jones conducted a valid diagnostic study in which he divided participants into three groups (probable malingering [failure on two validity tests], n = 66; probable to definite malingering [failure on three or more validity tests], n = 46; and not malingering [no validity tests failed], n = 122). Consistent with Guyatt et al. (2006), Jones (2013b) assembled an appropriate spectrum of patients, applied a reference standard to all of them, administered the diagnostic test, and explored potential cutoff scores’ accuracy in the same way for all participants. Table 17.1 reports characteristics of various VSVT measures, including number of correct responses for easy, hard, and total items; mean response times for easy and hard items; and mean difference between easy and hard items. Three good indicators for the VSVT are suggested by the results of Jones (2013b): scoring less than 19 on the hard items, scoring less than 44 on all items, and having a difference between easy and hard totals of more than 4. Jones (2013b) did not cross-validate these data, but Silk-Elgit, Lynch, and McCaffrey (2016) reported an effective cross-validation. Like Jones (2013b), Silk-Elgit et al. (2016) assembled an appropriate spectrum of patients, applied a reference standard to all of them, administered the diagnos-



17.  Feigned Amnesia and Memory Problems 319 TABLE 17.1.  VSVT Indicator Performance

Study

Indicator at optimum cutoff

Se + Sp at optimum cutoff

FPR at optimum cutoff

Jones (2013b)

Easy < 23

1.47—mediocre

.05

Hard < 19

1.85—very good

.02

Silk-Elgit et al. (2016)

Total score < 44

1.75—good

.10

Easy versus hard score > 4

1.76—good

.04

Mean easy time > 3 sec

1.26—mediocre

.04

Mean hard time > 4 sec

1.50—good

.07 .15

Hard < 20

1.59—good

Total score < 41

1.58—good

.10

Easy versus hard score > 4

1.57—good

.15

Note. FPR, false-positive rate.

tic test, and explored potential cutoff scores’ accuracy in the same way for all participants. Jones found “good” to “very good” VSVT indicators according to Se + Sp (see Table 17.1) at the optimum values for their own data. The Jones (2013b) and Silk-Elgit et al. (2016) samples are similar: (1) clinical samples with mild TBI and incentives to malinger, (2) classified in criterion groups by standard feigning indicators. Therefore, it is useful to combine their data to examine cutoff scores that minimize FPRs (see Table 17.2). The combined data (Jones and Silk-Elgit et al.) provide stable estimates of effective indicators. Despite the manual’s limitations, the VSVT nevertheless appears to have several effective indicators, which, when used as described in Table 17.2, have quite good sensitivity while retaining exceptional specificity. Practitioners would do well to use the VSVT manual as a source of information about how the VSVT should be administered. For useful interpretation of VSVT data, practitioners should look to postpublication analysis of VSVT indicators in well-validated studies such as Jones (2013b) and Silk-Elgit et al. (2016). The combined data in these

studies (see Table 17.2) serve as an excellent basis for decision making with the VSVT.

Test of Memory Malingering The Test of Memory Malingering (TOMM; Tombaugh, 1996) presents 50 simple line drawings for memorization for 3 seconds, with a 1-second interval between exposures. Following presentation of the stimulus items, examinees are presented with 50 pairs of simple drawings and asked to choose which they saw before. Immediately following this Trial 1, the 50 original drawings (in a different order) are presented again, and again the examinee is presented with 50 pairs of drawings for the second test (Trial 2). A third test of memory is optional after 15 minutes (Retention Trial with no further exposure of stimulus items). The development sample included 158 individuals (13 with no cognitive impairment, 42 with cognitive impairment, 21 with aphasia, 45 with TBI, and 37 with dementia) who were primarily referred for neuropsychological assessment and not in litigation or seeking compensation. A simulation

TABLE 17.2.  VSVT Indicator Performance from Jones (2013b) Combined with Silk-Elgit et al. (2016)

Indicator

Se

n

Se 95% CI

Sp

n

Sp 95% CI

Se + Sp

Hard < 18

.72

165

.65 to .79

.98

161

.95 to 1.00

1.70, good

Total score < 41

.73

165

.67 to .80

.98

161

.95 to 1.00

1.71, good

Easy versus hard score > 7

.62

165

.54 to .69

.97

161

.94 to 1.00

1.59, good

Note. CI, Confidence Interval.

320

I II .  Ps y c h o m e t r ic M e t h o ds TABLE 17.3.  Greve et al.’s (2006) Cross-Validation of Tombaugh’s (1996) TOMM Indicators

Indicator

Group

Se

Se 95% CI

Sp

Sp 95% CI

Se + Sp

Trial 2 < 45

All patients

.56

.41 to .71

 .93

.88 to .98

1.47, mediocre

Trial 2 < 45

Mild TBI only

.41

.26 to .56

 .98

.94 to 1.00

1.39, mediocre

Retention < 45

All patients

.46

.31 to .62

 .93

.88 to .98

1.36, mediocre

Retention < 45

Mild TBI only

.46

.31 to .62

1.00

—

1.46, mediocre

group of 27 undergraduate students was recruited. Tombaugh (1996) referenced a group of individuals “at risk for malingering” who were evaluated in Gansler, Tombaugh, Moczynski, and Rees (1995). However, neither the TOMM manual nor Gansler et al. referenced the standard by which these individuals were classified. The reported decision rules were “less than 90% correct” on Trial 2 or Retention. Based on the manual data, this results in Trial 2 < 45 meeting the standards for a good test (Se = .82, Sp = .93; Se + Sp = 1.75) and Retention Trial < 45 being classified as a very good test (Se = .89, Sp = .97; Se + Sp = 1.86). Tombaugh (1996) did not report a cross-validation when publishing this test. The first effective independent cross-validation study of the TOMM was reported by Greve, Bianchini, and Doane (2006). Their sample was taken from 161 individuals with reported TBI and 22 individuals with memory disorders. Final groups were (1) 41 individuals classified as probable or definite malingered neurocognitive dysfunction, (2) 46 individuals with mild TBI, (3) 30 with moderate to severe TBI, and (4) 22 patients with memory disorders. As is true in most of their research reports, Greve and colleagues effectively described test behavior by reporting frequency distribution tables of cutoff score classifications across the entire range of potential cutoff scores. Table 17.3 reports the TOMM manual’s recommended cutoff scores for the Greve et al. (2006)

data. As seen, all TOMM indicators are rated as just “mediocre,” primarily because sensitivity is so low for the Tombaugh cutoff scores. Table 17.4 reports the optimum cutoff scores for three TOMM indicators based on Trial 1, Trial 2, and Retention totals for Greve et al.’s data. Schroeder et al. (2013) evaluated 62 individuals in litigation for complaints of mild or uncomplicated TBI. Their data (Table 17.5) can serve to cross-validate the Greve et al. (2006) values reported in the same table. At these cutoff scores, the TOMM is not an impressive test; the primary TOMM strategy of performance accuracy on Trial 2 and the Retention trial are at best “mediocre” by the Se + Sp metric. The TOMM cutoff scores, which provide the greatest utility, are currently under active investigation in forensic neuropsychological research (e.g., Denning, 2012; Jones, 2013a; Fazio, Denning, & Denney, 2017; Mossman, Wygant, & Gervais, 2012; O’Bryant, Engel, Kleiner, Vasterling, & Black, 2007; O’Bryant et al., 2008; Stenclik, Miele, Silk-Eglit, Lynch, & McCaffrey, 2013; Wisdom, Brown, Chen, & Collins, 2012). It seems likely that the cutoff scores with the greatest utility and fairness (i.e., highest sensitivity while controlling for FPR) can be generated by combining published data generated from a broad spectrum of patients and applying comparable reference standards. Certainly, enough data are published on the TOMM not only to identify effective cutoff scores

TABLE 17.4.  Greve et al.’s (2006) Optimum Cutoff Scores for TOMM Indicators

Optimum cutoff score

FPR at optimum cutoff score

Se + Sp at optimum cutoff score

Trial 1

< 43

.16

1.43, mediocre

Trial 2

< 50

.30

1.66, good

Retention

< 50

.32

1.43, mediocre

Indicator



17.  Feigned Amnesia and Memory Problems 321 TABLE 17.5.  TOMM Sensitivity and Specificity for Greve et al. (2006) with Cross-Validation by Schroeder et al. (2013)

Study

Indicator

Greve et al. (2006) a

Schroeder et al. (2013)

aCutoff

Cutoff score

Se

Se 95% CI

Sp

Sp 95% CI

Se + Sp

Trial 1

< 37

.44

.29 to .59

 .95

.88 to 1.00

1.39, mediocre

Trial 2

< 44

.56

.41 to .71

 .95

.88 to 1.00

1.51, good

Retention

< 40

.34

.20 to .49

 .95

.88 to 1.00

1.29, mediocre

Trial 1

< 37

.54

.35 to .73

 .97

.83 to 1.00

1.51, good

Trial 2

< 44

.46

.27 to .65

1.00

—

1.46, mediocre

Retention

< 40

.39

.20 to .58

1.00

—

1.39, mediocre

scores were set with maximum 5% false positive.

for Trials 1 and 2 and Retention but also to adapt those cutoff scores for certain subpopulations (e.g., Ashendorf, Constantinou, & McCaffrey, 2004; Duncan, 2005; Gierok, Dickson, & Cole, 2005; Weinborn, Orr, Woods, Conover, & Feix, 2003). Clinicians who use the TOMM should review studies on the potential cutoff scores for Trials 1 and 2, and Retention. Blind adherence to the manual recommendations no longer seems justified. After surveying the research on the TOMM, clinicians should choose cutoff scores that are supported by the literature (as in Schroeder et al., 2013), and make them clear in their reports (or data summary tables). They should strenuously reject any temptation to choose a test cutoff score after they have administered the test, fitting their observed data to a study that supports a finding that is consistent with their a priori hypotheses.

The Green Tests The Word Memory Test (WMT; Green, 2003), the Medical Symptom Validity Test (MSVT; Green, 2004a), the Nonverbal Medical Symptom Validity Test (NVMSVT; Green, 2008), and the Memory Complaints Inventory (MCI; Green, 2004b) are procedures self-published by Paul Green. The first three tests have manuals that accompany their purchase; the MCI has no test manual. The essential element of the WMT, MSVT, and NVMSVT is the memorization of word pairs or other pairs of stimuli, followed by recognition and recall testing. The three test manuals have published reviews by the Buros Center for Testing (WMT: Haynes, 2014; Hubley, 2014; MSVT: Albanese, 2014; NVMSVT: Lambert, 2014; Reed, 2014). Each Buros review noted that the test manuals are poorly organized, difficult to read, and lacking in

conceptual information. Psychometrically, these reviewers also concurred that the Green tests lack information about development, reliability, and validity. Regarding the latter, no validation samples are reported. Therefore, the Green tests cannot be considered to meet any of the elements required to develop a diagnostic test (Guyatt et al., 2006) and do not meet any of the elements of STARD. The lack of development and validation information did not deter Green (2005, p. 16) from describing the WMT as having “100% sensitivity and 100% specificity . . . to poor effort.” To the contrary, Greve, Ord, Curtis, Bianchini, and Brennan (2008) demonstrated a tremendous FPR (30%) for the WMT among individuals with mild TBI and moderate to severe TBI (Table 17.6). Greve et al. also reported what the WMT cutoff scores should be set to in order to limit each indicator to a 2% FPR (see Table 17.6). Green, Flaro, and Courtney (2009) sharply criticized Greve et al.’s (2006) findings, claiming that the “WMT was designed to measure effort and not to predict malingering, and so a false positive for malingering does not equate to a false positive for poor effort” (pp. 741–742). However, Green has never reported on their construct validation to demonstrate what the Green tests actually capture, and so his distinction between “poor effort” and “malingering” is interesting but has yet to be demonstrated. Despite their criticism of Greve et al. (2008), Green et al. (2009) acknowledged that FPRs did occur with the WMT and MSVT and could be reduced through the use of post hoc analysis of WMT and MSVT scores with a “genuine memory impairment profile” (GMIP; Green, 2005, p. 26; for the MSVT, also referred to as the “dementia profile”; see Howe & Loring, 2009).

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TABLE 17.6.  WMT Accuracy for Individuals with TBI (Greve et al., 2008) Using (1) Green’s (2005) Recommended Cutoff Scores and (2) Cutoff Scores with a Maximum FPR of 2%

Standard

WMT indicator

Cutoff score

Se

Se 95% CI

Sp

Sp 95% CI

Se + Sp

Green Green Green

IR DR CI

< 82.50 < 82.50 < 82.50

.67 .67 .78

.49 to .85 .49 to .85 .62 to .94

.81 .81 .70

.69 to .93 .69 to .93 .66 to .90

1.48, mediocre 1.48, mediocre 1.48, mediocre

2% FPR 2% FPR 2% FPR

IR DR CI

< 60.00 < 62.50 < 57.50

.48 .52 .26

.29 to .67 .33 to .71 .37 to .75

.98 .98 .98

.94 to 1.00 .94 to 1.00 .94 to 1.00

1.46, mediocre 1.50, good 1.24, poor

Note. IR, immediate recognition; DR, delayed recognition.

Fazio, Sanders, and Denney (2015) reported the characteristics of WMT on 109 criminal defendants and hospitalized inmates undergoing neuropsychological evaluation. Of these individuals, 41 were classified as malingering neurocognitive dysfunction (MND) and 38 were classified as not malingering, independent of the WMT. Fazio et al. reported rates of WMT classification, with and without GMIP (Table 17.7). The FPR of the WMT without GMIP (31.6%) was essentially the same as reported by Greve et al. (2008), who noted that the Green tests are sorely in need of “detailed calibration study” (p. 914) because they showed that “while the WMT is very sensitive to malingering at published cutoffs, that superior sensitivity comes with the cost of unacceptably high [false-positive] error rates” (p. 908). Despite their apparent wholehearted acceptance among the neuropsychology community (Martin, Schroeder, & Odland, 2015), the Green tests, strategies, and manuals simply do not meet professional standards for use of the tests in forensic forums. Practitioners who use these tests should strive to identify a reliable and valid basis for sensitivity and specificity of decision rules in these tests. However, those rules will not be found in the Green test manuals. Greve et al. (2008) are correct to admonish practitioners to find and identify detailed calibration studies before using the

tests to classify. Studies such as that by Fazio et al. (2015) serve as a good basis for use of the WMT in a responsible manner.

Reliable Digit Span Reliable Digit Span (RDS) was conceived by Greiffenstein, Baker, and Gola (1994) as an embedded effort indicator (floor-effect detection strategy) in a regularly used psychological test—Digit Span of the Wechsler intelligence scales, obtained by “summing the longest string of digits repeated without error over two trials under both forward and backward conditions” (pp. 219–220). A comprehensive review of more than 20 studies and cross-validation of RDS was reported by Schroeder, Twumasi-Ankrah, Baade, and Marshall (2012). They reported cutoff scores for the RDS of individuals with mild TBI and postconcussive syndrome (Table 17.8). Schroeder et al. (2012) effectively demonstrated that a cutoff score of RDS < 8, although rated as “good” by Se + Sp, should be rejected for a less effective cutoff score, RDS < 7, rated as “mediocre” by Se + Sp. The Se + Sp metric characterizes only how effectively a cutoff score classifies the two groups under evaluation without regard to whether, for example, high FPRs are unfair to examinees. A cutoff score of RDS < 8 results in many more

TABLE 17.7.  WMT Accuracy for 109 Defendants and Inmates (Fazio et al., 2015)

WMT

Se

Se 95% CI

Sp

Sp 95% CI

Se + Sp

Without GMIP

.95

.88 to 1.00

.68

.54 to .83

1.64, good

With GMIP

.56

.41 to .71

.95

.88 to 1.00

1.51, good



17.  Feigned Amnesia and Memory Problems 323 TABLE 17.8.  RDS Accuracy for TBI and Postconcussive Patients and Litigants

Cutoff score

Se

Se 95% CI

Sp

Sp 95% CI

Se + Sp

RDS < 8

.72 (n = 134)

.18 to 1.00

.81 (n = 194)

.47 to 1.00

1.53, good

RDS < 7

.38 (n = 81)

0.00 to 1.00

.95 (n = 126)

.64 to 1.00

1.33, mediocre

correct classifications by an unacceptably high FPR of .19 (Sp = .81). Sensitivity (.72) is excellent for RDS < 8, but it is marred with problematic inflation of the FPR. Clinicians are encouraged to use the cutoff score of RDS < 7. The sensitivity is fairly low (.38), but the FPR is quite reasonable (.05) at this cutoff score.

Advanced Clinical Systems Advanced Clinical Systems (ACS; Holdnack & Drozdick, 2009) offers a review of all potential embedded validity indicators in the Wechsler Adult Intelligence Scale–IV (WAIS-IV) and Wechsler Memory Scale–IV (WMS-IV), including RDS, Logical Memory Recognition, Verbal Paired Associates II Recognition, Visual Reproduction II Recognition, and the Word Choice test of the ACS (5 indicators; Holdnack, Millis, Larrabee, & Iverson, 2013). In order to obtain the “ACS multivariate indicator of performance validity,” it is necessary to administer all these tests, then enter the results in the ACS scoring program. It is no simple matter to determine cutoff scores or sensitivity and specificity values for any ACS cutoff scores from the reported data. Instead, consistent with the multivariate analysis style of the ACS, a matrix is generated, showing how each score obtained relates to a large clinical sample (n = 371) of persons with learning disability, TBI, and many other conditions or statuses (e.g., low education, low IQ) who completed the WAIS-IV, WMS-IV, and Word Choice test as part of validation. A sample of 50 simulators (normal adults) and 50 “no stimulus” normal adults were also included to serve as criterion groups for feigning. The “no stimulus” group completed the five indicators without being exposed to the stimulus items; this allows for an analysis of scores expected for pure guessing. ACS classifies each score according to a “base rate,” depending on where the score fell in the overall clinical sample (e.g., at or below the 2, 5, 10, 15, and 25% levels, or above any of those levels. This comprises the “Effort Score Summary.” The evaluator then (according to an a priori de-

cision about which values to use—apparently on one’s honor) generates an “Effort Score Analysis.” For example, if a clinician determines a priori to look at scores at the 10% base rate level, the Effort Score Analysis will show the number of scores generated by the examinee at the 10% cutoff score for the No Stimulus group, the Simulators, the Overall Clinical Sample, and other summary groups. Such an analysis (as reported in Holdnak & Drozdick, 2009) provides evidence, for example, that while one score at the 10% base rate level is not unusual for any of the groups, two scores at the base rate level is quite uncommon, and three or more scores at this range is specific only to Simulators and No Stimulus groups. ACS strategy primarily depends on clinicians being honest when they make decisions after seeing the distributions of scores. For example, it would be dishonest to use the 15% base rate distributions to conclude that faking was likely when the a priori decision was actually for scores only at 10% base rate or lower. ACS Effort Score Analysis requires that clinicians routinely administer a large number of tests with no purpose other than to assess effort. First, besides administering particular tests, the tests underpinning them also need to be administered, with many requirements for repeated testing at certain intervals. This procedure will likely prove to be impractical except for psychologists who routinely administer the entire WMS-IV and are willing also to administer Digit Span and Word Choice to complete the tests in the Effort Score Analysis. Second, the Effort Score Analysis methodology is obtuse and nonintuitive. It seems unlikely that most clinicians and evaluators can comprehend ACS methodology, much less explain it accurately or effectively to legal decision makers. Finally, the use of many potential cutoff score values (2–25%) may lead many clinicians to look at the data before having a firm rule about which values to use. ACS offers a powerful strategy for evaluating effort and cooperation for clinicians willing to (1) administer all five tests, (2) learn a complicated strategy of interpretation, and (3) make a priori rules about decision points.

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Other Embedded Memory Validity Indicators Interest in the use of other embedded “effort indicators” (or “cognitive symptom validity measures”) has been prompted by a recent chapter by Schutte and Axelrod (2013), which details over 50 such indicators. Table 17.9 lists those indicators and utility estimates related to memory tests. Given that these indicators fail to satisfy the four elements of valid diagnostic studies as described by Guyatt et al. (2006), none are recommended for forensic evaluations. Babikian, Boone, Lu, and Arnold (2006) reported on elements of characteristics of Digit Span, including Digit Span Scaled Score and times to

repeat three- and four-digit forward Digit Span. None of these indicators is promising. Boone, Lu, and Wen (2005) investigated a wide range of indicators for the Rey Auditory Verbal Learning Test. Using a score of recognition < 11 was an optimum good score, but it had a high FPR (.14). A score of recognition < 9 was also a good score (Se = .59, Sp = .93) and showed a much lower FPR (.07). Additionally “primacy recognition” (ability to recognized the first five words on the stimulus list) was a good score (Se = .64, Sp = .91). Both scores are pending cross-validation. On a similar measure, Root, Robbins, Chang, and van Gorp (2006) did not find that the California Verbal Learning Test– II forced-choice recognition trial was an effective

TABLE 17.9.  Optimum Cutoff Scores for Embedded Memory Validity Indicators

Test

Author

Indicator

Se

Sp

Se + Sp

Crossvalidated?

Status

Digit Span

Babikian

Scaled Score < 7

.46

.77

1.23, poor

No

Unpromising

Babikian

Avg. time for 3 digits > 2.5 sec

.32

1.0

1.32, mediocre

No

Unpromising

Babikian

Avg. time for 4 digits > 6 sec

.16

1

1.16, poor

No

Unpromising

Boone

Trial 5 < 9

.57

.81

1.38, mediocre

No

Unpromising

Total trials 1–5 < 29

.39

.93

1.32, mediocre

No

Unpromising

Recognition < 11

.77

.86

1.63, good

No

Unpromising, high FPR

Primacy recognition < 4

.64

.91

1.55, good

No

Promising

Recency recognition < 4

.77

.76

1.53, good

No

Unpromising, high FPR Unpromising, high FPR

RAVLT

CVLT-II

Root

FCRT < 16

.61

.81

1.44, mediocre

No

RCFT

Lu

Immediate recall < 11

.45

.86

1.31, mediocre

Yes, in Reedy

.45

.88

1.33, mediocre

Reedy RCFT

RCFT RCFT

Unpromising

Lu

True positive recognition 171

.80

.80

1.64, good

No

Unpromising, high FPR

Test time > 206

.66

.91

1.56, good

No

Promising

Nitch

Recognition correct 1.53– >4 SD .96

WAIS DFA

DS Fwd

DS Rev

RDS

3 .21 >.11– >.21 .06

4 5 ≤4–≤5 .50

3 3 ≤2–≤3 .57

18 7 ≤6–≤8 .62

Sensitivity N Median Range SD

6 .41 .18–.72 .24

6 .66 .36–.88 .20

4 .63 .21–.95 .40

3 .53 .16–.53 .21

18 .50 .18–.86 .19

Specificity N Median Range SD

6 .86 .79–.89 .03

6 .84 .82–.94 .05

4 .95 .90–1.00 .06

3 1.00 .93–1.00 .04

18 .93 .57–1.00 .11

Hit rate N Median Range SD

6 .67 .51–.79 .10

6 .78 .64–.90 .10

4 .87 .57–.93 .17

3 .77 .71–.77 .03

14 .73 .54–.88 .09

Effect size (d) N 3 Median .44 Range .44–1.48 SD .60

3 .97 .97–2.10 .65

7 .96 .44–2.94 .92

6 .63 .44–1.92 .60

16 1.20 .53–1.81 .35

Note. Results from patient-controlled studies of the WAIS-R, WAIS-III, and WAISIV are combined. V – DS, Vocabulary minus Digit Span subtests; WAIS DFA, Wechsler Adult Intelligence Scale discriminant function analysis score from Mittenberg et al. (1995); DS Fwd, Digit Span forward raw score; DS Rev, Digit Span reverse raw score; RDS, Reliable Digit Span total score.

338

II I .  Ps y c h o m e t r ic M e t h o ds

ful in ruling out MND, though its NPP value fell just below .80. However, as previously noted, to meet Slick and colleagues (1999; Slick & Sherman, 2012) criteria for probable MND, converging findings must be present. Additional clinical research beyond TBI populations is limited but suggests that the WAIS DFA and V – DS are contraindicated for individuals with intellectual disability. The V – DS also appears to perform poorly among individuals with ADHD, LD, and mixed clinical populations. The lack of current published research on these indices for the WAIS-IV poses additional challenges. Overall, the WAIS DFA and V – DS provide only modest feigning discrimination and should not be used in non-TBI clinical populations until extensive validation is provided.

Attention and Executive Functions WAIS Digit Span Subtest

The Digital Span subtest of the WAIS-R, WAISIII, and WAIS-IV (WAIS DS), thought to tap attention, has been widely studied as a feigning detection tool. As noted earlier, malingerers often tend to overestimate the severity of the deficits on this test after a brain injury, perhaps believing it taps memory rather than attention. Thus, the DS subtest of the WAIS has been proposed as a potential index of cognitive feigning; it represents an application of the floor effect detection strategy. A variant of the DS subtest is the RDS, proposed by Greiffenstein et al. (1994). The RDS is based on the Wechsler DS but sums the longest string of digits correctly completed on both trials for digits forward and digits backward. In this way, information is combined into a single malingering indicator, which is less cumbersome than the original use of DS (cutoff scores from both the forward and reverse subtests). As noted, the WAISIV DS subtest added a new sequencing task. The RDS has been applied to the WAIS-IV using the original digits forward and backward tasks only. Efforts have been made to create a new Reliable Digit Span—Revised (RDS-R) measure to include the new sequencing task (Young, Sawyer, Roper, & Baughman, 2012). However, RDS-R data are limited; therefore, this chapter focuses on the traditional RDS, which incorporates only the digits forward and backward portions of the test. Review of the methodological criteria in published evaluations of the DS and RDS suggests sufficient evaluation using both simulation and

known-groups designs, research independent of the developers of the indices and with fair convergence on utility parameters. Data from DS and RDS appear in the final three columns of Table 18.1. For DS measures, limitations include the study of primarily patients with TBI and the unclear impact of psychiatric disorders and other neurological conditions. Median specificity rates for DS indices are strong, though sensitivity rates are modest (forward median sensitivity = .63, specificity = .95; reverse median sensitivity = .53, specificity = 1.00). Using an estimated MND base rate of .40 and the median sensitivity and specificity values for the DS indices, these translate into moderate to strong predictive powers for ruling in MND and limited predictive powers for ruling out MND (DS forward PPP = .89, NPP = .79; DS reverse PPP = 1.0, NPP = .76). For RDS, the breadth of information available is a strength. Among the EVSs discussed in this chapter, the RDS has the most research regarding its utility in clinical populations beyond TBI. It has tended to generate modest sensitivity (median = .50) and strong specificity (median = .93). At a MND base rate of .40, the RDS has an estimated PPP of .83 and NPP of .74. These results support the use of the DS subtest and RDS for identifying MND, as PPP values are moderate to very strong, particularly DS reverse. However, these procedures have modest NPP, which suggests they are not as useful at ruling out MND. Thus, clinically, if an examinee “fails” indices derived from the DS subtest, suspicion regarding MND should be raised, although converging findings from other MND procedures are needed to meet the Slick and colleagues (1999; Slick & Sherman, 2012) criteria. Turning to the impact of psychiatric disorders and non-TBI populations, unless indicated, studies are not presented in Table 18.1. Contemporary research on the RDS is extensive and has examined intellectual disability, ADHD, LD, chronic pain, schizophrenia spectrum disorders, toxin exposure, and mixed clinical populations. Graue et al. (2007), discussed earlier, examined feigned intellectual disability using the WAIS-III RDS. For RDS, at a cutoff score ≤ 7, sensitivity was .56, specificity was .15, and d = 0.23. Likewise, Smith et al. (2014) also examined potentially feigned intellectual disability using the WAIS-III RDS. This knowngroups study included “credible” examinees with mild intellectual disability, without litigation or financial incentive (i.e., WAIS-III FSIQ M = 69), and “noncredible” examinees identified as poten-



18. Assessment of Feigned Cognitive Impairment 339

tially feigning neurocognitive impairment, with litigation and/or financial incentive (i.e., meeting the Slick et al. [1999] probable and definite criteria; WAIS-III FSIQ M = 63). A wide range of comorbidities was included, from psychiatric and developmental (e.g., depression, psychosis, LD) to neurological (e.g., epilepsy, TBI) conditions. For RDS, at a cutoff score ≤ 6, specificity was .67 and d = 0.83. The cutoff score recommended in this sample was ≤ 4, with a sensitivity of .12 and specificity of 1.00. Dean, Victor, Boone, and Arnold (2008) studied the relationship of FSIQ and feigning classification rates in neuropsychology outpatients neither involved in litigation nor seeking disability. The sample included comorbidities such as depressive disorder, head injury, psychosis, and seizures. FSIQ in the clinical comparison group ranged from 56 to 133. The RDS produced a large correlation with FSIQ (rho = .58; p < .01). Moreover, for the RDS at a traditional cutoff score of ≤ 6, specificities were unacceptable for those with borderline to extremely low FSIQs (FSIQs 50–59 specificity = .33; FSIQs 60–69 specificity = .33; FSIQs 70–79 specificity = .81; IQs 80–89 specificity = 1.00), though sample sizes for these IQ groups were quite small. Adjusted cutoff scores were recommended for adequate specificities (FSIQs 3 0

9 ≤9 ≤6–≤9 1.27

3 56 for males. In summary, it appears the L scale is able to detect underreporting in adolescents, but further research is needed to examine validity scales that can detect overreporting in adolescents (Fabry, Bertinetti, & Guzman-Cavazos, 2011). Because the MMPI-A is used in court settings, it is essential

484

V.   S p e ci a l i z e d A p p l ic at i o n s

TABLE 24.3.  MMPI-A and Adolescent Deception

Author(s)

N

Design

Sex

Cutoff score

Utility estimates Sens

Spec

PPP

NPP

Faking pathology Baer, Kroll, Rinaldo, & Ballenger (1999)

 89

Mixed known-group

46 female 43 male

F > 79T F-K > 13

1.00

1.00 1.00

1.00  .93

.98

Lucio, Duran, Graham, & Ben-Porath (2002)

292

Mixed known-group

174 female

F > 82T F-K > 27

 .76  .69

 .87  .91

 .86  .88

.89 .75

118 male

F > 66T F-K > 14

 .90  .80

 .80  .88

 .82  .87

.89 .81

 .66  .45  .83

.91 .93 .91

 .94

.89

Rogers, Hinds, & Sewell (1996)

 53

Clinical known-group

11 female 42 male

F > 81T F(p) ≥ 9 F-K > 20

Stein, Graham, & Williams (1995)

276

Mixed known-group

160 female 116 male

F ≥ 71T

 .99

Baer, Ballenger, & Kroll (1998)

 77

Mixed known-group

36 female 41 male

L ≥ 64T K ≥ 60T F-K ≤ –10

 .76  .62  .67

 .91  .74  .44

Stein & Graham (1999)

277

Mixed known-group

118 female

L ≥ 64T K > 60T L + K ≥ 19

 .85  .72  .85

 .82  .82  .81

 .83  .80  .69

.84 .74 .91

159 male

L ≥ 56T K > 52T L + K ≥ 20

 .70  .67  .68

 .78  .65  .76

 .76  .60  .83

.73 .67 .58

Defensiveness

Note. Sens, sensitivity; Spec, specificity; PPP, positive predictive power; NPP, negative predictive power; mixed known-group, inclusion of both clinical genuine responders and nonclinical simulators.

that additional studies pertaining to validity be conducted (Hudgins v. Moore, 1999; McGrath et al., 2000; Pennuto & Archer, 2008). The MMPIA-RF was recently developed (Archer et al., 2016) and is similar to the restructured adult form (BenPorath & Tellegen, 2008; Tellegen & Ben-Porath, 2008; see also Wygant et al., Chapter 14, this volume). The adolescent restructured form has only recently been released and therefore requires further validation. Personality Assessment Inventory

The Personality Assessment Inventory (PAI; Morey, 1991, 2007b) is a well-validated multiscale inventory (see Boccaccini & Hart, Chapter

15, this volume). The PAI—Adolescent (PAI-A; Morey, 2007a) is intended for 12- to 18-year-olds and parallels the adult version. Both the PAI and the PAI-A were created with a fourth-grade reading level; however, the PAI-A contains fewer items (i.e., 264), which makes it more appropriate and approachable for younger clientele. The PAI-A contains two validity scales to detect careless (ICN) and idiosyncratic (INF) responses when respondents may not have allotted appropriate attention or efforts to their responses. The manual (Morey, 2007a) suggests that scores of 64T–72T on the ICN indicate probable distortion and should be interpreted within the context of the reason for referral and other scale scores; however, scores of 73T and above suggest marked distortion. Simi-



24.  Deception in Children and Adolescents 485

larly, scores on the INF between 60T and 74T are suggestive of probable distortion, and scores above 75T invalidate the protocol. Only one study has been conducted on the PAI-A validity scales. Specifically, Rios and Morey (2013) empirically evaluated feigning on the PAI-A with a simulation design. They instructed 100 undergraduates ages 17–18 to feign symptoms of ADHD (divided into coached and uncoached) and compared their profiles to 37 adolescents diagnosed with ADHD by their treating clinician. Simulators of ADHD scored higher on the Negative Impression Management (NIM), Malingering Index (MAL), and Rogers Discriminant Function (RDF) and lower on Positive Impression Management (PIM) than those with clinical diagnoses of ADHD. RDF was best at discriminating between ADHD feigners and genuine ADHD patients. However, Rios and Morey determined that adult cutoff scores yielded low sensitivity and therefore provided lower cutoff score recommendations (see Table 24.4). In summary, the PAI-A may have potential for detecting feigning among older adolescents, but needs further cross-validation with different diagnostic groups. Also, a major concern involves the lack of feigning research on younger adolescents. Millon Adolescent Clinical Inventory

The Millon Adolescent Clinical Inventory (MACI; Millon, Millon, & Davis 1993), a 160item, true–false inventory, is designed to assess a broad range of psychological problems in adolescents between ages 13 and 19 (McCann, 1997; Salekin, Leistico, Schrum, & Mullins, 2005). The MACI contains four validity scales: Disclosure (i.e., defensiveness), Desirability (i.e., social desirability), Debasement (i.e., exaggerated responding or feigning), and Reliability (item consistency). The validity scales evidence good internal consistency (i.e., alphas ranging from .73 to .87). One unique aspect of the MACI is that the scores are referred to as base rate (BR) scores. BR scores below 75 suggest that the profile is not very characteristic, whereas BR scores above 85 suggest that the profile is very characteristic of the adolescent. The Disclosure scale was developed to detect defensiveness in responding. However, the Disclosure scale has a high positive correlation with the Debasement scale and a moderate negative correlation with the Desirability scale (McCann, 1997). These findings are unexpected, and no explana-

tion is provided for their association (i.e., sample characteristics or methodology). In addition, the Reliability scale of the MACI includes only two items. However, Pinsoneault (2002) developed a scale that can also be used and reflects variable response inconsistency (M-VRIN), with a reported specificity of .95 and sensitivity of .89. Despite manual recommendations, studies on the MACI’s effectiveness in detecting defensiveness or feigning have yet to be established. As a consequence, it is unclear whether the MACI validity scales would consistently detect defensiveness or feigning in simulation or known-groups research. Much more research is needed on the MACI validity scales to evaluate their effectiveness, utility estimates and the generalizability across clinical groups, age groups, and ethnicity (see Table 24.4). McCann (1997) offers the following conceptual general guidelines for assessing the validity of MACI profiles; however, these guidelines are not based on empirical research. A single score is not sufficient to invalidate a MACI profile. Rather, a valid profile should exhibit Disclosure, Desirability, and Debasement BR scores below 75 and a Disclosure raw score above 200. Social desirability should be a concern when the Desirability BR score is above 75 and the Disclosure and Debasement BR scores are below 75. Exaggeration or overreporting should be a concern when Debasement BR scores are above 75 and Desirability BR scores are below 75. Personality Inventory for Youth

The Personality Inventory for Youth (PIY; Lachar & Gruber, 1995) is a 270-item, true–false, multidimensional instrument intended to assess the emotional and behavioral adjustment of children and adolescents between ages 9 to 18 years with a third-grade reading level or above. The PIY contains four validity scales: (1) Validity (VAL) and Inconsistency (INC) scales assess inconsistent responding; (3) the Dissimulation Scale (FB for “fake bad”) addresses feigning; and (4) the Defensiveness Scale (DEF) identifies responders who deny problems or overly represent their virtues. Two studies have examined the PIY for response styles. Stokes, Pogge, Wecksell, and Zaccario (2011) found that over- and underreporting tend to be similar across the youth and parent reports. Wrobel and colleagues (1999) asked adolescent psychiatric inpatients to “fake good” and also com-

486

V.   S p e ci a l i z e d A p p l ic at i o n s

TABLE 24.4.  Self-Report Measures and Adolescent Deception

Utility estimates Author(s)

N

Design

Sex

Cutoff score

Combined Child

F = 7–15

Combined Adolescent

L > 13 F = 7–15

Sens

Spec

PPP

NPP

 .89  .88

 .95  .93

 .83  .83

 .97  .97

 .97  .64  .38  .82

 .91  .73  .81  .76

BASC Reynolds & Kamphaus (2004)

13,000+

MACI Pinsoneault (2002)

   108

Matchedpairs

78 female 108 male

M-VRIN ≥ 8 M-TRIN ≥ 10

Millon, Millon, & Davis (1993)

   700

Clinical

Not reported

Disclosure ≥ 85BR Desirability ≥ 85BR Debasement ≥ 85BR

   137

Mixed known-group

Not reported

PIM < 25T NIM > 60T MAL > 2 (raw) RDF > –.0439 (raw)

Stokes, Pogge, Wecksell, & Zaccario (2011)

   153

Clinical group

59 female 94 male

DEF > 64T DEF > FB + 14 FB > 64T FB > DEF + 14

Wrobel et al. (1999)

   108

Nonclinical known-group

69 female 39 male

INC ≥ 64T DEF ≥ 59T VAL ≥ 2 (raw) FB = 70T–79T

Clegg, Fremouw, & Mogge (2009)

   116

Mixed known-group

76 female 40 male

>14 >16

 .86  .83

 .65  .74

 .65  .70

 .86  .86

Lewis, Sincox, & Berry (2002)

    55

Forensic evaluations

Male

>16

1.00

 .61

 .54

1.00

Rogers, Hinds, & Sewell (1996)

    53

Clinical known-group

11 female 42 male

Total score ≥ 40

 .49

 .94

PAI-A Rios & Morey (2013)

PIY

SIMS

Note. Sens, sensitivity; Spec, specificity; PPP, positive predictive power; NPP, negative predictive power.



24.  Deception in Children and Adolescents 487

pared honest and random responses by high school psychology students. The student simulators also had two feigning conditions: fake-moderate (i.e., significantly disturbed to win a lawsuit) and fakesevere (i.e., severely disturbed, requiring hospitalization). The fake-moderate, fake-severe, and random response groups attained clinically elevated mean VAL and FB T scores when contrasted with the clinical comparison group. From Wrobel and colleagues’ (1999) study, FB scores that range from 70T to 79T may be used to screen for exaggerated responders, but it is important to note that an isolated elevation of FB alone may reflect accurately severe emotional and social adjustment and not simple exaggeration. Additional research is required to determine which PIY validity scores can effectively detect malingering across clinical populations. Behavior Assessment System for Children— Third Edition

The Behavior Assessment System for Children— Third Edition (BASC-3; Reynolds & Kamphaus, 2015), a multidimensional measure of emotional and behavioral disorders, is available in self-report, parent, and teacher format for several age ranges. The BASC-3 Self-Report of Personality contains five validity indexes: (1) the F index, which detects the endorsement of very negative behaviors; (2) the Consistency index, which detects divergent responses; (3) the Response Pattern index, which detects random responding; (4) the L index, which detects unrealistically positive responding; and (5) the V index, which indicates nonsensical item endorsement. The general norms were gathered from over 4,000 participants ages 2 through 18 years across the United States. The BASC-3 manual (Reynolds & Kamphaus, 2015) offers raw-score ranges for cautionary or extreme cautionary endorsements for all of the BASC-3 validity indexes. No studies are available that evaluate the BASC-3 validity indexes and their ability to accurately detect defensiveness, inconsistency, or feigning with the exception of perhaps one study (Kirk, Hutaff-Lee, Connery, Baker, & Kirkwood, 2014). The absence of simulation or known-group studies is a considerable omission. Because of the lack of empirical studies examining the use of the BASC-3 to detect feigning, defensiveness, and inconsistency in responding, we would advise that the BASC-3 not be used to determine defensiveness or feigning.

Feigning Measures Structured Inventory of Malingered Symptomatology

The Structured Inventory of Malingered Symptomatology (SIMS; Widows & Smith, 2005) is a self-report, true–false questionnaire designed as a screener for feigning across specific conditions (Widows & Smith, 2005; Smith & Burger, 1997). The SIMS can be completed by persons with a fifth-grade reading level and contains 75 true–false items that map onto five nonoverlapping scales to detect feigned clinical conditions. Smith (Chapter 23, this volume) provides a full description of its scales and their validation. Two studies have evaluated the SIMS with adolescents. Specifically, Rogers et al. (1996) examined the SIMS’s utility with an adolescent inpatient population and found it to be moderately effective as a screen for adolescent feigning, although they noted that the NPP rate was low. Also, the sample had limited representation of psychotic disorders; its use for adolescents within this diagnostic category is unknown. On the other hand, Lewis, Simcox, and Berry (2002) found the SIMS to be effective in a known-groups design using the SIRS to classify honest and feigning responders. The two groups had highly significant differences on all the SIMS scales. In summary, the research on the SIMS remains relatively thin. However, elevations of one or more scales are an appropriate signal for further evaluation. With extensive cross-validation, particularly in adolescent samples, the SIMS may have potential to be an effective and brief screen for adolescent malingering. Structured Interview of Reported Symptoms

The SIRS (Rogers, Bagby, & Dickens, 1992) is a structured interview for the assessment of feigned mental disorders (see Rogers, Chapter 22, this volume). To date, one study has been conducted with adolescents. Rogers et al. (1996) recruited adolescent participants from a court-referred residential treatment program. After examining participant responses in a within-subjects simulation design, Rogers and colleagues recommended that the SIRS be used in collaboration with other measures rather than as the sole instrument for clinical classification. They found that lowering the cutoff score for primary SIRS scales from ≥3 to ≥2 in the probable-feigning range increased the PPP to .79, while the NPP remained at .98. The SIRS requires

488

V.   S p e ci a l i z e d A p p l ic at i o n s

further study in children and adolescents. For adolescents, it may provide ancillary data on feigning when limited to nonpsychotic clinical samples.

Psychological Testing and Feigned Neurocognitive Impairment Research in the past 15 years has demonstrated that children have the capacity to feign neuropsychological problems and impairment (Kirkwood, 2015; Rohling, 2004; Slick & Sherman, 2012). Multiple case articles have identified children who feign cognitive impairment during neuropsychological assessment (e.g., Chafetz, Abrahams, &

Kohlmaier, 2007; Kirkwood & Kirk, 2010; Kirkwood, Kirk, Blaha, & Wilson, 2010; McCaffrey & Lynch, 2009; see Table 24.5). Symptom validity tests (SVTs) and performance validity tests (PVTs) can be used in combination (Kirkwood, 2015; Larrabee, 2007, 2012; Slick & Sherman, 2012), but methodological issues must also be considered (see Rogers, Chapter 2, and Bender & Frederick, Chapter 3, this volume). The following section summarizes several SVT measures for which child and adolescent data are available. They include the Test of Memory Malingering, the Word Memory Test, the Medical Symptom Validity Test, the Computerized Assess-

TABLE 24.5.  Measures for Detecting Cognitive Impairment

Study

N

Design

Sex

Cutoff score

Utility estimates Sens

Spec

PPP

NPP

.95

.98

—

—

.62

.17

Blaskewitz, Merten, & Kathmann (2008)

 70

Nonclinical known-group

40 female 30 male

>45

Brooks, Sherman, & Krol (2012)

 53

Consecutive neuropsychiatric evaluations

N/A

>45

Chafetz, Abrahams, & Kohlmaier (2007)

 96

Consecutive disability evaluations

30 female 66 male

>45

.64

.74

.50

.83

1.0

1.0

Constantinou & McCaffrey (2003)

 61 Greek

Nonclinical unknown-group

31 female 30 male

>45

N/A

.98

N/A

.98

Constantinou & McCaffrey (2003)

 67 US

Nonclinical unknown-group

44 female 23 male

>45

N/A

.98

N/A

.98

Donders (2005)

100

Consecutive neuropsychiatric evaluations

42 female 58 male

>45

1.0

.989

.67

.97

Gunn, Batchelor, & Jones (2010)

 90

Nonclinical known-group

45 female 45 male

>45

.95

.98

.97

.96

Kirk et al. (2011)

101

Consecutive clinical evaluations

29 female 72 male

45

N/A

.96

Nagle, Everhart, Durham, McCammon, & Walker (2006)

 25

Nonclinical known-group

13 female 22 male

45

N/A

N/A

N/A

N/A

Perna & Loughan (2013)

 75

Consecutive neuropsychiatric evaluations

28 female 47 male

>45 >41a

1.0 .78

.72 .92

.33 .58

1.0 .97

Rienstra, Spaan, & Schmand (2010)

 48

Research neuropsychiatric evaluations

N/A

45

N/A

1.0

N/A

1.0

Note. Sens, sensitivity; Spec, specificity; PPP, positive predictive power; NPP, negative predictive power. aConsidered preferable cutoff score by researchers.

1.0

.97



24.  Deception in Children and Adolescents 489

ment of Response Bias, and the Victoria Symptom Validity Test. Test of Memory Malingering

The Test of Memory Malingering (TOMM) consists of two learning trials, each preceded by a recognition memory test, with an optional delayed recognition trial made available. It is described by Frederick (Chapter 17, this volume). Eleven studies have examined use of the TOMM with children. Several investigations demonstrated that the majority of typically developing children perform at or above the adult cutoff scores for the TOMM (Blaskewitz, Merten, & Kathmann, 2008; Constantinou & McCaffrey, 2003; Rienstra, Spaan, & Schmand, 2010; Gunn, Batchelor, & Jones, 2010). Research in pediatric clinical populations has shown that children as young as age 5 or 6 years perform at adequate levels on the TOMM (Donders, 2005; Kirk et al., 2011). Research has also shown that children (ages 6–16 years) with a wide range of clinical diagnoses (Donders, 2005; Brooks, Sherman, & Krol, 2012) and those with epilepsy and low IQ (MacAllister, Nakhutina, Bender, Krantzoulis, & Carlson, 2009) can surpass the established cutoff scores for the TOMM. However, youth with extremely low cognitive abilities can produce poor performances on the TOMM (see Mazur-Mosiewicz, Carlson, Bracken, & Erdodi, 2015). For simulation studies, children may have difficulty maintaining poor effort consistently on the TOMM (Blaskewitz et al., 2008). In feigned scenarios, the TOMM appears able to detect children asked to “fake” a brain injury. In a study of German school-age children (ages 6–11) placed in full-effort or simulating conditions, Blaskewitz and colleagues reported that 68% of the simulators yielded scores below the adult cutoff scores on Trial 2 and the Retention Trial. However, Nagle, Everhart, Durham, McCammon, and Walker (2006) determined that the TOMM is not effective at detecting simulators told to fake a brain injury from a car accident. However, Chafetz et al. (2007) found that if secondary gain is present, 28% of the children performed under the TOMM threshold criteria. In conclusion, adult cutoff scores for the TOMM yield high specificities, with children performing similarly to adults. For sensitivity, the TOMM may have the potential to identify feigned performances, although further research is needed on this topic.

Medical Symptom Validity Test

The Medical Symptom Validity Test (MSVT; Green, 2004) is a brief (e.g., 5 minute), computerbased, basic reading, forced-choice verbal memory test designed to evaluate response validity. Respondents are presented with 10 semantically related word pairs twice on a computer screen before being asked to choose the correct word from pairs consisting of the target and an incorrect word during Immediate Recognition (IR) and Delayed Recognition (DR) conditions, followed by Paired Association (PA) and Free Recall (FR) trials. Like the TOMM, respondents receive auditory and visual feedback about the correctness of each response. The IR, DR, and CNS (consistency variable) scores reportedly denote effort. Green (2004) recommended a cutoff score of 85% correct for IR, DR, and CNS. The MSVT has established high rates of sensitivity and specificity in experimental studies that have compared controls with simulated feigners instructed to imitate memory problems. Frederick (Chapter 17, this volume) provides a summary of its limitations. Several studies have examined the MSVT’s specificity in impaired children. For example, Chafetz et al. (2007) demonstrated that children with Wechsler Intelligence Scale for Children— Third Edition (WISC-III) Full Scale IQ scores less than 70 can pass the MSVT with perfect or near perfect scores. Carone (2008) found that, using the MSVT, 37 children with moderate/severe brain damage/dysfunction in a full-effort condition outperformed 67 adults who were engaging in symptom exaggeration in an outpatient neuropsychological evaluation. Regarding specificity, only two children (5%) failed the MSVT. However, no data are available in this study regarding the MVST’s sensitivity with children simulators. One simulation study of juvenile feigning produced excellent results with a German translation of the MVST. As reviewed earlier, Blaskewitz and colleagues (2008) administered the MSVT to German school-age children placed in full-effort or simulating conditions. The MSVT achieved a sensitivity of 90% and a specificity of 98%. With a sample with postconcussional disorder (PCD), Kirkwood, Peterson, Connery, Baker, and Grubenhoff (2014) used the MSVT to identify a subset (ages 8–17) of postconcussive mild traumatic brain injury (mTBI) pediatric patients who may have been exaggerating or feigning symptoms during a neuropsychological evaluation. Many performed below established cutoff scores on the TOMM.

490

V.   S p e c i a l i z e d A p p l i c at i o n s

In summary, most children with impaired cognitive/neurological functioning can perform in the normal range on the MSVT. In addition, the MSVT may be useful for detecting invalid performance in relatively high-functioning older children and adolescents. However, additional research is essential for more impaired and younger examinees. Word Memory Test

The Word Memory Test (WMT; Green, Allen, & Aster, 1996) assesses feigned cognitive impairment (see Frederick, Chapter 17, this volume). Two studies have examined the WMT in younger populations. Specifically, Green and Flaro (2003) found that children (four groups with mean ages of 8, 11, 14, and 16 years) showed no significant differences between themselves and their parents in ongoing litigation for child custody. Understandably, children with a reading level below third grade had difficulty with the computerized WMT and scored comparatively low on the effort subtests. Similarly, Courtney, Dinkins, Allen, and Kuroski (2003) found that children’s effort on the WMT was associated with reading ability and age, although children age 11 and older generated WMT effort scores comparable to adult normative data. Taken together, findings suggest that the WMT as a measure of deception is not yet ready for professional use with young children (i.e., younger than third grade) or those with a low reading ability. Computerized Assessment of Response Bias

The Computerized Assessment of Response Bias (CARB; Allen, Conder, Green, & Cox, 1997) presents a five-digit number, a timed delay, then asks the examinee to choose the correct number from two five-digit numbers. Only one study has been conducted with children. Specifically, Courtney and colleagues (2003) found that children age 11 years and older produced CARB effort scores with adequate effort validity scores. However, the average CARB Total score for children below age 10 was much lower; additional work is needed with this measure before it is used with children (Flaro, Green, & Allen, 2000). Rey 15‑Item Test

The Rey 15-Item Test (FIT or Rey 15; Rey, 1958) is a brief measure of symptom validity presented as

a memory test (see Frederick, Chapter 17, this volume). While the Rey 15 is commonly used, likely due to its low cost and brief administration time, it has consistently demonstrated low rates of specificity and sensitivity and should not be considered sufficient from the standpoint of assessing effort (Strauss, Sherman, & Spreen, 2006; Vallabhajosula & van Gorp, 2001; Vickery, Berry, Inman, Harris, & Orey, 2001). The Rey 15 has been examined in only two studies with children. When administered alongside the TOMM, the Rey 15 demonstrated poor sensitivity and specificity (Whitney, Hook, Steiner, Shepard, & Callaway, 2008). For child symptom validity testing, Constantinou and McCaffrey (2003) found the Rey 15 was highly correlated with age and education. They recommended that the Rey 15 not be used with children under age 9. In general, the Rey 15 should not be used with children when the TOMM is available. Raven’s Standard Progressive Matrices

Raven’s Standard Progressive Matrices (RPM; Raven, Court, & Raven, 1996), a multiple-choice, paper-and-pencil test, comprises a series of visual pattern matching and analogy problems. It is recognized as a useful neuropsychological measure of cognitive reasoning and concept formation (Lezak et al., 2004; Gudjonsson, & Shackleton, 1986). Only one feigning study has been conducted with children. McKinzey, Prieler, and Raven (2003) found a false-negative rate of 64%, an artificial increase that they attributed to their participants’ inability to fake the test. However, when the investigators used Raven, Raven, and Court’s (2000) item difficulty analyses to guide the creation of a three-item detection formula (A3, A4, and B1), they produced a 95% hit rate, with equal falsepositive and -negative rates of 5% (McKinzey et al., 2003). Given that there is only one study (N = 44; ages 7–17) of this test with children, additional testing is warranted, especially before implementing it in practice.

THRESHOLD AND CLINICAL DECISION MODELS Approximately 20 years of clinical practice and research have transpired since the works of McCann (1998) and Oldershaw and Bagby (1997) on child and adolescent deception. However, systematic research on juvenile response styles has made only modest advances. Defensiveness, for example, may be prevalent in many child and adolescent clinical



24.  Deception in Children and Adolescents 491

populations. However, the current research is insufficient to propose empirically validated threshold or clinical decision models. Moreover, despite available scales on the MMPI-A, BASC, and MACI to assess defensiveness, the research has not yet established cross-validated cutoff scores. Consequently, similar to Oldershaw and Bagby (1997), we cannot make any specific recommendations regarding definite cutoff scores. More research is available on malingering in adolescents, but this research is still very limited. Oldershaw and Bagby (1997) proposed a threshold model to determine when malingering should be fully evaluated. Although the classification rates were generally high for the SIRS and the MMPIA, the lack of systematic cross-validation did not allow for the development of a clinical decisionmaking model at that time. With this caveat in mind, we suggest several tentative threshold models in Table 24.6 to help consider when feigning and defensiveness should be more fully evaluated. The importance of clinical enterprise is emphasized in each threshold model. As such, there is an

TABLE 24.6.  Threshold Models for Adolescent Malingering and Defensiveness

Feigning of mental disorders should be assessed if . . . 1.  Clinical and structured interviewing indicative of a highly exaggerated pattern of reporting. 2.  On the SIRS or SIRS-2, any primary scale in the definite range or any two primary scales in the probable range. 3.  On the MMPI-A, an F index > 85 or an F-K index > 20. 4.  On the PAI-A, a PIM < 25 or a NIM, MAL, and RDF > 89T or Cashel > 160 (raw). 5.  On the SIMS, a total score > 40. Feigning of cognitive impairment should be assessed if . . . 1.  Clinical and structured interviewing is indicative of feigned cognitive impairment. 2.  On the TOMM, with children 9 and up, a total score on the second trial < 44. 3.  On the WMT, with children 11 and up, Effort Scores < 82.5. Defensiveness should be fully assessed if . . . 1.  Clinical and structured interviewing are indicative of defensive response style. 2.  On the MMPI-A, an L score ≥ 56. 3.  On the PAI-A, an elevated PIM or DEF index. 4.  On the PIY, a DEF score > 59.



Note. Full titles of measures are included in the text.

emphasis on data from clinical and structured interviewing. For feigning, different detection strategies are required for each domain: feigned mental disorders and feigned cognitive impairment. For the former, a diverse set of detection strategies is available via the SIRS, MMPI-A, and SIMS. For the latter, research has been mostly limited to overly poor performance (floor effect).

CONCLUSION Most of the literature on the assessment of malingering and deception has focused on adults. We note in this chapter an increasing need for applying reliable, valid, and practical methods for assessing malingering and deception among child and adolescent populations. Fortunately, research has begun on these important issues, but there are still a number of questions. Eight issues signal the need for research on deception in children. First, it is important to establish the prevalence rates of different types of deception, categorized by gender and setting. Second, the role of developmental status and deception requires extensive longitudinal research. Third, the relationship of motivation to deception should be further examined. Fourth, structured interviews, with their advantages over self-report measures, deserve additional investigation. Fifth, it might be beneficial to examine personality and psychopathology, and their relation to malingering. Sixth, additional research is needed on the classification accuracy of various measures (e.g., MMPI-A, MACI, BASC-3). Seventh, multiple-time-point evaluations of functioning might be helpful in understanding the nature of response styles. Eighth, treatment of young individuals that incorporates their deceptive practices deserves further research.

ACKNOWLEDGMENTS We appreciate Elizabeth Corning and Dane Hilton for their help with earlier versions of the tables.

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Tackett, J. L. (2006). Evaluating models of the personality–psychopathology relationship in chil­ dren and adolescents. Clinical Psychology Review, 26, 548–599. Talwar, V., & Crossman, A. (2011). From little white lies to filthy liars: The evolution of honesty and deception in young children. In J. B. Benson (Ed.), Advances in child development and behavior (Vol. 40, pp. 139–179). San Diego, CA: Elsevier/Academic Press. Talwar, V., Gordon, H. M., & Lee, K. (2007). Lying in elementary school years: Verbal deception and its relation to second-order belief understanding. Developmental Psychology, 43, 804–810. Talwar, V., & Lee, K. (2002). Development of lying to conceal a transgression: Children’s control of expressive behaviour during verbal deception. International Journal of Behavioral Development, 26, 436–444. Talwar, V., & Lee, K. (2008a). Little liars: Origins of verbal deception in children. In S. Itakura & K. Fujita (Eds.), Origins of the social mind: Evolutionary and developmental views (pp. 157–178). New York: Springer Science and Business Media. Talwar, V., & Lee, K. (2008b). Social and cognitive correlates of children’s lying behavior. Child Development, 79, 866–881. Talwar, V., Lee, K., Bala, N., & Lindsay, R. C. L. (2002). Children’s conceptual knowledge of lying and its relation to their actual behaviors: Implications for court competence examinations. Law and Human Behavior, 26, 395–415. Tellegen, A., & Ben-Porath, Y. S. (2008). MMPI–2–RF (Minnesota Multiphasic Personality Inventory–2–Restructured Form): Technical manual. Minneapolis: University of Minnesota Press. Todd, R. D., Joyner, A. C., Heath, A. C., Rosalind, D. P., Neuman, J., & Reich, W. (2003). Reliability and stability of a semi-structured DMS-IV interview designed for family studies. Journal of the American Academy of Child and Adolescent Psychiatry, 42, 1460–1468. Vallabhajosula, B., & van Gorp, W. G. (2001). PostDaubert admissibility of scientific evidence on malingering of cognitive deficits. Journal of the American Academy of Psychiatry and the Law, 29, 207–215. Vickery, C. D., Berry, D. R., Inman, T. H., Harris, M. J., & Orey, S. A. (2001). Detection of inadequate effort on neuropsychological testing: A meta-analytic review of selected procedures. Archives of Clinical Neuropsychology, 16, 45–73. Vrij, A. (2005). Criterion-based content analysis: A qualitative review of the first 37 studies. Psychology, Public Policy, and Law, 11, 3–41. Vrij, A., Akehurst, L., Soukara, S., & Bull, R. (2002). Will the truth come out?: The effect of deception,

age, status, coaching, and social skills on the CBCA scores. Law and Human Behavior, 26, 261–283. Vrij, A., Akehurst, L., Soukara, S., & Bull, R. (2004). Let me inform you how to tell a convincing story: CBCA and reality monitoring scores as a function of age, coaching, and deception. Canadian Journal of Behavioral Science, 36, 113–126. Watson, D., Kotov, R., & Gamez, W. (2006). Basic dimensions of temperament in relation to personality and psychopathology. In R. F. Krueger & J. L. Tackett (Eds.), Personality and psychopathology (pp. 7–38). New York: Guilford Press. Wells, R. D., Bruns, B., Wender, E. H., & Stein, M. T. (2010). Scott: An 11-year-old boy with repetitive lying. Journal of Developmental and Behavioral Pediatrics, 31, 30–33. Whitney, K. A., Hook, J. N., Steiner, A. R., Shepard, P. H., & Callaway, S. (2008). Is the Rey 15-Item Memory Test II (Rey II) a valid symptom validity test?: Comparison with the TOMM. Applied Neuropsychology, 15, 287–292. Widiger, T. A. (2015). Assessment of DSM-5 personality disorder. Journal of Personality Assessment, 97, 456–466. Widiger, T. A., & Trull, T. J. (2007). Plate tectonics ion the classification of personality disorder: Shifting to a dimensional model. The American Psychologist, 62, 71–83. Widows, M., & Smith, G. P. (2005). Structured Inventory of Malingered Symptomatology (SIMS) and professional manual. Odessa, FL: Psychological Assessment Resources. Williams, S., Leduc, K., Crossman, A., & Talwar, V. (2016). Young deceivers: Executive functioning and antisocial lie-telling in preschool aged children. Infant and Child Development, 26(1). [Epub ahead of print] Wilson, A. E., Smith, M. D., & Ross, H. S. (2003). The nature and effects of young children’s lies. Social Development, 12, 21–45. Wrobel, T. A., Lachar, D., Wrobel, N. H., Morgan, S. T., Gruber, C. P., & Neher, J. A. (1999). Performance of the Personality Inventory for Youth validity scales. Assessment, 6, 367–376. Yates, B. D., Nordquist, C. R., & Schultz-Ross, R. A. (1996). Feigned psychiatric symptoms in the emergency room. Psychiatric Services, 47(9), 998–1000. Yuille, J. C. (1988). The systematic assessment of children’s testimony. Canadian Psychology, 29, 247–262. Zahner, G. E. (1991). The feasibility of conducting structured diagnostic interviews with preadolescents: A community field trial of the DISC. Journal of the American Academy of Child and Adolescent Psychiatry, 30, 659–668.

CHAPTER 25

Use of Psychological Tests in Child Custody Evaluations Effects of Validity Scale Scores on Evaluator Confidence in Interpreting Clinical Scales Jonathan W. Gould, PhD Sol R. Rappaport, PhD James R. Flens, PsyD

The use of psychological tests in child custody evaluations remains controversial. Some scholars argue that psychological testing provides little, if any, useful information to assist the evaluator. Voicing their concerns about the usefulness of child custody evaluations, in general, and psychological testing, in particular, Emery, Otto, and O’Donohue (2005, p. 7) wrote: More difficult to explain and more problematic, however, are other aspects of evaluation practices including the widespread use of well established measures with no clear relevance to the custody context (e.g., measures of intelligence), attempts to measure constructs created to apply to child custody decision making (e.g., “parent alienation syndrome”), efforts to identify “parent of choice” (e.g., the Bricklin Perceptual Scales), and the use of measures that a significant number of psychologists view with skepticism (e.g., the Rorschach Inkblot Technique).

Other scholars view information obtained via psychological testing as an important component of any psychological evaluation, whether it is a forensic or clinical assessment (Meyer et al., 2001).

Applying the criteria described by Meyer et al. to child custody assessment, Gould, Martindale, and Flens (2009) wrote that the psychological test battery provides an empirically based dataset that allows for more precise measurement of individual characteristics than is usually obtained from interviews alone. The multiple tests that comprise a test battery allow for cross-checking of hypotheses using the data obtained by the tests. By incorporating multiple measures of multiple dimensions, evaluators are able to gather a wide range of information to increase their understanding of the examinee, as well as facilitate an understanding of the comparative strengths and limitations of each examinee. In assessing parenting strengths and deficiencies, this procedure makes it possible to compare each parent with the other and to compare each parent with the normative group. Use of a psychological test battery makes it possible to obtain data that shed light on a large number of personality, cognitive, emotional, or other dimensions at one point in time. Many of these dimensions (i.e., variables) have been shown to impact parenting (e.g., anger, anxiety, depression, substance abuse). Test batteries tend to be inclu-

497

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sive, covering a range of domains, many of which may be overlooked during less formal evaluation procedures. Each examinee is presented with a uniform stimulus that serves as a common yardstick by which to measure his or her characteristics. Standard administration and scoring procedures also may reduce legal and ethical problems, because they minimize the prospects that unintended bias may adversely affect the examinee and/or the evaluator’s interpretation of the examinee’s responses. Comparison of each examinee’s data with context-specific normative data allows the evaluator to formulate opinions concerning the strengths and weaknesses of examinees compared with a relevant group of peers.1 For example, the Minnesota Multiphasic Personality Inventory–2 (MMPI2), the MMPI-2 Restructured Form (MMPI-2-RF), the Millon Clinical Multiaxial Inventory–III (MCMI-III), the Personality Assessment Inventory (PAI), and the Parenting Stress Index (PSI) have context-specific normative data available. Each of these tests has normative data reported for male and female custody litigants. Use of these normative data allows the evaluator to consider the degree to which a particular examinee’s scores deviate from the context-specific normative group. An experienced evaluator is able to take the obtained data and examine subtle differences in it that may be relevant to questions about individual functioning along relevant dimensions associated with parenting. We want to add a caution at this point. The concept of “custody norms” can be misleading. The MMPI-2 scoring, for example, is based on the original standardization data used in determining the psychometrics of the instrument (i.e., raw scores, translated scores, etc.). The MMPI-2 also has data showing how individuals who are involved in child custody assessment typically score. These data that are often referred to as “contextspecific normative data” are not “norms” in the traditional sense. These scores based on MMPI-2 results of male and female custody litigants are comparison data based on group-mean data from various studies. The MMPI-2 cannot be rescored using “custody norms.” What we refer to as “custody norms” may be used to plot for comparing the parent’s MMPI-2 scores against a sample of individuals undergoing child custody assessment. Manuals for psychological tests used in forensic evaluations should have information about reliability and validity. Such psychometric information allows the evaluator to consider the strength

and limitations of the information obtained from the test. Without such information, evaluators have little ability to gauge the accuracy of the data they interpret when making judgments. In addition, the interscale correlation matrix provides information about how the various scales influence each other in a given context, and provides assistance in understanding if the scales are measuring different constructs. The lack of reported reliability data should raise questions about the utility of the individual test data generated by the individual in the assessment battery. Psychological testing in child custody evaluations constitutes a professional standard among psychologists (Ackerman & Ackerman, 1997; Bow & Quinnell, 2002; Quinnell & Bow, 2001), although the frequency of use of specific psychological tests has been challenged (Hagan & Castagna, 2001). The Association of Family and Conciliation Courts (AFCC), in its Model Standards of Practice for Child Custody Evaluation (AFCC, 2007) declares, in Model Standard 6.1: “The use of formal assessment instruments is not always necessary. Where those who are legally permitted to administer and score psychological assessment instruments elect not to do so, they shall recognize that they may be called upon to articulate the basis for that decision.”

SPECIFIC PSYCHOLOGICAL TESTS IN CHILD CUSTODY EVALUATIONS Psychological testing in child custody assessment has examined the emotional adjustment of the parents (Posthuma, 2016, p. 68): “The traditional custody battlefield is characterized by allegations of emotional, behavioural, or other personality shortcomings of one parent over the other, favouring the superior parent, who should be awarded primary, or even sole, care of the child or children.” Concerns are raised about evaluators’ use of psychological tests such as the MMPI-2, the PAI, the MCMI-III, and the NEO Personality Inventory (NEO-PI) because, although frequently used in child custody assessment (Ackerman & Pritzl, 2011; Bow & Quinnell, 2002), these multiscale inventories have been scientifically developed to assess “normal” psychological functioning. Only the MCMI-III was actually normed on a psychiatric population. Context-specific normative data have been developed for several psychological tests frequently used in child custody evaluations. They have been



25. Psychological Tests in Child Custody Evaluations 499

available for the MMPI-2 since 1997 (Bathurst, Gottfried, & Gottfried, 1997). They are also available for male and female custody litigants for the MCMI III (McCann et al., 2001), the PAI (e.g., Hynan, 2013), the MMPI-2-RF (Archer et al., 2012), and the NEO-PI (e.g., Langer, 2011). Rappaport (see Sol Rappaport at [email protected]) has developed context-specific normative data for male and female custody litigants who have taken the Sixteen Personality Factor Questionnaire (16PF). Similar data have been developed for the PSI (Abidin, Flens, & Austin, 2006; Abidin, Austin, & Flens, 2013). Context-specific normative data are useful for evaluators because they provide the ability to compare the score pattern of a particular parent to other male or female custody litigants rather than comparing them to general normative data described in test manuals. However, it is important for evaluators to remember that context-specific normative data do not enable the evaluator to know the meaning of the score pattern. Only tentative hypotheses may be developed from analysis of a test profile based on context-specific normative data. The clinical impressions drawn from these data must be viewed with caution, as they have not been empirically derived and therefore do not meet the Daubert threshold (Posthuma, 2016). A similar caution is suggested in drawing clinical inferences from the statements contained in the computer interpretations of the MMPI-2 and MCMI-III custody reports, and the PAI computer interpretive report. The interpretations are based upon the normative data contained in the manual and not data derived from child custody litigants. Only the MMPI-2-RF provides score comparisons between custody litigants and manual-based norms. To date, none of the computergenerated narratives are based on context-specific normative data. When used in child custody assessments, results of psychological tests need to be interpreted cautiously. The commonly administered standardized tests used in child custody assessment have not been validated on a custody population. There are no data comparing parents to stepparents. There are no data comparing parents engaged in high conflict versus low conflict litigation.

RESPONSE STYLE ISSUES Discussions of psychological testing in child custody assessment often focus on how best to inter-

pret clinical scales. Little attention has been paid to the importance of understanding validity scales as they affect accurate interpretation of clinical scales. In this section, we discuss the importance of understanding data from validity scales in psychological testing and how validity scale data influence the evaluator’s ability to provide an accurate explanation of the meaning of the clinical test data. The stakes are high in a child custody case for the parents who seek to obtain full custody of their children or the lion’s share of custodial time. Parents’ motivation to put forth their most positive presentation is common, yet it interferes with evaluators’ abilities to gain a clear understanding of parents’ test data. Tests used in child custody assessment need to include measures of response bias to assess whether the parent has attempted to place him- or herself in a favorable light, and if so, the degree to which the parent is attempting to present a more favorable picture of self. At the broadest level, response bias may be categorized as faking good, faking bad, and genuine (i.e., open and responsive to the context of the evaluation). In the child custody arena, however, most parents approach testing with the motivation to fake good. They wish to convince the evaluator and the court that they are psychological healthy and emotionally capable to parent their child or children. It is very unusual in a custody evaluation to find parents who fake bad. Evaluators need to investigate when validity scales suggest an open, nondefensive response style, which is unexpected in contested child custody cases. This may indicate that the parent has been coached to avoid those items contained in several of the validity scales. Because of the highconflict nature of child custody litigation, coached presentations should remain as one of many interpretations to nondefensive responding (e.g., Baer, Wetter, & Berry, 1992; Graham, Watts, & Timbrook, 1991; Victor & Abeles, 2004). Other issues include parents who are so confident in themselves and their views that they do not see how their views and beliefs are problematic. The first step in interpretation of a parent’s psychological test responses is an analysis of the validity scales. Different response styles, discussed in the literature, include malingering, defensiveness, irrelevant responding, random responding, honest responding, and hybrid responding (Heilbrun, 2001; Rogers, 2008). Research examining how male and female custody litigants perform on the MMPI-2 and the MCMI-III suggest two

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common response styles: defensive responding and honest responding. As noted, most parents are motivated to present themselves in a favorable light. This is often called a defensiveness or a faking good response style. The detection of a faking good response style is more difficult to detect than a faking bad response style (Baer et al., 1992). This dilemma is aptly described by Graham (2000): “It is not possible to tell if such a profile is indicative of a well-adjusted person who is motivated to appear even more well [sic] adjusted or of a poorly adjusted person who is trying to appear to be well adjusted” (p. 58). Response styles help evaluators determine how much confidence and certainty can be placed in their interpretation of the test data (Bathurst et al., 1997; Butcher, Morfitt, Rouse, & Holdren, 1997; Heilbrun, 2001). For example, it is likely that the influence of a “faking good” response style on clinical scales is to underestimate the parent’s true score. Failure to take response style into account may result in erroneous interpretations, faulty opinions, and recommendations. The “costs” are high in child custody case for the parents—and the evaluator. A review of the item content from the MMPI2, MCMI-III, and PAI quickly shows that many of the items are quite transparent. Although the validity scales typically show these “faking good” attempts, the clinical scales are unlikely to accurately represent the mental health of the parent. The defensive individual is likely to address items measuring healthy and abnormal functioning by developing a response style with two elements:

(Bagby & Marshall, 2004; Paulhus, 1998; Strong, Greene, Hoppe, Johnston, & Olesen, 1999):

1. Assertion of mentally healthy attributes that are inaccurate. 2. Denial of characteristics that are accurate but less than desirable.

MMPI‑2

Nichols and Greene (1997) noted the following about the defensive test-taker: “They may endorse items not merely to conceal symptoms and maladjustment, but to assert a degree of soundness, virtue, prudence, strength and well-being that is, if anything, superior to ‘normal’ levels. . . . In child custody, the evaluator is likely to see underreporting by a mix of trying to conceal any indication of psychological distress (dissimulation) and trying to assert superior adjustment) simulation” (p. 254). Custody evaluators see willfully manipulated data in almost every custody evaluation. Research findings have revealed that the common “faking good” response style has two separate factors

1.  Impression management. Impression management (IM) is a situational and context-specific variable that involves conscious and deliberate attempts to present a favorable impression. As noted earlier, it can include asserting favorable but inaccurate characteristics and denying unfavorable but accurate characteristics. The parents try to fool the evaluator into believing they are more virtuous and mentally healthy than may actually be the case. 2.  Self-deceptive enhancement. Self-deceptive enhancement (SDE) involves a more subtle characteristic in which persons actually believe they embody the favorable image presented. It may be said that the individual in this case is actually fooling him- or herself. A parent with SDE may be described as having a lack of insight, narcissistic qualities, and becoming easily angered when confronted. The parent in this case believes that his or her responses are accurate and justified. It is believed that IM reflects behavior that is under conscious control, whereas SDE does not. One option for persons with high IM scores involves the test being readministered, with directions to the test-taker to approach the test in an open and honest manner. However, two studies found this approach to be unsuccessful, producing almost no changes on the MMPI-2 clinical scales (Butcher et al., 1997; Cigrang & Staal, 2001).

Assessment of IM and SDE factors can be easily integrated into an analysis of the MMPI-2 validity scale scores. Scales for both are summarized: 1. IM: L Scale (L), Other Deception Scale (ODecp2), and Wiggins Social Desirability Scale (Wsd). 2. SDE: K Scale (K), Superlative Scale (S), and Edwards Social Desirability Scale (Esd). Values for the L and K scales are reported on the MMPI-2 profile. For computerized scoring, the S scale has been added to the validity scale configuration on the National Computer Systems/Pearson (NCS) Extended Score, interpretive and forensic profiles. The ODecp, Wsd, and Esd scales must be hand-scored. The item composition (p. 559)



25. Psychological Tests in Child Custody Evaluations 501

and T-score transformation tables (p. 523) can be found in Greene (2000). We believe that the Overcontrolled Hostility (O-H) scale is a widely misunderstood and misused scale in the child custody arena. The O-H scale was originally designed to measure angerrelated issues but it is correlated highly with the L Scale (.62) and K Scale (.51; see Megargee, Cook, & Mendelsohn, 1966), suggesting an aspect of positive self-presentation. The typical custody litigant averages approximately 60T–61T on the O-H scale (Bathurst et al., 1997), a full standard deviation higher than the normal sample. Interpretation within a child custody context suggests that the O-H scale is best described as an attempt by the parent to portray a characteristic of good emotional control (Flens, 2008). Data collected from a sample of over 1,000 child custody litigants (Flens, 2008) show the following values for the IM and SDE scales and are presented in Table 25.1. Cluster analysis of the IM and SDE scales using data from male and female custody litigants reveal three distinct profiles: 1. An honest and open presentation, with a relatively flat IM and SDE profile. These profiles present little interpretive difficulty for the evaluator. 2. An elevated SDE factor. This factor suggests that these parents have a lack of insight, narcissistic qualities, and may become angry when confronted. 3. Elevated IM and SDE factors. These profiles present the most interpretive difficulties for the evaluator. These parents are presenting a faking good response style and are some-

TABLE 25.1.  MMPI-2 IM and SDE Scales in Child Custody

Scale

Mean

SD

Median Min Max

L Scale

58

12

56

33

100

ODecp

57

11

57

32

 87  93

Wsd

54

11

53

30

K Scale

60

 9

61

30

 81

S Scale

61

10

62

30

 80

Esd

58

 7

60

30

 66

Other indicators False %

67

 6

68

37

 80

O-H Scale

61

10

62

30

 86

what narcissistic, rigidly overconfident, and sanctimonious about others’ problems. These individuals create an initial favorable impression but are later seen as arrogant, hostile, and domineering. There is an absence of a separate IM cluster in these data due to an elevated L Scale, without the corresponding elevation in ODecp and Wsd. Along these lines, Greene (2000) noted the following: “Thus, the L scale can be construed as a measure of psychological sophistication with high scores indicating a lack of such sophistication when the elevation is not [emphasis in original] a reflection of impression management. In this latter circumstance, the L scale will be elevated, but the Other Deception (ODecp) and Wiggins’ Social Desirability (Sd) scales will not be elevated” (p. 91). For this reason, the IM and SDE supplemental validity scales should be scored when either the L or K scale is elevated. Graham (2000) recommends that T-scores > 65 on the L scale indicate extreme denial and/or defensiveness, and that similar elevations on the K scale suggest a “faking good” response style, both of which raise questions about the ability of an evaluator to accurately interpret the profile. Nichols and Greene (1997) suggest that some degree of deception is present if the False % ≥ 70%. Using these decision rules on an unpublished database of more than 1,000 custody cases (Flens, 2008), approximately 45% of the cases would be of questionable interpretive value. Using only interpretation rules for the L and K scales, approximately 40% of the cases would qualify. Clearly, the “faking good” response style becomes a significant issue in the interpretation of MMPI-2 profiles in the child custody arena. As observed, response style issues influence interpretation of clinical scale scores. For the MMPI2, for example, elevations on the L and K scales often result in a set of relatively flat clinical scales. The lack of elevations on the clinical scales, however, cannot be interpreted as an absence of psychopathology (Butcher et al., 2001). The elevated L and K scales suggest that the individual has been unwilling to be open and honest with his or her endorsement of the test items. Because the items are quite transparent, the resulting profile may not accurately capture situational and personality characteristics of the parent being tested. Flens (2006) has named these profiles (high validity scales and low clinical scales) the fake dead profile or opossum profile.

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Custody litigants tend on average to answer “false” to approximately 67% of the items. This “false” response set affects other scales on the MMPI-2, such as the content scales (which tend to be low), several supplemental scales (Repression, Social Responsibility, Dominance, OH, and Gender-Male) and several Harris–Lingoes subscales (Hy1, Hy2, Hy5, Pd3, Pa3, Ma3). The substance abuse scales are also affected by the “faking good” response style (Otto, Lang, Megargee, & Rosenblatt, 1988; Wasyliw, Haywood, Grossman, & Cavanaugh, 1993). The prudent evaluator should be alert to the impact of a “faking good” response style on the clinical scales of any test, such as the MMPI-2. Knowing how the “faking good” response style affects the MMPI-2 items, scales, and profile configurations is very important given the complexity of this these issues in the child custody context. Response style factors, such as the K correction issue, can create artificially elevated scales, often leading to false-positive interpretations of these elevated scales. It is strongly recommended that clinicians use a non-K-corrected profile in cases of a faking good response style. There are equivocal data on the utility of the K-correction. Even Butcher and Han (1995) had concerns about the use of the K-correction: One problem with the K scale is that it was not developed for use with non-inpatient psychiatric samples (e.g., nonclinical groups such as family custody cases or applicants for employment who have a clear motivation to assert extremely good adjustment in order to present a favorable picture of themselves usually have extreme K scores). There is no research to guide practitioners to apply K in this context, or even to ensure that any K correction should be made. The K scale was not developed with samples of nonhospitalized subjects who have a known motive to deceive through asserting that they are better adjusted than “normals” usually report. Rather, it was simply assumed (because of their low scores in a psychiatric setting) that the original K samples underreported symptoms. (p. 26)

The assertion of positive mental health and virtue, and denial of psychopathology, are common among custody litigants’ response style to the MMPI-2 and other similar tests. Evaluators need to be familiar with the effects of validity scale scores on other clinical scales. We call this a configural analysis approach to test interpretation. Interpretation of test results needs to occur with the knowledge of correlation between and among validity scales and other scales and subscales on each test.

MCMI‑III/IV Scores on the MCMI-III personality scales are affected by a “faking good” response style differently than are scores on the MMPI-2 clinical scales. A prototypical child custody profile on the MCMIIII involves elevations on the Desirability validity scale and the Histrionic, Narcissistic, and Compulsive personality scales (Millon, Davis, & Millon, 1997, pp. 119, 125). However, the discussion in the MCMI-III manual of the Dependent scale as a factor in a “faking good” profile is inconsistent with research findings examining how child custody litigants score on the MCMI-III. Research on the Histrionic, Narcissistic, and Compulsive personality scales have shown a relationship between moderate elevations on each of these scales (base rate [BR] below 85) and healthy adjustment and functioning. In fact, elevations on these three scales have been labeled elevations on the healthy triad and, when including scores on the Desirability scale, these four scales have been called the normal quartet. Generally, male and female custody litigants’ MCMI-III profiles show a lack of significant elevations on most other scales, with occasional exceptions of elevations on some of the Clinical Syndrome scales. As noted earlier, the configural approach requires knowledge of the interscale correlation matrix. For the MCMI-III, this correlation matrix shows correlations between and among the normal quartet to be high and in the positive direction. All other scales on the MCMI-III correlate negatively with each other, with the exception of the Narcissism and the Anxiety scales. At this point in time, data are not available for the MCMI-IV with reference to child custody. The MCMI-IV has 20 additional items and a new scale (i.e., Turbulent Personality). Its interscale correlation matrix shows some striking changes. Desirability still correlates highly with Scale 4, as does the new Turbulent Personality scale. On the other hand, other, expected correlations plummeted. It is our opinion that these significant changes in the interrelatedness of the MCMI-IV scales will require significant research efforts to understand the impact of these changes on clinical interpretations within the custody context.

PAI The PAI (Morey, 2007) is frequently used in forensic settings (Archer, Buffington-Vollum, Stredny, & Handel, 2006). The PAI has been reported to be among the most widely used inventories in



25. Psychological Tests in Child Custody Evaluations 503

child custody evaluations (Ackerman & Pritzl, 2011). The PAI is a 344-item (4-point Likert scale answer for each item) measure, with 22 nonoverlapping scales. These scales are divided into 11 clinical scales, four validity scales, five treatment scales, and two interpersonal scales (see Boccaccini & Hart, Chapter 15, this volume). There is a growing body of research supporting the validity of the PAI in clinical and forensic samples (Edens, Cruise, & Buffington-Vollum, 2001). Overview of Validity Scales

The PAI standard validity indicators are summarized by Boccaccini and Hart (Chapter 15, this volume). Morey (1996, 2003) later examined supplemental response style measures. They include measures of defensiveness (Defensiveness Index and the Cashel Discriminant Function) and feigning (Malingering Index and Rogers Discriminant Function). Feigning

As previously observed, child custody litigants rarely are motivated to present an unfavorable picture of themselves. Therefore, they are unlikely to score at an elevated level on the Negative Impression Management (NIM) scale (Hynan, 2013). In the unusual circumstance in which NIM is elevated, the evaluator should consider why a custody litigant would score high on this scale; that is, child custody litigants are expected to present an overly favorable picture of themselves. When a parent’s approach to the test suggests a greater focus on bizarre and rare symptoms than on presenting a favorable impression, the evaluator needs to investigate what it is about this individual that did not follow expected demand characteristics. The Malingering Index (MAL; Morey, 1996) was developed as a more specific measure of malingering that is relatively independent from psychopathology. Like the NIM, it would be unusual to see an elevated MAL index in a custody evaluation. Elevations may suggest that the individual is so impaired that he or she did not fully understand the demand characteristics of completing the testing in a custody evaluation and therefore reported having more symptoms than he or she likely has. A second hypothesis is that the litigants are overwhelmed by the stress of the divorce. A third hypothesis is that litigants may wish to be viewed by others as having significant problems; that is, the scores may reflect a cry for help.

The Rogers Discriminant Function (RDF; Rogers, Sewell, Morey, & Ulstad, 1996) was developed to distinguish between clinical patients with true psychiatric disorders and those feigning psychiatric disorders. If a custody litigant scores high on this measure, a first step is to recheck the scoring. If there are no scoring errors, an in-depth interview investigating the reported symptoms is appropriate. Social Desirability

Individuals involved in a child custody evaluation are motivated to place themselves in the best possible light. The Positive Impression Management (PIM) scale assesses the motivation both to present a very favorable impression and to deny common but minor faults (Morey, 2007). PIM items are selected based on low endorsement rates in clinical and normal individuals. PIM elevations may reflect intentionally lying to deceive the examiner or a lack of awareness of the difficulties reflected in the item content. However, examiners should not assume that a high PIM score is a function of one of these interpretations. It may also be that the examinee has fewer negative characteristics than most people. Unlike the MCMI-III, the PIM is not positively correlated with any PAI clinical scales. Morey (2003) found that psychopathology might be predicted, in part, based on PIM scores. Morey found a negative correlation (median r = –.38) between PIM scores and clinical scale scores, suggesting that a significant amount of variability in the clinical scales is explained by attempts to present positively on the PAI. Morey and Hopwood (2007) developed a regression formula based on the relationship between the PAI clinical scale scores and the PIM scale. The result of the regression formula is that there is a PIM-predicted score for each clinical scale and subscale. In essence, conceptually, a PIM-predicted score allows for an adjustment to the PAI clinical scale and subscale scores similar to the K-correction on the MMPI2 and the correction used on the MCMI-III, albeit with a different approach for developing the modified scores than was used for other tests. It is relevant to custody evaluators when high PIM scores are encountered; PIM-predicted scores may be used to see which scales would be predicted to be higher than actually observed. For example, if the PIM-predicted score for the Depression scale was high, even if the original clinical scale is not, this elevation may allow the evaluator to deter-

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mine whether there are other data to support the hypothesis that the litigant may have depressive features. In essence, the PIM-predicted clinical scores can help generate hypothesis that might have not been generated from viewing only the clinical scale scores. Kurtz, Bupp, Henk, and Dresler (2015) recently studied the usefulness of PIM-predicted scoring and found encouraging evidence that it might be helpful in identifying hidden symptoms or problems on the PAI. Kurtz and his colleagues administered the PAI to 334 university students under standard and socially desirable (i.e., job applicant) conditions. Most clinical scales were significantly lower than the standard administration. The overall exceptions were the Mania, Treatment Rejection, Dominance, and Warmth scales, with the latter three being significantly higher. Positive interpersonal traits of dominance and warmth were stressed, as well as the absence of treatment needs. Kurtz et al.’s results show promise for using PIM-predicted deviation scores in detecting concealed problems or symptoms, which are especially important in custody evaluations in which parents often do not present themselves as having psychological difficulties. It is critical that evaluators are aware that faking good may yield false-negative results. When an evaluator assumes that a parent displays no psychological problems when a psychological problem does exist, there may be a risk to children as a result of impaired parenting. Although we believe that the PIM-predicted deviation scores hold promise in helping to minimize false negatives, we are hesitant to suggest their use in making determinations about psychological problems. First, the Kurtz et al. (2015) study is the only peer-reviewed article that used the regression formula to address deviation scores. Second, the sample of university students was primarily white and female, and is therefore not be representative of the typical custody litigant. The Defensiveness Index (DEF; Morey, 1996) was developed to evaluate intentional defensive responding. However, Morey (2007, p. 32) notes that “defensive people may not score high on the index, and scores within normal limits should not be considered to rule out the possibility of a dissimulated protocol.” Interestingly, high DEF scores may relate to more sophisticated attempts to minimize problems (Morey, 2003). However, when a score is elevated, it is likely that the person presented an overly positive impression. The Cashel Discriminant Function (CDF; Cashel, Rogers, Sewell, & Martin-Cannici, 1995)

was developed to distinguish between honest and defensive responding. The CDF has been found to show similar scores in patient and community samples. As with the RDF, scores on the CDF have been shown to be independent of whether an examinee is a bona fide patient. Some studies have shown that the CDF is weaker at detecting defensive responding than the DEF and PIM in simulation research (Baity, Siefert, Chambers, & Blais, 2007; Morey & Lanier, 1998). Thus, PIM or DEF scale should be considered the principal measure of PAI defensiveness. Substance Abuse

The PAI estimated Alcohol (ALC Est.) and Drug (DRG Est.) indices were designed to detect the underreporting of substance abuse. The method involves comparing an individual’s score on the ALC and DRG scales with estimated scores based on other scores on the PAI profile. Specifically, Morey (1996) used linear regression to identify possible underreporting of alcohol and substance use problems using five PAI subscales, which have moderate correlations with the ALC and DRG scales. The belief is that if these five scales are elevated, then professionals would expect to see the ALC and DRG scales elevated. Examinees with low scores on the ALC and DRG scales but high scores on the ALC Est. and DRG Est. may be underreporting substance use or alcohol use. The PAI and Its Application to Deception in Custody Disputes

Two studies have investigated the use of the PAI among parents involved in child custody (Hynan, 2013) or parenting capacity evaluations (Carr, Moretti, & Cue, 2005). While parents in each group may be motivated to put forth a favorable self-presentation, the results from these two studies suggest that custody litigants approach the custody assessment with somewhat different motivation to take tests than do those involved in parental capacity evaluations for dependency court. Hynan (2013) published the only known study providing context-specific normative data on the PAI with child custody litigants. His sample of 250 litigants (125 mothers and 125 fathers) is from Chicago, Illinois, limiting its generalizability to urban areas. As expected, averages for inconsistency scales were unremarkable, while the NIM was low average (45.57T). Predictably, the PIM was mildly elevated (M = 60.24T) and nearly identical across



25. Psychological Tests in Child Custody Evaluations 505

gender. Using a PIM cutoff score ≥ 57T, most parents (73.6%) would be viewed as defensive; this percentage drops dramatically (22.0%) when a more conservative cutoff score ≥ 68T is applied. Interestingly, PIM was moderately associated (r = .54, p < .001) with the Treatment Rejection scale. Hynan’s most pronounced finding involved the uniformly low scores on the PAI clinical scales that ranged 40.0T (SCZ) to 46.04 (DRG). Overall, the clinical scales were clearly unelevated with an average of 43.86T. Carr et al. (2005) evaluated 91 biological mothers, and 73 fathers were assessed at a family court center. Parents were primarily of European descent. They differed from most parents used in child custody studies: The majority did not complete high school, and most were unemployed and/or receiving social assistance. For defensiveness, 17.2% of mothers and 18.2% of fathers obtained PIM elevations (≥ 66T). Moreover, moderately high correlations were found between the PAI PIM scale and the MMPI-2 L (.66) and K (.73) scales. Carr et al. should be interpreted for parent capacity but not child custody cases, because of their marked differences from parents in child custody cases. Summary

The PAI shows promise for use in custody evaluations, yet only one published study provides context-specific normative data for custody litigants. One of the authors of this chapter (Sol Rappaport) is currently analyzing data to create a second set of norms. The data from Hynan suggest caution when applying conventional interpretation of some PAI scales. Custody litigants present with much lower clinical scales than the standardization sample, calling into question the accuracy of scale interpretations drawn from the PAI manual. For example, the custody sample’s PIM elevations appear to be related to Treatment Rejection and Warmth scales, suggesting that elevations on these clinical scales may be an artifact of attempting to present themselves in a favorable light.

16PF–Fifth Edition The fifth edition of the 16 Personality Factor Questionnaire (16PF) was first published in 1949 and has since has undergone numerous revisions (Institute for Personality and Ability Testing [IPAT], 2009). The 16PF comprises 185 questions designed to measure 16 primary personality factors, which Cattell (1945) believed were the main

components to personality. Each of the 16 factor scales has 10–15 questions. Test-takers are given three options, with the option of using the middle choice (“?”) if the other options do not fit. Unlike many tests using T-score distribution, the 16PF uses sten (sten = standard 10) scores. Sten scores are based on a 10-point scale (1–10), with a mean of 5.5 and a standard deviation of 2. Scores falling farther from the mean are viewed as more extreme, and the more extreme the score, the greater likelihood that characteristics purported to be measured by scale will be observed through the individual’s behavior. Scores of 1–3 are considered in the low range, while scores of 8–10 are considered in the high range. Impression Management

The Impression Management (IM) scale is a bipolar scale, with high scores representing faking good and low scores representing faking bad. Using one scale to assess both ends of the continuum is a popular method of response style measurement (IPAT, 2009). Importantly, the 16PF IM scale is not used to correct factor scores. Its goal was to develop a scale that measures both self-deception and other-deception. For convergent validity, the IM scale strongly correlates with other measures of social desirability (see IPAT, 2009) including (1) the Marlowe–Crowne Social Desirability scale (r = .54), (2) the Self-Deception Enhancement scale of the Balanced Inventory of Desirable Responding (r = .54) and (3) the Other-Deception scale (r = .49). Infrequency

The 16PF Infrequency scale is designed to assess for random responding; it includes items with low endorsement rate for the “?” response of 6.5% or less. The mean scores for the normative sample and custody litigants are statistically significantly different. Although custody litigants endorsed more of the infrequent items than the normative sample, the meaning of this difference is uncertain. Acquiescence

The Acquiescence scale was created to assess the tendency of an examinee to agree with an item independent of its content. The Acquiescence score is obtained by adding the number of true responses to 103 of its items. Professionals might expect no

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differences between custody litigants and the normative sample, because this scale measures acquiescence, not necessarily positive traits. Yet custody litigants are much less likely to endorse items as true than the normative sample. In fact, the mean scores for the custody sample place these litigants at the 14th percentile, while the normative sample mean score is at the 50th percentile. Without additional research, the meaning of these scores differences remains unknown. It appears that the Acquiescence scale is much higher in custody litigants, with a mean difference of over 10 points between the normative sample and custody litigants.

er et al., 2001). Many other tests use similar approaches. There are two major problems with the cutoff score approach. First, the cutoff score approach does not take into account measurement error. Tests are not 100% reliable. As a result, a score that an examinee obtains may be an overestimate or underestimate of his or her true score. If an evaluator uses the cutoff score approach, he or she will inevitably find some examinees who have met the cutoff score but whose true score is below the threshold. The reverse is true as well. Evaluators need to be mindful of the standard error of measurement. If an examinee obtains a score 4 points above or below the cutoff score, it may be within the standard error of measurement. In this case, the evaluator cannot rely on the cutoff score. For example, a score of 62 on the Depression scale of the MMPI-2 may still reflect a clinical elevation due to the standard error measurement (which is 4.59 for males and 4.45 for females). In custody disputes, evaluators may reach the wrong conclusion about the absence of depression. Please see Rogers (Chapter 2, this volume) about tooclose-to-call scores.

The 16PF and Its Application to Deception in Custody Disputes

The 16PF manual states that the cutoff score used for the IM scale should be determined based on the test setting. However, the computer-generated report does not report IM scale interpretation based on reasons for the testing. Because a recent survey has shown that most custody evaluators now score psychological tests using computer-generated scoring programs (Rappaport & Flens, 2014), we are concerned that evaluators will come to accept the generic scoring information. It should not be applied when validity scale scores are known to be different from the normative sample, yet not included in the test publisher’s computerized printout. No currently published studies are available describing 16PF context-specific normative data for male and female custody litigants. However, our unpublished data with a sample of approximately 140 custody litigants showed elevated IM scales across genders (see Table 25.2).

CUTOFF SCORES AND RELEVANT SAMPLES IN CUSTODY EVALUATIONS The use of cutoff scores has increasingly become a topic of discussion among custody evaluators. Some evaluators assume, rightly or wrongly, that custody litigants are no different in general than the population at large; therefore, the elevations on the PAI PIM scale or the 16PF IM scale should be considered as recommended in the test manuals. For the PAI, a cutoff score of 57 would result in more than half of the custody litigants being considered defensive. A second approach is to suggest that the means used for custody litigants should be used for comparative purposes, and that a cutoff score for an individual would need to be higher than the average for custody litigants. Of course, this would result in much fewer parents being considered defensive. If an evaluator is more

CUTOFF SCORES IN CUSTODY ASSESSMENTS Test manuals often provide cutoff scores for a score that is considered elevated or significantly high. For example, the MMPI-2 uses 65T as the cutoff score between moderate and high scores (Butch-

TABLE 25.2.  Normative and Child Custody Data on the 16PF

Normative Custody both genders both genders IM Infrequency Acquiescence

Normative male

Custody male

Normative female

11.5

15.3

11.6

14.5

11.7

1.4

2.4

1.5

2.6

1.3

46.9

56.1

48.2

56

56

Custody female 15 2.7 46.3



25. Psychological Tests in Child Custody Evaluations 507

concerned with wrongly stating that a person has mental health problems when he or she does not, an evaluator may choose a higher cutoff score on the PIM or the 16PF than is recommended in the manual. Conversely, if an evaluator is more concerned with not minimizing problems, the evaluator may choose a lower cutoff score, indicating that parents exceeding it are minimizing problems and may therefore have more difficulties than the test results suggest. A third approach is to use classification statistics to develop optimal cutoff scores. In essence, this is what Morey (1996) did in developing the PAI. Several studies have found that using the score of 57 is the most effective at classifying defensiveness (see Baity et al., 2007; Fals-Stewart, 1996; Peebles & Moore, 1998). Others have found that a score of 61 is the best cutoff score for classifying defensiveness (Morey & Lanier, 1998), but only marginally more effective. Using a receiver operating characteristic (ROC) curve may be the best statistical way to develop a cutoff score. Morey and Lanier used an ROC to determine that a score of 61 was ideal. We believe that the best approach is not to choose one method over another, but rather to make a decision on a case-by-case basis. In assessing for defensiveness in custody evaluations, evaluators must understand that custody litigants more likely than not will complete test measures in a more defensive manner than the general population. At times, it may be a function of parents being unaware of their difficulties; and at other times, their deceptions are intentional, in order to attempt to present themselves in the most favorable light. The best approach to answering this question is based on an assessment of all of the tests, as well as information from interviews with the other parent as collateral sources. These data can assist evaluators in understanding whether a parent’s high scores on PIM scales are a function of self-deception, other-deception, or a combination of both, or perhaps the parent is actually more virtuous than most. Our main point is that the use of one clear cutoff score on a test is not recommended. All test results must consider other information as well. Defensiveness is a dimensional construct, which cannot be captured by cutoff scores alone. We believe that is best to talk about levels or degrees of defensiveness rather than making categorical judgments. For example, a PIM score of 80T means something different than a PIM score of 59T, even though both are above the cutoff score of 57 described in the manual. When scores are near the cutoff score, evaluators may discuss

these findings in their report but prudently avoid any definite conclusion. Evaluators typically use multiple measures in a child custody examinations (Ackerman & Pritzl, 2011). Many commonly used measures (e.g., MMPI-2, MMPI-2 RF, PAI, 16PF, and MCMI-III) have response-style scales. When litigants produce high scores across multiple measures of defensiveness, many evaluators often conclude that the parent is attempting to present him- or herself in a favorable light throughout the evaluation. However, many measures of defensiveness are highly correlated. When that occurs, there is little incremental validity in combining these various measures (Borum, Otto, & Golding, 1993), yet evaluators may mistakenly believe that they have more evidence, when this is not the case. Evaluators would be wise to look at the correlations among the various response-style indices before developing any tentative hypotheses about defensiveness. As a final concern, coaching must be considered in the interpretation of the validity scales, especially in custody evaluations. Baer and Wetter (1997), using the PAI, found that coaching about defensiveness significantly decreased the classification accuracy of the PIM and DEF. They found that lower cutoff scores (a PIM of 50T and a DEF of 4 raw score) were optimal in the coached simulators. This creates an important dilemma for custody evaluators. While a lower cutoff score may be more effective for coached simulators, it runs the serious risk of misclassifying an honest uncoached parent.

MEASURES OF PARENTING ABILITIES An evolving development in child custody assessments is to administer measures of parenting. A major problem with parenting tests involves their item transparency (Martindale & Flens, 2011), which increases vulnerability to impression management. Thus, consistency between parenting test results may simply reflect impression management with no clinical reference (Posthuma, 2016).

Parenting Stress Index—4th Edition The Parenting Stress Index—4th Edition (PSI-4; Abidin, 2012) is a 101-item inventory designed to assess the level of stress in the parent–child relationship. Standardized on 534 mothers and 522 fathers, the PSI-4 contains two domains, one each for the child and the parents. Within the child domain, six subscales measure sources of stress re-

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lated to the parents’ report of the child’s characteristics. Within the parent domain, seven subscales assess sources of stress related to the parents’ own characteristics. Defensive Responding Scale

Low scores (≤ 24) on the Defensive Responding scale are suggestive of this response style. There are two main reasons parents may score high on the Defensive Responding scale. First, parents may minimize their stress as a way to present positively to the evaluator if undergoing a custody evaluation, or involved with child protective services. However, high Defensive Responding scores may also reflect a lack of awareness of parenting difficulties or disengagement with parenting (Abidin, 2012). Besides the Defensive Responding scale, extremely low Total Stress scores may also suggest defensiveness. Some parents with extremely low Total Stress scores are, in fact, very defensive, somewhat paranoid, and fearful, whereas others actually are experiencing little stress (Abidin, 2012). If the both scales are at or below the 15th percentile, Abidin recommends that evaluators should consider defensiveness. Custody Applications

Research on the use of the PSI is limited in custody evaluations. Yet the PSI, as well as the Parent–Child Relationship Inventory described next, is frequently used in custody evaluations (Ackerman & Pritzl, 2011; Quinnell & Bow, 2001). Abidin et al. (2013) presented preliminary data on the PSI with 107 male and 107 female custody litigants. No significant gender differences were found on domain scores, subscale scores, or Total Stress score. However, approximately 33% of the sample was defensive, compared with 15% in the standardization sample. Among custody litigants, fathers were found to have higher defensive scores than mothers. The data suggest that the defensive responding among custody litigants is strongly related to SDE rather than IM. Parents scoring in the PSI defensive range tended to have limited insight into their own behavior and to have narcissistic overconfidence in their skills.

Parent–Child Relationship Inventory The Parent–Child Relationship Inventory (PCRI) is a 78-item questionnaire that assesses parents’ at-

titudes toward parenting, as well as attitudes toward their children (Gerard, 1994). The PCRI contain seven Content scales addressing certain aspects of the parent–child relationship. There are two validity scales: the Social Desirability Scale and the Inconsistency Scale. Hurley, Huscroft-D’Angelo, Trout, Griffith, and Epstein (2014) examined 164 measures of parenting skills. The PCRI was among the five parenting skills measures that met seven or more of 10 psychometric criteria of validity, reliability, and response bias. They concluded that it was psychometrically sound and likely to withstand a Daubert challenge. Social Desirability

The Social Desirability (SOC) scale consists of five items rarely endorsed in a positive direction. Low scores (? 9) are reason to question the validity of the PCRI due to positive impression management. Custody Applications

The PRCI SOC may be ineffective when used in custody disputes. In a sample of 214 child-custody litigants, Hynan (2013) found that only 3% were categorized as defensive according to the PCRI manual. The Hynan (2013) study also has limited generalizability as a European American sample from one Midwestern city. In addition, the study found several elevated PCRI scales. They include the Parent’s Satisfaction with Parenting Scale and the Limit-Setting Scale. These elevations may reflect positive IM, which was undetected by the SOC scale. Tobin, Seals, and Vincent (2011), using a simulated custody procedure, asked undergraduate parents to take the PRCI under standard instructions and custody dispute instructions. They reported that the SOC was effective in detecting attempts to present an overly positive image of the parent– child relationship. However, they recommended that a cutoff score of 12 be used for custody litigants, because it had the best predictive utility.

ASSESSMENT OF CHILDREN IN CUSTODY EVALUATIONS Evaluators sometimes overlook the literature addressing the assessment of children whose parents are involved in child custody evaluations (Posthu-



25. Psychological Tests in Child Custody Evaluations 509

ma, 2016). The American Psychological Association (2010) has published Guidelines for Child Custody Evaluations in Family Law Proceedings. These guidelines instruct evaluators to focus on “parenting attributes, the child’s psychological needs, and the resulting fit” (p. 864). Understanding the psychological and developmental functioning of children is vital to the assessment of the parent–child fit. Assessment methods include (1) descriptions from parents, teachers, and other collateral sources (2) interviews with children, and (3) psychological testing. Professional guidelines from both the American Psychological Association (2010) and the AFCC (2007) Model Standards of Practice for Child Custody Evaluation recommend multiple methods of data gathering. This section focuses on tests/questionnaires that parents complete with regard to their children. According to Ackerman and Pritzl (2011), the three most frequently used tests that parents complete regarding their children in custody evaluations are Achenbach’s Child Behavior Checklist (CBCL), the Conners Rating Scale (CRS), and the Behavior Assessment System for Children (BASC). None of these scales was developed for use in custody evaluations, and we are unaware of any context-specific normative data that exists for custody litigants completing these scales. One advantage is that these tests have versions that can be completed by each parent, as well as by teachers. They also have versions that can be completed by children if they have reached a minimum age. Evaluators can make direct comparisons of the parents, teachers, and, if applicable, the child’s scores on various subscales. Where discrepancies exist among parent, teacher, or child reports, evaluators can follow-up with further interviews to better understand why the discrepancies exist. Parents, teachers, and children may report different types, severity, and frequency of problems. Discrepancies among reporters can occur for several reasons, including a parent’s lack of awareness. Parents may be unaware that difficulties exist, because the problems occur at school rather than home, or because the child hides it well. In other situations, a parent may be unaware of the child’s difficulties due to having little involvement with his or her child. Some parents are aware of their child’s difficulties, yet they attempt to present their child as having no problems for fear that a custody evaluator will attribute the child’s difficulties to them. At the other end of the spectrum, some parents may exaggerate a child’s difficulties. Parents with

mental health problems, such as depression, may be prone to distort their children’s difficulties (Chi & Hinshaw, 2002). Parents under high levels of stress may also be more likely to view their child as having more difficulty than other informants (Youngstrom, Loeber, & Stouthamer-Loeber, 2000). Other parents may see in their children a variety of difficulties that are, in fact, projections of the parents’ problems. Discrepancies among informants’ reports of children’s difficulties using various assessment measures are well established in the scientific literature (Achenbach, McConaughy, & Howell, 1987; Conners, 1997, 2009; De Los Reyes & Kazdin, 2005; Stokes, Pogge, Wecksell, & Zaccario, 2011). When discrepancies in child ratings exist among informants, evaluators must attempt to understand the basis for informant discrepancies. Evaluators may wish to determine which informant has more understanding of the child’s difficulties and their potential amelioration. Recent versions of two measures have responsestyle scales: the Conners 3 and the BASC-3. As described earlier, tests without such measures have limited utility in custody evaluations. The third major measure does not have any measure of response style: The Achenbach System of Empirically Based Assessment (ASEBA), often referred to as the Achenbach CBCL, has numerous forms. In fact, the manual reports that deliberate lying is only one of multiple factors that can lead to high or low scores. Social desirability can also impact ratings. While acknowledging possible influences, Achenbach chose to include only meaningful items and therefore omitted responsestyle items. In our opinion, this omission makes the ASEBA less useful in custody evaluations than the Conners 3 or BASC-3. The Conners 3 (Conners, 2009) has three scales that assess for response styles: Positive Impression, Negative Impression, and Inconsistency Index. Because these scales are only available on the Long and Short Form versions, we recommend their use in custody cases. Elevations on these scales can occur for a variety of reasons. According to Conners (2009), an elevated Positive Impression scale can result in not only an informant’s defensiveness but also children’s exemplary behavior in the presence of the informant. Elevations can also occur when an informant completes the Conners 3 by comparing the child to another person who has many more problems (Conners, 2009). Similarly, high scores on the Negative Impression scale can not only result from a biased reporter but it also

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can occur when a child/adolescent has significant behavioral problems. Evaluators need to consider multiple hypotheses when any response style scale is elevated. Comparing parents’ and teachers’ (or other informants’) scores to each may also assist in the assessment of response styles. As described earlier, children may behave differently in various settings, resulting in differences in informants’ reports. If this is observed, then the evaluator needs to investigate the reasons for these observed behavior differences. Conversely, it may be that multiple persons report in interviews that the child behaves similarly across various settings. If this occurs, but the Conners 3 scale scores appear discrepant, the evaluator needs to consider whether any of the informants appear to have biased reporting, even if none of the three response style scales were elevated. The BASC-3, like the Conners 3, has multiple forms for different informants: a Teacher Rating scale form (TRS), a Parent Rating Scale form (PRS), and a Self-Report of Personality for children (ages 8–25). The BASC-3 has validity scales for the TRS, PRS, and SRP, but not all of them have the same validity indices. All three have an F Index to assess whether an informant described the child in an excessively negative fashion (Reynolds & Kamphaus, 2015). High scores on the F Index may reflect an informant overreporting problems or describing a child as having exceedingly maladaptive behaviors. The BASC-3 manual (Reynolds & Kamphaus, 2015) reported very low percentages, with the F Index in the very low Caution (≤ 3%) or Extreme Caution (≤ 1%) range. Clearly, scores in these ranges needs to be further investigated. It is recommended different F indices be compared across informants. If multiple informants have elevated F scores, it is likely that the child has serious behavioral problems and suggests at least two rival hypotheses. First, the elevated score is not an exaggerated response style but represents a true concern for a child with significant problems. Second, the elevated F Index reflects a shared but false view of the child as having significant problems. If the BASC is completed by several informants, yet only one informant has an elevated F score, it is vital to assess whether the child acts in a significantly different manner with that adult than with other adults who completed the BASC-3. A rival hypothesis is that the one informant with a high F score has a distorted view of the child. The SRP (student version) has two validity indices not found on the TRS and PRS. The L Index

measures a child’s tendency to give an unusually positive view of him- or herself. The V index is made up of nonsensical or highly implausible statements, such as “I drink 50 glasses of milk every day” (p. 20). A parent may invalidate the test if the parent responds to an item with “true,” “often,” or “almost always.” For evaluators who hand-score the BASC-3, the manual describes a method for assessing response bias for all three forms that do not include a standardized score (Reynolds & Kamphaus, 2015). Evaluators should review the Item Scoring Page to assess for patterned responding. One approach to examine is whether the informant tended to answer all questions in the same manner. If so, the validity of the results should be questioned. For evaluators using the BASC-3 Q-global reports, a Response Pattern Index is generated, which is designed to identify answer sheets that may be invalid due to the informant not being attentive to item content. The Response Pattern Index is the total number of times an item response differs from the prior item response. The computer scoring provides a description of whether the response pattern is in the low or high caution range. Two questions need to be explored. The first question is, “Did the informant tend to choose the same response repeatedly?” The second question is, “Did the informant mostly never choose the same response from one question to the next? Scores that fall in the low or high caution range identify extreme response patterns, as less than 1% of informants completing the BASC fall into each range. The BASC-3 has a Consistency Index for evaluators on its computer-scoring program. This scale identifies situations in which the informant has given different response to items that are typically answered in the same manner. The Consistency scale was developed by pairing for each form items that are highly correlated with each other. If an informant’s answers on the Consistency Index fall in the caution or extreme caution range, the computer-generated report lists the questions contributing to the high score, allowing evaluators to follow-up on the questions with the informant.

Summary There are numerous questionnaires and tests that may be administered during a child custody evaluation to parents regarding their children. This section has focused on the three the most commonly used child-related assessment tools. Of them, the



25. Psychological Tests in Child Custody Evaluations 511

BASC-3 and the Conners 3 have response-style indices, which may be helpful in understanding parents’ and teachers’ approaches to completing the questionnaires. Understanding validity scale scores on children’s measures can also lead to follow-up interviews of parents and teachers to explore further the motivation or intention behind their responses. Ultimately, understanding the motivation or intention behind how a parent, teacher, or other caretaker may have completed a test may assist the evaluator in developing a greater understanding of not just the parent and child, but also the parent’s relationship with his or her child.

NOTES 1.  The practitioner is able to select context-specific normative data from numerous clinical populations, including child custody litigants. 2.  The Positive Malingering scale [Mp] is also used to assess the IM factor. The Mp scale was updated by Nichols and Greene (1997) and renamed as the Other Deception scale [ODecp].

REFERENCES Abidin, R. R. (2012). The Parenting Stress Index: Professional manual (4th ed.). Lutz, FL: Psychological Assessment Resources. Abidin, R. R., Austin, W. G., & Flens, J. R. (2013). The forensic uses and limitations of the Parenting Stress Index. In R. P. Archer & E. M. A. Wheeler (Eds.), Forensic uses of clinical assessment instruments (2nd ed., pp. 346–379). New York: Routledge/Taylor & Francis Group. Abidin, R., Flens, J. R., & Austin, W. G. (2006). The Parenting Stress Index. In R. P. Archer (Ed.), Forensic uses of clinical assessment instruments (pp. 297–328). Mahwah, NJ: Erlbaum. Achenbach, T. M., McConaughy, S. H., & Howell, C. T. (1987). Child/adolescent behavioral and emotional problems: Implications of cross-informant correlations for situational specificity. Psychological Bulletin, 101(2), 213–232. Ackerman, M. J., & Ackerman, M. C. (1997). Custody evaluation practices: A survey of experienced professionals (revised). Professional Psychology: Research and Practice, 28(2), 137–145. Ackerman, M. J., & Pritzl, T. B. (2011). Child custody evaluation practices: A 20-year follow-up. Family Court Review, 49(3), 618–628. American Psychological Association. (2010). Guidelines for child custody evaluations in family law proceedings. American Psychologist, 65(9), 863–867.

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(PAI) and the detection of defensiveness. Assessment, 2(4), 333–342. Cattell, R. B. (1945). The description of personality: Principles and findings in a factor analysis. American Journal of Psychology, 58, 69–90. Chi, T., & Hinshaw, S. (2002). Mother–child relationships of children with ADHD: The role of maternal depressive symptoms and depression-related distortions. Journal of Abnormal Child Psychology, 30, 387–400. Cigrang, J. A., & Staal, M. A. (2001). Re-administration of the MMPI-2 following defensive invalidation in a military job applicant sample. Journal of Personality Assessment, 76, 472–481. Conners, C. K. (1997). Conners’ Rating Scales—Revised. North Tonawanda, NY: Multi-Health Systems. Conners, C. K. (2009). Conners’ manual (3rd ed.). North Tonawanda, NY: Multi-Health Systems. De Los Reyes, A., & Kazdin, A. E. (2005). Informant discrepancies in the assessment of childhood psychopathology: A critical review, theoretical framework, and recommendations for further study. Psychological Bulletin, 131(4), 483–509. Edens, J. F., Cruise, K. R., & Buffington-Vollum, J. K. (2001). Forensic and correctional applications of the Personality Assessment Inventory. Behavioral Sciences and Law, 19, 519–543. Emery, R. E., Otto, R. K., & O’Donohue, W. T. (2005). A critical assessment of child custody evaluations: Limited science and a flawed system. Psychological Science in the Public Interest, 6, 1–29. Fals-Stewart, W. (1996). The ability of individuals with psychoactive substance use disorders to escape detection by the Personality Assessment Inventory. Psychological Assessment, 8, 60–68. Flens, J. R. (2006, October). Psychological Testing in Child Custody. Presented at the Association of Family and Conciliation Courts, 7th International Symposium on Child Custody Evaluations, Atlanta, GA. Flens, J. R. (2008, September). Advanced issues in psychological testing. Presented at the Association of Family and Conciliation Courts, 45th Annual Conference, Vancouver, British Columbia, Canada. Gerard, A. B. (1994). Parent–Child Relationship Inventory (PCRI) manual. Los Angeles: Western Psychological Services. Gould, J., Martindale, D. A., & Flens, J. R. (2009). Use of psychological tests in child custody evaluations. In R. M. Galatzer-Levy, L. Kraus, & J. Galatzer-Levy (Eds.), The scientific basis of child custody decisions (2nd ed., pp. 85–124). Hoboken, NJ: Wiley. Graham, J. R. (2000). MMPI-2: Assessing personality and psychopathology (3rd ed.). New York: Oxford University Press. Graham, J. R., Watts, D., & Timbrook, R. E. (1991). Detecting fake-good and fake-bad MMPI-2 profiles. Journal of Personality Assessment, 57(2), 264–277. Greene, R. L. (2000). MMPI-2/MMPI: An interpretive manual (2nd. ed.). Boston: Allyn & Bacon.

Hagan, M. A., & Castagna, N. (2001). The real numbers: Psychological testing in custody evaluations. Professional Psychology: Research and Practice, 32(3), 269–271. Heilbrun, K. (2001). Principles of forensic mental health assessment. New York: Kluwer Academic/Plenum. Hurley, K. D., Huscroft-D’Angelo, J., Trout, A., Griffith, A., & Epstein, M. (2014). Assessing parenting skills and attitudes: A review of the psychometrics of parenting measures. Journal of Child and Family Studies, 23(5), 812–823. Hynan, D. J. (2013). Use of the Personality Assessment Inventory in child custody evaluation. Open Access Journal of Forensic Psychology, 5, 120–133. Institute for Personality and Ability Testing. (2009). 16PF Questionnaire manual (5th ed.). Champaign, IL: Author. Kurtz, J. E., Bupp, L. L., Henk, C. M., & Dresler, C. M. (2015). The validity of a regression-based procedure for detecting concealed psychopathology in structured personality assessment. Psychological Assessment, 27(2), 392–402. Langer, F. (2011). Using the NEO Personality Inventory in child custody evaluations: A practitioner’s perspective. Journal of Child Custody: Research, Issues, and Practices, 8(4), 323–344. Martindale, D. A., & Flens, J. R. (2011). Test item transparency: The undisclosed threat to test validity. The Matrimonial Strategist, 29(7), 3–4. McCann, J. T., Flens, J. R., Campagna, V., Colman, P., Lazzaro T., & Connor, E. (2001). The MCMI-III in child evaluations: A normative study. Journal of Forensic Psychology Practice, 1(2), 27–44. Megargee, E. I., Cook, P. E., & Mendelsohn, G. A. (1966). Development and validation of an MMPI scale of assaultiveness in overcontrolled individuals. Journal of Abnormal Psychology, 72, 519–528. Meyer, G. J., Finn, S. E., Eyde, L. D., Kay, G. G., Moreland, K. L., Dies, R. R., et al. (2001). Psychological testing and psychological assessment: A review of evidence and issues. American Psychologist, 56(2), 128–165. Millon, T., Davis, R., & Millon, C. (1997). MCMI III manual (2nd ed.). Minneapolis, MN: National Computers Systems. Morey, L. C. (1996). An interpretive guide to the Personality Assessment Inventory. Lutz, FL: Psychological Assessment Resources. Morey, L. C. (2003). Essentials of PAI assessment. Hoboken, NJ: Wiley. Morey, L. C. (2007). Personality Assessment Inventory: Professional manual (2nd ed.). Lutz, FL: Psychological Assessment Resources. Morey, L. C., & Hopwood, C. J. (2007). Psychological conflict in borderline personality as represented by inconsistent self-report item responding. Journal of Social and Clinical Psychology, 26, 1065–1075. Morey, L. C., & Lanier, V. W. (1998). Operating characteristics of six response distortion indicators for the



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Personality Assessment Inventory. Assessment, 5(3), 203–214. Nichols, D. S., & Greene, R. L. (1997). Dimensions of deception in personality assessment: The example of the MMPI-2. Journal of Personality Assessment, 68(2), 251–266. Otto, R. K., Lang, A. R., Megargee, E. I., & Rosenblatt, A. I. (1988). Ability of alcoholics to escape detection by the MMPI. Journal of Consulting and Clinical Psychology, 56, 452–457. Paulhus, D. L. (1998). Paulhus Deception Scales (PDS): The Balanced Inventory of Desirable Responding–7. North Tonawanda, NY: Multi-Health Systems. Peebles, J., & Moore, R. J. (1998). Detecting socially desirable responding with the Personality Assessment Inventory: The Positive Impression Management Scale and the Defensiveness Index. Journal of Clinical Psychology, 54, 621–628. Posthuma, A. (2016). Current and new developments in psychological testing for child custody disputes. In M. L. Goldstein (Ed.), Handbook of child custody (pp. 67–84). Zurich: Springer. Quinnell, F. A., & Bow, J. N. (2001). Psychological tests used in child custody evaluations. Behavioral Sciences and the Law, 19, 491–501. Rappaport, S. R., & Flens, J. R. (2014, May). Psychological testing and cross-examination: Help! There is so much to know. Presented at the Association of Family and Conciliation Courts, 51st Annual Conference, Toronto, Ontario, Canada. Reynolds, C. R., & Kamphaus, R. W. (2015). Behavior Assessment System for Children manual (3rd ed.). Bloomington, MN: PsychCorp. Rogers, R. (2008). Clinical assessment of malingering and deception (3rd ed.). New York: Guilford Press. Rogers, R., Bagby, R. M., & Dickens, S. E. (1992).

Structured Interview of Reported Symptoms professional manual. Lutz, FL: Psychological Assessment Resources. Rogers, R., Sewell, K. W., Morey, L. C., & Ulstad, K. L. (1996). Detection of feigned mental disorders on the Personality Assessment Inventory: A discriminant analysis. Journal of Personality Assessment, 67(3), 629–640. Stokes, J., Pogge, D., Wecksell, B., & Zaccario, M. (2011). Parent–child discrepancies in report of psychopathology: The contributions of response bias and parenting stress. Journal of Personality Assessment, 93(5), 527–536. Strong, D. R., Greene, R. L., Hoppe, C., Johnston, T., & Olesen, N. (1999). Taxometric analysis of impression management and self-deception on the MMPI-2 in child-custody litigation. Journal of Personality Assessment, 73(1), 1–18. Tobin, N. L., Seals, R. W., & Vincent, J. P. (2011). Response patterns on the Parent–Child Relationship Inventory in a simulated child custody evaluation. Journal of Child Custody, 8(4), 284–300. Victor, T. L., & Abeles, N. (2004). Coaching clients to take psychological and neuropsychological tests: A clash of ethical obligations. Professional Psychology: Research and Practice, 35(4), 373–379. Wasyliw, O. E., Haywood, T. W., Grossman, L. S., & Cavanaugh, J. L. (1993). The psychometric assessment of alcoholism in forensic groups: The MacAndrew Scale and response bias. Journal of Personality Assessment, 60, 252–266. Youngstrom, E., Loeber, R., & Stouthamer-Loeber, M. (2000). Patterns and correlates of agreement between parent, teacher, and male adolescent ratings of externalizing and internalizing problems. Journal of Consulting and Clinical Psychology, 68, 1038–1050.

CHAPTER 26

Malingering Considerations in Reporting and Testifying about Assessment Results Eric Y. Drogin, JD, PhD Carol S. Williams, LLB

We address in this chapter, in an overtly practical fashion, considerations for mental health professionals when undertaking to write reports and testify in court about the presence or absence of malingering and related dissimulative behaviors. We first address the surprisingly minimal grounding in these concepts that attorneys—the usual targets of such reports and testimony—are likely to possess on the basis of the literature germane to their own profession. We then reviews enforceable and aspirational codes and guidelines in light of the considerations they identify with respect to intentionally distorted responding. We conclude with an overview of additional points for developing appropriate and effective communication of malingering assessment results.

WHAT ARE ATTORNEYS LIKELY TO “KNOW” ALREADY ABOUT MALINGERING? Definitions Attorneys who strive—as attorneys will—to obtain an “official” definition of malingering are almost inevitably going to seek out the current version of the American Psychiatric Association’s Diagnostic and Statistical Manual of Mental Disorders (2013; currently the fifth edition, DSM-5) once they determine that it does not appear in

Black’s Law Dictionary (Garner, 2014). They would be forgiven for concluding that this notion—like “multiple personality disorder” or “mental retardation”—simply no longer exists, since they will be unable to find “malingering” in either the Contents or the Index of DSM-5. This notion lurks, without fanfare, under the heading of Other Circumstances of Personal History within the section devoted to Other Conditions That May Be a Focus of Clinical Attention (pp. 726–727). If they can find the DSM-5 passage in question, attorneys will read about feigned presentations motivated by an external goal. They will also learn that there are other conditions, such as factitious disorder and conversion disorder, that may be a better fit when a litigant is claiming to have problems that are not as bad as claimed or maybe do not exist at all.

The Legal Literature When it comes to gaining a deeper understanding of malingering, beyond mere definitions of terms, attorneys have surprisingly few resources on which to draw within their own professional discipline. A recent WESTLAW search of all “Law Reviews and Journals” yielded only seven examples in the last 20 years of articles—written mostly by psychologists—for which the notion of malingering was

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a primary focus. This result was so counterintuitive that the search was repeated several times to ensure that it had been performed properly. The shallowness of this literature pool, and the revealing nature of the assertions found therein, make it worth discussing the contents of each of these legal publications in turn. Being “Alert” to Malingering

Cahn (1997, p. 77) warned fellow trial attorneys, “several symptoms should alert you to a client who may be malingering,” including, among others, the following: • Does your client have a history of other incapacitating injuries? If so, what were the injuries, and how long was he or she out of work? • Does your client have a poor work record or does he or she express general dissatisfaction with his or her job? Has the client had conflicts with authority figures? • Is the client blaming all of life’s problems on the accident? Does he or she portray life before the injury as perfect and life since the injury as being nothing but problematic? • When does your client allege the symptoms began? Has he or she suffered no apparent disability for months or years since the injury? • Did the client never try to return to work after the first phase of recovery from the alleged injury? Has the client refused to work part-time? • Is the client uncooperative, evasive, or guarded when you ask for descriptions of aspects of his or her life other than the alleged impairments and the injury? • Does the client pursue allegations about injuries tenaciously, with numerous visits to doctors and consultations with you? Does he or she contradict this tenacity by alleging depression, anxiety, or other disabling problems to explain shirking personal responsibilities? None of these clinical presentations was offered with reference to malingering as a quasi-diagnostic entity—for example, the then-current Diagnostic and Statistical Manual of Mental Disorders (DSMIV; American Psychiatric Association, 1994). Instead, the focus of this article was entirely on ways to “avoid wasting time and money on preparing a malingerer’s case for trial” (Cahn, 1997, p. 77). Seasoned mental health professionals and attorneys who consult them with any degree of regularity will readily recognize that none of these

so-called “symptoms” would constitute conclusive, independent confirmation of malingering. Indeed, professionals could go so far as to argue that many are not only consistent with but in fact emblematic of legitimate psychological injury. Testing for Malingering

From an attorney’s perspective, Friedland (1998) offered a comprehensive overview of malingering as a diagnostic entity, with detailed reference to the legal significance of such a finding and specific identification of standard clinical assessment instruments, responses to which may vary depending on the truthfulness and investment of effort displayed by the examinee. Friedland’s article demonstrates just how difficult it can be for attorneys to master and explain the finer points of psychological testing and assessment. For example, Friedland offered the following: The comprehensive section [sic] of the revised WAIS [Wechsler Adult Intelligence Scale] has been perceived as an intelligence test with the easier questions early on and the latter questions ascending in degree of difficulty. Malingerers attempting to pass as mentally retarded will answer the early questions correctly and purposely answer questions near the end of the test incorrectly. . . . Believing these questions to be of greater difficulty, they will purposely give wrong answers, unaware the deceiver is being deceived. (p. 354)

Attorneys or experts attempting to put such logic to the test could, of course, wind up concluding precisely the opposite of what has truly occurred, thus wrongly ascribing a classification of malingering. Scant mention was made in this article of freestanding tests of malingering. The only such measure mentioned by name was what the author identified as the “Memorization of Fifteen Items Test,” curiously described as one that “asks the respondent a series of questions, forcing the respondent to choose from two alternatives, only one of which is correct” (Friedland, 1998, p. 354). Even more problematically, this measure was offered as an example of the utility of “probability theory” in malingering assessment, with the following explanation: Probability theory is premised on the assumption that a person asked to answer a large number of basic true–false questions will, based on random chance alone, answer approximately fifty percent of

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the questions correctly. If a person answers less than fifty percent of the questions correctly—which is worse than random chance—this yields an inference of intentional failure. (p. 354)

Properly trained forensic evaluators are aware, of course, that statistical chance is defined by a range of responses and not a single bright-line cutting point. For example, during the period in which this article was generated, Tombaugh (1996) explained with regard to a 50-item trial of Test of Memory Malingering (TOMM) that “the 95% confidence interval for chance performance” would rest with a range of 18 to 32, as opposed to resting precisely at 25.” He then concluded that “scores below 18 are unlikely to occur by chance” (p. 19). Overall, with respect to “such a test” Friedland (1998, p. 355), deferred to an earlier assertion by fellow attorney Cahn (1995) that these were “accepted based more on past hope than current research” (p. 321). Malingering Unpacked

In 2001, Drogin (a psychologist and lawyer) offered attorneys a first look within the confines of one of their own publications at the broader notion of “dissimulation.” This was accomplished by means of citing the second edition of Clinical Assessment of Malingering and Deception (Rogers, 1997) as the basis for explaining that “malingering” was not the only scientifically studied form of inaccurate responding. “Malingering” was described, instead, as just one form of “dissimulation” among response styles such as “defensiveness,” “irrelevant responding,” “random responding,” “honest responding,” and “hybrid responding” (p. 711). Citing scientific characteristics and uses of freestanding malingering assessment measures, Drogin (2001) also encouraged counsel to accept that “the presence of malingering, even when accurately classified, does not automatically mean that litigants are not experiencing sufficient impairment to meet a requisite legal standard” (p. 712), urging attorneys to consider, for example, the situation of a capital murder defendant whose legitimate mental illness is accompanied by limited intellectual ability and dependent personality characteristics. Spurred on by the admonition of his lawyer not to “hold anything back” about his condition, and encouraged by his cellmate to “tell the doctor how crazy you are, so they won’t give you the death penalty,” this individual pursues his own agenda when examined

by a forensic psychiatrist. When he goes out of his way to endorse every fanciful hallucination he can fabricate, the defendant is properly assigned a diagnosis of malingering. Lost in the shuffle, however, are the paranoid delusions he was actually suffering at the time of the crime, amounting to a state of diminished capacity that, if detected, would have reduced his murder charge to one of manslaughter. Such situations have their civil analogues—for example, the plight of a woman injured in a workplace accident who has endured examination after examination in the context of seemingly endless worker’s compensation proceedings. Suffering acutely from chronic pain, she has been denied access to treatment because medical benefits from her former employer have been terminated. . . . The examinee’s deliberately incorrect answers are recognized as such by the psychologist, who correctly applies a diagnosis of malingering, even though there exists, in fact, a significant degree of disability preventing gainful employment for the foreseeable future. (pp. 712–713)

This article also distinguished malingering from factitious disorder, examined each component of DSM-IV (American Psychiatric Association, 1994) malingering indicators in detail for potential legal relevance, and offered that the conclusion that a person manufacturing or exaggerating symptoms of mental illness or disability must therefore be free of mental illness depends upon the acceptance of one or both of two inherently flawed propositions. The first is that persons must lack mental illness to make false assertions; the second, that persons with mental illness always tell the truth. (Drogin, 2001, p. 712)

The import of this admittedly wordy passage was that counsel should not assume, in effect, either that “those who misrepresent symptoms don’t have any symptoms at all” or that “persons with symptoms never misrepresent them.” It does not assist the legal system to conclude that persons who misreport cannot be subject to a disability, or to patronize persons with disabilities by insisting that they would never exaggerate their conditions out of misunderstanding, frustration, or desperation. Malingering for Prosecutors

Rubenzer (2004), in a law journal entitled The Prosecutor, provided the following guidance, in a fashion that no doubt struck a chord with its intended audience. Here, importantly, legal professionals found for the first time an overt reference



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to ways in which mental health experts may manipulate a forensic evaluation with respect to the issue of malingering: Of course, information that is shared with the evaluator may be shared with the defendant or his attorney, so prosecutors must weigh sharing information with the defense that is not even discoverable against withholding information from an honest evaluator. If the sense is that the evaluator is a “hired gun,” providing such information will not change the evaluator’s conclusions and may enable him to prepare for your cross-examination more thoroughly. On the other hand, a truly neutral evaluator may take information gleaned from the prosecutor—say, the 911 tape where the defendant is heard calmly and accurately reciting a lengthy address and giving one false explanation for the victim’s injuries—and compare it to the defendant’s story to him of a frenzied attack that took place without premeditation or intent, and come to the conclusion that the defendant was sane at the time of the offense and currently malingering and lying to avoid responsibility. (p. 45)

The ethically intriguing notion of an active decision-making process for attorneys in whether to make clinically relevant information available, based on an assessment of the bona fides of the examiner in question, is presumably without parallel anywhere else in the legal—or, for that matter, scientific—literature. While asserting that “some forensic evaluators are overzealous” (Rubenzer, 2004, p. 41) and questioning whether some are even “willing to look for” malingering (p. 43), Rubenzer—a forensic psychologist—did acknowledge in this article (pp. 44–45) that “the presence of exaggeration or even faking cannot definitively rule out the presence of a mental disorder or the possibility the person might be incompetent (or legally insane at the time of the offense).” How to “Detect” Malingering

In 2005, Tysse—a practicing large-firm attorney— undertook to address perceived drawbacks to both the primacy of malingering in legal considerations and the allegedly shoddy way in which this phenomenon was assessed. He referred in the context of a law review article addressing amnesia and trial competency to “the legal preoccupation with malingering,” finding that this perspective, “though inconsistent and perhaps not theoretically sound, is not absurd” (p. 378). Maintaining that “most of the expert testimony in courts is little more than

educated guesses by psychological experts untrained in the finer points of memory loss,” Tysse called for improved measures that might “convince courts to reconsider the theoretical bases of their amnesia approaches and their competency jurisprudence generally” (p. 379). He singled out “two techniques [that] deserve special mention” (p. 382) in this regard: The first is “Symptom Validity Testing” (SVT), where “the defendant is asked a series of dichotomous (true–false) questions about the crime and the circumstances under which it took place” (Cima et al., 2002, pp. 29–30). With purely random guessing, the defendant’s answers should be correct about 50% of the time, thus, “individuals who perform significantly below chance avoid correct alternatives, which means that they have knowledge about the correct answers, and [implying] that they are feigning memory impairment” [Cima et al., 2002, p. 30]. The researchers describe several studies in which suspected or confirmed malingerers in forensic settings showed a response score significantly below chance. The other promising method for forensic evaluators to detect malingering “is provided by selfreport questionnaires that capitalize on the tendency of malingerers to exaggerate their memory complaints” [Cima et al., 2002, p. 30]. Specifically, the researchers describe the “Structured Inventory of Malingered Symptomatology” (SIMS) questionnaire, which consists of a series of self-report questions where defendants are asked to answer questions about the way they experience amnesia, under the theory “that malingerers will exaggerate and so will endorse bizarre, unrealistic, and atypical symptoms” [Cima et al., 2002, p. 30]. Like the SVT, studies with SIMS found excellent results, identifying 90% or more of the malingerers correctly, with similarly promising results in forensic settings. (Tysse, 2005, pp. 382–383)

Here, one finds a rendering of the notion of “chance” that, while not particularly detailed, is at least not overtly misleading in content. SVT, however, was acknowledged in forensic neuropsychological circles—some time before the publication of this article—to have “move[d] beyond the standard two-item forced choice recognition test, concealing the level of chance responding through sophisticated administrative manipulations” (Bianchini, Mathias, & Greve, 2001, p. 29). Also, the SIMS, while it indeed addresses amnesia, also concerns itself with additional constructs such as psychosis, low intelligence, neurological impairment, and affective disorders (Merckelbach & Smith, 2003).

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Malingering and Coaching

Lawyers consider it bad enough that litigants would exaggerate or fabricate symptoms, and even worse that their own attorney colleagues would become complicit in such shenanigans. Hickling, Blanchard, and Hickling (2006)—two psychologists and a research assistant—described how, in the context of personal injury law, “sometimes the question becomes whether a person is exaggerating his or her symptoms or even faking those symptoms in an effort to realize financial gain” (p. 626). Citing documented reports of “lawyers coaching clients to fake symptoms prior to a psychological evaluation,” these authors identified “a growing concern about whether an individual may be exaggerating or even faking his or her disorder,” and explained: With psychological disorders, we are often required to base our diagnoses on the history and description of symptoms given to us by a patient. There are not x-rays, CAT scans, or other medical tests that can definitively diagnose these conditions. Even our uses of psychological testing and psychophysiological assessment have been found problematic. All carry risks and problems about their sensitivity and their ability to discriminate true cases from simulated efforts. Some recent articles have even warned about the coaching of psychological symptoms that can take place prior to an evaluation. Some have argued that with the availability of information today, it is nearly impossible to have a truly naïve individual show up for an evaluation without any ideas of what possible symptoms are probed, and even ways that are used to detect malingering. (Hickling et al., 2006, p. 627)

This last assertion was one guaranteed to raise the suspicions—and, indeed, the hackles—of civil and criminal legal practitioners alike, but presumably the notion of an examinee’s almost certain ability to “show up” prepared to subvert the process would find a more supportable home in the broad-spectrum personal injury realm than that of, say, criminal trial competency, given the various preexisting cognitive deficiencies routinely uncovered in that context (Tussey, Marcopulos, & Caillouet, 2013). A “Balanced Approach” to Consultation

The preceding articles occurred over the course of two decades of minimal coverage of malingering in the extant legal literature, with often incomplete

and even downright misleading characterizations of assessment techniques. Against this backdrop, Drogin (2016) posited the following excerpted example of “how counsel might interact with a properly trained forensic psychologist” in getting to the bottom of “the murky and enigmatic topic of malingering” (p. 46): Counsel: Well . . . so, how did my client do? Doctor: The IQ testing would mean intellectual disability—that’s what we call “mental retardation” now—and the personality testing would mean paranoid schizophrenia . . . Counsel: Great! Well, I mean, not great, but . . . you know . . . but wait, you said “would mean.” Doctor: That’s because he also flunked Rey’s 15Item Test and the Miller-Forensic Assessment of Symptoms Test (M-FAST). Counsel: So he’s even worse? Again, I’m not necessarily saying “great,” but . . . Doctor: Those are what we call “measures of effort.” They suggest he’s malingering. Counsel: What?! He was in special ed classes since the third grade, and he’s been to the state psychiatric hospital maybe 17 times . . . Doctor: Tests don’t always tell the whole story. Counsel: Well, so far I’m not enjoying this story at all. What’s going on here? Doctor: Just because your client failed these tests doesn’t mean he doesn’t have a legally relevant disability. Counsel: Either he’s faking or he isn’t . . . and if there’s nothing wrong with him, then forget incompetency and insanity. Doctor: “Faking” doesn’t have to mean “no disability.” . . . If anyone caught “faking” is automatically competent, then anytime a criminal defendant is slated for “competency restoration” all I’d have to do is get them to lie. Counsel: I’m with you so far . . . I think. Doctor: I’d just walk into the jail and say “this may sound weird, but humor me . . . tell me that your first name is “Napoleon” and that one plus one equals three. Thanks . . . was that so hard? You’re competent, because you’re faking. Congratulations!” Counsel: What would that accomplish? Doctor: Nothing, unless you subscribe to the strange notion that anyone with mental illness who isn’t 100 percent forthcoming about it is all of a sudden “better.” . . . Counsel: Tell me how this sort of thing happens. Doctor: Maybe a defendant with PTSD is tired of seeing doctor after doctor after doctor and not getting any results. The next time there’s an



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exam, he really sells it, to make sure that finally someone “gets it.” Maybe a defendant with intellectual disability intends to tell the truth . . . but a cellmate says to her “look, this exam is your ticket out of here; make sure you get everything wrong and they’ll have to cut you loose.” Counsel: Interesting theory. Do you see this sort of thing in real life? Doctor: All the time! Think about it . . . who’s most likely to get caught faking? It’s the person who’s too psychotic or dimwitted to do it convincingly. Counsel: Do you think that’s what happened in this case? Doctor: Maybe. I can tell you this much: I read the school records you sent me, and if your client’s faking, then he’s not just normal—he’s a child genius who’s been putting up numbers in the same narrow low-IQ range since he was five years old, in order to plan the perfect crime . . . in this case, walking out of a store without paying for a candy bar. Doesn’t really fit, does it? Counsel: No. Doctor: Also, interviews with your client and his family are ringing true . . . and the jailer has a soft spot for your client. When I was leaving he said, “See if you can do something for this guy . . . he doesn’t belong here, and it’s costing us a fortune.” Counsel: This is terrific stuff. I think I understand your reasoning about the tests, but I’d just as soon not have to sell it to the prosecutor, the judge, and the jury. Can’t you just write up your report and leave out the “testing” part? Doctor: No, I really can’t. Even if it weren’t unethical to engage in selective reporting, I’m a psychologist—testing is what we do. Any halfbright prosecutor . . . would have to ask, “Why didn’t you do any testing?” The answer would have to be “I did” . . . and you wouldn’t like my answer about why I left it out of the report. Counsel: How do I fix this? Doctor: I know how hard you had to fight for the funds to hire me, so I know you won’t be able to get another psychologist—especially with a trial in three weeks. Check with your colleagues, but my advice would be this: go talk with your client, let him tell you why this happened, and then let him know everyone makes mistakes. Then I’ll go test him again—using different malingering tests—and see if he’s changed his tune. Counsel: That’s not an ideal solution, but it’s better than leaving things the way they are. Doctor: In fact, your arguments about mental health become more credible because your socalled expert flagged a problem with your client’s cooperation, didn’t just roll with whatever

your client said, and was willing to go back to get truly valid information. Counsel: Okay, let’s do this. (pp. 46–50)

Here, exposed to the time-honored “teaching” mode most likely to be effective with retaining counsel as well as with judges and juries (Tanay, 1980), counsel learns not only that malingering is a more nuanced phenomenon that anticipated but also that its mere presence does not lead inexorably to the conclusion that all—in particular, the legal case at hand—is lost. There is more to assessment than standardized testing, and there is almost always a practical approach to evidentiary setbacks that can at least serve to minimize the potential damage. Acknowledging the “balanced approach” (Drogin, 2016, p. 50) touted by the running column in which this particular article appeared, Drogin offered the following advice as a means of ensuring that no constituency would feel excluded: Prosecutors, beware the defense report that doesn’t refer to testing for malingering. It’s a problem if such testing wasn’t done. It’s an even bigger problem if a defendant’s initial lack of cooperation was never acknowledged. Yes, defense counsel can score some points by sending the expert back to get it right, but who could blame you for asking “was the defendant lying then, or is the defendant lying now?” (p. 50)

It is critical to ensure, to the extent possible, that malingering not become a polarizing legal topic that leads to one side steeping itself in the extant scientific literature and the other electing to avoid the whole notion like the plague. If malingering is reduced to a condition that prosecutors are overdriven to establish and defense attorneys dismiss as overblown, then the legal process itself is poorly served—similar to what occurs when, for example, an inclination to address antisocial personality disorder becomes the province of the prosecution than the defense (Edens & Cox, 2012).

ETHICALLY DRIVEN REPORTING AND TESTIMONY ABOUT MALINGERING Armed with a greater understanding of what attorneys are likely to “know” about malingering, how should the forensic mental health professional seek to convey the results of forensic evaluations in which this notion is a factor? A good place to start is with a review of relevant ethical obligations and

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aspirations and the path they create toward appropriate techniques for reporting and testimony.

An “Enforceable” Code and Malingering The American Psychological Association’s “Ethical Principles of Psychologists and Code of Conduct” (2002; hereinafter the “Ethics Code”) contain specific reference to psychologists’ obligations when proffering clinical opinions of any kind, including those arising in the course of “forensic activities” (p. 1061). The Ethics Code comprises not only “General Principles” as “aspirational goals,” but also “Ethical Standards” meant to serve as “enforceable rules for conduct” (p. 1061). Failure to adhere to these Standards could result in expulsion from the Association or even disciplinary and criminal sanctions levied by institutions, agencies, and other entities that have incorporated the Ethics Code “by reference” (Farrell & Drogin, 2016, p. 33). Integrity

A General Principle of “Integrity” sets the scene for ethically driven and professionally appropriate report writing and testimony, with the following broad encouragement: Psychologists seek to promote accuracy, honesty, and truthfulness in the science, teaching, and practice of psychology. In these activities psychologists do not steal, cheat, or engage in fraud, subterfuge, or intentional misrepresentation of fact. Psychologists strive to keep their promises and to avoid unwise or unclear commitments. In situations in which deception may be ethically justifiable to maximize benefits and minimize harm, psychologists have a serious obligation to consider the need for, the possible consequences of, and their responsibility to correct any resulting mistrust or other harmful effects that arise from the use of such techniques. (American Psychological Association, 2002, p. 1062)

This Principle, should one choose to take a “lawyerly” approach to interpreting it, does not tell psychologists to be accurate, honest, and truthful as much as it encourages them to find ways to ingrain these notions in professional practice. Topics, such as “fraud, subterfuge, or intentional misrepresentation of fact,” however, are addressed directly. This goes beyond Rubenzer’s concern regarding forensic evaluators unwilling to “look for” malingering (2004, p. 43) into cases—and we have witnessed this in our own practice—in which experts are caught “shaving points” on malingering

measure when raw data are subpoenaed and analyzed. We have also been involved in cases in which malingering test scores are accurately reported, but deliberately misinterpreted. The vignette offered by Drogin (2016) in which counsel urged an expert to “write up your report and leave out the ‘testing’ part” (p. 50) was not merely a cautionary tale. In our own practice we have been asked to do this by attorneys in both civil and criminal cases within the preceding year. One capital murder case found defense counsel seemingly incapable of grasping why such “selective reporting” would be unethical, and going so far as to claim that she had consulted with other experts, who had wondered why we felt compelled to “give those kinds of tests at all.” Informed Consent

Some colleagues grapple with how the presence of “measures of effort” should be conveyed to prospective examinees. Naming the tests in question or describing just how they work is clearly counterproductive, but it makes sense to convey more generally that the contemplated battery will contain tests—and portions of tests—that are designed to gauge honesty and consistency in responding. According to Standard 3.10 (“Informed Consent”) of the Ethics Code, “when psychological services are court ordered or otherwise mandated, psychologists inform the individual of the nature of the anticipated services” (American Psychological Association, 2002, p. 1065). This guidance is supplemented by Standard 9.03 (“Informed Consent in Assessments”), which calls for proffering information about the “nature and purpose of the proposed assessment services” (p. 1071). It is not necessary to specify every aspect of the informed consent colloquy in a written report, but forensic evaluators will, of course, be prepared to offer any requested specifications when testifying in court. Addressing such matters in a forthright, readily understandable fashion that avoids resort to scientific—or legal—jargon helps to avoid the possibility that jurors will resort instead to “peripheral (superficial) cues” (Koehler, Schweitzer, Saks, & McQuiston, 2016, p. 402) in reaching their decisions. False or Deceptive Statements

Consistent with the General Principle of “Integrity,” Standard 5.01 (“Avoidance of False or De-



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ceptive Statements”) specifically encompasses “statements in legal proceedings,” and directs that “psychologists do not knowingly make public statements that are false, deceptive, or fraudulent concerning their research, practice, or other work activities” (American Psychological Association, 2002, p. 1067). This infrequently cited portion of the Ethics Code lends enforceability “teeth” to what otherwise might be deemed by courts or other sanctioning authorities as little more than helpful advice. Commons, Miller, Li, and Gutheil (2012) observed that other forensic mental health professionals may be helpful to counsel in identifying expert witness bias and related concerns, with relevant initial cues, including an expert’s cultivated reputation as a willing and pliable expert for one side as opposed to another on dichotomous psychological or psychiatric issues. Interpreting Results

Not only do forensic evaluators sometimes fail to convey interpretive limitations in their reports and courtroom testimony—they sometimes, when it comes to tests for malingering and other dissimulative response styles, fail to acquaint themselves with these important considerations in the first place, on either a global or measure-by-measure basis. According to Standard 9.06 (“Interpreting Assessment Results”): When interpreting assessment results, including automated interpretations, psychologists take into account the purpose of the assessment as well as the various test factors, test-taking abilities, and other characteristics of the person being assessed, such as situational, personal, linguistic, and cultural differences, that might affect psychologists’ judgments or reduce the accuracy of their interpretations. They indicate any significant limitations of their interpretations. (American Psychological Association, 2002, p. 1072)

We have repeatedly encountered reports and testimony in recent years by experts who are unaware of the following—unlikely, to be fair, to have been anticipated—caution that is provided in the Inventory of Legal Knowledge: Professional Manual (ILK; Otto, Musick, & Sherrod, 2010): The ILK has not been specifically validated for use with intellectually or cognitively impaired populations; therefore, caution should be used when interpreting results produced by defendants with mental

retardation or other significant cognitive deficits. Ultimately, it is unclear whether such examinees possess the requisite verbal ability to comprehend the items and whether they score similarly to individuals whose intellectual and cognitive functioning are within normal limits. (p. 7)

Here, in a measure described as a “measure of feigning” with respect to “adjudicative competence” (Otto et al., 2010, p. 13), the evaluator learns that test outcomes may be questionably applicable in situations where the examinee is of substantially less than average intelligence or, arguably, suffers from the cognitive limitations that may attend more severe manifestations of acute or chronic mental illness. We recently consulted on a case in which a forensic mental health expert in a criminal matter was a “deer in the headlights” on cross-examination with respect to such ILK cautions, having failed to include them in his report or to profess awareness of them in any meaningful way, such that defense counsel—for this and other stated reasons—claimed to be considering seeking administrative sanctions. The ILK is not the only test of this nature for which examinee limitations are stated in the professional manual. Here are some representative examples: • Individuals who are severely decompensated or who have profound cognitive impairment should not be administered the M-FAST [Miller Forensic Assessment of Symptoms]. Individuals who are mentally retarded or of borderline intellectual ability may also have considerable difficulty understanding the items. Clinicians should remember that some individuals may attempt to malinger both psychiatric illness and cognitive impairment. The M-FAST may be appropriate in these cases despite the examinee’s apparent impairment on cognitive testing. Additionally, individuals who are not fluent in English may provide invalid responses because of their inability to sufficiently understand the nuances of the items. (Miller, 2001, pp. 5–6) • Computations to determine sentence length and word length were performed. . . . Use of the Dale–Chall procedure for the PDS indicates a North American fifth grade reading level. As such, the PDS [Paulhus Deception Scales] can confidently be administered to those respondents age 16 years or older. (Paulhus, 1998, p. 3) • The SIRS-2 [Structured Interview of Reported Symptoms, 2nd Edition] is intended to be used with older adolescent and adult populations. . . . At present, the SIRS-2 lacks sufficient data on effect sizes and cut scores to be employed with younger

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adolescents in clinical practice. . . . Importantly, the SIRS-2 should not be used in cases where is a valid determination of moderate or severe mental retardation. . . . As with all standardized measures, care must be taken that the severity of the impairment does not invalidate the findings. For example, grossly psychotic thinking may compromise an examinee’s ability to attend to and respond to SIRS-2 inquiries. (Rogers, Sewell, & Gillard, 2010, p. 14)

Aspirational Guidelines and Malingering The American Psychological Association’s “Specialty Guidelines for Forensic Psychology” (2013; hereinafter the “Specialty Guidelines”) are acknowledged by the authors to be “aspirational in intent” and neither “mandatory” nor “exhaustive.” Indeed, the authors go out of their way to characterize the Specialty Guidelines as “not intended to serve as a basis for disciplinary action or civil or criminal liability” and that that the “standard of care is established by a competent authority, not by the Guidelines” (p. 8). Despite these sweeping disclaimers, the Specialty Guidelines remain worthy of consideration—as do largely similar “Ethics Guidelines for the Practice of Forensic Psychiatry” (2005) promulgated by the American Academy of Psychiatry and the Law. Experts’ credibility could, of course, still be questioned if they failed to “aspire” in even one of the recommended instances. Impartiality and Fairness

According to Guideline 1.02 (“Impartiality and Fairness”): When conducting forensic examinations, forensic practitioners strive to be unbiased and impartial, and avoid partisan representation of unrepresentative, incomplete, or inaccurate evidence that might mislead finders of fact. This guideline does not preclude forceful presentation of the data and reasoning upon which a conclusion or professional product is based. (American Psychological Association, 2013, p. 9)

With this advice in mind, the reader is encouraged to contemplate the following fictitious examples of forensic psychological report excerpts, each based on the same outcome on an administration of the M-FAST. A reasonably good-faith argument could be made that none of these excerpts—arranged in increasing order of anti-examinee negativity—would be “inaccurate,” at least to the apparent extent that scores are consistent across the

board. None of the assertions is entirely untrue, however “partisan” each is, but perhaps #3 may be. Given that almost all of them contain some degree of unique information, perhaps each of the others could be deemed “incomplete” if only by comparison, but it is rarely possible (and almost never feasible) to include every single scrap of potential information in a forensic report—at least, a report than one expects anyone to read. The notion of what is “unrepresentative” is a difficult one to pin down, but few would deny that #5 is nothing if not “forceful.” 1. “The examinee’s raw score of 6 (out of a possible 25) points on the M-FAST rests right at the cutoff point for determining inconsistent responding. In light of the standard error of measurement, no definitive assessment could be made with respect to this instrument.” 2. “The examinee’s raw score of 6 (out of a possible 25) points on the M-FAST would normally generate some concerns about accuracy; however, this court should bear in mind that—if valid—the score in question is only barely significant and represents a variance from the norm that turns upon only 4% of the total items available. It is also worth considering that the M-FAST was only one of the tests administered and that none of those measures raised the specter of invalidity.” 3. “The examinee’s raw score of 6 (out of a possible 25) points on the M-FAST is considered a significant finding. According the test manual, ‘examinees who endorse six or more M-FAST items are presenting in a manner highly suggestive of malingering.’ The test manual provides the following example for reporting such outcomes: ‘The examinee’s Total score was significantly elevated, indicating that this individual may be malingering mental illness.’ ” 4. “The examinee’s raw score of 6 (out of a possible 25) points comports with the presence of malingering.” 5. “The examinee’s raw score of 6 (out of a possible 25) represents a positive finding for the presence of malingering. As this Court is aware, ‘malingering’ is a form behavior often pursued for the purpose of ‘evading criminal prosecution,’ and has been proven to be associated to correlate with antisocial personality disorder. As a result, this Court is encouraged to consider whether any of the examinee’s self-servings assertions should be taken at face value.”



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Along these lines, Rogers and Granacher (2011) pointed out that that when it comes to “communicating conclusions about malingering to the courts and other decision makers,” it is often “equally important to clarify what the term does not mean” (pp. 674–675, emphasis in original). They provided the following example, which in overall styling contrasts instructively with the preceding examples of attempts to convey results from the M-FAST: The claimant is inconsistent and unreliable in describing her Axis I symptoms; therefore, it was critically important to rely more on collateral sources than her own account. It is my conclusion that she was generally attempting to disclose her symptoms, but was hampered by the severity of her impairment. I found no credible evidence during interviews and testing that she was attempting to fabricate or grossly exaggerate her symptoms. (p. 675)

Multiple Sources of Information

Not only should evaluators avoid “relying solely on one source of data”—they should also avoid relying, without a very good explanation, on one source of data to the exclusion of other sources that appear to tell a different story. We have encountered innumerable forensic reports and associated testimony that ascribed malingering when the examinee passed a handful of tests for dissimulation but managed to fail just one, literally “by the numbers,” without any disclosed investigation of the reasons for which one test’s results were favored over the others. Guideline 9.02 (“Use of Multiple Sources of Information”) recommends that forensic practitioners ordinarily avoid relying solely on one source of data, and corroborate important data whenever feasible. . . . When relying upon data that have not been corroborated, forensic practitioners seek to make known the uncorroborated status of the data, any associated strengths and limitations, and the reasons for relying on the data. (American Psychological Association, 2013, p. 15)

As with any other incidence of conflicting data, the ethically supportable strategy is one that enables a frank discussion as to why different tests wind up yielding different results. It may include consideration of different items, variable item difficulty, and significant intervals in the course of extant legal proceedings. Logical explanations may therefore conceivably include some combination of the following, in no particular order:

 1. One test was more comprehensive, reliable, and or valid than another; for example, one was a “screen,” while the other was a highly evolved, comprehensive measure of malingering.   2. Two tests, essentially equivalent in approach and content, were administered on different dates, during which the status of the case or the examinee’s own emotional status may have been markedly distinct.  3. For reasons unknown, the examinee malingered when taking one of the tests, but not when taking another test.   4. The examinee may have possessed the optimal reading or receptive oral vocabulary skills for effectively understanding the items in one of the tests, but was confused as the result of a less than optimal ability to grasp the literal meaning or broader import of some items in another test.   5. One test addressed or included different specific content or themes than another test, although from a broader perspective the two tests were typically marketed and commonly assumed to be equivalent in such aspects.  6. One test was administered by an examiner whose overall level of training, available time to conduct an assessment, or unexplored personal or professional biases differed markedly from those of another examiner who administered another test.  7. One test was designed for or normed upon a different ethnic group, cultural minority, gender, or age cohort than those that were targeted—or simply ignored—during the development of another test.   8. As a result of exposure to multiple malingering tests, it eventually became apparent to the examinee what was the true import of these measures—a realization that dawned during the administration of one of the tests, but after the administration of another test.   9. Initially diligent and focused on accurate and truthful responding when taking one test, the examinee became so frustrated, insulted, and fatigued by what seemed like an endless procession of psychological assessment measures that when it came time to take another test, this resulted in some dissimulative combination of malingering, defensiveness, irrelevant responding, random responding, honest responding, and hybrid responding. 10. Both tests were transparently designed to identify the presence of malingering, leading

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the examinee to engage in random responding that wound up amounting to a significantly different outcome on one test than on another test. Would it be necessary to run serially through each of these and other potential considerations in each and every forensic report, in cases where conflicting results were apparent, and perhaps in other cases as well? Certainly not. A given case may, however, lend itself to—or, indeed, cry out for—this sort of searching analysis. This is a decision that is driven not by the degree to which a case is funded, externally scrutinized, or consequential for the examinee, but rather by the extent to which a potentially problematic lack of “fit” (Scherr, 2003, p. 1) has been identified. Fellow Professionals

Guideline 7.03 (“Resolving Ethical Issues with Fellow Professionals”) addressed what may occur when evaluators differ strongly concerning not only what sort of malingering-related results are being expressed but also whether matters have progressed beyond a mere difference of opinion to some actual or potential malfeasance: When forensic practitioners believe there may have been an ethical violation by another professional, an attempt is made to resolve the issue by bringing it to the attention of that individual, if that attempt does not violate any rights or privileges that may be involved, and if an informal resolution appears appropriate. If this does not result in a satisfactory resolution, the forensic practitioner may have to take further action appropriate to the situation, including making a report to third parties of the perceived ethical violation. In most instances, in order to minimize unforeseen risks to the party’s rights in the legal matter, forensic practitioners consider consulting with the client before attempting to resolve a perceived ethical violation with another professional. (American Psychological Association, 2013, p. 14)

This advice is consistent with various provisions of the aforementioned “Ethics Code,” (American Psychological Association, 2002), but with a twist: the specter of injury to a litigant’s legal status as a by-product of a perceived reporting obligation. To the extent that the Guidelines have correctly applied the term client to the attorney retaining the evaluator, it is worth observing that this is not the only lawyer to whom this issue should be referred.

It is here that the support of one’s own counsel may wind up being particularly useful. The retaining attorney may, in fact, like nothing better than for the evaluator to escalate this matter to, for example, a state psychology board. For added momentum, the attorneys may argue to the judge that the report of the other side’s evaluator should be rewritten, redacted, deemed inadmissible, or simply ignored. Timing may be everything. What the evaluator may construe as fulfilling a debt to one’s profession may be viewed by a judge or—ironically—the state board in question as something tantamount to witness intimidation (Turner, 2006). As a general matter, evaluators would do well to hold their fire for the pendency of current legal proceedings, and also to consider along these lines that many cases become subject in short order to reconsideration, postconviction review, or appeals. Duly informed of what may be unprofessional conduct on the other side of the aisle, the retaining attorney is as capable and entitled as anyone else to approach the disciplinary arbiter in question. That attorney may also wish for the evaluator to address these issues in a pending or supplemental report, consistent with Guideline 11.05 (“Commenting upon Other Professionals and Participants in Legal Proceedings”), which repeatedly indicates that any such remarks be both “impartial” and “fair” (American Psychological Association, 2013, p. 17). Among the questions that would arise at this juncture are (1) whether the ethicality per se of the other evaluator’s methodology is truly relevant to one’s own forensic opinion; (2) whether similar concerns of intimidation or harassment arise in this slightly different context; and (3) whether it is overall unseemly and reflects badly upon the retaining attorney’s case to indulge in finger-pointing of this nature. This sort of disclosure accomplishes nothing more in the short term than to alert the other evaluator to questions that will inevitably be asked in the course of a subsequent cross-examination. Appropriate Methods

It is axiomatic that there is “more than one way to skin a cat.” Nowhere in the extant forensic mental health literature, for example, has anyone credibly posited the existence of a requisite battery of psychological tests that obviates the use of any other measure, either individually or as a fixed sequence grouping of scientific instruments. This is not to assert, of course, that “anything goes,” or that advantages do not exist in broader principles such



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as reducing the redundancy of similar tests—for malingering or otherwise—in a single evaluation (Weakley, Williams, Schmitter-Edgecombe, & Cook, 2015). Guideline 9.01 (“Use of Appropriate Methods”) states: Forensic practitioners strive to utilize appropriate methods and procedures in their work. When performing examinations, treatment, consultation, educational activities, or scholarly investigations, forensic practitioners seek to maintain integrity by examining the issue or problem at hand from all reasonable perspectives and seek information that will differentially test plausible rival hypotheses. (American Psychological Association, 2013, pp. 14–15).

These admonitions resurface the issues of appropriate test selection and interpretation addressed by other previously analyzed provisions, but with a supplemental focus on how far beyond the tests an evaluator is capable of and willing to go in order to determine whether assumptions borne of standardized assessment instruments truly capture the degree to which an examinee may or may not be indulging in dissimulative behavior. Such investigations are further supported by Guideline 8.03 (“Acquiring Collateral and Third Party Information”), which advises that “forensic psychologists strive to access information or records from relevant sources with the consent of the relevant attorney or the relevant party, or when otherwise authorized by law or court order” (American Psychological Association, 2013, p. 14). This is an increasingly commonplace avenue of forensic investigation that has lent itself to more and more sophisticated technological means (Pirelli, Otto, & Estoup, 2016).

underlying the opinions, the salient data or other information that was considered in forming the opinions, and an indication of any additional evidence that may be used in support of the opinions to be offered. The specific substance of forensic reports is determined by the type of psycholegal issue at hand as well as relevant laws or rules in the jurisdiction in which the work is completed. Forensic practitioners are encouraged to limit discussion of background information that does not bear directly upon the legal purpose of the examination or consultation. Forensic practitioners avoid offering information that is irrelevant and that does not provide a substantial basis of support for their opinions, except when required by law. (American Psychological Association, 2013, p. 17)

The “accurate” presentation is one that maintains precision not just at the opinion level, but also with respect to obtaining, scoring, and interpreting the data that underlie clinical and forensic opinions, whether these are expressed in written reports or courtroom testimony. Scoring errors are as relevant to—and as potentially damaging in—testing for malingering as well as for any other circumstance, condition, or diagnosis (Frederick & Tyner, 2013).

ADDITIONAL CONSIDERATIONS Finally, there are several supplemental points that every mental health professional may wish to consider, beyond those explicitly driven by codified ethical considerations, when preparing and delivering reports and testimony in matters that involve malingering and other forms of dissimulative behavior.

Comprehensive and Accurate Presentation

Dawning Realization . . . in Real Time

Seeking to encapsulate the foregoing considerations, and achieving this goal in a straightforward fashion that neatly dovetails with standard issues in the assessment of malingering and related activities, Guideline 11.05 (“Comprehensive and Accurate Presentation of Opinions in Reports and Testimony”) opines:

Earlier in this chapter, we discussed interpretive concerns that arise when, for example, overly similar malingering measures are administered at different times, or too many malingering measures are administered at once, leading an examinee to suspect that he or she is being “set up.” Scant attention is ever paid, however, to an examinee’s dawning realization, during the course of a single measure, that a test addresses malingering—or, in the alternative, his or her erroneous conclusion that a measure of impairment is headed in the wrong direction and that the time has come, literally, to “even up the score.”

Consistent with relevant law and rules of evidence, when providing professional reports and other sworn statements or testimony, forensic practitioners strive to offer a complete statement of all relevant opinions that they formed within the scope of their work on the case, the basis and reasoning

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We recently administered the M-FAST to a male pretrial criminal detainee in order to determine whether he might be exaggerating or fabricating symptoms of major mental illness. The examinee faithfully answered item after item in a nonpathological fashion, but with visibly growing discomfort. Eventually, he wound up endorsing every fanciful notion that arose in the last dozen M-FAST items, no matter how transparently outrageous. Follow-up querying established that he had “panicked” because he was afraid that this “personality test” [sic] was “asking all the wrong questions” and thus failing to capture the legitimate symptoms that had occasioned multiple state psychiatric hospitalizations in the course of the last few years. Evaluators should remain on the alert for such phenomena, explaining these in reports and preparing to describe them during testimony.

“Malingering Culture” In some geographical areas, there exists a deeply embedded culture of benefits-driven symptom exaggeration or fabrication that works very much to the disadvantage of legitimate psychological evaluators. Professionals need not delve all that deeply into recent national news reports to find examples of lawyers and local doctors being subjected to state and federal investigations for complicity in Medicaid or Social Security fraud (Altshuler, Creekpaum, & Fang, 2008). Again and again, we have encountered civil and criminal litigants who, despite being pointedly cautioned against misrepresenting their current and prior psychological conditions, patiently wait for us to finish these admonitions, then proceed to malinger in the most shocking fashion. Subsequent inquiry in such cases often touches on the examinee’s sense that it is the current doctor who somehow does not “get it.” Reexamination may be feasible in such matters. Counsel may be able to grasp, as noted earlier in this chapter, that while problems must be acknowledged in reporting and courtroom testimony, a frank discussion of these issues and an explanation of how they were managed may be deemed acceptable and contributory by the courts.

Excessive Preparation Some examinees—perhaps guilelessly on their own, or perhaps at counsel’s manipulative encouragement—will arrive for a psychological evaluation alert to the names and even item content of

tests designed to identify the presence of malingering or other dissimulative behaviors. How difficult is this to research? Readers may be surprised to see what sorts of results they obtain when conducting a simple Google search for the terms psychology malingering tests, catch people faking, and psychological evaluations. Armed with test names and acronyms, the previously uninitiated can learn a great deal about certain measures by entering such additional search terms as MFAST, SIRS-2, Validity Indicator Profile, TOMM, and the like (Bauer & McCaffrey, 2006). Evaluators can counter such excessive preparation, at least in part, by avoiding disclosure of test names, keeping title pages of test protocols from direct view, asking examinees at the beginning of an evaluation whether they are aware of what sorts of measures will be employed, and asking examinees as that end of an examination whether they recognized any of the tests that were used. Relevant responses will find their place in well-constructed reports and optimally prepared courtroom testimony.

“Past” Malingering and Retrospective Assessments The difficulties inherent in retrospective assessment of legally relevant mental states (Simon & Shuman, 2002) are well-known to clinical and forensic practitioners alike. Three issues arise in the context of this chapter: Is an examinee malingering about prior mental illness, was an individual malingering at a certain time in the past, and what are the implications for potential present malingering of an examinee’s documented prior malingering? Malingering about Prior Illness

Evaluators need to take a very close look at items comprising the particular malingering measures being employed. To what period in the examinee’s life are allegedly exaggerated or fabricated symptoms ascribed? The SIRS-2, to cite one example, makes this easier by enabling evaluators to designate a “timeframe” for certain inquiries as part of a patterned “administration and scoring” induction sequence (Rogers et al., 2010, pp. 22–23). If an examinee’s malingered responses pertain entirely or primarily to “current” symptoms, this may limit the utility of the protocol in question for a report or testimony concerning retrospective assessment of mental states.



26. Malingering 527

Malingering in the Past

Evaluators are certainly not enjoined from mentioning the fact that an examinee is alleged—or essentially proven—to have malingered at one or more times in the recent or distant past, when expanding upon one’s own reasons for a forensic opinion premised upon recently administered testing and interviewing. Although, as noted previously, DSM-5 (American Psychiatric Association, 2013) addresses malingering in the context of certain “conditions” (pp. 726–727), this form of dissimulative behavior should not be treated as some sort of chronic disease entity that should be presumed present in the here and now, because it apparently has occurred before. Circumstances, incentives, capacity, and behavior may vary dramatically with the passage of time. Present Malingering

By the same token, even when an evaluator is entirely convinced that a current testing and examination performance is malingered, this does not function as proof positive that an examinee was malingering when examined on a prior date by the same or another evaluator. This does not mean that it is presumptively “punitive” or “unfair” to raise such issues in report writing or courtroom testimony, but interpretations of earlier examinations generally must ultimately stand or fall on their own merits, absent some newly discovered evidence of contemporaneous inadequacies, such as improperly chosen tests or test scoring errors (Drogin & Biswas, 2016).

Conflating “Malingering” with “Dishonesty” When an examinee fails a malingering test, he or she is presumed to be endorsing “false” or “grossly exaggerated symptoms” for one of a number of specific reasons—reasons that may have to do with extant legal proceedings (American Psychiatric Association, 2013). When this occurs, it does not amount to a license for evaluators to extrapolate from this behavior an assertion in a report or on the witness stand that the examinee is more globally a “liar” or that he or she is not inclined to offer truthful or accurate representations across the board. By the same token, of course, the examinee’s willingness or ability to pass a malingering test should not be touted as proof that he or she must have been responding honestly, for example, when, being questioned by the police, or when tes-

tifying under oath during an earlier deposition or court hearing.

Report Writing versus Testifying Reports about malingering reflect professional opinions developed at a certain point in time, while courtroom testimony—unless taken by avowal for future use at trial—is a product of the present moment. This crucial difference has everything to do with the fact that reports are produced from the perspective of the evaluator, and presumably under the evaluator’s sole control, while testimony is elicited by attorneys for one side or the other (and occasionally by the presiding judge) in response to their questions (Lavin, 2003). Our reason for identifying and emphasizing this distinction is that we all too often hear even the most experienced expert witnesses insist, “I’ll tell the court this” and “On cross-examination, I’ll say that.” When writing reports that address malingering and other dissimulative behaviors, evaluators should not assume that they will be allowed or otherwise able to supplement this evidence at their whim on the witness stand.

SUMMARY Mental health professionals who undertake to write reports and testify about malingering should start by assessing what their target audience understands—or, at least, thinks it understands— concerning this topic. Opinions on the presence or absence of malingering or other dissimulative behaviors should reflect mandatory codes and aspirational guidelines for ethical performance. It is important to develop and maintain a thorough grasp of notions such as internal test item analysis, the specter of a “malingering culture,” and the implications of excessive examinee preparation. Other factors include pitfalls inherent in variable time frames for malingering assessment, the conflation of malingering with dishonesty, and critical differences between report writing and testimony.

REFERENCES Altshuler, M., Creekpaum, J. K., & Fang, J. (2008) Health care fraud. American Criminal Law Review, 45, 607–644. American Academy of Psychiatry and the Law. (2005). Ethics guidelines for the practice of forensic psychiatry. Available from www.aapl.org/ethics.htm.

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American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: Author. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington, VA: Author. American Psychological Association. (2002). Ethical principles of psychologists and code of conduct. American Psychologist, 57, 1060–1073. American Psychological Association. (2013). Specialty guidelines for forensic psychology. American Psychologist, 68, 7–19. Bauer, L., & McCaffrey, R. J. (2006). Coverage of the Test of Memory Malingering, Victoria Symptom Validity Test, and Word Memory Test on the Internet: Is test security threatened? Archives of Clinical Neuropsychology, 21, 121–126. Bianchini, K. J., Mathias, C. W., & Greve, K. W. (2001). Symptom validity testing: A critical review. The Clinical Neuropsychologist, 15, 19–45. Cahn, G. (1995). Head injury: Evaluating malingering. Trial Diplomacy Journal, 18, 319–321. Cahn, G. (1997). Is your client malingering? Trial, 33(12), 77. Cima, M., Merckelbach, H., Nijman, H., Knauer, E., & Hollnack, S. (2002). I can’t remember your honor: Offenders who claim amnesia. German Journal of Psychiatry, 5, 24–34. Commons, M. L., Miller, P. M., Li, E. Y., & Gutheil, T. G. (2012). Forensic experts’ perceptions of expert bias. International Journal of Law and Psychiatry, 35, 362–371. Drogin, E. Y. (2001). “When I said that I was lying, I might have been lying”: The phenomenon of psychological malingering. Mental and Physical Disability Law Reporter, 25, 711–713. Drogin, E. Y. (2016). My client failed a malingering test: Now what? Criminal Justice, 31(2), 46, 50. Drogin, E. Y., & Biswas, J. (2016). Forensic assessment. In J. C. Norcross, G. R. VandenBos, & D. K. Freedheim (Eds.), APA handbook of clinical psychology: Vol. 3. Applications and methods (pp. 167–188). Washington, DC: American Psychological Association. Edens, J. F., & Cox, J. (2012). Examining the prevalence, role and impact of evidence regarding antisocial personality, sociopathy and psychopathy in capital cases: A survey of defense team members. Behavioral Sciences and the Law, 30, 239–255. Farrell, H. M., & Drogin, E. Y. (2016). Comparing psychiatric and psychological ethics in forensic practice. Ethics, Medicine and Public Health, 2, 27–35. Frederick, R. I., & Tyner, E. A. (2013). Rates of computational errors for scoring the SIRS primary scales. Psychological Assessment, 25, 1367–1369. Friedland, S. I. (1998). Law, science, and malingering. Arizona State Law Journal, 30, 337–393. Garner, B. A. (Ed.). (2014). Black’s law dictionary (10th ed.). St. Paul, MN: West.

Hickling, E. J., Blanchard, E. B., & Hickling, M. T. (2006). The psychological impact of litigation: Compensation neurosis, malingering, PTSD, secondary traumatization, and other lessons from MVAs. DePaul Law Review, 55, 617–633. Koehler, J. J., Schweitzer, N. J., Saks, M. J., & McQuiston, D. E. (2016). Science, technology, or the expert witness: What influences jurors’ judgments about forensic science testimony? Psychology, Public Policy, and Law, 22, 401–413. Lavin, T. J. (2003). The art of direct examination. Illinois Bar Journal, 93, 305–306. Merckelbach, H., & Smith, G. P. (2003). Diagnostic accuracy of the Structured Inventory of Malingered Symptomatology (SIMS) in detecting instructed malingering. Archives of Clinical Neuropsychology, 18, 145–152. Miller, H. A. (2001). Miller Forensic Assessment of Symptoms Test: Professional manual. Lutz, FL: Psychological Assessment Resources. Otto, R. K., Musick, J. E., & Sherrod, C. B. (2010). Inventory of Legal Knowledge: Professional manual. Lutz, FL: Psychological Assessment Resources. Paulhus, D. L. (1998). Paulhus Deception Scales: User’s manual. North Tonawanda, NY: Multi-Health Systems. Pirelli, G., Otto, R. K., & Estoup, A. (2016). Using Internet and social media data as collateral sources of information in forensic evaluations. Professional Psychology: Research and Practice, 47, 12–17. Rogers, R. (Ed.). (1997). Clinical assessment of maling­ering and deception (2nd ed.). New York: Guilford Press. Rogers, R., & Granacher, R. P. (2011). Conceptualization and assessment of malingering. In E. Y. Drogin, F. M. Dattilio, R. L. Sadoff, & T. G. Gutheil (Eds.), Handbook of forensic assessment: Psychological and psychiatric perspectives (pp. 659–678). Hoboken, NJ: Wiley. Rogers, R., Sewell, K. W., & Gillard, N. D. (2010). Structured Interview of Reported Symptoms, 2nd Edition: Professional manual. Lutz, FL: Psychological Assessment Resources. Rubenzer, S. (2004). Malingering of psychiatric disorders and cognitive impairment in criminal court settings. The Prosecutor, 38(5), 40–45. Scherr, A. (2003). Daubert and danger: The “fit” of expert predictions in civil commitments. Hastings Law Journal, 55, 1–90. Simon, R. I., & Shuman, D. W. (2002). Retrospective assessment of mental states in litigation: Predicting the past. Washington, DC: American Psychiatric Publishing. Tanay, E. (1980). The expert witness as teacher. Journal of the American Academy of Psychiatry and the Law, 8, 401–411. Tombaugh, T. (1996). Test of Memory Malingering: TOMM. North Tonawanda, NY: Multi-Health Systems.



26. Malingering 529 Turner, J. A. (2006). Going after the “hired guns”: Is improper expert witness testimony unprofessional conduct or the negligent practice of medicine? Pepperdine Law Review, 33, 275–309. Tussey, C. M., Marcopulos, B. A., & Caillouet, B. A. (2013). Neuropsychological evaluation of competency in criminal forensic contexts. Psychological Injury and Law, 6, 31–40.

Tysse, J. D. (2005). The right to an “imperfect” trial: Amnesia, malingering, and competency to stand trial. William Mitchell Law Review, 32, 353–387. Weakley, A., Williams, J. A., Schmitter-Edgecombe, M., & Cook, D. J. (2015). Neuropsychological test selection for cognitive impairment classification: A machine learning approach. Journal of Clinical and Experimental Neuropsychology, 37, 899–916.

CHAPTER 27

Evaluating Deceptive Impression Management in Personnel Selection and Job Performance Julia Levashina, PhD

The most common workplace lies: “I’m sick. I won’t be in today.” “Sorry I’m late. My alarm clock didn’t go off.” “No, I didn’t get your email.” “It wasn’t me. It was their fault.” “I’m fine, thanks.” —www.negotiations.com

Many professionals, including psychologists, consult with employers on issues of response style as part of their initial assessment of prospective hires. Deceptive impression management represents a key issue. The beginning of this discussion started in the 1920s, when researchers noted that testtakers were unlikely to provide sincere responses to paper-and-pencil personality tests (Hartshorne & May, 1928). “The subject’s answers are doubtless dictated, in part, not only by his notion as to what the test is meant to measure (notions which may be more or less incorrect), but also by his relations to the people who are going to see these answers . . . and it is seldom that his attempts at sincerity can be checked in any way” (Vernon, 1934, p. 166). Later, it was argued that people intentionally manage (e.g., create, modify, distort) the images they wish to convey to others regarding their behaviors, motivation, and other attributes, in order to gain desirable outcomes, such as job offers, favorable performance evaluations, or desirable promotions (Gilmore & Ferris, 1989; Goffman, 1959; Stevens & Kristof, 1995).

Employment interviews and psychological measures must take into consideration what is likely being masked and its potential effects on future job performance. Dissimulation is not always a detriment to work effectiveness. For example, first responders to medical emergencies may find that intentionally masking their negative emotions and reactions is an effective tool in treating lifethreatening injuries. Besides personnel selection, periodic appraisals of work performance should take into account the potential concealment of relevant psychological issues (e.g., severe depression among airline pilots).

DEFINING DECEPTIVE IMPRESSION MANAGEMENT IN PERSONNEL SELECTION During job searches, job applicants are commonly advised to sell themselves and their skills to potential employers. They are advised to show enthusiasm and ambition even when they are not enthusiastic or ambitious, to express likability

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and confidence even when they are not likable or confident, to prepare memorable stories in order to “put the best foot forward,” even when this is not the case, and provide answers signaling positive traits (e.g., assertive, achievement-oriented) while avoiding answers signaling negative traits (e.g., rigidity, lack of self-worth. See the following websites: www.wikihow.com/impress-an-interviewer; http://theundercoverrecruiter.com/how-to-impressyour-interviewer; www.dummies.com/how-to/content/how-to-perform-well-during-a-personality-test0. html; www.thesocial.ca/real-life/money/how-to-putyour-best-foot-forward-at-a-job-intervi). As such, it is likely to expect that job applicants engage in different forms of dishonest or deceptive impression management in order to control and define images they create about themselves (e.g., their motivation, skills, behaviors, experiences, and personality) while completing different employment tests during selection process. Different terms have been used to describe job applicants’ tendencies to intentionally distort their responses on psychological (i.e., noncognitive) measures to increase their chances of being hired. These terms include response distortion, socially desirable responding (SDR), impression management (IM), and faking (Ones, Viswesvaran, & Reiss, 1996). In order to define deceptive IM in job selection, I use the accumulated knowledge about this concept from the literatures on personality measures, as well as social behaviors in organizations (Levashina & Campion, 2006). In the personality literature, IM has been defined as one of the two components of SDR (Paulhus, 1984). SDR refers to the tendency to describe or present the self more favorably. It has two main components: selfdeception (SD), which refers to the unintentional tendency to describe or present the self more favorably, and IM, which refers to the situation-specific intentional tendency to describe or present the self more favorably than is warranted. Typically, IM is the primary focus in the personality literature because of its intentional nature and the implications for understanding intentional distortions in selfreports. Intentional response distortion, or faking, has been equated with the IM component of SDR. Many researchers have considered responses saturated with SD as non-deceptive responses, because they are truly believed by respondents (Zerbe & Paulhus, 1987). In the literature on social behaviors, IM has been defined as intentional or unintentional attempts to regulate and control (1) information

people present to others and (2) images others form of them (Leary & Kowalski, 1990; Schlenker & Weigold, 1992). As such, “IM is not necessarily intentional or deceptive” (Levashina & Campion, 2006, p. 300). Researchers have argued that impressions or disclosed information may reflect either what people truly believe to be true or their attempts to deceive others. During an employment interview, a job applicant may use an honest self-promotion tactic by telling an interviewer that she was a leader who successfully motivated team members to accomplish challenging tasks. She may also use a deceptive selfpromotion tactic by claiming undeserved credit for leadership, when in fact she was just a team player. The recent research on faking in employment interviews has also acknowledged the two types of IM: deceptive and honest (Levashina & Campion, 2006; Roulin, Bangerter, & Levashina, 2015; Swider, Barrick, Harris, & Stoverink, 2011; see Box 27.1). Both deceptive and honest IM commonly coexist. Job applicants may use IM to look good without being untruthful, or they may use it and be untruthful (Levashina & Campion, 2006). Moreover, job applicants may engage in deceptive IM in order to eliminate any discrepancies between what they think they can offer and what is required for the job by inventing, masking, exaggerating, or tailoring the description of their self-presentation (Levashina & Campion, 2007). Deceptive IM is especially problematic and deserves more attention from researchers because it may result in the hiring of less competent, more deceptive job applicants (Roulin, Bangerter, & Levashina, 2014; Roulin et al., 2015).

BOX 27.1.  Definition of Deceptive IM in Selection • Both types of IM—deceptive and honest— commonly coexist. • Deceptive IM in selection is an intentional distortion of responses, such as exaggeration, misrepresentation, omission, or falsification of responses on selection measures (e.g., per‑ sonality tests, biodata measures, employment interviews) to create a specific impression or to provide the best answer in order to enhance chances of being selected. • The intentional act of deceptive IM is aimed to convince an employer about something that a job applicant considers to be false.

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The definition of deceptive IM should integrate the distinctions from the personality literature (intentional vs. distortion) and that on social behaviors (deceptive vs. honest IM; Levashina & Campion, 2006). Thus, I define deceptive IM in selection as intentional distortion of responses, such as exaggeration, misrepresentation, omission, or falsification of responses on selection measures (e.g., personality tests, biodata measures, employment interviews) to create a specific impression or to provide the best answer in order to enhance chances of being selected. The intentional act of deceptive IM is aimed to convince an employer about something that a job applicant considers to be false. As such, faking (the most commonly used term to describe intentional response distortion in selection) should be equated with deceptive IM and not with IM as a whole (Levashina & Campion, 2006). In this chapter, I use the concepts of deceptive IM and faking interchangeably.

BASE RATE OF DECEPTIVE IM IN SELECTION The empirical evidence suggests that job applicants are able and engage in deceptive IM when they complete various selection measures (see Box 27.2). The ability to fake has primarily been measured in experimental settings by utilizing the induced-faking paradigm (i.e., participants are instructed to fake a test). This research demonstrates that participants are able to distort their responses when instructed to do so. Furthermore, meta-analytical evidence suggests that when participants are instructed to “fake-good” on personality mea-

BOX 27.2.  Base Rate of Deceptive IM among Job Applicants • Somewhere between 49 and 72% of job appli‑ cants distort information provided on resumes or application forms. • Somewhere between 21 and 45% of job ap‑ plicants claim nonexistent experiences or to have performed nonexistent tasks. • Somewhere between 25 and 90% of job applicants admit engaging in faking during selection. • Somewhere between 24 and 50% of appli‑ cants inflate test scores in a motivated context (e.g., high-stakes selection) compared with unmotivated context (e.g., training).

sures, they can inflate their responses by about 0.65 SD (Viswesvaran & Ones, 1999). Several approaches have been used to estimate prevalence of response distortion among job applicants. The first approach provides an indirect assessment of job applicant faking by comparing responses provided by job applicants and those provided by nonapplicants (e.g., job incumbents). This approach is based on the notion that job incumbents have little motivation to distort their responses, whereas applicants are motivated to distort their responses in order to increase their chances of getting a job. A recent meta-analysis of studies utilizing this approach demonstrates that job applicants distort their responses on personality measures by about 0.25 SD (Birkeland, Manson, Kisamore, Brannick, & Smith, 2006). Several recent large-scale empirical studies reported larger differences. On samples of 5,629 applicants and 303 job incumbents, Bott, O’Connell, Ramakrishnan, and Doverspike (2007) reported standardize difference between applicants’ and incumbents’ scores, ranging from 0.66 for emotional stability to 1.05 for conscientiousness and agreeableness. On samples of 355,738 applicants and 2,295 job incumbents, Levashina, Weekley, Roulin, and Hauck (2014) reported standardize differences between applicants’ and incumbents’ scores across 10 personality traits, ranging from 0.54 to 0.99. The second approach provides a direct assessment of applicant faking by utilizing verification of information provided by job applicants on resumes and applicant forms. Goldstein (1971) reported that 72% of 111 job applicants provided inaccurate information on the application form for the nurse’s aide position. Among these applicants, 15% claimed previous employment that they did not have, 25% provided reasons for leaving a previous job that were discrepant with information obtained from the prior employer, 41% overestimated the duration of employment by on average 16 months, and 6% underestimated the duration of employment by on average 3 months. In its 2009 Screening Index, ADP Screening and Selection Services reported that 46% of reference and credential verifications revealed a discrepancy between information provided by job applicants and what the background check revealed (www. adp.com/media/press-releases/archive/2009-newsreleases/twelfth-annual-adp-screening-index.aspx). Moreover, the discrepancies increased by 5% from 2007 to 2009. This group of studies suggests that about 50% of job applicants distort information they provide on resumes or application forms.



27.  Deceptive Impression Management in the Workplace 533

The third approach provides also a direct assessment of applicant faking by utilizing a bogus-item technique, an unobtrusive measure of faking. Job applicants should not endorse bogus items, because they ask applicants to assess their familiarity with nonexistent tasks, events, and principles. Bogus items capture deliberate deception, because endorsement of bogus items indicates that job applicants claim job-related experiences they could not possibly have, therefore providing verifiable factual evidence of deception. Pannone (1984) reported that approximately 35% (n = 221) of electrician applicants indicated that they had used a piece of electrical equipment that, in reality, did not exist. Similarly, Anderson, Warner, and Spencer (1984) found that 45% (n = 351) of applicants for state jobs indicated that they had observed or performed one or more of the several nonexistent tasks. Donovan, Dwight, and Schneider (2008) found that 21% of job applicants reported experience with at least three out of five bogus tasks. Levashina, Morgeson, and Campion (2009) reported that 24% (n = 17,368) of applicants for entry-level government jobs endorsed at least one out of three bogus items on a biodata measure. Similarly, Kung, Tristan, and O’Connell (2011) found that around 20% (n = 30,705) of job applicants for entry-level production positions in a global manufacturing company endorsed at least one out of five bogus items. The fourth approach involves asking job applicants directly to admit their faking. Keenan (1980) reported that 25% of graduates admitted to faking in interviews. Levashina and Campion (2007) reported that over 90% of undergraduate job applicants indicated that they faked during employment interviews; however, fewer job applicants reported engaging in faking that was semantically closer to lying, ranging from 28% to 75%. McDaniel, Douglas, and Snell (1997) reported that about 30% of job applicants admitted exaggerating their work experiences on resumes, exaggerating their skills in an interview, and distorting their responses to personality tests. Using the randomized response technique, Donovan, Dwight, and Hurtz (2003) reported that 30% of job applicants admitted providing completely false or fabricated responses on selection tests, and about 50% exaggerated their personality characteristics on personality tests. The fifth, and final, approach involves collecting test data from the same person in an unmotivated context (e.g., training and development on the job) and in a motivated context (e.g., highstakes selection). Several studies were conducted

utilizing this design. Two studies (Arthur, Glaze, Villado, & Taylor, 2010; Griffith, Chmielowski, & Yoshita, 2007) found that roughly 30% of job applicants faked on personality measures. Twentyfour percent of applicants were classified as fakers in Peterson, Griffith, Isaacson, O’Connell, and Mangos (2011). Donovan, Dwight, and Schneider (2014) classified 49.7% of applicants as fakers. The reported findings provide compelling evidence of job applicant faking. Job applicant faking is a common occurrence, and many applicants fake extensively across different selection tests. Job applicants distort information they provide on resumes and job applications; they fake on biodata and personality tests, and during employment interviews. Yet, they fake to a different degree, which results in variability in faking.

TYPES AND FORMS OF DECEPTIVE IM Many types of IM behaviors, tactics, or strategies have been identified and discussed in the literature (Bolino, Long, & Turnley, 2015). Yet the typology of deceptive IM has received less attention, despite the several calls to investigate it (e.g., Gilmore & Ferris, 1989). Previous research has viewed deceptive IM or faking as a relatively uniformed phenomenon (Griffith, Lee, Peterson, & Zickar, 2011), differentiating between “faking good” (creating a good impression) and “faking bad” (creating a bad impression), faking in a job-desirable (claiming qualities perceived to be important for the job) and socially desirable (claiming socially acceptable traits and denying socially unacceptable ones) ways. Recently, researchers have identified and discussed several types of deceptive IM in job selection. Levin and Zickar (2002) proposed three classes of faking on personality measures, including (1) self-presentation, (2) lies, and (3) bullshit. Self-presentation is defined as selective presentation of truthful information and accurate capabilities directed toward increasing the probability of being hired. “Of necessity, employers can only sample a small subset of an applicant’s actual capabilities. Applicants who select a set of information that they deem will increase their likelihood of selection from entirely within their true set of capabilities engage in presentation behavior” (p. 256). Lies are defined as false information and falsification behaviors directed toward increasing the probability of being hired. Finally, bullshit is defined as expedient or self-serving behaviors di-

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rected toward increasing the probability of being hired by presenting information without regard to truth or falsehood. When applicants engage in bullshit, they present a mix of truthful and false information, yet they do not use deceptiveness or honesty as the criterion for their presentations. Instead, they rely solely expediency as the criterion for their self-presentations. The authors argue that faking is closer to bullshit than to lying because faked information on a personality test is not necessarily false or true. Griffith et al. (2011) proposed four forms of faking on personality measures, including (1) self-presentation, (2) exaggeration, (3) reactive responding, and (4) fraudulent responding. The typology is based on the underlying motives and the cognitive processes of faking. Self-presentation is defined as an attempt to maintain a specific reputation; it is viewed as “a sign of social competence” (p. 342). Based on socioanalytic theory (Hogan & Holland, 2003; Johnson & Hogan, 2006), Griffith et al. (2011) argue that faking on personality tests reflects applicants’ evaluations of their reputation or how other people view them. “Applicants engaging in self-presentation will reference their self-schema and ideal applicant schema. . . . This leads to a more nuanced process of faking in which applicants balance enhancement with being true to oneself. . . . Self-presentation has the potential to be beneficial to the organization by enhancing validity of personality assessments” (p. 343). Exaggeration is defined as score elevation on important traits or attributes: “These inflations do not invalidate the responses. In the case of exaggeration, scores are still reflective of the individual but in an idealized form” (p. 344). Reactive responding is defined as “an opportunistic form of faking” (p. 344), when applicants impulsively fake items they perceive to be easily faked. “Reactive respondents will endorse items based on the perceived desirability of the item, without regard to self or ideal applicant schema” (p. 344). Finally, fraudulent responding is defined as intentional “attempts to conceal and/or fabricate information to create and maintain in another a belief that the communicator considers false. The goal of the fraudulent responder is to match the perceived ‘scoring key’ of the ideal applicant and ignore his or her selfschema” (p. 345). Levashina and Campion (2007) argue that applicants engage in four types of deceptive IM (see Box 27.3) in interviews: (1) slight image creation (SIC)—distorting prior experiences or qualifications by embellishing or tailoring answers to em-

BOX 27.3.  Types of Deceptive IM • Deceptive IM is a multidimensional construct that includes not only lying and fabrication but also exaggeration, omission, and masking. • Deceptive IM may be self-oriented or otheroriented. • Job applicants may distort their responses in job-desirable, socially desirable, or reputationcongruent ways. • Deceptive IM types may not be equally decep‑ tive (e.g., omission and exaggeration are less deceptive than fabrication). • Different types of deceptive IM may lead to different selection and work outcomes.

phasize what the interviewer is looking for to create an image of a qualified applicants; (2) extensive image creation (EIC)—intentional fabrication of prior experiences or qualification by creating stories about nonexistent experiences, inventing accomplishments that did not happen, or borrowing other people’s accomplishments or job experiences in order to create an image of a qualified applicants; (3) image protection (IP)—intentionally omitting or masking undesirable characteristics or experiences in order to protect an image of a qualified job applicant; and (4) deceptive ingratiation (DI)—insincere praise of the interviewer or organization in order to increase being liked by the interviewer or to appear likable. The Interview Faking Behavior (IFB) scale was developed and validated to measure the identified types of deceptive IM. Although they developed the typology of deceptive IM for the employment interview context, it could be applied to other selection tests. There are several common themes across described typologies of deceptive IM. First, deceptive IM is a multidimensional construct. Yet it is unclear what the dimensions of deceptive IM are. It might include not only lying and fabrication but also exaggeration, omission, masking, reactive responding, and other dimensions. Two of the typologies considered self-presentation as a type of deceptive IM (Levin & Zickar, 2002; Griffith et al., 2011). Yet it has been defined as presentation of truthful information. The proposed definition of deceptive IM emphasizes two main components: (1) deceptive IM as an intentional and deliberate action, and (2) deceptive IM designed to mislead and misinform an employer about jobrelated qualification of the job applicant. Thus, self-presentation that is based on truthful infor-



27.  Deceptive Impression Management in the Workplace 535

mation should probably be considered honest IM rather than deceptive IM. Deceptive IM includes self-presentation based on exaggerated and invented experiences, credentials, or accomplishments. Future research is needed in this area. Second, orientation of deceptive IM should be considered. Numerous typologies of IM have differentiated between self-oriented or self-focused and other-oriented or other-focused IM (e.g., Bolino et al., 2015). Self-oriented deceptive IM is used to protect a job applicant’s own needs and/ or save face. Other-oriented deceptive IM is used to protect others’ needs and/or face or to evoke an interpersonal attraction and to appear likable. Third, directionality of distortion should be considered and incorporated in the typology of deceptive IM. When applicants are motivated to create an image during the selection process, they need to determine what kind of impression they should create. Job applicants can distort their responses in a job-desirable (e.g., using an ideal applicant schema) or a socially desirable (e.g., using an ideal self-schema; Barrick & Mount, 1996) way, or they can create an image that is congruent with their reputation. Self-presentations, fueled by applicants’ perceptions of their reputation should be closer to honest IM than deceptive IM, because reputation is based on observers’ evaluations of applicants’ traits and behaviors (e.g., helpful, talkative, and competent). Fourth, not all deceptive IM tactics/types are equally deceptive. A deceptive IM that is based, or perceived to be based, on presentation of true and false information (e.g., exaggeration) may be considered less deceptive compared with the deceptive IM based on presentation of false information. Omission may be considered less deceptive than any other types of deceptive IM because of its passive nature. Different types of deceptive IM may lead to different selection and work outcomes. Future research should explore this further.

MODELS OF FAKING OR DECEPTIVE IM IN SELECTION At least 11 conceptual models have been proposed to identify predictors and antecedents of faking behavior on selection measures (Ellingson & McFarland, 2011; Goffin & Boyd, 2009; Griffith, Malm, English, Yoshita, & Gujar, 2006; Griffith et al., 2011; Levashina & Campion, 2006; McFarland & Ryan, 2000, 2006; Mueller-Hanson, Heggestad, & Thornton, 2006; Roulin, Krings,

& Binggelli, 2016; Snell, Sydell, & Lueke, 1999; Tett et al., 2006; Tett & Simonet, 2011). I briefly describe conceptual models by focusing on higherlevel predictors of faking. Lower-level predictors that comprise or influence higher-level predictors in at least three different models are summarized in Figure 27.1. Snell et al. (1999) proposed that faking on noncognitive measures is affected by ability and motivation to fake. They argue that dispositional factors, experiential factors, and test characteristics affect ability to fake. Demographic, dispositional, and perceptual factors also affect motivation to fake. This pioneering work represents the earliest attempt to describe factors that account for individual differences in faking. They called their model as “interactional model of faking,” yet the nature of the interactional process was not described. The subsequent models tried to describe the nature of interaction and incorporated an opportunity as a third high-level predictor of faking. Three models (Levashina & Campion, 2006; Tett et al., 2006; Tett & Simonet, 2011) propose that faking is a type of performance that can be understood as the joint product of opportunity, ability, and motivation. Faking would not occur when any one factor equals zero. Four models (Ellingson & McFarland, 2011; McFarland & Ryan, 2000, 2006; Roulin et al., 2016) proposed that motivation (or intention) to fake has a direct effect on faking, and ability to fake moderates that relationship. In addition, opportunity to fake was included as a moderator in two of the models (McFarland & Ryan, 2000; Roulin et al., 2016). Goffin and Boyd (2009) argued that perceived ability to fake influences motivation to fake, which leads to faking. Individual differences and contextual antecedents influence motivation and perceived ability to fake. Mueller-Hanson et al. (2006) proposed that intention to fake has a direct impact on faking. Perceptions of situation, willingness to fake, and ability to fake have an impact on intentions. Different theoretical frameworks were used to identify antecedents of motivation to fake. McFarland and Ryan (2006) used the theory of planned behavior (Ajzen, 1991). Ellingson and McFarland (2011) used the classic valence–instrumentality– expectancy theory of motivation (Vroom, 1964). Roulin et al. (2016) presented a model of applicant faking grounded in signaling theory (Bangerter, Roulin, & Konig, 2012). Griffith et al. (2011) proposed that motivation to fake is influenced by the interaction of applicants’ individual differences and their anticipatory psychological short-term

Tett & Simonet (2011)

+

Tett et al. (2006)

+

Snell et al. (1999)

  2.  Belief that faking is acceptable

Roulin et al. (2016)

Mueller-Hanson et al. (2003) +

Griffith et al. (2011)

+

Griffith et al. (2006)

  1.  Belief that faking helps getting a desired outcome (e.g., job)

Goffin & Boyd (2009)

McFarland & Ryan ((2006)

Levashina & Campion (2006)

V.   S p e ci a l i z e d A p p l ic at i o n s Ellingson & McFarland (2011)

536

ANTECEDENTS OF FAKING Motivation/Willingness/Intentions to Fake + +

+

 3. Conscientiousness

–

–

–

–

  4.  Emotional stability

–

–

–

–

  5.  Faking is a norm

+

 6. Integrity/honesty

+ –

  7.  External locus of control

– +

– + +

+

+

10.  Moral code

–

–

–

+

  8.  Low probability of getting caught while faking  9. Machiavellianism

+

+

+

+

+

+

– +

+ +

+ +

–

+

+

–

11.  Perceived behavior control (e.g., faking is easy, expectations for successful faking) 12. Self-monitoring

+

– +

+

+

Ability/Perceived Ability/Capacity to Fake 1.  Cognitive ability/GMA

+

+

2.  Communication/oral skills 3.  Emotional intelligence (e.g. empathy)

+

+

+

+

+ +

+

5.  Knowledge of the traits/constructs being measured

+

+

7.  Social skills

+

+

4.  Knowledge of the job/role prescriptions (e.g., prior job experience)

6. Self-monitoring

+

+ +

+ +

+

+

+ +

+

+

+ (continued)

FIGURE 27.1.  Framework of antecedents and outcomes of faking on noncognitive measures. “+” indicates proposed positive relationship; “–” indicated proposed negative relationship; “?” indicates undefined relationship.

Tett et al. (2006)

Tett & Simonet (2011)

Snell et al. (1999)

Roulin et al. (2016)

Mueller-Hanson et al. (2003)

McFarland & Ryan ((2006)

Levashina & Campion (2006)

Griffith et al. (2011)

Griffith et al. (2006)

Goffin & Boyd (2009)

27.  Deceptive Impression Management in the Workplace 537 Ellingson & McFarland (2011)



+

+

Opportunity to Fake/Situational Factors 1.  Empirical scoring

–

2.  Low true score on the targeted trait/construct

–

–

+

OUTCOMES OF FAKING 1.  Construct validity

?

2.  Criterion-related validity

?

3.  Factor structure

?

4.  Job offer

?

?

5.  Job performance

?

?

6.  Scale reliability

?

7.  Test scores

+

FIGURE 27.1.  (continued)

(or transactional) or long-term (or relational) contract with the hiring organization. They also argue that applicants’ assessment of the organization’s susceptibility to deception, which is formed during the selection process, will affect motivation to fake. Many causal theories concerning faking have been proposed, yet a comprehensive theory of faking in selection does not exist. Theoretical research on faking has been fragmented by a focus on faking on one selection measure at a time. Most of the described models focus on faking in the context of personality tests. In a special issue of Human Performance, which was devoted to theoretical perspectives on applicant faking behavior, Griffith and Peterson (2011) called for a better theory in faking. Yet they defined applicant faking as “a volitional attempt at increasing one’s score on a personality assessment in order to obtain a desired outcome” (p. 291, emphasis added). A comprehensive theory of deceptive IM should focus on under-

standing and predicting applicant faking across all selection measure that are part of the entire selection process and not on just one measure, such as personality tests, biodata, or interviews. For long period of time, researchers have argued that questionnaires are not all equally fakable (e.g., Furnham, 1986). The same logic might be applied to different selection methods. Selection tests may provide applicants with different opportunities to fake and may require different abilities to fake. Limited empirical findings support this notion. McFarland and Ryan (2000) have examined faking across three noncognitive measures, such as personality, biodata, and integrity tests. They found that biodata measures were the most fakable (d = 1.86), followed by conscientiousness (d = 1.82), integrity tests (d = 1.78), neuroticism (d = –1.66), agreeableness (d = 1.06), and extraversion (d = 0.98). Openness was the least fakable measure (d = 0.19). Similarly, Douglas, McDaniel, and Snell (1996) found that a biodata measure

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V.   S p e ci a l i z e d A p p l ic at i o n s

was more fakable (d = 1.23) than a personality test (d = 0.91), measuring the same constructs of agreeableness and conscientiousness. In one unpublished meta-analysis, Stanush (1997) reviewed the susceptibility of four measures to faking and found integrity tests to be the most fakable (d = 1.11), followed by biodata (d = 0.94), interest (d = 0.66) and personality inventories (d = 0.45). Alliger and Dwight (2000) explored further the fakability of integrity tests and found that the effects of faking good and coaching were much larger for overt (d = 0.90 and d = 1.32, respectively) than for covert (d = 0.38 and d = 0.36, respectively) integrity tests. Overt tests measure thoughts, expected behaviors, and feelings toward honesty, deviance, and punishment of deviance, as well as admissions of past deviant behaviors. Covert integrity tests measure general psychological characteristics (e.g., conscientiousness, impulse control) predicting future counterproductive behaviors. Finally, Van Iddekinge, Raymark, and Roth (2005) showed that the extent of faking in interviews is lower than faking on personality measures, suggesting that it is more difficult for applicants to fake on structured interviews than on personality tests. Thus, selection tests may provide applicants with different opportunities to fake and should be incorporated into the theory of applicant faking probably as one of the components of the opportunity to fake. The described models of faking identified also low-level antecedents of faking that predict or comprise high level antecedents (see Box 27.4). Across all 11 models, 57 variables were linked to motivation to fake, 30 variables were linked to ability to fake, and 21 variables were linked to opportunity to fake. Some of the variables were linked to more than one factor, including self-monitoring, coaching, self-esteem, and warnings. Figure 27.1 describes low-level antecedents that were identified in at least three different models. The most

BOX 27.4.  Modeling (or Models) of Deceptive IM • At least 11 conceptual models have been pro‑ posed to identify predictors and antecedents of faking behavior, yet a comprehensive theory of faking in selection does not exist. • Deceptive IM is affected by three high-level antecedents: (1) ability, (2) motivation, and (3) opportunity.

frequently identified antecedents of motivation to fake are Machiavellianism and self-monitoring. The most frequently identified antecedents of ability to fake are cognitive or mental abilities, knowledge of the job or role prescriptions, and knowledge of the traits being measured. The most frequently identified antecedents of opportunity to fake are low true score on the trait being measured and empirical scoring. I concur with Sackett (2011) that future conceptual research should focus more on antecedents that offer a basis for action and provide insights into ways to prevent or detect faking, and not just better understanding of faking as a phenomenon. Only two models (McFarland & Ryan, 2006; Roulin et al., 2016) have considered outcomes of faking behavior, such as distorted test scores, scale reliabilities, factor structure, and construct and criterion-related validity. Yet the identified outcomes are briefly described and not fully incorporated into the models. Future conceptual research should integrate and incorporate faking outcomes more fully into faking models, because considerable research has been conducted to examine the effects of faking on various outcomes.

OUTCOMES OF DECEPTIVE IM Deceptive IM may influence selection outcomes (see Box 27.5). It may lead to artificially increased scores on noncognitive measures, decreased score variance, changed score reliability, changed rankorder of job applicants, and likely decreased test validity. It may also influence job performance and other work-related outcomes.

Selection Outcomes Increased Scores on the Selection Measures

One of the few consistent findings in the literature is that faking leads to increased scores on noncognitive selection measures. According to the classical test theory (Anastasi & Urbina, 1997), obBOX 27.5.  Selection Outcomes of Deceptive IM • • • • •

Artificially increased test scores Decreased score variance Changed score reliability Changed rank order of job applicants Decreased test validity



27.  Deceptive Impression Management in the Workplace 539

served individual test scores comprise a true score component and an error component. When applicants fake, they increase their low true score, thus introducing a systematic error component into their observed test scores (Heggestad, 2012; Smith & Ellingson, 2002). For example, a high score on a self-rated Dutifulness scale could reflect (1) the true high score on the trait, (2) an average true score and some faking, or (3) a low true score and extensive faking. Decreased Score Variance

A few studies have demonstrated that faking decreases the variance of the observed scores (e.g., Douglas et al, 1996). When applicants fake, their observed scores, composed of low true scores and extensive faking, are similar to the observed scores of applicants who respond honestly with the true high scores on the trait. Changed Score Reliability

A few studies have investigated the impact of faking on noncognitive test reliabilities with inconsistent findings. Some researchers have argued that reliabilities of measures can be artificially increased due to faking. When applicants respond honestly, they likely report true inconsistences in their behaviors, leading to reduced homogeneity of the scale and, therefore, lower reliabilities (Douglas et al., 1996). When applicants fake, they present themselves as consistently engaging in job-desirable or positive behaviors with increased scale homogeneity and, therefore, increased reliabilities. Consistent with this notion, Douglas et al. reported slightly higher reliabilities of biodata and personality tests in faking (average rxx = 0.81) than honest (average rxx = .75) conditions. Similarly, Griffith et al. (2007) found higher reliability for faking (rxx = .93) than honest responses (rxx = .83) on a personality measure. Other researchers have found that faking decreases test reliability. Stark, Chernyshenko, Chan, Lee, and Drasgow (2001) reported lower internal consistency reliabilities for applicant (average rxx = .68, n = 1,135) than nonapplicant scores (average rxx = .74, n = 1,023). Using within-subject design, Donovan et al. (2014) also found lower reliabilities for applicant (average rxx = .28, n = 162) scores than for incumbent scores (average rxx = .83, n = 162). Future research should explore these inconsistences further.

Changed Rank Order of Applicants

The increased test scores due to faking can lead to the changed rank order of job applicants. When selection ratios are low and companies are using a top-down selection strategy, fakers have disproportionately good chances of being placed at the top of the score distribution of noncognitive measures and, thus, being hired. Several studies using various methods and designs have confirmed this notion, demonstrating the sensitivity of top-down hiring decisions to faking (e.g., Donovan et al., 2014; Douglas et al., 1996; Griffith et al., 2007; Rosse, Stecher, Miller, & Levin, 1998; MuellerHanson, Heggestad, & Thornton, 2003). Thus, when faking occurs, job applicants hired under a top-down selection strategy are likely to be fakers. Test Validity

The test validity depends on the extent to which observed test scores are saturated with true score variance. Conceptually, the test validity is undermined when test scores are saturated with error variance. It can be argued that deceptive IM represents a real threat to the validity of selection decisions. Selection decisions are valid when the required job-related knowledge, skills, abilities, and other qualifications are accurately measured during selection process. Deceptive IM can interfere with this process by decreasing the measurement accuracy, resulting in decreased accuracy of predictions made from the faked scores. For example, work performance of applicants selected on the high Dutifulness score, composed of the low true score and extensive faking, is likely to be worse than that of applicants whose high Dutifulness score reflects their true score on the trait. Although it makes sense conceptually, it is widely contested empirically. Some studies have demonstrated the decline in validity due to faking (Anderson et al., 1984; Douglas et al., 1996; Rosse et al., 1998), whereas other studies have shown a negligible effect of faking on validity (Barrick & Mount, 1996; Christiansen, Goffin, Johnston, & Rothstein, 1994; Ones et al., 1996). Similar controversial findings have been reported for both construct and predictive validity. CONSTRUCT VALIDITY

Some studies provide evidence that faking has deleterious effects on the factor structure of noncognitive tests (Cellar, Miller, Doverspike, & Klausky,

540

V.   S p e ci a l i z e d A p p l ic at i o n s

1996; Ellingson, Sackett, & Hough, 1999; Paulhus, Bruce, & Trapnell, 1995; Schmit & Ryan, 1993; Zickar & Robie, 1999). Others fail to find evidence that faking poses a threat to the construct validity of tests (Ellingson, Smith, & Sackett, 2001; Smith, Hanges, & Dickson, 2001). PREDICTIVE VALIDITY

Some researchers suggest that faking has decreases the predictive validity of noncognitive tests (Anderson et al., 1984; Douglas et al., 1996; Harold, McFarland, & Weekley, 2006; Komar, Brown, Komar, & Robie, 2008; Pannone, 1984). Other researchers have failed to establish this prediction (Barrick & Mount, 1996; Christiansen et al., 1994; Ones et al., 1996; Ones, Viswesvaran, & Schmidt, 1993; Schmitt & Oswald, 2006). Several possible explanations may explain these seemingly contradictory findings. First, the overall criterion-related validity coefficient might be not an appropriate indicator of faking (Alliger & Dwight, 2000). Simulation (e.g., Zickar & Robie, 1999; Zickar, Rosse, Levin, & Hulin, 1996) and empirical studies (e.g., Douglas et al., 1996; Pannone, 1984) have demonstrated that many applicants must engage in faking before criterion-related validity coefficients are significantly reduced. Thus, an overall criterion-related validity might not be sensitive to faking. When a sample is divided into two groups (fakers and nonfakers), criterion-related validity can be calculated for each group separately. A few studies that have employed this approach demonstrated decreased validity for fakers compared with nonfakers (e.g., Dunnette, McCartney, Carlson, & Kirchner, 1962; Pannone, 1984). For example, Pannone reported the total criterion-related validity of a biodata measure as .45 for 221 job applicants. However, when the sample was divided into faker and nonfaker subgroups, the difference in criterion-related validity was substantial (r = .26 for fakers and r = .55 for nonfakers). Second, studies with negligible impact of faking on predictive validity have measured faking with SDR scales (e.g., Hough, Eaton, Dunnette, Kamp, & McCloy, 1990; Hough, 1998; Ones et al., 1996). Accumulated empirical evidence suggests that SDR scales do not necessarily measure applicant response distortion. Some empirical studies have demonstrated that SDR scores are related to personality traits such as emotional stability and conscientiousness (e.g., McCrae & Costa, 1983; Ones et al., 1996). Other empirical studies have demon-

strated that SDR scales themselves can be faked (Ellingson et al., 1999; Griffith & Peterson, 2008). Third, different types of faking might have different impacts on criterion-related validity. Exaggeration or embellishing is likely to have much smaller effects than outright lying or fabrication. For example, it is likely that SDR scales measure exaggeration or embellishing, while bogus items tend to measure lying or fabrication. The results of studies on SDR scales failed to demonstrate that faking can adversely affect predictive validity of a selection measure. On the contrary, the results of studies with bogus items (e.g., Anderson et al., 1984; Pannone, 1984) demonstrate that faking can adversely affect predictive validity of a selection measure.

Faking and Job Performance The research that examines the relationship between faking and job performance is limited and controversial. Some researchers argue that faking leads to better job performance, whereas others suggest that faking leads to counterproductive work behaviors and other negative work outcomes (see Box 27.6). Faking Increases Performance

It has been argued that faking positively relates to performance (Hogan, 1991; Hough et al., 1990). Following socioanalytic theory, Hogan and his colleagues (Hogan & Holland, 2003; Hogan, 1996) BOX 27.6.  Two Principal Views on the Relationship between Faking and Job Performance • Faking increases performance: ||Deceptive IM as a lubricant of interpersonal relationships. ||Feigned (or exaggerated) enthusiasm as an expected signal of motivation. ||Displaying deceptive positive emotions and masking negative emotions are expected on jobs with high demands for emotional labor. ||Some types of deceptive IM are normalized in some occupational settings. • Faking decreases job performance: ||Deceptive IM may destroy individual cred‑ ibility. ||Deceptive IM may destroy interpersonal relations. ||Deceptive IM may lead to counterproductive work behaviors.



27.  Deceptive Impression Management in the Workplace 541

suggested that faking reflects self-presentations that generalize to the work context. They argue that fakers are more socially competent and likely to receive higher performance ratings. Moreover, some researchers argued that deceptive IM is expected and rewarded in occupational settings, and employees who do not engage in deceptive IM are more likely to be penalized or reprimanded (DePaulo, DePaulo, Tang, & Swaim, 1989; Hunt & Manning, 1991). Lying is a feature of everyday life found in a variety of personal, occupational and political interactions. Although telling the full truth may be formally encouraged throughout life, it is not always admired or rewarded. . . . Those who continue to tell the truth and do not understand communications as complex negotiations of formal and informal behavioral norms, find themselves in social dilemmas, and are vulnerable to a variety of labels used in everyday life like “tactless,” “undersocialized,” “deviant,” or “mentally ill.” (Hunt & Manning, 1991, p. 65)

Thus, some forms of deceptive IM are considered appropriate and ethical in work settings. DECEPTIVE IM AS A LUBRICANT OF INTERPERSONAL RELATIONSHIPS

Researchers have considered deceptive IM (i.e., deceptive ingratiation, omissions, and masking) as a lubricant of interpersonal relationships (DePaulo et al., 1989; Ekman, 1992). Employees who dislike a coworker’s personality, work habits, or taste in clothing, food, or music create and maintain better interpersonal relationships when they conceal their true feelings and opinions. Such omission and masking helps coworkers interact smoothly and perform better. The truth might hurt people’s feelings unnecessarily, potentially leading to decreased motivation and job performance. Some researchers also consider deceptive IM to be an indicator of communication competence that allows people to better manage interpersonal relations (Derlega, Metts, Petronio, & Margulis, 1993). FEIGNED (OR EXAGGERATED) ENTHUSIASM AS AN EXPECTED SIGNAL OF MOTIVATION

During employment interviews, job applicants are expected to demonstrate enthusiasm about a new employment opportunity. Low levels of displayed enthusiasm might be perceived by employers as a lack of motivation or interest in the new opportunity, resulting in potential rejection of job ap-

plicants. Thus, it might be argued that exaggerated motivation and enthusiasm about a new job is expected and desirable in the context of employment interview. DePaulo et al. (1989) labeled it as feigned affect or pretending to like something more or less than one actually does. DISPLAYING DECEPTIVE POSITIVE EMOTIONS AND MASKING NEGATIVE EMOTIONS IN JOBS WITH HIGH DEMANDS FOR EMOTIONAL LABOR

Some jobs (e.g., day care workers, flight attendants, desk receptionists, sales staff, journalists, teachers, and health care employees) require job incumbents to engage in emotional labor and to manage emotions as a part of the work roles and responsibilities. They are encouraged to suppress their felt emotions and display job-congruent emotions (Gabriel, McDaniel, Diefendorff, & Greguras, 2015). Sociologist Arlie Hochschild introduced the term emotional labor in 1983 to describe the process of manipulating emotions to fit work expectations by suppressing feelings of anger or hostility and displaying positive feelings. Typically, customer service employees are expected to express positive emotions and suppress negative emotions (Hochschild, 1983; Scott & Barnes, 2011). No matter how irate or obnoxious a customer may be, the customer service employee’s duty is to remain pleasant and to make each customer feel welcomed and comfortable. Such situations might require some deceptive IM, such as feigned affect or deceptive ingratiation. For example, waiters or waitresses may be required to pretend to like guest more than they really do. Employees skillful at deceptive IM are more likely to be successful using different emotional labor strategies. Deceptive IM might include surface acting to hide or suppress the felt emotions and show the required emotions. Another example involves doctors and first responders to medical emergencies. They may find that intentionally masking their negative emotions and reactions to be an effective tool in treating life-threatening injuries (e.g., O’Sullivan, 2009; Tuckett, 2004). Moreover, providing more optimistic prognoses to seriously ill patients may give them hope and encourage them to do better in treatment (Ruddick, 1999). NORMALIZED DECEPTIVE IM IN OCCUPATIONAL SETTINGS

Lies can be deviant or normal (Manning, 1974). Deviant lies represent socially unacceptable lies.

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V.   S p e ci a l i z e d A p p l ic at i o n s

Normal lies are socially acceptable lies used as a tool of negotiated occupational order (Ruane, Cerulo, & Gerson, 2016). Different occupations have their own unique understanding of what constitutes normal and deviant lies. Such understanding regulates behaviors of employees. When certain forms of deception are considered to be normal lies or a necessity, employees demonstrate high levels of tolerance and acceptance by receiving and engaging in such forms of deceptive IM. By contrast, when certain forms of deception are considered to be deviant lies or moral transgression, employees should demonstrate negative views and less motivation toward engaging in such forms of deceptive IM. I consider normal deceptive IM in the context of four occupations: doctors and nurses, customer service workers, police, and real estate agents.

gotiated occupational order” (p. 51). Based on an 18-month ethnographical field study, the researchers showed types of lies rewarded by police. “In the police academy, instructors encouraged recruits to lie in some situations, while strongly discouraging it in others. Learning to lie is a key to membership” (p. 54). Police normalize lying by using excuses (denying responsibility for a wrongdoing, but acknowledging its immorality) and justifications (accepting responsibility for a wrongdoing but denying its blame-worthiness). Examples of normal lies are constructed lies to rationalize drinking, sleeping on duty, staying inside during bad weather, and changing reports to avoid unnecessary paperwork. The authors noted that “in a crisis, ability to display solidarity by telling a proper and effective lie is highly valued and rewarded” (p. 65).

Doctors and Nurses. Truth telling and therapeutic lying have received a lot of attention in medical literature and clinical practice (Tuckett, 2004). In telling the truth and not telling the truth, intentions are important. To maximize treatment outcomes, the truth about a medical condition might be masked and not revealed in acute and emergency situations. Telling the truth in such situations might cause pain or a response causing death (Tuckett, 2004). Tuckett identified three principles for withholding the truth: benevolence, nonmaleficence, and uncertainty. The first two principles dictate that doctors be deceptive, because true disclosures might diminish hope and cause distress, depression, pain, or anger. As such, deception should be used to preserve and protect lives, to save another’s feelings, to avoid harm, and to promote well-being. The uncertainty principle suggests that the truth should not be revealed, because there is no certainty in health care; therefore, there is no absolute truth to be revealed.

Real Estate Agents. Ruane et al. (2016) conducted a study to examine normal lying in the real estate occupation. By utilizing three different data-gathering techniques, such as observation, personal interviews, and document analysis, they concluded that “the presence of normal lying was undeniable” (p. 94). All forms of lies were observed, including falsification (e.g., denying of the presence of the termites), omission (e.g., not mentioning that property is located in flood zones or next to multifamily units), and equivocations (e.g., a large hole in a garage floor was explained as an old tradition of making garages). Realtors do not view these lies as deviant; rather, they legitimate such lies, arguing that they are necessary and viable occupational strategies. Faking has also been shown to be positively related to selling ability (Ruch & Ruch, 1967).

Customer Service Workers. Customer service

jobs require employees to remain pleasant when dealing with irritated or obnoxious passengers, customers, or patients and enact deception in the forms of feigned affect, faked ingratiation, omission, and masking to handle irate or obnoxious customers. Thus, displaying faked positive emotions is considered an example of normal lies. Police. Hunt and Manning (1991) have examined the social context of normal lies told by the police: “Police learn to lie and to carefully distinguish normal (or acceptable) lies from unacceptable lies, suggesting that lies are a part of ne-

EMPIRICAL EVIDENCE

Barrick and Mount (1996) found that deceptive IM, measured with the Balanced Inventory of Desirable Responding (BIDR; Paulhus, 1998) during the selection process, was significantly related with increased job performance ratings (r = .17) and decreased turnover (r = –.15) among 147 job applicants hired by transportation companies as long-haul semi-truck drivers. However, they found no relationships between deceptive IM and job performance and turnover in the second sample of job applicants. O’Connell, Kung, and Tristan (2011) have found statistically significant positive relationships between deceptive IM, measured with a short version of the Marlowe–Crowne Social Desirability Scale (Crowne & Marlowe, 1960)



27.  Deceptive Impression Management in the Workplace 543

and supervisor ratings of contextual performance (r = .17, n = 391). Faking Decreases Performance

Some researchers argue that deceptive IM results in negative job outcomes. Empirical research on this topic is very limited, and more research is desperately needed. DECEPTIVE IM MAY DESTROY INDIVIDUAL CREDIBILITY

Employees try to create and manage favorable images (e.g., being well-liked, knowledgeable, effective, and sincere) and desirable reputations at work (Payne, 1989). Some researchers argue that an image of truthfulness is the prerequisite for most, if not all, of the other images people create and manage at work (DePaulo, Stone, & Lassiter, 1985). When individuals are suspected in deceptive IM, their arguments may be ineffective and their promises ignored (DePaulo et al., 1989). DECEPTIVE IM MAY DESTROY INTERPERSONAL RELATIONS

Healthy peer coworker relationships lead to enhanced job performance and well-being, because they create an emotional and task-related supportive environment (Bryant & Sias, 2011; Cahill & Sias, 1997). Deceptive IM can harm relationships, because it destroys trust among employees (Burgoon, Buller, Floyd, & Grandpre, 1996). When employees discover that their coworkers have deceived them, they try to avoid the deceptive coworkers by breaking the relationships or interacting only when work requires doing so (Bryant & Sias, 2011). Such limited and unhealthy work relationships may decrease performance, especially when job tasks require coworkers to interact frequently. DECEPTIVE IM MAY LEAD TO COUNTERPRODUCTIVE WORK BEHAVIORS

Payne (1989) argued that “the presence of many impression managing employees in an organization may actually contribute to theft motivation and reduce the effectiveness of organizational processes normally associated with controlling theft losses. . . . The individual process of impression management and resulting self-presentational biases appear to be contributing factors in actually

permitting greater incidents of employee theft” (pp. 399, 401). Employees who are skilled at IM have wide-ranging repertoires of IM tactics (e.g., denial, rationalization, minimization, neutralization, justification, blaming others) that may be used to justify or rationalize employee theft (Payne, 1989). EMPIRICAL EVIDENCE

On 391 incumbents from a global manufacturing company, O’Connell et al. (2011) found statistically significant positive relationships between deceptive IM, measured with bogus items, and personnel records of tardiness (r = .23). They have also reported negative relationships deceptive IM and production simulation scores (r = –0.19). Deceptive IM was unrelated to the supervisor ratings of task performance. For 196 applicants to manufacturing positions in a large automotive and industrial component manufacturer, Peterson et al. (2011) found that faking, measured as difference scores across applicant and research conditions, was significantly positively related to self-reported counterproductive work behaviors (r = .22). Similarly, Rosse, Levin, and Nowicki (1999) found faking to be positively related to ratings of counterproductive behaviors. In addition, Donovan et al. (2014) examined faking, measured as a difference score across applicant and research conditions, in a sample of 162 applicants for pharmaceutical sales positions. They found that fakers demonstrated lower training (d = 0.76) and sales performance (d = 0.39) than nonfakers.

DETECTION AND PREVENTION OF DECEPTIVE IM Both researchers and practitioners have tried to control job applicants’ tendencies to inflate their responses through detection and prevention of deceptive IM (Lievens & Peeters, 2008; Vasilopoulos, Cucina, & McElreath, 2005).

Detection of Deceptive IM Detection methods are designed to identify the occurrence of faking. In all these methods, items designed to measure deceptive IM are embedded in selection tests. After job applicants complete the tests, their scores are corrected in order to remove social desirability or deceptive IM biases.

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Several proposed deception detection techniques in the literature have been examined and used in the practice, including idiosyncratic item responses (Kuncel & Borneman, 2007), an eye-tracking technique (Van Hooft & Born, 2012), response time (Holden & Hibbs, 1995), and a covariance index (O’Connell et al., 2011; Spector & Brannick, 1995). Other methods involve validity scales such as SDR scales (Crowne & Marlowe, 1960), IM scales (Paulhus, 1998), unlikely virtue scales (Hough, 1998), and lie scales (Dahlstrom, Welsh, & Dahlstrom, 1975). Burns and Christiansen (2011) provide a comprehensive review of various faking detection methods, their advantages and disadvantages, and underlying assumptions. Here, I focus only on a few faking detection methods. SDR and IM Scales

SDR and IM scales have received significant research attention and are the most frequently used methods to detect faking. However, recent research has questioned the validity of SDR and IM scales as indicators of faking (Peterson et al., 2011). There is empirical evidence suggesting that SDR and IM scales are more likely to measures personality traits of conscientiousness and neuroticism (e.g., McCrae & Costa, 1983; Ones et al., 1996) than to measure response bias. Finally, empirical evidence suggests that removing SDR and/or IM does not improve criterion-related validity. As a result, researchers are arguing that SDR and IM scales should not be used to detect deceptive IM or to correct scores (e.g., Sackett, 2011). Bogus‑Item Techniques

A bogus-item technique involves asking job candidates to assess their familiarity with nonexistent tasks, events, and principles. Although this technique has received little attention from researchers and practitioners, several studies have shown that the bogus-item techniques may be effective in identifying individuals who provide deceptive responses (e.g., Alliger & Dwight, 2000; Anderson et al., 1984). For example, Anderson et al. asked job applicants for 13 different positions to rate their experience with different tasks on a job task inventory that included actual and bogus job tasks. Bogus job tasks described nonexistent tasks that were superficially similar to the job-related items (e.g., preparing routine renselaer reports, cleaning chartels, dusting votres, training new clerical staff on tertiary methods). Job applicants were warned

that (1) they should not give themselves credit for having performed tasks whose meaning they did not understand, and (2) misrepresentation of their training and experience could result in potential failure on the exam. Also, they were asked to provide names and contact information of people who could verify their past experiences. Despite these warnings, 45% of job applicants indicated that they had observed or performed at least one of the nonexistent tasks. Applicants who endorsed bogus job tasks also had higher scores on real tasks. As other evidence of its usefulness, Pannone (1984) created a single bogus item that asked job applicants for an electrician position to indicate whether they used a nonexistent piece of electrical equipment. The results indicated that 35% of job applicants endorsed the bogus item and enhanced their scores on the biographical questionnaire. Four recent studies have also demonstrated usefulness of the bogus-item technique to detect deceptive IM (Donovan et al., 2008; Levashina et al., 2009; Delgado, Tristan, Kung, & O’Connell, 2011; Kung et al., 2011). Past researchers have used this technique primarily with biodata measures. Future researchers should use the bogus-item technique in personality tests and employment interviews. Overclaiming Technique

The overclaiming technique has received renewed attention from researchers as a promising deceptive IM detection method (e.g., Bing, Kluemper, Davison, Taylor, & Novicevic, 2011). Developed by Phillips and Clancy (1972), the overclaiming technique measures a tendency to claim knowledge of nonexistent topics, events, and entities (Paulhus, Harms, Bruce, & Lysy, 2003), or to undertake a fictitious activity (Calsyn, Kelemen, Jones, & Winter, 2001). When applicants claim knowledge or familiarity with nonexistent books, poems, science, and so forth, they are assumed to be faking or overclaiming. The overclaiming technique is similar to the bogus-item technique, because both techniques focus on detecting faking via endorsement of nonexistent items. The overclaiming technique is more general and can be used across different jobs, while bogus items are job-specific. The initial evidence suggests that the overclaiming technique might be useful in measuring faking (Paulhus et al., 2003; Bing et al., 2011; Mesmer-Magnus, Viswesvaran, Deshpande, & Joseph, 2006). Yet we do not know whether the overclaiming technique will be useful in a high-stakes selection environment. It is



27.  Deceptive Impression Management in the Workplace 545

possible that job applicants might have negative perceptions of overclaiming items because of their non-job-specific content. Blatant Extreme Responding

Blatant extreme responding (BER), a type of deceptive IM, consists of endorsing socially or job-desirable extreme values on the rating scale endpoints (e.g., “1s” and “7s” on a 7-point rating scale) in order to maximize the overall score on a noncognitive measure. When applicants respond honestly to self-report noncognitive tests, they are less likely to answer with extreme alternatives and to receive maximum scores across different traits, because people possess varying levels of different traits. Past research has mainly considered extreme responding as a bias (e.g., extreme response style; Berg, 1953; Greenleaf, 1992; Zax, Gardiner, & Lowy, 1964). However, recent research has explored BER as a type of faking and a new faking detection method. It has also provided initial empirical evidence of the usefulness of BER (Konig, Mura, & Schmidt, 2015; Landers, Sackett, & Tuzinski, 2011; Levashina et al., 2014; Weekley & McKee, 2009).

Researchers have argued that the RET may decrease deceptive IM. They have found that requiring elaboration for biodata item reduces selfreport test scores (e.g., Levashina, Morgeson, & Campion, 2012; Lievens & Peeters, 2008; Ramsay, Schmitt, Oswald, Kim, & Gillespie, 2006; Schmitt & Kunce, 2002; Schmitt et al., 2003). The RET may reduce both the opportunity and motivation to fake by increasing accountability of responses and requiring applicants to engage in explanatory reflection (Levashina et al., 2012). Warnings

Warnings refers to verbal, oral, or written statements that advise job applicants to provide honest responses to self-report tests and caution applicants that deceptive responses will be detected and there might be consequences of deception. Past research provides empirical support that warning reduces faking. For example, a meta-analysis Dwight and Donovan (2003) suggested that warning reduces faking by 30% (d = 0.23). Warnings that mention the punitive consequences of faking reduce it more than warnings that do not mention such consequences. Vasilopoulos et al. (2005)

Prevention of Deceptive IM A rare consensus in the literature on faking involves the challenge of its detection. As such, researchers and practitioners are tried to develop strategies that prevent applicants from using deceptive IM (see Box 27.7). The prevention methods are designed to either decrease the motivation to fake (e.g., the response elaboration technique [RET]; Schmitt & Kunce, 2002) or decrease the opportunity to fake (e.g., imposing forced-choice responses; Christiansen, Burns, & Montgomery, 2005; Travers, 1951). RET

A recently proposed method of reducing score inflation on selection tests involves requiring respondents to elaborate their responses (Schmitt & Kunce, 2002). The RET requires job applicants to provide supporting information to justify their answers to test questions (Schmitt & Kunce, 2002). For example, for a biodata question (e.g., “During the last year, how many presentations have you made to groups of 10 or more people?”), a required elaboration might be “List the title(s) or provide brief description(s) of the presentation(s).”

BOX 27.7.  Detection and Prevention of Deceptive IM • Job applicants’ tendencies to inflate their re‑ sponses can be controlled through detection and prevention of deceptive IM. • Detection methods are designed to identify the occurrence of faking and include the fol‑ lowing: ||Bogus items ||The overclaiming technique ||Idiosyncratic item responses ||The eye-tracking technique ||Response time ||Covariance index ||Validity scales ||Blatant extreme responding • The prevention methods are designed to decrease the motivation or the opportunity to fake and include the following: ||The response elaboration technique ||Warnings ||Imposing forced-choice response formats ||Using verifiable items • Faking prevention methods might offer a better solution to control faking than faking detection methods.

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found that warning of response verification had a greater impact on personality scores for applicants with low cognitive ability (d = 0.26) than for those with high cognitive ability (d = –0.00). Past research has identified some issues associated with the use of warnings. In particular, Vasilopoulos et al. (2005) argue that warnings increase the complexity of the responses with personality test scores, and that warning conditions correlate higher with cognitive ability scores than do no warning conditions. Researchers have also raised ethical concerns about using warnings. Warnings misleadingly caution job applicants that faking can be detected, when in fact it is unlikely to happen (Goffin & Woods, 1995). To address this ethical concern, Fan et al. (2012) have proposed and examined a strategy for applicants taking computer-based test; applicants receive warnings only if identified as fakers. The results showed some promise for the proposed procedure. Despite limited research, it is recommended that warnings be included in a selection process to decrease faking on noncognitive measures. Forced‑Choice Response Formats

Forced-choice questions ask job applicants to choose among two, three, or four statements that are typically assess different traits (Graham, 1958). Statements are balanced in terms of social or job desirability. A forced-choice format reduces the opportunity to fake, because applicants cannot answer questions on the basis of job or social desirability anymore. An example of a question is: “Choose the one option that is ‘most’ like you, and the one that is ‘least’ like you: (a) Work according to a routine; (b) Adapt easily to new situations; (c) Like to gossip; and (d) Try to avoid complex people.” Despite substantial criticism throughout the 1950s and 1960s, this approach received renewed interest in the 2000s (Rothstein & Goffin, 2006). Empirical research has demonstrated that a forced-choice format may reduce faking when forced-choice statements are developed correctly (Christiansen et al., 2005; Converse et al., 2010; Jackson, Wroblewski, & Ashton, 2000). Verifiable Test Items

Item verifiability can be defined as the degree to which item responses can be confirmed or disconfirmed based on the information obtained from an independent source (e.g., school or work

records, coworkers, and managers). Researchers have suggested that verifiable (e.g., “How many graduate-level courses did you take last year?”) and nonverifiable (e.g., “How often do you spread false rumors?”) items provide different opportunities for job applicants to engage in deceptive IM (Levashina et al., 2012). Nonverifiable items allow applicants to engage in “safe” faking, because it is difficult to determine whether the truthful response was provided. On the contrary, verifiable items do not allow applicants to engage in undetectable faking. The nature of verifiable items signals to applicants that the accuracy and truthfulness of their responses may be assessed, and deceptive IM may be detected. The limited research also demonstrates that verifiable items are more valid (Harold et al., 2006) and less often faked than nonverifiable items (Becker & Colquitt, 1992). Deceptive IM is a multidimensional construct that includes various forms, ranging from mild exaggeration and omission to severe fabrication. A single universal measure of faking is unlikely (Burns & Christiansen, 2011; Rothstein & Goffin, 2006). Faking detection methods are likely to measure particular types of deceptive IM. For example, bogus items are intended to measure fabrication and are probably less sensitive to exaggeration. Thus, it is important to explore what types of faking are better measured by which faking detection method. Faking prevention methods might offer a better solution to deception than faking detection methods and should be examined further. Future research should also examine strategies that combine detection and prevention methods to mitigate faking during selection (Fan et al., 2012; Rothstein & Goffin, 2006).

SUMMARY The researchers have provided opposing assessments of the progress made in the research on deceptive IM. “The faking literature has failed to provide theory-based research that can directly inform organizational practices” (Griffith & Peterson, 2011, p. 291). In the same journal issue, Sacket (2011) argues “that we’ve made real progress in understanding faking in recent years and that much of this progress is due to new and accumulated empirical data” (p. 380). Job applicant faking or deceptive IM continues to be a controversial topic that requires future research attention.



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CHAPTER 28

Assessment of Law Enforcement Personnel The Role of Response Styles Rebecca L. Jackson, PhD Kimberly S. Harrison, PhD

A large body of literature supports the notion that job applicants tend to present themselves in the best possible light when navigating the employment process (see Levashina, Chapter 27, this volume). For example, Griffith, Chmielowksi, and Yoshita (2007) reported that between 30 and 50% of applicants produce elevated (i.e., more socially desirable) test scores on preemployment personality-based selection measures. Rosse, Stecher, Miller, and Levin (1998) found that response distortion scores of applicants were significantly higher than those of job incumbents. Furthermore, they demonstrated that scores on response distortion were more strongly correlated with those characteristics thought to be more job-relevant. In many employment settings, individuals who generate overly positive profiles are hired and go on to become well-performing employees. However, the risks associated with law enforcement selection are much greater. The potential harm that could result from an unsuitable candidate being selected on the basis of distorted assessment results is incalculable. Selection of appropriate candidates to serve on municipal and state police forces is critical to the functioning of the police force and the safety of communities. The selection of suitable individuals to serve our communities is necessarily a multifaceted endeavor, as successful officers must possess a variety of traits and abilities. For example, police officers must be in good physical health, be capable of

engaging with the public, be competent at administrative tasks, be able to follow as well as give orders, be skilled at quickly judging situations, and be willing to utilize force or restraint as the situation demands. The demands of officers may further vary depending on the location of their departments. For example, Bartol (1991) notes that in small towns, officers may work for days without encountering any significant incidents. Therefore, certain departments may demand high boredom tolerance from their officers, while busy urban departments might prefer officers who enjoy constant activity. Due to the multifaceted demands of law enforcement officers, the job selection process is also multifaceted. Similar to other personnel selection settings, law enforcement selection makes use of traditional job selection methods such as interviews, letters of recommendation, and screening tests, such as the Civil Service Exam. Unlike some other employment settings, law enforcement selection also often includes physical examinations, drug testing, and criminal background checks (Hibler & Kurke, 1995). Law enforcement agencies have also been making greater use of assessment centers in their job selection procedures (More & Unsinger, 1987). Assessment centers are generally large-scale simulations that enable potential applicants to engage in job-related tasks while being scored on a variety of domains related to their performance. They have demonstrated validity

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in predicting future job performance but are very expensive to launch and maintain (Coulton & Feild, 1995; McEvoy & Beatty, 1989). Despite their value, many departments cannot afford to adopt the use of assessment centers as part of routine job selection procedures. A final element in law enforcement selection is the psychological assessment process. According to the International Association of Chiefs of Police (2014) Preemployment Psychological Evaluation Service Guidelines, “In most jurisdictions, the minimum requirements for psychological suitability are that the applicant be free from any emotional or mental condition that might adversely affect the performance of safety-based duties and responsibilities and be capable of withstanding the psychological demands inherent in the prospective position” (p. 1). Given this high standard, law enforcement candidates are likely to be motivated to present themselves in the best possible light. Cochrane, Tett, and Vandecreek (2003) identified commonly used procedures in the selection of police personnel. Psychological testing was the fifth most commonly used procedure. Among the 155 departments that responded, psychological assessment was employed by 91.6% and was used only less frequently than background investigation (99.4%), medical exams (98.7%), interviews (98.1%), and applications forms (95.5%). Interestingly, psychological assessment was more prominent than procedures that are considered standard in law enforcement settings, such as drug testing (88.4%), or general personnel selection approaches, such as recommendation letters (46.5%) or tests of knowledge, skills, and abilities (46.5%). The use of standardized psychological measures in law enforcement personnel selection has increased over the course of a decade. For example, in 1988, Delprino and Bahn reported that 52% of police agencies conducted psychological screening, compared to 91% in 2003 (Cochrane et al., 2003). In 2014, the International Association of Chiefs of Police adopted revised guidelines for preemployment psychological evaluations. The Guidelines outline who is eligible to conduct assessments, the role of testing in the overall evaluation, and qualities the tests must possess. Specifically, the Guidelines caution professional users to be familiar with the tests and the research related to their use, particularly in law enforcement candidate selection. Under the umbrella of psychological assessment in preemployment screening reside two distinct activities. The first (screening in) assesses a candidate’s personality to identify aspects that may

be suitable for law enforcement work, while the second (screening out) identifies potential psychopathology in applicants. Similar to other employers, law enforcement agencies must adhere to the Americans with Disabilities Act (ADA; 1990) when conducting preemployment screening. Most relevant to the current discussion is that the ADA allows medical evaluations to occur only after the individual has been tendered a conditional offer of employment. In terms of psychological measures, those designed to identify psychopathology are considered medical tests. In contrast, tests of “normal” personality are not considered medical tests and are therefore permissible even in the preoffer stage of the selection process. Besides the screening discussed earlier (i.e., for psychologically unstable candidates and those particularly suited to police work), psychological assessment is also used to evaluate officers who have been found or are thought to be unfit for duty. Our goals in this chapter are to identify standardized psychological measures that are frequently used during law enforcement assessment, to describe the common response patterns on these measures, and to explain the effects of these patterns on these instruments and their results. Strong demand characteristics to distort one’s responses in a favorable manner encourage the use of psychological tests that incorporate validity scales. Common instruments used in police selection include the Minnesota Multiphasic Personality Inventory (MMPI; Hathaway & McKinley, 1940) and its revisions, the MMPI-2 (Butcher, Dahlstrom, Graham, Tellegen, & Kaemmer, 1989) and MMPI-2-RF (Restructured Form; Ben-Porath & Tellegen, 2008/2011). Besides MMPI measures, other common tests are the Inwald Personality Inventory (IPI; Inwald, Knatz, & Shusman, 1982) and the California Psychological Inventory (CPI; Gough, 1987). The first two measures are considered tests of psychopathology, while the CPI is considered a test of personality. A more recent measure to be used in the assessment of law enforcement is the Personality Assessment Inventory (PAI) Law Enforcement, Corrections, and Public Safety Selection Report (Roberts, Thompson, & Johnson, 2004). Beyond the validation data reported in the manual, this version of the PAI has not been extensively tested or adopted for use in the assessment of law enforcement. Because of its clear relevance, however, we include a brief description of the measure following our discussion of the three most commonly employed measures.

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PERSONALITY ASSESSMENT The California Commission on Peace Officer Standards and Training (POST; Spilberg & Corey, 2014) conducted a statewide analysis of personality and interpersonal requirements of successful police officers. The 10 identified personality-based competencies are outlined in Table 28.1. Many of these traits can be assessed through the use of personality testing conducted at the preoffer stage. For example, conscientiousness has long been known to be a predictor of positive work behavior across occupations and settings, as well as a predictor of problematic behavior such as poor training performance and inappropriate work behavior (see, e.g., Barrick, Mount, & Judge, 2001). Likewise, integrity test scores have been found to be positively correlated with work performance. This finding is not surprising given the high correlations found between conscientiousness and integrity. At the same time, the traits presented in Table 28.1 are rather face-valid in their relationship to desirable and productive employees, making the “right” answer on tests of these constructs evident. Additionally, POST reported that police officer candidates tend to score higher than candidates for other professions on these and related dimensions, including emotional adjustment, extraversion, tolerance, warmth, rule-consciousness, and tough-mindedness. The POST Guidelines noted, “High scores on these many positive attributes appear to indicate high psychological functioning; they may also be reflective of impression management techniques (i.e., socially desirable responding or underreporting). A reluctance to admit psychologically negative content is quite common for peace officer candidates . . . as well as other job candidates completing psychological inventories in high stakes situations” (pp. 41–42). For this reason, it is recommended that tests that TABLE 28.1.  Personality-Based Police Officer Competencies

•• Integrity/ethics •• Assertiveness/ persuasiveness

•• Conscientiousness/ dependability •• Teamwork

•• Decision making and judgement

•• Adaptability/ flexibility

•• Impulse control/attention to safety

•• Service orientation

•• Emotional regulations and stress tolerance

•• Social competence

include validity scales be employed at both points in the screening process. It is also recommended that law enforcement norms be used to compare a current applicant’s scores. The CPI is one such measure of personality.

CPI Personality tests, such as the CPI (Gough, 1987, 1996/2002), are often used in law enforcement selection to help identify those potential officers who have personality characteristics similar to those of successful officers. The CPI consists of 434 true–false items that assess a variety of personality characteristics. Cochrane et al.’s (2003) survey indicated that approximately one-fourth of police departments (24.5%) reported utilizing the CPI. The CPI yields scores on 18 dimensions that are organized into four classes: 1. Class I (Poise): Dominance (Do), Capacity for status (Cs), Sociability (Sy), Social presence (Sp), Self-acceptance (Sa). 2. Class II (Normative Orientation and Values): Well-being (wb), Responsibility (Re), Socialization (So), Self-control (Sc), Tolerance (To), Good impression (Gi), Communality (Cm). 3. Class III (Cognitive and Intellectual Functioning): Achievement via conformance (Ac), Achievement via independence (Ai), Intellectual efficiency (Ie). 4. Class IV (Role and Interpersonal Style): Psychological mindedness (Py), Flexibility (Fx), Femininity (Fe). The Good Impression Scale (Gi; a Class II variable) is used to detect positive impression management. It consists of 40 items which identified desirable high school students, who were instructed to endorse the items as if they were applying for an important job or trying to make a favorable impression. Similar to the IPI’s GD scale, the Gi scale was intended as a measure of socially desirable responding (Gough, 1987). T scores at or above 69 (raw score 31) are thought to raise the issue that an individual may be “faking good” (Gough, 1987, p. 36). In the CPI manual, Gough (1987) reports normative data on a variety of law enforcement samples, including parole and probation officers (n = 65), police officers (n = 84), and correctional officers (n = 221). Each of these occupational groups scored above the mean on Gi, though none averaged above the critical value of 69T (raw = 31). Po-



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lice officers (mean T= 62, raw = 26.18, SD = 6.30) scored significantly higher than both the parole and probation officer group (mean T = 55, raw M = 21.17, SD = 6.38) and the correctional officers (mean T = 55, raw M = 21.45, SD = 6.23). The manual does not report the number or percentage of individuals who exceeded the suggested cutoff score. In addition to the Gi scale, Gough (1987) identified a raw-score equation that was designed to improve upon the Gi scale in detecting invalid responding. The formula (44.67 + .15Do + .18Em + .35Gi – .11Wb – .13To – .12Fx) makes prominent use of the Gi scale by weighing it heavily in the equation. A cutoff score of 56.65 was reported to detect those individuals who were “faking good.” Among the 84 police officer applicants in the normative sample, this equation identified 10.7% (n = 9) of the sample as faking good. Police officers as a group achieved the highest mean score on this index (M = 53.54, SD = 2.38). To our knowledge, this index has not been independently cross-validated in law enforcement samples. Compared to the MMPI, considerably less attention has been given to law enforcement applicants’ response styles on the CPI. Hogan (1971) suggested that the response styles of highly rated officers would emphasize self-assurance and functional intelligence. Hargrave and Hiatt (1989) determined that the primary difference between problem and nonproblem officers was found in the Class II variables of the CPI. Class II variables, including the Gi scale, assess normative orientation and values. Nonproblem officers scored significantly higher than problem officers on these Class II variables, indicating that nonproblem officers present themselves on the CPI as socially desirable, mature, self-controlled, tolerant, and social. Class II variables also factor into the CPI’s Social Maturity index (Gough, 1966; Hogan & Kurtines, 1975). Using four Class II variables, plus one Class I and one Class IV variable, the Social Maturity Index was developed as a measure of responsible adaptation to the social environment. Its six scales entail Dominance, Responsibility, Socialization, Good Impression, Communality and Flexibility. First developed to discriminate between delinquent and nondelinquent youth, the Social Maturity Index had a point-biserial of .63. High scorers on this index were considered to be rational, idealistic, wholesome, clear thinking and organized. Low scorers on the index were shallow, inconsiderate, intolerant, nervous, temperamental, and frivolous.

Hogan and Kurtines (1975) tested the Social Maturity Index on two samples of law enforcement officers in Maryland and in Oakland, California. Both groups of police officers scored slightly, though not significantly, below the mean on social maturity. Unfortunately, social maturity scores failed to predict any of the performance criteria for the Oakland police officers. The CPI continues to be popular in law enforcement selection (Cochrane et al., 2003), with demonstrated validity in predicting job performance (Varela, Boccaccini, Scogin, Stump, & Caputo, 2004). However, little research has investigated the effects of response styles on the validity of the CPI. Extant research suggests that candidates who present themselves in a positive light, along with a general orientation toward normative values, will be more successful in law enforcement tasks (Hogan, 1971; Hogan & Kurtines, 1975; Hargrave & Hiatt, 1989).

POSTOFFER PSYCHOLOGICAL ASSESSMENT MMPI/MMPI‑2: Response Profiles of Law Enforcement Personnel and Applicants The MMPI and its revision, the MMPI-2 (Butcher et al., 1989), are the longest-standing and most commonly used psychological tests in police selection (71.6% of police departments endorsed its use; see Cochrane et al., 2003). The MMPI-2 consists of three “traditional” validity scales and 10 primary clinical scales. In addition, supplementary scales, content scales, and dozens of specialized indices have been developed. The newer MMPI-2-RF is rapidly being adopted for use in law enforcement selection and addresses some of the challenges inherent in utilizing the MMPI-2. A review of the research utilizing the MMPI-2 is presented below, followed by a discussion of the MMPI-2-RF. A substantial body of research in law enforcement selection utilizing the MMPI exists. However, the published research regarding a “normal” MMPI profile for law enforcement applicants is likely to be largely irrelevant to evaluations currently being conducted. Significant changes during the past two decades compel us to revisit the notion of a “normal” profile for law enforcement applicants. At least two important considerations play a role. First, the ADA (1990) triggered changes in preemployment psychological testing. The Act declared it illegal to conduct psychological evaluation that would lead to the identification of a mental or emotional disorder or impairment

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until after the applicant has been tendered a conditional offer of employment. The practical importance of this change in legislation is that tests of psychopathology, such as the MMPI-2 or IPI, may only be administered following a conditional offer of employment. Second, more women and minorities are joining the law enforcement professions than did so 40 years ago. It is unknown how closely a “normal” law enforcement profile fits nonwhite male and female applicants. Since the MMPI-2’s publication (Butcher et al., 1989, 2001), the need for updated norms, and norms particularly for law enforcement applicants, became evident (Detrick, Chibnall, & Rosso, 2001; Kornfeld, 1995). Despite reports of general concordance between the MMPI and MMPI-2 (e.g., Ben-Porath & Butcher, 1989), at least two reports have demonstrated differences between MMPI and MMPI-2 scores (i.e., Chojnacki & Walsh, 1992; Munley, 1991). Only recently have published normative data on police officer applicants become available (Ben-Porath & Tellegen, 2008/2011; Detrick et al., 2001; Kornfeld, 1995). In addition, newer data will be based on MMPI2 assessments within the parameters of the ADA. Specifically, data from pre-1990 MMPI studies may include applicants from all stages of the employment pool. In contrast, post-1990 data are more

likely from applicants who passed the first rounds of screening and have received a conditional offer of employment. Table 28.2 presents average MMPI-2 validity scale scores from three studies of law enforcement candidates (Detrick et al., 2001; Devan, 2004; Kornfeld, 1995). Each of these studies presents means and standard deviations separately by gender, and two include normative data on minority law enforcement candidates (Detrick et al., 2001; Kornfeld, 1995). Kornfeld (1995) reported normative data for a group of police officer candidates (61 European American males, 12 European American females, and 11 minority male candidates). As Table 28.2 demonstrates, across gender and ethnicity, candidates produced moderately high K scores. Furthermore, Scales 2 and 0 tend to be low across groups (T scores < 50). Male samples tended to score low on Scale 5, while females averaged a nearly clinically significant range (T = 64.20). Individuals with low scores on Scale 2 of the MMPI-2 are less likely to have easily bruised feelings, to worry, to have problems in making decisions, and to have concern about being rejected. They are also more likely to be self-confident, and to laugh and joke with people. Males scoring low on Scale 5 are described as presenting themselves as extremely masculine, as having stereotypical masculine interests,

TABLE 28.2.  K-Corrected Means (SD s) for MMPI-2 Police Selection Studies

Scale

Study

White males (n = 61a ; 395b ; 6,207c)

L

Kornfeld (1995)

56.90 (11.71)

53.45 (8.83)

60.00 (10.50)

61.1 (11.5)

65.4 (13.4)

60.00 (11.00)

Detrick et al. (2001)

F

White females (n = 12a ; 34b ; 935c)

Devan (2004)

51.53 (9.46)

   Weighted mean (SD)

52.15 (9.62)

62.54 (12.80)

51.73 (10.13)

Kornfeld (1995)

39.79 (3.07)

38.27 (3.54)

39.50 (5.35)

41.3 (4.6)

43.6 (5.2)

Detrick et al. (2001)

K

Minority males (n = 11a ; 35b)

43.3 (4.3)

43.72 (6.45)

   Weighted mean (SD)

43.54 (6.33)

42.33 (4.99)

45.79 (7.77)

Kornfeld (1995)

62.48 (6.23)

58.83 (8.09)

64.60 (5.43)

63.8 (7.2)

63.1 (7.6)

Devan (2004)

56.19 (9.47)

   Weighted mean (SD)

56.70 (9.32)

Weighted mean

(7.4)

51.32 (10.09)

Devan (2004)

Detrick et al. (2001)

Minority females (n = 3b)

67.7 (7.4)

52.16 (9.71)

(5.1)

45.96 (7.88)

63.1 (7.7)

45.7 (5.1)

43.82 (6.52)

64.3 (9.6)

56.13 (9.74) 62.08 (7.90)

56.48 (9.65)

64.3 (9.6)

56.71 (9.36)

Note. Devan (2004) reported means and standard deviations for male and female applicants, but reported ethnicity for only a subset of the sample; 93.7% of the applicants were European American. aKornfeld (1995); bDetrick, Chibnall, & Rosso (2001); cDevan (2004).



28. Assessment of Law Enforcement Personnel 557

and being action oriented. Low MMPI Scale 0 descriptors include sociable and outgoing, extraverted, and friendly. Scale 5 elevations for women (64.20) are described as typically rejecting the traditional female role and as having interests that are stereotypically more masculine than feminine. Kornfeld (1995) interpreted the high Scale 5 scores of females as perhaps unique to, or especially relevant for, females entering a traditionally maledominated profession. While females who reject the traditional feminine role may be more likely to gravitate to law enforcement and other male-dominant professions, other interpretations are possible. For example, Graham, Watts, and Timbrook (1991) reported that female students who were instructed to create a favorable impression of themselves also produced elevations on Scale 5. It is possible then, that women trying to create a favorable impression, such as in a personnel selection situation, will emphasize assertiveness, competitiveness, and an achievement orientation, which leads to elevations on Scale 5 in female test-takers. Detrick et al. (2001) reported mean scale scores for 467 police officer applicants who had received conditional job offers; they were mostly European Americans (395 men and 34 women), and few were minorities (35 men and three women). As Table 28.2 demonstrates, the pattern of scale scores is similar to those reported by Kornfeld (1995), with moderately elevated K scores and moderately depressed F scores. Detrick et al., also reported similarly low scores on Scale 0, low Scale 5 scores for male police applicants, and moderately elevated Scale 5 scores for female applicants. Devan (2004; see also the following section) examined a large sample of police officer applicants (6,207 males and 935 females) but had ethnicity data available for only 3,411 participants, mostly European Americans (93.7%). K scores were more moderate (M = 56.18) than other studies but with similar low scores on Scales 2 and 0. The majority of law enforcement applicants produce clinical profiles within normal limits. Devan, Hungerford, Wood, and Greene (2001) reviewed 42 studies in the literature that utilized the MMPI or MMPI-2 for officer screening and selection. Using averages, they found no MMPI or MMPI-2 scale elevated to the level of clinical significance. Taken together, these results suggest that despite some gender and ethnicity variation, police applicants as a whole produce somewhat defensive, but valid, MMPI-2 profiles that describe psychologically healthy people who are relatively free of

distress or worry, assertive and achievement oriented, and comfortable in the presence of others.

Altered MMPI‑2 Response Styles among Police Officer Candidates Psychologists involved in the selection of appropriate police officers are very concerned with the veracity of the officer’s self-report. Presenting oneself in a positive light is expected for job applicants. For example, Devan (2004) reported that in a large sample (N = 7,142) of law enforcement applicants, the mean MMPI-2 profile was within normal limits. However, Scale K was the most frequently elevated scale (≥65T) at 19.2%, followed by L (11.9%). The question for the psychologists is whether this is “typical” social desirability associated with positive impression management or whether the response style represents defensiveness (minimized psychopathology). In the context of personnel selection, trying to present in the most positive light may be different than concealing psychopathology. The first is typical of job applicants in general and, in most respects, these individuals continue to be acceptable job candidates. The latter type of applicant, however, is potentially unacceptable. Borum and Stock (1993) studied a small group (n = 18) of applicants for law enforcement positions, who were found to be deceptive and subsequently admitted they had intentionally lied in one or more areas of the application process. They were compared an equal number of police applicants who admitted no deception and for whom none could be detected. The authors asked the following questions: Do these two groups differ on the validity indexes of the MMPI and the IPI? Do any of these scales or indexes discriminate between the groups so that they could be used to identify candidates, who need closer scrutiny? Of particular interest, Borum and Stock (1993) proposed the Es-K scale as a potential indicator of defensive responding. Ego Strength (Es) is believed to measure a general factor of capacity of personality integration or ego strength. High Es scores are indicative of stable, reliable, responsible, and self-confident individuals. High K scores are more indicative of defensiveness in individuals trying to give an appearance of adequacy, control, and effectiveness, but who lacks self-insight and self-understanding. However, Caldwell (1988) suggested that interpretation of the Es scale is most effective when seen in comparison to the K scale. The positive characteristics of “personal organi-

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zation” associated with high Es scores seem to be more prominent when Es is increasingly higher than K. To some extent, both scales measure psychological defenses against distress. Consequently, the comparison is made to differentiate the healthy defensiveness from the intentional effort to ignore or minimize difficulties. This is identified as the Es-K Index. The Es-K scale was highly significant in the expected direction (d = 1.85; see Table 28.3). A cutoff score of –3 correctly classified 83.3% of the deceptive applicants, with a false-positive rate of only 5.5%. The Es-K scale outperformed the IPI’s validity scale (i.e., Guardedness; d = .73), as well as other MMPI indicators of impression management including K, L, and MP in terms of false positives (44.4%, 38.9%, and 16.7%, respectively) and effect sizes (see Table 28.3). Importantly, Borum and Stock’s (1993) investigation was limited to the MMPI. To our knowledge, the Es-K scale has not been validated with the MMPI-2. Finally, the notion of ego strength as a construct distinct from a more blatant attempt to present a distorted impression of oneself is similar to the distinction that has been made between self-deceptive enhancement and impression management (Paulhus & Reid, 1991).

Self‑Deception Enhancement versus Impression Management Overly positive response sets can take one of two forms: self-deceptive enhancement (SDE) or impression management (IM). SDE is similar to ego

strength, as defined earlier, in the sense that the individual engaging in this response style is exhibiting a healthy level of psychological defense and is responding in a way he or she believes to be genuine. Paulhus and Reid (1991; see also, Paulhus, 2002) characterized SDE respondents as honest in their responses despite inaccurate, highly positive views of themselves. In a sense, SDE can be said to occur outside the examinee’s conscious awareness. IM, on the other hand, is a conscious attempt to distort one’s presentation. Applicants for highstakes positions, such as law enforcement, can be expected to present themselves in overly positive terms. Individuals who genuinely see themselves in a positive light and possess a healthy amount of psychological guardedness would be more suitable for police work than individuals consciously denying difficulties or blatantly lying. Therefore, the distinction between SDE and IM within a psychological testing protocol may be an important one. The Balanced Inventory of Desirable Responding (BIDR; Paulhus, 1988), and later the Paulhus Deception Scales (PDS; Paulhus, 1998), purport to measure both SDE and IM. Compared to anonymous administrations, Lautenschlager and Flaherty (1990) found that BIDR-IM scores increased when students believed that their responses were being monitored. A similar pattern was not noted for the BIDR-SDE scale. These results demonstrated the relative sensitivity of IM and relative insensitivity of SDE to testing conditions of public versus anonymous disclosure. Furthermore, research suggests that IM is taxonic in nature (see Devan, 2004) and more situ-

TABLE 28.3.  Validity Scales for a Sample of Deceptive versus Honest Police Applicants

Deceptive (n = 18) Mean (SD)

Comparison (n = 18) Mean (SD)

F

d

58.06 (8.67)

50.22 (5.40)

10.60**

1.09

MMPI scale L K Total O-S

66.94 (5.65)

62.11 (7.33)

4.91*

0.74

–88.33 (33.19)

–71.89 (34.30)

2.14

0.49

Mp Scale

18.39 (3.78)

15.72 (3.79)

4.48*

0.71

F-K Index

–18.39 (3.48)

–15.89 (5.61)

2.58

0.54

Es-K Index

–7.00 (4.31)

2.22 (5.59)

30.72***

1.85

49.39 (8.71)

43.06 (8.59)

4.82*

0.73

IPI scale GD

Note. Adapted from Borum and Stock (1993). *p < .05; ** p < .01; ***p < .001.



28. Assessment of Law Enforcement Personnel 559

ationally dependent (Paulhus, 2002), while SDE is more closely related to traits that are present to individuals to varying degrees (Paulhus, 2002). Essentially, IM will or will not be present in a particular protocol. SDE, on the other hand, is dimensional and assumed to be present to varying degrees within all individuals. Devan (2004) divided her sample of 7,142 police officer candidates into taxon (IM present) and SDE groups; the taxon group scored higher on all of the indices thought to measure intentional positive distortion (L, ODecp [ODecp replaced the Mp scale in the MMPI-2], Wsd, K, S, So), but the differences were most pronounced for L (d = 2.98), ODecp (d = 1.32) and Wsd (d = 1.02). Devan’s findings provide additional support for Bagby et al.’s (1997) recommendation that ODecp and S (d = 0.87) are useful with normal individuals, such as job applicants, who might be presenting themselves in the most favorable light. These results suggest that the L and Wsd scales may also be important with this population. The K scale (d = 0.73) and So (d = 0.55) were less discriminating between the groups. Interestingly, the clinical scale profiles of the two police groups were essentially identical. Devan (2004) highlighted that respondents who were found to be engaging in IM through the use of specialized indices would have gone undetected using only the traditional validity scales. Moreover, IM respondents scored significantly lower on six content scales (OBS, ANG, ASP, TPA, FAM, and WRK) and six supplementary scales (MT, PK, PS, AAS, APS, and HO) and significantly higher on two supplementary scales (O-H and Re) than those in the complement group. This pattern of scores would indicate individuals who are denying negative traits such as anger and antisociality, while endorsing socially desirable traits such as confidence and a sense of duty. This may provide evidence that supplemental scales, content scales, and nontraditional validity indices may have greater sensitivity for differentiating respondents engaging in IM in preemployment psychological screening of law enforcement applicants. Devan also estimated that roughly 33% of her sample were engaging in IM, which is similar to rates in child custody settings (37%; Strong, Greene, Hoppe, Johnston, & Olesen, 1999), and higher than estimates in general personnel screening (25%; Strong, Greene, & Kordinak, 2002). In this sample of law enforcement applicants, the items contributing most to the IM taxon reflected selfconfidence, fearlessness, and bravery.

Similar findings have been reported using the Marlowe–Crowne Social Desirability Scale (MCSD; Crowne & Marlowe, 1960). The MCSD, a 33-item, true–false measure, typically is used as an adjunct to other personality assessment measures to evaluate “faking good” response styles. Reynolds (1982) developed a 13-item short form of the MCSD, which was used in a study of 241 active police officers and deputy sheriffs to examine its internal consistency and its external validity through comparison with other measures (Greenberg & Weiss, 2012). Results indicated significant correlations between the Marlowe–Crowne short form and the MMPI-2 L (r = .57) and K scales (r = .40). According to Greenberg and Weiss, the moderate range of correlations suggests that measures address slightly different aspects of socially desirable responding (i.e., a desire to present oneself to others in the best possible light) than the MMPI-2 validity scales.

MMPI Response Styles and Future Job Performance As noted, most MMPI research suggests that police officer candidates often respond in a characteristically defensive manner. However, data regarding its predictive value are lacking. Isolated reports of slight defensiveness being predictive of better job performance (Bernstein, Schoenfeld, & Costello, 1982; Neal, 1986) are offset by largerscale analyses, including a meta-analysis. For example, Aamodt (2004) conducted a meta-analysis of personality inventories and job performance in law enforcement settings. Defensiveness on the MMPI (Scales K and L) did not predict any of the job performance outcomes, including academy grades, supervisor ratings of performance, discipline problems and complaints, citizen and department commendations, and absenteeism. On the other hand, Aamodt reported that Scale F may be the most useful predictor of both poor academy performance and poor supervisor ratings. More recently, Weiss, Vivian, Weiss, Davis, and Rostow (2013) reported data from 4,348 officers who took the MMPI-2 as a condition of preemployment; officers with L scale raw scores ≥ 8 had significantly more performance problems (e.g., citizen complaints, termination for cause, inappropriate use of a weapon) than officers with lower L scores. Notably, the association between L scores and problematic behavior appears to be independent of other potentially confounding variables (e.g., level of education, ethnicity, or psychopa-

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thology). These authors recommended using a cut off score ≥ 8 on the L scale for poor police performance. With the exceptions of F and L scales reported earlier, the majority of evidence does not indicate that a linear relationship exists between individual response style scales and future job performance. However, attempts have been made to identify predictors of performance based on scales that combine response style information (via validity indices) and clinical information. For example, using the MMPI’s Lie scale as a centerpiece, Bartol (1991) developed what he termed an Immaturity Index, which consisted of the combined raw scores of Scales L, Pd, and Ma. Of 600 police officers, 44 eventually were dismissed or asked to resign due to failure to meet job performance requirements. Another 471 officers remained on the police force. Terminated officers scored significantly higher on all three scales than retained officers, and they scored significantly higher on the summed Immaturity Index (terminated officers M = 54.22, SD = 8.89 vs. retained officers M = 45.37, SD = 5.83, d = 1.44). The officers in the terminated group were described by their supervisors as immature and inappropriate in their work. These officers engaged in irresponsible behavior, such as inappropriate use of firearms, frequent accidents in police vehicles, frequent tardiness and absenteeism, and failure to complete written work on time. Using discriminant function analysis, the Immaturity Index correctly classified 74% of the officers. In addition, the Immaturity Index also correlated significantly with the MacAndrew Alcoholism Scale (MAC) scale (r = .38), which provides a measure of addiction proneness. Bartol (1991) cross-validated the Immaturity Index on a small sample of 22 additional officers who were later terminated, and a matched group of retained officers. On cross-validation, the index correctly classified 81.2% of the officers. Bartol also suggested Immaturity Index cutoff scores. Summing raw scores, 70% of terminated officers received immaturity scores of 49 or above, compared with 23% of the retained group. Raising the cutoff score to 54 correctly identified 53% of the terminated group and 95% of the retained group. To date, the Immaturity Index has not been replicated with other police departments or with the MMPI-2. In summary, research with the MMPI-2 and law enforcement assessment suggests that law enforcement applicants, similar to job applicants in gener-

al, are likely to present themselves in a socially desirable and defensive manner. In terms of response styles, their validity scales for underreporting are likely to be higher than average (> 50T), but not clinically significant (< 65T). T scores within the normal range on each of the MMPI’s traditional validity scales are unlikely to be meaningful or to predict future job performance (Aamodt, 2004). However, combinations of scales (both validity scales and clinical scales) may be useful in identifying socially desirable protocols (Borum & Stock, 1993; Devan, 2004) and perhaps identifying individuals likely to perform poorly on the job (Aamodt, 2004; Bartol, 1991; Weiss et al., 2013). MMPI‑2‑RF

The MMPI-2-RF consists of 338 items from the original MMPI-2 (Ben-Porath & Tellegen 2008/2011; Tellegen & Ben-Porath, 2008/2011). It yields nine validity scales and nine Restructured Clinical (RC) scales. Importantly, the MMPI-2-RF technical manual includes descriptive data on a sample of law enforcement candidates for comparison. In preemployment settings, the MMPI-2-RF would appear to be the measure of choice. First, and importantly, the T scores generated by the MMPI-2-RF are nongendered, which is valuable, because Federal law prohibits the use of gendered norms in preemployment settings (U.S. Civil Rights Act, 1991). Second, the T scores generated by the MMPI-2-RF are non-K-corrected. On this point, a significant body of literature questions the utility of the K correction, particularly in nonclinical samples where an incentive to underreport may be present (Detrick et al., 2001; Sellbom, Fischler, & Ben-Porath, 2007). Sellbom et al. (2007) described the factors likely to impact the validity of MMPI-2 scores in the selection of law enforcement personnel. First, since the MMPI-2 can only be administered after the conditional offer, many unsuitable candidates have already been eliminated, resulting in a restriction of range of scores. Second, among law enforcement candidates, clinical elevations of scores are rare (Detrick, Chibnall, & Luebbert, 2004). As described by Sellbom et al. (2007), important variance may exist at subclinical elevations, necessitating lower cutoff scores to predict problematic behavior for this population. Sellbom et al. (2007) also demonstrated that the use of the K correction is counterproductive in a sample of law enforcement candidates. For ex-



28. Assessment of Law Enforcement Personnel 561

ample, the noncorrected scores on Scales 8 and 9 and low scores on Clinical Scale 3 were the best predictors of problematic behaviors. Sellbom and colleagues concluded that “the K-correction may substantially attenuate, if not altogether eliminate, the validity of the clinical scale scores in non-clinical settings” (p. 999). Another advantage of the MMPI-2-RF is the Police Candidate Interpretive Report (PCIR; Corey & Ben-Porath, 2014), a computer-based interpretive measure developed specifically for evaluating law enforcement candidates. The report yields several sections relevant for the selection of suitable candidates, including Protocol Validity, which discusses implications associated with underreporting. The report also includes Clinical Findings, Diagnostic Considerations, and Comparison Group Findings, in which substantive scale findings are described in the context of a Multisite Police Candidate comparison group made up of 2,074 North American candidates. In addition, a section on Job-Relevant Correlates describes the candidate on 10 domains that are similar to the POST domains outlined in Table 28.1. Detrick and Chibnall (2014) reported on 62 police officer candidates who took the MMPI-2-RF under both high-demand (preemployment) and low demand (following completion of the police academy) conditions. Overall, the scores obtained in the high-demand condition did not differ from the law enforcement norms reported for the MMPI-2-RF. However, scores in the low-demand condition were significantly higher than the law enforcement norm. The effects of underreporting in the high-demand condition were most evident in the higher-order scales of Emotional/Internalizing Dysfunction and Behavioral/Externalizing Dysfunction. The authors concluded that police officer candidates tended to minimize aggressive behaviors and affective symptoms that may result in an underestimate of future behavioral or performance problems. Due to recent publication of the MMPI-2-RF, the body of published literature on it is not extensive. Given its relevance for law enforcement selection, additional validation studies are certainly expected.

IPI The IPI (Inwald, Knatz, & Shusman, 1982) is a 310-item true–false inventory designed to measure a variety of problematic characteristics thought to be relevant to law enforcement. Cochrane et al. (2003) reported that approximately 12% of police

department in their survey use the IPI for law enforcement selection. The IPI consists of 26 scales, including one validity scale, Guardedness (GD). The remaining 25 scales are organized around four areas. The areas and representative scales are as follows: 1. Acting-out behaviors: Alcohol use (AL), Drug use (DG), Driving violations (DV), Job difficulties (JD), Trouble with society and the law (TL), and Absence abuse (AA). 2. Acting-out attitudes: Substance abuse (SA), Antisocial attitudes (AS), Hyperactivity (HP), Rigid type (RT), and Type A (TA). 3. Internalized conflict: Illness concerns (IC), Treatment programs (TP), Anxiety (AN), Phobic personality (PH), Obsessive personality (OB), Depression (DE), Loner type (LO), Unusual experience and thoughts (EU). 4. Interpersonal conflict: Lack of assertiveness (LA), Interpersonal difficulties (ID), Undue suspiciousness (US), Family conflict (FC), Sexual concerns (SC), and Spouse/mate conflicts (SP). The IPI GD scale was designed to detect socially desirable responding (Haaland & Christenson, 1998) and identify law enforcement personnel who have minimized shortcomings and denied faults (Inwald et al., 1982). Detrick et al. (2001) reported that the IPI’s GD scale was more related to the MMPI-2’s L scale (r = .64) than to the K scale (r = .30). As previously summarized, Borum and Stock (1993) found that the GD scale was significantly higher in the deceptive than in nondeceptive groups. Therefore, the GD scale may have some utility to detect individuals engaged in less sophisticated IM and/or deception (see Table 28.3). Detrick and Chibnall (2002) investigated IPI profiles in newly hired police officers and found no differences on any IPI scales for officers retained after 1 year (n = 108) compared to those who dropped out (n = 30). These authors also utilized IPI scale scores to predict changes in performance ratings. Performance ratings increased the most between 2 months and 1 year for officers who scored high in GD (≥T = 60) and low in DV (≤ T = 40). Importantly, the overall GD scores were not significantly elevated in this study (i.e., M = 43.6). Detrick and Chibnall reported that high GD scores are related to level of education and socioeconomic status. Thus, higher, but not significantly elevated, GD scores in this sample may simply

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V.   S p e ci a l i z e d A p p l ic at i o n s

reflect greater psychological sophistication rather than overt defensiveness. Similarly, Inwald and Shusman (1984) noted that police officers who demonstrated heightened awareness, discernment of others, and some wariness tended to be rated higher by supervisors. The authors also noted that these traits can be beneficial in police work. Other researchers have not found a relationship between GD scores and job performance. Surrette and Serafino (2003), for example, found a negative, though not significant (r = –.23) GD relationship to performance ratings at 1 year in a sample of 129 police officers from small towns in New Mexico. These authors found the only IPI scale that was significantly correlated with performance was Loner (r = .40). Likewise, Kauder and Thomas (2003), utilizing a sample of 149 officers in Oregon, found that GD was unrelated to performance measures of Driving Performance, Performance in Dealing with People, and Overall Performance. Of the 26 IPI scales, only Unusual Experiences and Family Conflicts were both positively and relatively strongly related to all three performance measures. In his meta-analysis of personality inventories and police officer performance, Aamodt (2004) reported that across six studies, the mean corrected correlation between GD and performance ratings was negligible at –.04. Aamodt also grouped scales from different measures according to the constructs they measure. Across these studies, defensiveness did not predict either performance ratings or discipline problems. Haaland and Christiansen (1998) proposed that the lack of a consistent relationship between social desirability (as measured by the IPI GD scale) and performance may be due to the unique nature of the relationship of IM to performance. Specifically, IM may have a curvilinear relationship with job performance. Individuals higher in cognitive ability may be more successful in presenting in a socially desirable fashion. Cognitive ability is also predictive of job performance. To the extent that cognitive ability, job performance, and elevated (socially desirable) responses are correlated, validity of these personality tests to predict performance may not suffer. However, at the extreme end of the distribution, predictive validity may suffer most when the variance in personality scales is explained by social desirability. Haaland and Christiansen (1998) tested the curvilinear hypothesis utilizing the IPI in a sample of 442 law enforcement recruits. The authors constructed a personality composite scale of four

IPI scales that were predictive of academy performance. The GD scale was adopted as the measure of social desirability. In the total sample, a positive relationship was observed between the personality composite and performance. However, in the top 12% of the distribution (n = 53), the relationship was actually negative. Among these individuals, higher scores on the personality composite were associated with lower performance scores. Furthermore, the relationship between personality and social desirability was strong in the upper 50% of the sample, but much stronger among top scorers. The authors concluded that recruits with scores in the uppermost range inflated their scores by endorsing only the most socially desirable items. Of interest, the mean GD score in highest group was only slightly elevated at 54.39 (SD = 9.56), which is about 1 standard deviation higher than the means reported in other studies (see Table 28.4) and nearly five T score points above the total sample in their study (M = 50.00, SD = 9.58). Haaland and Christiansen’s (1998) analysis demonstrates that validity scales within the normal range may in fact be clinically meaningful when assessing high-functioning, motivated individuals. Certainly, their findings must be replicated. To our knowledge, no investigator has replicated the analysis with the IPI or attempted to extend the findings to other multiscale inventories, such as the MMPI-2. To the extent that their results generalize to other samples, it appears that cutoff scores below what is considered clinically significantly (i.e., 65T) might be meaningful in samples of law enforcement recruits.

PAI Law Enforcement, Corrections, and Public Safety Selection Report The PAI Public Safety Selection Report (Roberts, Thompson, & Johnson, 2004) is an add-on scoring system for the existing PAI Software Portfolio, TABLE 28.4.  Mean IPI Guardedness Scores in Three Samples of Law Enforcement Recruits

n

Guardedness M

Detrick & Chibnall (2002)

138

44.16 (SD = 11.25)

Surrette & Serafino (2003)

 30

47.47

Kauder & Thomas (2003)

149

42.01

  Pooled mean

317

43.46

Note. Surrette and Serafino (2003) and Kauder and Thomas (2003) did not report a standard deviation.



28. Assessment of Law Enforcement Personnel 563

designed to display results specific to public safety employment screening (see Boccaccini & Hart, Chapter 15, this volume, for a more general description of the PAI). The PAI Public Safety Selection Report uses the full PAI plus relevant demographic data collected from the examinees in order to produce detailed and tailored interpretations of the data. The authors report that it is appropriate for preemployment screening as well as fitness for duty evaluations and evaluations for special unit assignments, such as Special Weapons and Tactics Team (SWAT) or hostage negotiations. The PAI Public Safety Selection Report is based on a normative sample of nearly 18,000 law enforcement officers, of whom 20% were female and 40% were nonwhite. All individuals in the normative group were hired in law enforcement and spent at least 1 year in that position. Profiles are generated against (1) the full normative sample, (2) the specific ethnic and gender sample, and (3) the specific job type (e.g., police officer, corrections officer, or firefighter). For comparison, it also plots scores against the PAI community norms. The program, then, generates four different profiles. Roberts et al. (2004) note that one main advantage of the PAI Public Safety Selection Report is that validity scales are sometimes elevated in law enforcement examinees when their scores are plotted against community norms. Validity scale elevations are not necessarily meaningful unless they are elevated when compared to similar individuals taking the test under similar conditions. For example, the authors include a profile illustration of a female applicant’s scores plotted against community norms and against the norms for the job for which she applied. Her Positive Impression Management (PIM) T score when plotted against the community sample was 70. However, when plotted against other law enforcement officers, her T score was only 56. Also, by controlling for response bias in this fashion, the authors report that the clinical scales are more often elevated when plotted on the law enforcement norms than on the community norms. Therefore, Lowmaster and Morey (2012) recommend using a PIM cutoff score of 60T in determining a result as clinically meaningful. In a second example, Roberts et al. (2004) present a poorly suited male individual whose PIM and Negative Impression Management (NIM) score were within normal limits when plotted on community norms. When plotted against job-specific norms, his NIM was highly elevated (T ≥ 90) and his PIM plummeted (T = 37). The effect on the

clinical scales was also remarkable. Previously normal scores became highly significant (≥75T), including paranoia, schizophrenia, alcohol problems, drug problems, aggression, and nonsupport. In addition to plotting the traditional PAI scales against this new set of norms, the PAI Public Safety Report also provides risk levels (low, moderate, high) for several problematic job-related behaviors, including substance abuse proclivity, illegal drug use concerns, alcohol use concerns, anger management problems, integrity problems, and job-related problems. It appears that the PAI Public Safety Report may be useful for law enforcement assessment purposes of screening for psychopathology, as well as identify potentially effective and ineffective officers. The PAI Public Safety Report needs to be independently validated with peer-reviewed research. However, initial validation data are promising for its use in law enforcement assessment.

Candidate and Officer Personnel Survey Created as a predictor of job performance in public safety job candidates, the Candidate and Officer Personnel Survey (COPS; Guller & Guller, 2003) is a 240-item, true–false, self-report measure. The 18 scales include two validity indices: Lie (LIE; “attempt to make a good impression”) and Inconsistency (INC; “item pairs of essentially equivalent meaning;” McGrath & Guller, 2009, p. 152). In an initial study of 2,509 law enforcement candidates administered the COPS, the largest negative correlations with the LIE scale were observed with the Personality Problems (r = –.31, p < .05) and Depression scales (r = –.37, p < .05; McGrath & Guller, 2009).

FITNESS‑FOR‑DUTY EVALUATIONS While it may be reasonably assumed that law enforcement applicants are motivated to minimize psychopathology (defensiveness), the same assumption cannot be made for examinees required to undergo fitness-for-duty evaluations. According to the International Association of Chiefs of Police (IACP) Psychological Fitness-for-Duty Evaluation Guidelines (2013), a fitness-for-duty evaluation is “a formal, specialized examination of an incumbent employee that results from (1) objective evidence that the employee may be unable to safely or effectively perform a defined job; and (2) a reasonable basis for believing that the cause may

564

V.   S p e ci a l i z e d A p p l ic at i o n s

be attributable to a psychological condition or impairment” (p. 2). Officers referred for evaluation may or may not be motivated to receive “a clean bill of health”; instead, some may be motivated to maximize the likelihood that they will be placed or remain on inactive or disability status. A full discussion of fitness-for-duty evaluations is beyond the scope of this chapter, but the issue is sufficiently important to warrant a brief discussion. As outlined by Corey and Ben-Porath (2015), psychological fitness-for-duty evaluations of armed officers are dually complex due to the lower referral threshold, combined with a higher fitness standard than in other psychological evaluations. In other words, the problematic behavior that triggers a referral for a law enforcement officer is likely less severe than behavior triggering a referral in lower-stakes occupations. Due to the potential impact on public safety, any erratic or unusual behavior might result in a referral, and expectations for psychological stability of law enforcement are high. The Courts also recognize the critical nature of psychological fitness-for-duty in rulings that support both the lower referral threshold and the higher standard. For example, in Bonsignore v. New York (1982), a case involving an officer who shot his wife and then himself, the Court held the city negligent. The Court opined that the agency did not identify him as a problem officer in need of intervention despite his having displayed behavior that should have raised concern. Similarly, in Watson v. City of Miami Beach (1999, para. 11) the Court held, “In any case where the department reasonably perceives an officer to be even mildly paranoid, hostile, or oppositional, a fitness-forduty evaluation is job relevant and consistent with business necessity.” Fitness-for-duty evaluations raise the possibility of at least two distinct response styles, depending on the examinee’s motivation. Officers who wish to return to work would be expected to minimize psychopathology while presenting themselves most favorably. Officers wishing to be placed on disability or remain on disability may maximize their impairment. Grossman, Haywood, Ostrov, Wasyliw, and Cavanaugh (1990) investigated the effects that these different motivations have on the MMPI validity scales. These authors compared three groups of officers (20 each): positive motivation (wanting to return to work), negative motivation (wanting disability), plus a control group from active police officers who were not undergoing fitness evaluations. The differences between positive and negative motivation were in the predicted directions, but no

means reached the 65T cutoff on L, F, Ds, or Mp scales. Officers in the negative motivation group produced significantly higher mean scores on the F scale, F-K index, O-S index, and Ds scale. No differences were observed for the L and Mp scales. Despite the minimization across all groups, a substantially greater degree of minimization was found for positive motivation group (range of 20–85%), than for the negative motivation group (15 to 45%). Thus, even officers not wishing to return to work still engaged in some minimization on the MMPI. Of the feigning validity indices, Ds was most sensitive to exaggeration, which was observed in 15% of the negative motivation group and none of the positive motivation group on any of the indices used (Ds, O-S, F-K, F). It appears that police officers in general were reluctant to admit psychological problems. The authors caution that the use of traditional validity scales is insufficient to detect response-style alterations in police officer examinees. The use of special scales and indices, such as the F- K index, Ds scale, and the Obvious–Subtle index are recommended by these authors. Corey and Ben-Porath (2015) reviewed the MMPI-2-RF profiles of 709 law enforcement and public safety officers being evaluated for fitness for duty. Overall, validity scale elevations were quite rare in this sample. The most frequently elevated scale was K ≥ 72T, found with only 9.4% of male law enforcement officers. Of interest, a small body of literature suggests that targeted instructions following the production of invalid profiles by individuals facing high-stakes evaluations may be helpful. For example, Walfish (2011) reported on a sample of medical professionals being evaluated for fitness to practice (n = 53). On first administration of the MMPI-2, 59% (n = 31) produced profiles indicative of “faking good.” Following a brief coaching script regarding exaggeration and minimization, individuals were encouraged to repeat the testing with “a mindset of rigorous openness and honesty on a conscious basis” (p. 77). On second administration, only 10% of the profiles remained invalid. Significant differences were still found on L, F, and K scores. Similar results have been found in other highstakes evaluations in which motivation to “fake good” may be at issue (Butcher, Morfit, Rouse, & Holden, 1997; Walfish, 2007). To our knowledge, this methodology has not been replicated with a sample of law enforcement personnel, but it may provide one avenue for consideration should the evaluator be faced with such a situation. Spilberg and Corey (2014) do not recommend this practice



28.  Assessment of Law Enforcement Personnel 565

among preemployment candidates, but it is unclear whether similar concerns extend to fitnessfor-duty evaluations. Fischler et al. (2011) suggested that one possible outcome of a psychological fitness-for-duty evaluation is an “invalid evaluation” when subjects are clearly not cooperating with the evaluation, as demonstrated through validity scales on formal measures or behavior indicators of noncooperation. In these cases, the authors opine that the fitness-for-duty question cannot be answered with a reasonable degree of psychology certainty. Decisions regarding the officers’ continued placement on the force therefore become administrative in nature.

SUMMARY Psychological assessments constitute a cornerstone of law enforcement selection and fitness-for-duty evaluations. This chapter has provided professionals with an overview of the specific effects that altered response styles may have on commonly used instruments. In most instances, assessments conducted under preemployment conditions will result in overly positive self-portrayals. The reviewed research suggests that a typical preemployment profile will have slight elevations on validity scales designed to detect positive IM, yet still be within normal limits. Traditional approaches to test interpretation suggest that scores within the normal range are not meaningful and not interpretable (Greene, 1991). The review of research presented here demonstrates that subclinical elevations may be important when assessing high-functioning individuals who are motivated to present themselves in a particularly positive fashion. It seems reasonable to suggest that desirable job candidates will be attempting to present in a positive fashion. Their efforts suggest that they are motivated to present well, indicating a desire to obtain the position. In contrast, candidates who do not attempt to pre­ sent well may not be very invested in the position. A more difficult challenge is to differentiate the motivated individuals engaging in defensiveness (minimization of psychopathology) from extreme social desirability to the extent that all of their responses are saturated with social desirability. We have presented several approaches to discriminating extreme desirability in this review (e.g., Borum & Stock, 1993; Devan, 2004; Haaland & Christiansen, 1998). Future research is likely to focus on distinguishing between SDE and IM with the

MMPI-2 and other instruments (Devan, 2004). Most notably, Haaland and Christiansen’s (1998) finding that the effects of socially desirable responding on performance are curvilinear clearly deserves further research attention.

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lice recruits. Journal of Police Science and Administration, 12, 1–11. Kauder, B. S., & Thomas, J. C. (2003). Relationship between MMPI-2 and Inwald Personality Inventory (IPI) scores and ratings of police officer probationary performance. Applied HRM Research, 8, 81–84. Kornfeld, A. D. (1995). Police officer candidate MMPI-2 performance: Gender, ethnic, and normative factors. Journal of Clinical Psychology, 51, 536–540. Lautenschlager, G. J., & Flaherty, V. L. (1990). Computer administration of questions: More desirable or more social desirability? Journal of Applied Psychology, 75, 310–314. Lowmaster, S. E., & Morey, L. C. (2012). Predicting law enforcement officer job performance with the Personality Assessment Inventory. Journal of Personality Assessment, 94, 254–261. McEvoy, G. M., & Beatty, R. W. (1989). Assessment centers and subordinate appraisals of managers: A seven-year examination of predictive validity. Personnel Psychology, 42, 37–52. McGrath, R., & Guller, M. (2009). Concurrent validity of the Candidate and Officer Personnel Survey (COPS). International Journal of Police Science and Management, 11, 150–159. More, H. W., & Unsinger, P. C. (1987). The police assessment center. Springfield, IL: Charles C Thomas. Munley, P. H. (1991). A comparison of MMPI-2 and MMPI T scores for men and women. Journal of Clinical Psychology, 47, 87–91. Neal, B. (1986). The K scale (MMPI) and job performance. In J. Reese & H. Goldstein (Eds.), Psychological services for law enforcement (pp. 83–90). Washington, DC: U.S. Government Printing Office. Paulhus, D. L. (1988). Manual for the Balanced Inventory of Desirable Responding (BIDR-6). Unpublished manual, University of British Columbia, Vancouver, British Columbia, Canada. Paulhus, D. L. (1998). Manual for the Paulhus Deception Scales: BIDR version 7. Toronto: Multi-Health Systems. Paulhus, D. L. (2002). Socially desirable responding: The evolution of a construct. In H. I. Braun, D. N, Jackson, & D. E. Wiley (Eds.), The role of constructs in psychological and educational measurement (pp. 49– 69). Mahwah, NJ: Erlbaum. Paulhus, D. L., & Reid, D. B. (1991). Enhancement and denial in socially desirable responding. Journal of Personality and Social Psychology, 60, 307–317. Reynolds, W. M. (1982). Development of reliable and valid short forms of the Marlowe–Crowne Social Desirability Scale. Journal of Clinical Psychology, 38, 119–125.

Roberts, M. D., Thompson, J. A., & Johnson, M. (2004). PAI Law Enforcement, Corrections, and Public Safety Selection Report. Lutz, FL: Psychological Assessment Resources. Rosse, J. G., Stecher, M. D., Miller, J. L., & Levin, R. A. (1998). The impact of response distortion on preemployment personality testing and hiring decisions. Journal of Applied Psychology, 83, 634–644. Sellbom, M., Fischler, G. L., & Ben-Porath, Y. S. (2007). Identifying the MMPI-2 predictors of police officer integrity and misconduct. Criminal Justice and Behavior, 34, 985–1004. Spilberg, S. W., & Corey, D. M. (2014). Peace Officer Psychological Screening Manual. California Commission on Peace Officer Standards and Training (POST) Standards, Evaluation, and Research Bureau. Retrieved from www.post.ca.gov. Strong, D. R., Greene, R. L., Hoppe, C., Johnston, T., & Olesen, N. (1999). Taxometric analysis of impression management and self-deception on the MMPI-2 in child custody litigants. Journal of Personality Assessment, 73, 1–18. Strong, D. R., Greene, R. L., & Kordinak, S. T. (2002). Taxometric analysis of impression management and self-deception in college student and personnel evaluation settings. Journal of Personality Assessment, 78, 161–175. Surrette, M. A., & Serafino, G. (2003). Relationship between personality and law enforcement performance. Applied HRM Research, 8, 89–92. Tellegen, A., & Ben-Porath, Y. S. (2011). MMPI-2-RF: Technical manual. Minneapolis: University of Minnesota Press. (Original work published 2008) U.S. Civil Rights Act of 1991, Public Law 102–166 (102d Congress, 1991). Varela, J. G., Boccaccini, M. T., Scogin, F., Stump, J., & Caputo, A. (2004). Personality testing in law enforcement employment settings: A meta-analytic review. Criminal Justice and Behavior, 31, 649–675. Walfish, S. (2007). Reducing MMPI defensiveness: The effect of specialized instructions on validity in a sample of pre-surgical bariatric patients. Surgery for Obesity and Related Disorders, 3, 184–188. Walfish, S. (2011). Reducing MMPI-defensiveness in professionals presenting for evaluation. Journal of Addictive Diseases, 30, 75–80. Watson v. City of Miami Beach, 177 F.3d 932 (11th Cir. 1999). Weiss, P. A., Vivian, J. E., Weiss, W. U., Davis, R. D., & Rostow, C. D. (2013). The MMPI-2 L scale, reporting uncommon virtue, and predicting police performance. Psychological Services, 10, 123–130.

PA R T V I

SUMMARY

CHAPTER 29

Current Status of the Clinical Assessment of Response Styles Richard Rogers, PhD

Important advances in the clinical assessment of response styles have ensued between past (1988, 1997, and 2008) and current editions of this book. Beginning in 2008, one major contribution was the move beyond clinical correlates and isolated validity scale differences to the systematic examination of detection strategies. As I outlined in Chapter 2 of this volume, detection strategies represent conceptually based and empirically validated methods of evaluating a specific response style as contrasted with its most relevant comparison groups. To be considered a detection strategy, validation must include multiple measures with multiple studies, using relevant clinical populations. Statistical differences have only marginal relevance. Instead, the focus is on the magnitude of the differences, most commonly Cohen’s d, and their effectiveness at classifying relevant groups of response style. This systematic application of detection strategies represents a major contribution of this edition to both applied research and clinical practice. The next section begins with an examination of feigning across the domains of mental disorders and cognitive impairment.

DETECTION STRATEGIES FOR FEIGNING Detection of Feigned Mental Disorders Major strides have been made in the development of detection strategies for persons feigning

mental disorders. Table 29.1 summarizes the established detection strategies for feigned mental disorders, with relevant information on validation designs and assessment methods. Of major importance, these established strategies have met rigorous standards with validation that combines the respective strengths of internal validity (i.e., simulation design) and external validity (i.e., known-groups comparisons [KGCs]). In instances in which a KGC is impractical, a partial criterion design (PCD) can provide moderate (but not strong) evidence of external validity (see Rogers, Chapter 2, this volume). In most instances, detection strategies from simulation and KGC studies have yielded large to very large effect sizes across multiple scales. One exception, included in Table 29.1, exemplifies the need for such rigorous evaluations based on multiple types of validation and multiple methods. For “spurious patterns of psychopathology,” the Malingering Index (MAL) produces generally large effect sizes across simulation designs and KGCs (see Hawes & Boccaccini, 2009). As observed in their meta-analysis, the Rogers Discriminant Function (RDF) works well with simulation design but not KGCs, suggesting fundamental problems in its real-world applications to criminal–forensic settings. The following list provides mental health professionals with practice-based recommendations that focus on the comprehensive assessment of feigned mental disorders.

571

572

V I .  S u m m a r y

TABLE 29.1.  Established Detection Strategies for Feigned Mental Disorders

Validation methods Established strategies

Sim

KGC

PCD

Test

Interview

Effect size

Primary scales

Rare symptoms

Y

Y

Y

Y

Y

Very large

SIRS-2 RS, MMPI-2 Fp, and PAI NIM

Quasi-rare symptoms

Y

Y

Y

Y

Y

Very large

MMPI-2 F and Fb

Improbable symptoms

Y

Y

Y

Y

Y

Variable

SIRS-2 IA and MCMI-III Validity Index

Symptom combinations

Y

Y

Y

N

Y

Very large

SIRS-2 SC and M-FAST RC

Spurious patterns of psychopathology

Y

Y

Y

Y

N

Largea

PAI MAL and RDF

Indiscriminant symptom endorsement

Y

Y

Y

Y

Y

Very large

SIRS-2 SEL and SADS SEL

Symptom severity

Y

Y

Y

Y

Y

Very large

SIRS-2 SEV and MMPI-2 LW

Obvious symptoms

Y

Y

Y

Y

Y

Very large

SIRS-2 BL and MMPI-2 O-S

Reported versus observed symptoms

Y

Y

Y

N

Y

Variableb

SIRS-2 RO and M-FAST RO

Erroneous stereotypes

Y

Y

Y

Y

N

Very large

MMPI-2 Ds scale

Note. Sim, simulation design; KGC, known-groups comparison; PCD, partial criterion design; SIRS-2, Structured Interview of Reported Symptoms–2; MMPI-2, Minnesota Multiphasic Personality Inventory–2; MCMI-III, Millon Clinical Multiaxial Inventory–III; M-FAST, Miller Forensic Assessment of Symptoms Test; PAI, Personality Assessment Inventory; SADS, Schedule of Affective Disorders and Schizophrenia. For scale names, refer to relevant chapters. aConsistently large for MAL; variable for RDF. Effect sizes are general estimates based on major studies. bThe SIRS-2 RO has a very large effect size; the M-FAST RO has a moderate effect size (see Jackson, Rogers, & Sewell, 2005).

1. The rare-symptoms strategy is the “workhorse” for the feigning of mental disorders; practitioners may wish to adopt a multimethod approach using both test-based (e.g., Personality Assessment Inventory [PAI] Negative Impression Management [NIM] scale or Minnesota Multiphasic Personality Inventory–2 [MMPI-2] Fp scale) and interviewbased (e.g., Structured Interview of Reported Symptoms–2 [SIRS-2] Rare Symptoms [RS] scale) methods for its assessment. 2.  The quasi-rare symptom strategy continues to be popular, based more on tradition than effectiveness for clinical practice. However, practitioners must bear in mind that scale development for this strategy focuses on an irrelevant contrast (i.e., feigned mental disorders vs. healthy controls). Genuinely disordered examinees often produce high elevations, an expected finding, because the key comparison (i.e., feigned vs. genuine disorders)

was not used in scale development. Despite large to very large effect sizes, the cutoff scores are highly variable, thus vitiating classificatory accuracy. At best, practitioners should consider the quasi-rare symptom strategy as a supplementary approach. 3.  Improbable symptoms represent an extreme form of rare symptoms that have a fantastic or preposterous quality to them. The majority of feigners do not feel the need to simulate such extreme presentations. Practitioners may wish to examine improbable symptoms as evidence of extreme feigning, but their absence is obviously not evidence of genuine responding. 4.  Two strategies (symptom combinations and spurious patterns of psychopathology) offer a sophisticated approach to feigning that address the relationships between symptoms and associated features. Their added complexity works against effective coaching. For instance, what easily ab-



29.  Clinical Assessment of Response Styles 573

sorbed information would assist feigners in avoiding atypical pairs of symptoms? It is strongly recommended that at least one of these detection strategies be included in comprehensive assessments of feigned mental disorders. 5. The domain of amplified detection strategies should also be included in these assessments. Both “indiscriminant symptom endorsement” and “symptom severity” produce very large effect sizes. They are helpful in evaluating whether examinees are feigning the breadth and intensity of symptoms and psychopathology. Thus, the focus is not on the legitimacy of specific symptoms but rather on the overall intensity reported (i.e., breadth, frequency, and severity). These strategies constitute a useful component in the evaluation of feigned mental disorders. 6.  The final three strategies provide further insights into feigning. “Reported versus Observed” adds a unique dimension to feigning assessment in comparing currently reported symptoms to concurrent observations. Although some inconsistencies are noted in genuinely disordered populations, feigners are sometimes detected by the discernable pattern of reported symptoms in the absence of clinical observations. In addition, the strategy of “erroneous stereotypes” produces very large effect sizes for the MMPI-2 Dissimulation (Ds) scale. As subsequently described (see next section), this strategy has been successfully applied to feigned attention-deficit/hyperactivity disorder (ADHD [Ds-ADHD]; Robinson & Rogers, in press). As a practical matter, the MMPI-2 Ds scale is recommended for clinical practice because of its consistent cutoff score and effectiveness among sophisticated feigners. Practitioners must consider whether stringent (≤ 10%) or lax criteria (≤ 20%) were applied as the maximum threshold for including items of an RS scale. Beyond the maximum threshold, practitioners must consider what represents the average or typical percentage in clinical population. This is highlighted in Box 29.1 using the PAI NIM scale. An alternative approach would be to use percentiles when they are available, because they are less skewed by outliers. For example, a score of “1” represents the 54th percentile on the SIRS-2 RS scale (Rogers, Sewell, & Gillard, 2010, p. 114, Appendix C). Out of a total of 16, genuine examinees typically report only 1 (6.3%) of the RS items, placing it within the stringent criterion for RS scales.

BOX 29.1.  Calculating the Accuracy of Rare Symptom Scales for Genuine Examinees Using the PAI NIM Scale 1. Check the M for NIM with the Representative Clinical Sample (Morey, 2007, p. 85). 2. Take the M of 4.38 and divide by the maxi‑ mum score of 27. 3. The result is 16.2% of NIM, which is the aver‑ age endorsed by genuine examinees. Note. See the test manual (Morey, 2007).

The study of feigning on projective measures has remained essentially stalled during the last several decades. At present, independent measures of feigned mental disorders should be administered in conjunction with projective methods. When evidence of feigning is found, the Rorschach and other projective measures should not be interpreted. In summary, mental health professionals can effectively evaluate feigned mental disorders and psychopathology. Because all measures are vulnerable to feigning, comprehensive assessments should include multiple detection strategies that integrate findings across test-based and interviewbased methods.

Detection of Specific Disorders Both practitioners and researchers wrestle with two related questions regarding specific disorders. First, can persons feigning a specific disorder to be accurately categorized by general feigning indicators and their cutoff scores? Second, can disorderspecific feigning indicators accurately identify a particular feigned disorder? The answer to the first question is generally affirmative, although differences are observed across specific disorders. Using the PAI as an example, simulators were understandably more easily detectable when they attempted to feign more severe (e.g., schizophrenia) than less severe (e.g., generalized anxiety disorder) disorders (see Rogers, Sewell, Morey, & Ustad, 1996). Ambitious efforts in the last 15 years have sought to establish disorder-specific feigning indicators. For these indicators to be successful, they must be able to differentiate disorder-specific feigning from general feigning. According to Merten and Rogers (2017), two critical steps are involved in establishing disorder-specific feigning,

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V I .  S u m m a r y

BOX 29.2.  Basic Questions That Should Be Addressed before Using Disorder-Specific Feigning Scales 1. Does the scale clearly differentiate feigners of this disorder from genuine examinees with this disorder? 2. Does the scale clearly differentiate specific feigners of this disorder from other feigners?

which are summarized in Box 29.2 in the form of questions. Most feigning research has focused on the first question and totally ignored the second. Both practitioners and investigators can readily recognize the perils of this approach. For example, the Infrequent Somatic Responses or Fs (Wygant, BenPorath, & Arbisi, 2004) looked for uncommon medical complaints and extensively examined the first but not the second question. A recent metaanalysis (Sharf, Rogers, Williams, & Henry, 2017) found that the Fs scale is far more effective with general feigning (i.e., sensitivity from .60 to .85) than feigned medical complaints (i.e., .26 to .56). Thus, Fs should not be used to conclude that examinees are feigning their medical complaints. As a general matter, disorder-specific feigning must address the second question before it can be applied to clinical practice. As mentioned, Robinson and Rogers (in press) developed the Ds-ADHD by addressing both questions in the development of a validity scale for disorder-specific feigning. Applying the detection strategy of erroneous stereotypes, the first step was to identify items that the majority of simulators wrongly assumed to be common ADHD characteristics. For the second step, ADHD simulators were compared to general psychiatric simulators, producing a very large effect size (d = 1.84). Thus, scales for disorder-specific feigning must include both steps in order to produce clinically relevant conclusions (Merten & Rogers, 2017).

Feigned Cognitive Impairment Phenomenal growth has been experienced during the last decade in the development of assessment methods for feigned cognitive impairment. Implementation of detection strategies has not always been consistent in the development of new measures. Even among prominent researchers, construct drift has been observed. Practitioners must

exercise considerable care when citing research supporting their clinical practice to ensure that terms are used accurately. Otherwise, slipshod terms and definitions can undermine the credibility of assessment reports. Neuropsychologists and other practitioners are also faced with a major challenge in generalizing across studies, because research often uses different terms and criteria for establishing comparison groups. As a specific example, I list (Chapter 1, this volume) suboptimal effort and incomplete effort as imprecise terms that should be avoided in clinical and forensic practice. Yet, some researchers continue to rely on these terms in designating criterion groups. Conceptually, problems in defining optimal effort and complete effort militate against the use of these terms for classification purposes. In light of their psychological and physical functioning, how do we ever know when examinees are truly putting forth complete effort? Detection Strategies for Feigned Cognitive Impairment

Most detection strategies for feigned cognitive impairment have been extensively cross-validated using multiple designs that capitalize on internal and external validity. It is recommended that psychologists employ a multimeasure, multistrategy approach for determinations that consider the following three questions: 1. What clinical data via specific detection strategies support the likelihood of genuine responding? 2. What clinical data via specific detection strategies support the likelihood of feigned cognitive impairment? 3. What clinical data via specific detection strategies support the likelihood of a mixed presentation with genuine and feigned impairment? The goal of these three questions is to ensure a balanced and methodical approach to the analysis of impaired cognitive presentations, which may have one of three clear conclusions, or alternatively, one indeterminate conclusion. At the ends of the spectrum, practitioners can be very confident about the prospect of likely genuine and likely feigned presentations. In other instances, the mixed presentation will be clear, with some genuine (e.g., poor performance of the Verbal Comprehension Index) and some feigned (e.g., purported memory deficits) presentations. In still other cases,



29.  Clinical Assessment of Response Styles 575

the most candid conclusion is indeterminate responding. In those cases, the available data do not allow any definite conclusions to be drawn about feigned or genuine cognitive impairment. Practitioners are invited to use the threequestion approach when reviewing reports by other psychologists, whether others’ conclusions agree or disagree with their own. An important issue is to identify those professionals who may be implicitly engaging in the taint hypothesis. As I noted in Chapter 1, this volume, the taint hypothesis unjustifiably assumes that nongenuine responding indicates a much broader, but not yet discovered, dissimulation. The taint hypothesis is easily uncovered when only the second question is considered and when any instance of nongenuine responding is given undue weight. APPLICATION OF DETECTION STRATEGIES

Commonly used detection strategies for feigned cognitive impairment are summarized in Table 29.2. I provide a more detailed description of these strategies in Chapter 2, this volume. Practitioners are also referred to Bender and Frederick (Chapter

3), Bender (Chapter 7), Frederick (Chapter 17), and Garcia-Willingham, Bosch, Walls, and Berry (Chapter 18) in this volume for clinical recommendations regarding specific feigning measures and their detection strategies. Traditionally, most cognitive feigning measures have focused on short-term memory to the exclusion of other cognitive abilities. A significant contribution to this volume is its inclusion of Garcia-Willingham et al.’s (Chapter 18) examination of feigned intelligence and neuropsychological deficits beyond memory. In selecting measures for feigned cognitive impairment, practitioners should select multiple detection strategies, with scales that cover both memory and other domains. The following list summarizes for mental health professionals recommendations on the comprehensive assessment of feigned cognitive impairment. 1.  Two detection strategies (magnitude of error and performance curve) are sophisticated methods that address issues often overlooked by malingerers, who often attempt to get many items wrong, without considering item difficulty or which incorrect responses would be credible. In particular,

TABLE 29.2.  Detection Strategies for Feigned Cognitive Impairment

Validation methods Sim

KGC

Test

Embedded

Targeted abilities

Established strategies Magnitude of error

Y

Y

Verbal, nonverbal

Performance curve

Y

Y

Verbal, nonverbal

Violation of learning principles

Y

Floor effect

Y

Y

Significantly below chance performance (SBCP)

Y

Alt.a

Forced-choice testing (FCT)

Y

Pattern analysis

Y

Memory, concentration Y

Y

Memory, concentration, motor Memory, verbal, nonverbal Memory

Y

Y

Verbal, neuropsych.b

Provisional strategies Consistency across comparable items

Y

Atypical test pattern

Y

Symptom frequency

Y

Y Y

Verbal, nonverbal Y

Y

Neuropsych.b Cognitive symptoms

Note. Partial criterion design (PCD) was not reviewed for this table. Sim, simulation design; KGC, known-groups comparison; Test, a stand-alone specialized measure; Embedded, derived scale or indicator for a standardized cognitive or neuropsychological measure. aRather than KGCs, binomial probability is used to establish a high likelihood of a known group of feigners. bGeneral abilities on neuropsychological test batteries.

576

V I .  S u m m a r y

the magnitude-of-error strategy appears to produce large effect sizes, even when simulators are coached (Bender & Rogers, 2004). It is recommended that practitioners include at least one of these strategies. As an example of magnitude of error, “d” errors on the “b” test are very rare in diverse clinical populations (Boone et al., 2000). 2.  Violation of learning principles can be applied using both standard measures and clinical judgment. Most scales focus on memory functions and well-known principles (e.g., recognition memory should exceed free recall). In contrast, the Dot Counting Test (DCT) compares concentration under differing conditions that facilitate or complicate counting. Beyond empirically tested comparisons, violations of learning principles can also be considered on an individualized basis. Substantially better performance on delayed rather than immediate memory (e.g., Word Memory Test [WMT] or Test of Memory Malingering [TOMM]) violates a basic learning principle and can be used as evidence of feigning. 3. The floor effect is used by the majority of cognitive feigning measures. Its simplicity constitutes both its intuitive appeal and major limitation. It identifies test items that can be successfully passed by most cognitively impaired persons. Especially with stand-alone measures, feigners can be easily coached simply to succeed when presented with questions in a specific format (e.g., Rey 15-Item Test). However, this strategy is strongly recommended for embedded measures. 4.  Significantly below Chance Performance (SBCP) is the only detection strategy that can provide definite evidence of cognitive feigning. However, most feigners do not perform substantially below chance, thereby limiting SBCP to demonstrating the presence but not the absence of feigned cognitive impairment. 5.  Forced-choice testing does not have a strong conceptual basis and simply refers to “lower than expected” performances. In some isolated cases, the clinical threshold has been established for cognitively compromised individuals, including those with comorbid Axis I disorders. In most instances, forced-choice testing should be avoided, because conceptually based detection strategies yield more robust results. 6.  Pattern analysis is a valuable detection strategy using embedded measures. As noted by Bender and Frederick (Chapters 3) and Bender (Chapter 7) in this volume, severity indexing can be used as

a benchmark to evaluate whether the test findings are generally consistent with the purported brain trauma. Fluctuating symptoms present a formidable challenge to “consistency across comparable items.” For instance, examinees with dementia may perform at varying levels throughout the day. However, the Validity Indicator Profile (VIP) has achieved considerable success in examining the stability of performance across comparable items. This strategy is placed in the provisional category, because it has not been rigorously tested with measures besides the VIP. “Symptom frequency” represents a unique perspective for feigned cognitive impairment. Practitioners may wish to consider a measure, such as the Neuropsychological Symptom Inventory (NSI; Gelder, Titus, & Dean, 2002) for screening purposes (see Rogers, Chapter 2, this volume). POSTTEST PROBABILITIES

Bender and Frederick (Chapter 3, this volume) provide an excellent summary of how posttest probabilities can be applied in producing the likelihood of feigning and genuine responding. As a critically important point, both positive and negative indicators must be considered. Using the Larrabee (2008) “3+ positive signs” makes sense only if three measures were given and all indicated feigning. However, forensic practice often considers 10 or more feigning indicators (see, e.g., Ardolf, Denney, & Houston, 2007). If three indicators are positive and seven are negative, then the Larrabee “3+ positive signs” grossly overestimate the likelihood of malingering, which should plummet to a very small percentage. The Malingered Neurocognitive Dysfunction Model and Feigned Cognitive Impairment

Many practitioners have applied clinical indicators of feigned cognitive impairment that were based on the original malingered neurocognitive dysfunction (MND) model for more than a decade. They are likely to take issue with the following paragraphs that raise questions about whether the MND model is sufficiently validated for continued use in clinical and forensic practice. Practitioners should review the relevant sections of Chapter 3 (Neuropsychological Models of Feigned Cognitive Deficits) and Chapter 30 (Researching Response



29.  Clinical Assessment of Response Styles 577

Styles) in providing a background to this portion of the current chapter. Only the key points are summarized here in the form of two enumerated questions. 1.  Which MND model should be used in professional practice? In response to strong criticisms of the original MND model’s (Slick, Sherman, & Iverson, 1999) weaknesses, Slick and Sherman (2013) attempted to implement substantial improvements. For example, the simple requirement of a “discrepancy” (Slick et al., 1999) was replaced with the far more stringent requirement of a “marked and implausible discrepancy” (Slick & Sherman, 2013, p. 63). Six of the seven (85.7%) “possible indicators of exaggeration/fabrication” (p. 63) in the revised model are substantially affected by this major change. At present, the two alternatives are both unappealing: Do practitioners go with admittedly weak original criteria because they have been extensively researched? Alternatively, do they go with improved criteria that lack research? 2.  Should practitioners base far-reaching conclusions about feigning on a model with unproven reliability? At its heart, the MND model represents the elaborate creation of three psychologists from British Columbia in an ambitious attempt to bring (or impose) order on the assessment of feigned cognitive impairment. A fundamental issue contributing to its popularity involves the recycling of archival data by invoking unwarranted, and often untested, assumptions, such as (a) external incentives can be equated with internal motivations, and (b) all discrepancies should be accorded equal weight. Its widespread popularity has also spawned malingered pain-related disability (Bianchini, Greve, & Glynn, 2005). Although an elaborate creation, basic steps of developing and refining the MND model were never undertaken. The reliability of the model has simply been assumed rather than rigorously tested. Using the DSM models as an instructive comparison, no MND field trials were conducted. As a result, its reliability is yet to be established. Practitioners may wish to refer to Chapter 30, Box 30.3, this volume, in deciding whether to apply the MND model or even rely on research based on the MND model. To date, this model lacks any evidence of interrater, test–retest, or even internal reliability. Its measurement error remains uninvestigated. Without reliability, validity cannot be established.

In concluding this section, practitioners should be aware of other MND limitations, many of which are covered by Bender and Frederick (Chapter 3, this volume). Two articles (Boone, 2011; Rogers, Bender, & Johnson, 2011a) and one commentary (Rogers, Bender, & Johnson, 2011b) will help readers to fully appreciate the MND’s conceptual strengths, and its many conceptual and empirical shortcomings.

ASSESSMENT OF SIMULATED ADJUSTMENT Most studies of simulated adjustment do not provide multiple comparisons to distinguish specific response styles, such as defensiveness and social desirability (see Rogers, Chapter 1, this volume). As a rare exception, Williams, Rogers, and Sharf (2017) systematically investigated the two response styles on the Personality Inventory for DSM-5 (PID-5). When compared to defensiveness, they found that examinees in the social desirability condition more frequently engaged in the total denial of problematic personality traits, while also overstating their perfectionism. In general, studies of clinical populations tend to focus on defensiveness (i.e., the minimization of symptoms and impairment). In contrast, research on job applicants emphasizes social desirability (i.e., an overly positive and prosocial presentation). However, practitioners need to know that these distinctions are often blurred, when researchers develop experimental conditions. Research on simulated adjustment is based almost entirely on simulation and differential prevalence designs. Independent classifications for KGCs are generally not possible, because defensiveness and social desirability are typically conceptualized as exclusively dimensional constructs. As I noted in Chapters 1 and 30 of this volume, differential prevalence design has limited clinical relevance and provides only indirect evidence of response styles. As summarized in Table 29.3, most scales for evaluating simulated adjustment emphasize (1) the denial of personal faults (e.g., L scale) or (2) a blend of denied personal faults with the affirmation of virtuous behaviors (e.g., MMPI-2 S and PAI PIM). In general, the blended approach produces stronger effect sizes than does denial alone. However, both strategies are vulnerable to coaching that can greatly reduce their effectiveness. As a more sophisticated approach, the PAI Defensive-

578

V I .  S u m m a r y TABLE 29.3.  Detection Strategies for Overly Positive Presentations

Strategies

Effect sizes

Coaching

Primary example

Deny personal faults

Moderate

Vulnerable

MMPI-2 L scale

Affirm virtuous behavior and deny personal faults

Moderate to large

Vulnerable

MMPI-2 S; PAI PIM

Spurious patterns of simulated adjustment

Moderate to largea

Difficult to coach or prepare

PAI CDF and DEF

Denial of patient characteristics

Moderate

Variable

MMPI-2 K

Social desirability

Large to very large

Moderately effective

MMPI-2 Wsd

Note. L, Lie; S, Superlative; PIM, Positive Impression Management; CDF, Cashel Discriminant Function; DEF, Defensiveness Index; K (unknown); Wsd, Wiggins’s Social Desirability Scale; MMPI-2, Minnesota Multiphasic Personality Inventory–2; PAI, Personality Assessment Inventory. aBased on Morey (1996) and on Boccaccini and Hart, Chapter 15, this volume: DEF typically has large effect sizes, whereas CDF has mostly moderate effect sizes.

ness (DEF) index is based on the detection strategy involving “spurious patterns of simulated adjustment” (see Rogers, Chapter 2, this volume). It performs very well (see Boccaccini & Hart, Chapter 15, this volume) and, given its complexity, should be more resistant to coaching.

Defensiveness and Denied Substance Abuse Significant progress has been made with the use of physiological markers for certain disavowed behaviors (see Stein, Rogers, & Henry, Chapter 8, this volume). Of these, the detection of denied substance abuse, especially through the application of hair analysis, has proven to be highly effective. Obviously, the use of global measures of deceit (i.e., polygraph)—by definition—may produce mostly false positives unless the polygraph questioning specifically involved illegal and undisclosed substance abuse. Detection strategies for denied substance abuse have advanced very little during the last decade. Despite the high prevalence of denied and minimized substance abuse, most screens and measures are content to merely ask face-valid questions (i.e., easy to deny or distort) without use of validity scales. When response-style scales are available, they typically measure either general social desirability (e.g., the Personal Experience Inventory [PEI]) or capitalize on clinical correlates, such as antisocial characteristics (e.g., the Substance Abuse Subtle Screening Inventory [SASSI]).

Given these limitations, practitioners need to rely heavily on collateral sources and laboratory testing for examining denials of substance abuse. As noted by Stein et al. (Chapter 8, this volume) disacknowledgment and honest misappraisals complicate the assessment of response styles. For example, individuals may have purchased an illicit drug, but they were deceived about its psychoactive ingredients. Regarding intentionality, this genuine misappraisal differs markedly from an intentional denial.

Denied Sexual Arousal Formidable challenges continue to confront practitioners responsible for the evaluation and treatment of sex offenders. Sex offenders are typically placed in a double-bind: (1) continued denial of sex offenses is generally viewed as a negative predictor of treatment potential, and (2) openness about deviant sexual behaviors and ongoing sexual fantasies is likely to receive criminal, and possibly civil (e.g., sexually violent predator [SVP]), sanctions. From a therapeutic perspective, one alternative is to acknowledge this double-bind as part of a trust-building process. A very different alternative is the adoption of an adversarial role, with the periodic use of polygraphs as an external incentive or threat for paraphilic disclosures. Mental health professionals are likely to avail themselves of most available methods, including polygraphs (see Iacono & Patrick, Chapter 19, this



29.  Clinical Assessment of Response Styles 579

volume) and penile plethysmographs (see Witt & Neller, Chapter 21, this volume), despite their mixed validity in assessing denied sexual deviance. Within a treatment context, the use of such measures as safeguards can assist practitioners, perhaps only modestly, in their therapeutic efforts. When used in an evaluative context with significant sanctions, these measures typically fall short of established standards for tests and may also be scrutinized via the Daubert criteria.

Denied or Minimized Cognitive Impairment Persons with limited intellectual abilities sometimes minimize their cognitive deficits. One potentially adaptive approach is acquiescence, whereby persons simply “go-along” with persons in authority, including mental health professionals (Gudjonsson, 2003). Such acquiescence is sometimes preferred by intellectually challenged persons rather than either acknowledge a lack of understanding or attempt to construct a different verbal response. The challenge for mental health professionals is to be aware of “yea-saying” and to minimize its effects on assessments. One approach is to ask questions that offer two, simply stated alternatives. Even in this instance, patterns sometimes emerge (e.g., consistent endorsement of the latter choice). Practitioners must be able to differentiate acquiescence, sometimes used as a method of defensiveness (i.e., hiding cognitive deficits), from other forms of defensiveness. Cognitive defensiveness is almost completely overlooked in clinical and personnel assessments. Persons with mental disabilities may seek to mask their cognitive issues to maintain employment and avoid stigmatization (Jans, Kaye, & Jones, 2012). It is especially germane in employment settings in which sophisticated cognitive skills are essential to successful completion of work-related tasks. I describe (Chapter 2, this volume), for example, widespread defensiveness among airline pilots. Without standardized methods or established detection strategies, practitioners may wish to use cognitive tasks with low face validity.

CRITERIA FOR EVALUATING RESPONSE‑STYLE MEASURES Mental health professionals are often confronted with highly atypical or anomalous findings based on individual interviews and psychological mea-

sures. Of critical importance is the relationship between these findings and response styles. I summarize in this section principal findings for the use of specific diagnostic methods. Clinical methods can be conceptualized in relationship to dissimulation on four levels: susceptibility, detection, detection-coached, and interpretability. 1.  Susceptibility. The most basic consideration is whether a particular method is vulnerable to dissimulation. For example, projective methods once were viewed as inaccessible to feigning, but this notion has subsequently been disproved (see Sewell and Helle, Chapter 16, this volume). When methods are susceptible to feigning, then the crucial issue is detection. 2.  Detection strategies and scales. The second consideration is whether a measure includes empirically validated detection strategies that have been cross-validated across scales and measures. 3.  Detection of coached dissimulators. Practitioners and researchers are poignantly aware that dissimulating persons are not necessarily naive and unprepared. On the contrary, some dissimulators have considerable knowledge of disorders, based on direct observations or personal experiences. Some individuals also possess some knowledge regarding the purpose and design of assessment methods. Therefore, the third consideration is whether the detection strategies and scales remain effective, despite coaching and other forms of preparation. 4.  Interpretability. Most early research designs implicitly assumed an either–or paradigm for response styles (e.g., malingering or mentally disordered). Given this simplistic dichotomization, researchers found no need to investigate the interpretability of dissimulated profiles. Spurred originally by research on the MMPI K-correction, investigators can explore the issue of interpretability: What clinical conclusions can be rendered, despite the observed dissimulation? Practitioners should consider the levels of dissimulation in their selection of assessment methods. As I noted in Chapter 1 of this volume, most clients are not completely forthcoming, even under seemingly optimal conditions. Moreover, research has convincingly demonstrated that all measures are susceptible to response styles. A circumscribed exception may be defensiveness on certain cognitive measures. But even in that case, clients can

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prepare and enhance their performances (see cognitive defensiveness; Chapter 2, this volume). Levels of dissimulation should be considered across clinical domains and referral issues. Because of their complexity, this section features common psychological measures used in the assessment of reported symptoms and psychopathology. Table 29.4 summarizes major trends across multiscale inventories, projective measures, and structured interviews. First and foremost, it should be unquestionably clear that all measures of psychopathology are susceptible to response styles. Put bluntly, decisions simply to neglect response styles likely reflect professional naiveté. As is obvious in Table 29.4, some major areas of clinical assessment have not advanced in their assessment of response styles. Two major areas of neglect have been structured interviews and projective measures. Regarding the former, clinical methods have been developed for comparing (1) client and collateral data and (2) the client’s structured and unstructured interview data (see Rogers, Chapter 22, this volume). However, little progress except for the Schedule of Affective Disorders and Schizophrenia (SADS) has been made on the implementation of empirically validated scales. Regarding projective measures, researchers have largely neglected this critical component of clinical interpretation. Multiscale inventories vary markedly in their sophistication with respect to response styles. Clearly, the MMPI-2, MMPI-2-RF, and PAI outstrip other measures in both their conceptualization of detection strategies and the breadth of their clinical research. In contrast, other inventories are comparatively limited in their usefulness. For example, a concern with the Millon Clinical Multiaxial Inventory–IV (MCMI-IV; Millon, Grossman, & Millon, 2015) is that the feigning scale (Scale Z) is moderately to highly correlated with many clinical scales among presumably genuine populations (rs ≥ .70 include 2A, 2B, 8A, S, C, A, H, D, SS, CC and X; see Millon et al., 2015, Table 6.4). For evaluations involving significant stakes for the examinees, a useful consideration is whether the measure has been tested and found to be effective with those individuals who are prepared (i.e., coached by others or self-coached). The SIRS-2 is widely used in clinical and forensic evaluations. As a specialized measure, it has substantial advantages over more general assessment measures in the development of its scales and extensive validation (see Rogers, Chapter 22,

this volume). The next section focuses briefly on forensic evaluations and specialized measures.

FORENSIC ASSESSMENT INSTRUMENTS AND RESPONSE STYLES Forensic assessments and other high-stakes evaluations place special demands on psychologists and other evaluators given that the context is often perceived as adversarial and its conclusions are far-reaching. Therefore, the systematic evaluation of relevant response styles should be an integral component of forensic assessments. As discussed by Rogers and Shuman (2005), a major contribution to forensic psychology and psychiatry involves the development of empirically validated forensic measures. More specifically, forensic assessment instruments (FAIs) are designed to evaluate a specific legal standard, such as competency to stand trial. In contrast, forensically relevant instruments (FRIs) are used to evaluate clinical constructs that often have direct bearing on forensic evaluations, such as psychopathy and risk assessments. Measures of malingering are also be considered FRIs; however, they are omitted from this section because they are covered extensively throughout the book. Rogers and Fiduccia (2015) provided a thorough review of FAIs and FRIs that includes the principles of their development and validation. Forensic practitioners may wish to consult their detailed critiques of these specialized measures and issues of FAI/FRI admissibility under the Daubert standard (Daubert v. Merrell Dow Pharmaceuticals, Inc., 1993). This section briefly highlights issues of response styles commonly found with FAIs and FRIs. As summarized in Table 29.5, FAIs vary considerably in their sophistication at addressing potential feigning. Some FAIs simply ignored issues of feigning and malingering in both their development and subsequent validation. In contrast, the Evaluation of Competency to Stand Trial—Revised (ECST-R; Rogers, Tillbrook, & Sewell, 2004) was constructed so that its items are less face-valid than those of other competency measures. As a result, at least 80% of college simulators could not successfully feign incompetence (Norton & Ryba, 2010). In addition, the ECST-R (see Rogers, Chapter 22, this volume) developed multiple scales and decision rules. Forensic practitioners are alerted by ECST-R scales to when feigning should be fully in-



29.  Clinical Assessment of Response Styles 581

TABLE 29.4.  Summary Table of Common Psychological Measures That Assess Malingering and Defensiveness

Effectiveness Measure

Response style

Susceptibility

Scales

Scales— Coached

Interpretability

Well developed

Effective

Effective

No Partialb

MMPI-2 and MMPI-2-RF

Malingering Defensiveness

Yes

Well developed

Effective

Partiala

PAI

Malingering

Yes

Well developed

Effective

Partialc

Partiald

Defensiveness

Yes

Well developed

Effective

Partial

No

Malingering

Yes

Developede

Partially effective

No

No

Defensiveness

Yes

Developed

Partially effective

No

No

Malingering

Yes

Developedf

Effective

No

No

Defensiveness

Yes

Developedf

Effective

No

No

Malingering

Yes

Nog

No

No

No

Defensiveness

Yes

No

No

No

No

Other projectives

Malingering

Yes

No

No

No

No

Defensiveness

Yes

No

No

No

No

SADS

Malingering

Yes

Developed

Partially effective

No

No

Defensiveness

Yes

Developed

Partially effective

No

No

SMD interviewsh

Malingering

Yes

No

No

No

No

Defensiveness

Yes

No

No

No

No

PD interviews

Malingering

Yes

No

No

No

No

Defensiveness

Yes

No

No

No

No

SIRS-2

Malingering

Yes

Well developed

Effective

Effective

Partiali

Defensiveness

Yes

Developed

Partially effective

No

No

MCMI-III

NEO-PI-R

Rorschach

Yes

Strategies

Note. Refer to the individual chapters for the full names of measures and details about their effectiveness. SMD, serious mental disorders; PD, personality disorders. aOnly specialized scales (i.e., Wiggins’s [Wsd] and Other Deception [ODecp]) remain effective. bUse of K-correction scales continues despite virtually no research in the two last decades; it is not available on the MMPI2-RF. cVery sophisticated users (doctoral students) can suppress NIM but not RDF. d Some researchers (e.g., Hopwood, Morey, Rogers, & Sewell, 2007) attempt to identify which PAI scales are artificially elevated (see Salekin, Kubak, Lee, Harrison, & Clark, Chapter 24, this volume). eThe Debasement scale is a broader category than feigning, partially confounded by “personal unattractiveness.” fPostdevelopment scales were constructed by Schinka, Kinder, and Kremer (1997). gResearch suggested possible detection strategies, but they remain to be validated. hExcluding the SADS. iA review of SIRS-2 scales can give information about the type of feigning (e.g., endorsement of symptoms with extreme severity), but not genuine disorders.

582

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TABLE 29.5.  Summary Table of FAI and FRI Measures That Assess Feigning

FAI/FRI construct

Measure

Type

Susceptibility

Strategies

Scales

Interpret

FAI: competency

CAI/CAI-R

Malingering

Yes

No

No

No

FAI: competency

CAST-MR

Malingering

Yes

No

No

No

FAI: competency

ECST-R

Malingering

Limiteda

Well developed

Multiple

Yesb

FAI: competency

GCCT

Malingering

Yes

Developed

One

No

FAI: competency

MacCAT-CA

Malingering

Yes

No

No

No

FAI: Miranda

MRCI

Malingering

Yes

No

No

No

FAI: Miranda

SAMA

Malingering

Yes

Well developed

Multiple

No

FRI: psychopathy

LSRP

Denial

Yes

No

No

No

FRI: psychopathy

PPI-R

Denial

Yes

Well developed

One

No

FRI: psychopathy

SRP-4

Denial

Yes

Developed

One

No

FRI: risk assessment

HCR-20

Denial

Yes

No

No

No

FRI: risk assessment

PICTS

Denial

Limitedc

No

No

No

Note. FAI, forensic assessment instrument; FRI, forensic relevant instrument; Type, predominant type of response style; Interpret, the interpretability of a faked measure. For instruments, CAI, Competency to Stand Trial Assessment Instrument (McGarry, 1973); ECST-R, Evaluation of Competency to Stand Trial—Revised (Rogers, Tillbrook, & Sewell, 2004); GCCT, Georgia Court Competency Test (GCCT; Wildman et al., 1978; MacCAT-CA, MacArthur Competence Assessment Tool— Criminal Adjudication (Poythress et al., 1999); MRCI, Miranda Rights Comprehension Instruments (Goldstein, Zelle, & Grisso, 2014); SAMA, Standardized Assessment of Miranda Abilities (Rogers, Sewell, Drogin, & Fiduccia, 2012); LSRP, Levenson Self-Report Psychopathy Scale (Levenson, Kiehl, & Fitzpatrick, 1995); PPI-R, Psychopathic Personality Inventory–R (Lilienfeld & Widows, 2005); SRP-4, Self-Report Psychopathy Scale–Fourth edition (Paulhus Neumann, & Hare, 2016); HCR-20, Historical–Clinical–Risk Management-20 (Webster, Douglas, Eaves, & Hart, 1997); PICTS, Psychological Inventory of Criminal Thinking Styles (Walters, 2001). aNorton and Ryba (2010) found most simulators did not succeed at producing sufficient impairment to be considered incompetent to stand trial. bThe ECST-R has separate decision rules for the classification of feigned incompetence. cGillard and Rogers (2015) found relatively small differences (mean d = 0.49) on PICTS scales when offenders engaged in positive impression management.

vestigated. If feigning is established, they are then provided with more stringent rules for interpreting the ECST-R Atypical scales as indicative of feigned incompetence. Most FRI’s assess forensically relevant constructs that are understandably viewed as negative by most examinees (e.g., psychopathy or high risk of recidivism). As a result, a subset of examinees will be motivated to present a more favorable image to forensic practitioners and may engage in denial or minimization. Knowing psychopaths’ propensities to engage in various forms of deception (Rogers & Robinson, 2016), it seems remarkably naive to develop face valid, self-report measures of psychopathy and to take on “good faith” that psychopaths will openly disclose their psychopathy at their own personal detriment. Taking the Self-Report Psychopathy Scale–4 (SRP-4) as an example, offenders

can markedly suppress their SRP-4 scores for both moderate (d = 1.91) or high (d = 1.91) psychopathy (Kelsey, Rogers, & Robinson, 2015). However, none of the SRP versions included a Positive Impression Management (PIM) scale. Only recent work by independent investigators has tackled this major omission via the Social Desirability—Psychopathy (SDP) index (Rogers et al., 2002; Kelsey et al., 2015). In general, self-report measures of psychopathy should be avoided in forensic assessments, because their results substantially diverge from the established Psychopathy Checklist—Revised (PCL-R), and very few studies with offender samples have addressed defensiveness or PIM. Singh, Grann, and Fazel (2011) identified 120 measures associated with risk assessment. However, most have not been systematically studied for the effects of social desirability and impression



29.  Clinical Assessment of Response Styles 583

management. Gillard and Rogers (2015) examined several measures with varying success. Interestingly, the Psychological Inventory of Criminal Thinking Styles (PICTS) was less susceptible to PIM than others, likely because of its focus on thinking styles rather than overt behaviors. In closing, forensic practitioners should always consider response styles in selecting FAIs, FRIs, and more general psychological measures. Otherwise, both their professional credibility and the perceived value of their forensic reports may be jeopardized.

CLINICAL RELEVANCE OF RESPONSE STYLES The clinical relevance of dissimulation varies directly with the degree of distortion, the type of dissimulation, and the specific referral question. For example, a person with paranoid schizophrenia and an extensive history of auditory hallucinations may attempt to feign command hallucinations. The clinical relevance of such fabrications would depend greatly on the referral question and vary considerably in relevance depending on whether the examinee is seeking voluntary hospitalization or attempting an insanity plea. Indeed, the relevance of any type of dissimulation can only be established with reference to a specific referral question. Vocational assessments have obvious potential for dissimulation. The determination of benefits as related to rehabilitation efforts may provide motivation in a minority of cases for feigned performances. In contrast, some job applicants are likely to be highly motivated to present themselves in the best possible light. To address this issue with law enforcement hires (see Jackson & Harrison, Chapter 28, this volume), the PAI examines validity indicators with reference to specific norms. Such data provide a valuable reference point: Compared to other applicants in this occupation, is this person engaging in a larger-than-expected level of defensiveness and social desirability? I discussed briefly in Chapter 1 (this volume) the potential effects of disclosing disabilities. Despite protection from the Americans with Disability Act (ADA), individuals may be motivated to hide or minimize their disabilities. In other contexts, the motivation may be different. For example, increasing numbers of persons are seeking special accommodations in higher education. Although many requests for special accommodation appear unwarranted, the overall proportion of per-

sons fabricating symptoms to circumvent academic requirements needs further investigation. Special education placements often vary in the quality of services provided but almost always carry a social stigma. We should therefore expect that the proportion of defensiveness to vary accordingly, based on these two parameters (i.e., quality of services and level of stigmatization). A commonsensical review of the forensic issues suggests that dissimulation is highly dependent on the legal issues and the specifics of the case. Although plaintiffs in personal injury cases may have something to gain from feigned deficits, the amount of gain varies directly with the degree of genuine disorder. For example, a person with unequivocal memory impairment coupled with major depression is not likely to be compensated more for the fabrication of additional symptoms. Insanity cases may provide an apparent motivation to malinger. However, this apparent motivation may evaporate in noncapital cases when the length and conditions of institutionalization (prison vs. maximum security hospital) are considered. In summary, Table 29.6 provides a heuristic framework for estimating the expected likelihood of malingering, defensiveness, and social desirability by the type of assessment being conducted. Please note, however, that even a “high” likelihood rarely reaches 50%. These designations are used for comparative purposes only. The relevance of dissimulation ranges widely with the degree of distortion and the referral question. One pivotal consideration underlying the analysis of clinical relevance includes the following: “If the dissimulation were true, what appreciable difference would it make to intervention or disposition?” For instance, a psychotically depressed woman, who exaggerates her suicidal ideation to ensure her hospitalization in a crowded public facility, should be hospitalized irrespective of her malingering. Likewise, a job applicant, who minimizes past episodes of anxiety out of fear of stigmatization, ought to be considered for most positions, irrespective of past anxiety episodes. In such cases, the dissimulation may have only a minor influence on the professional opinion and subsequent decision making. Each case of suspected dissimulation must be evaluated thoroughly, since evaluators cannot determine prior to assessment what the potential relevance of a specific response style will be to an individual case. The issue of relevance must be raised following each assessment in assigning importance of the dissimulation to the overall evaluation of the examinee.

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TABLE 29.6.  Potential for Dissimulation by Types of Professional Evaluation

Likely response styles Domain

Referral issue

Malingering

Defensiveness

Social desirability

Treatment

1. Voluntary outpatient

Low

Low

Variablea

2. Voluntary inpatient

Variableb

Low

Low

3. Involuntary inpatient

Low

Moderate

Low

1. Rehabilitation assessments

Moderate

Low

Variablec

2. Job placement/performance

Low

Variabled

Moderate to highe

1. Special education placement

Low

Variablef

Low

2. Special accommodation under ADA

Variableg

Low

Variable

1. Child custody

Low

Moderate

High

2. Disability

High

Low

Variablec

3. Insanity

High

Low

Low

4. Presentence

Low

Variableh

High

Vocational

School

Forensic

aOutpatients

often present with “safe” problems and do not discuss personally sensitive issues until trust and rapport have been developed. bWhen inpatient resources are scarce, voluntary patients may feel compelled to malinger in order to qualify for services. cOverly virtuous presentations have been observed in vocational and disability assessments. dFor positions requiring emotional stability (e.g., pilots and police officers), defensiveness is commonly observed. eThis response style is common among job applicants and can occur very frequently for competitive positions. f Some persons with cognitive impairments have learned to avoid social stigma by “covering up” their deficits. g Sometimes the issue involves the gross exaggeration of impairment rather than feigned symptoms. hIn minor offenses, defendants may hope for treatment alternatives to incarceration by appearing mentally disordered. In other cases, defendants may worry that the appearance of serious mental disorders will lead to harsher sentences.

Treatment Considerations The relationship of malingering or defensiveness to treatment outcomes is likely to be context-specific. Malingering in terms of disability determinations may vary with the objective, as well as the context; if a settlement is reached in a personal injury case, then the motivation to feign may be attenuated or cease altogether. However, the change from malingering to nonmalingering confounds the accurate assessment of positive treatment outcomes. As a parallel, defensiveness—especially at the beginning of therapy—may obscure much of the treatment gains (Addleson, 2004). While a response style, such as feigning, may complicate the initial assessment, it does not necessarily predict subsequent treatment success (see, e.g., DeViva & Bloem, 2003). Regarding the context, evaluators must exercise care in offering treatment predictions when circumstances are likely to change. For instance, a parent in a child custody dispute may have a strong motivation to deny all psychological problems prior to the child custody hearing, then manifest strong

motivation for treatment, if it becomes a condition of the custody. Similarly, an involuntary examinee may be markedly defensive during the civil commitment proceedings, yet show at least some motivation to participate in treatment once the decision to hospitalize has been determined judicially. Simply put, any striking change in life circumstances signals the need to reevaluate the relationship of response styles to clinical interventions. A common example of time-limited defensiveness can involve “presenting complaints.” Examinees often choose to omit or mask their most pressing psychological issues (e.g., suicidal ideation) and choose less-disclosing problems until they test the trustworthiness and competence of their therapists. In other words, their defensiveness can represent a naturally understandable and likely healthy step in the therapeutic process. The question must also be considered whether the client’s response style itself should be the focus of therapy. Other obstacles to successful treatment, such as a lack of insight, can be considered legitimate foci for therapy. Why not malingering?



29.  Clinical Assessment of Response Styles 585

Malingering could be considered from a cognitive perspective as a salient example of poor decision making with undesired consequences. Unfortunately, many practitioners consider even the possibility of malingering, as a reason not to attempt treatment (e.g., Pedroza, 2003). Instead, an important alternative is suggested: Malingering and other forms of dissimulation constitute major obstacles to treatment, but not categorical excuses to avoid treatment efforts.

avoided, since an underlying premise of such, often pejorative interpretations, is that only “bad” persons dissimulate. A preferable approach is to describe an examinee’s apparent motivation rather than make unsubstantiated inferences regarding character or personality.

Motivational Considerations

An important consideration in the clinical determination of response styles involves some feedback regarding examinees’ specific response styles. This process might well include a summary of a particular examinee’s presentation and the difficulties or problems that the practitioner has in accepting it at face value. Feedback on the examinee’s response style requires sensitivity, tact, and timing from the practitioner. As a general principle, practitioners should be parsimonious in their feedback and judicious in their communication with examinees seen for evaluation. The goals of providing feedback may range from (1) informing examinees regarding the status of the evaluation, (2) asking examinees for assistance in clarifying incongruities, (3) eliciting more complete information, and (4) giving examinees an opportunity to elaborate on their selfreports. Except under very unusual circumstances, the feedback should not become a confrontational attempt to extract from examinees an “admission of dissimulation.” Instead, it provides examinees with an opportunity to clarify areas of ambiguity and, more importantly, to offer insight into possible motivation for the dissimulation. Feedback to a defensive examinee typically requires the use of corroborative data. Naturally, an evaluator should attempt to present his or her observations in a respectful and nonpejorative manner. The following are examples of feedback with a defensive examinee:

A final issue in considering the clinical relevance of dissimulation concerns its adaptive functions for the dissimulator (see also the adaptational model in Chapter 1, this volume). Although practitioners frequently view dissimulation as a negative and complicating factor in clinical assessment, this same behavior may be viewed as positive and adaptive by the dissimulator. The goals of dissimulation may include (1) the maintenance of an individual’s sense of autonomy, (2) securing unfairly denied benefits, (3) avoidance of painful circumstances, and (4) circumvention or disengagement from a difficult or involuntary process. Please note that no professional judgment is rendered regarding either the accuracy or the merits of these goals. Rather, it is an effort to understand and appreciate examinees’ metacognitions, as they affect the professional relationship (Pellecchia et al., 2015). For many forensic practitioners, perspective taking may seem to be an unnecessary step when considering the motivation for response styles. However, it can reflect an important component of effective treatment. For instance, Cacioppo et al. (2015) asked military personnel about the acceptability of malingering in seeking sick call for minor issues or to avoid difficult/dangerous duties. In other nonvoluntary settings, feigning may be motivated by a desire to maintain a sense of integrity, or to minimize avoidable pain or coercion. In contrast, examinees who resist involuntary hospitalization or mandatory treatment may be defending, whether wisely or not, their own right to selfdetermination. An informal review of clinical reports suggests that some practitioners extrapolate from the observed response style to make attributions about a particular examinee. Reports may sometimes describe these individuals as manipulative, selfserving, dishonest, uncooperative, and oppositional. As a rule, linking such inferences to response styles with personality characteristics should be

FEEDBACK TO EXAMINEES ON SPECIFIC RESPONSE STYLES

1. “Although you are telling me that everything is going fine, when I hear about . . . [i.e., description of current problems], I am having some trouble understanding this.” 2. “Life is not all black and white. Whenever someone tells me only the good side, I become interested in what is being left out. . . . ” Malingerers, unlike their defensive counterparts, are commonly characterized by wide range of rarely observed symptoms and magnified im-

586

V I .  S u m m a r y

pairment. Representative probes include the following: 1. “Earlier in the evaluation you told me . . . , now you are telling me. . . . I am having trouble putting this together.” 2. “Although you have told me about . . . [i.e., description of current problems or symptoms], when I observed you, you have not appeared. . . . ” 3. “According to you, you have . . . [i.e., current problems], but according to . . . [i.e., a reliable informant], you are. . . . Can you help me understand this?” Irrelevant responders typically are disengaged from the assessment process. This disengagement may make feedback particularly difficult, since the examinee is uninvolved and emotionally distant from the practitioner. Representative probes are listed below: 1. “I don’t think we got off on the right foot. Can we start again? Tell me in your words what you see as your problems.” 2. “I know that you’re not particularly pleased about being here. How can we make sure that this is not a waste of time for you?” 3. “I know you took these . . . [e.g., psychological tests] for me, but I don’t think you paid much attention to how you answered them. Can you give me an idea what is going on?” As discussed, the purpose of feedback and discussion is to provide a greater understanding of the examinee’s motivation. Experienced practitioners have found that matter-of-fact questions with respect for the examinee’s personal dignity often provide an avenue for further understanding of that examinee. An alternative to direct feedback is to offer the examinee possible motivations for dissimulation. In a child custody case, the practitioner might remark, “Many parents do themselves more harm than good by trying to appear perfect.” For a chronic patient feigning hallucinations, evaluators might caution indirectly, “Sometimes patients make up symptoms to get special attention or more medication. The danger in doing this is that professionals begin to doubt their credibility, even when they are really in crisis.” The indirect discussion of possible motivation has the advantage of providing a face-saving method of discussing and possibly changing dissimulation.

SYNTHESIS OF CLINICAL DATA Practitioners must integrate an array of clinical findings on response styles and discuss these findings in terms of both the referral question and clinical relevance. Such synthesis of dissimulation material requires an examination of the following dimensions: (1) the strength and consistency of results across psychological measures, (2) the absence of alternative explanations, (3) the possibility of hybrid styles of dissimulation, and (4) the methods of reporting dissimulation. Each of these dimensions is discussed individually.

Strength and Convergence of Findings Among the dozens of standardized methods and specific techniques, the strength and robustness of measures must be considered in the determination of response styles. Several methods are particularly robust and produce very few false positives. For feigned psychopathology, the SIRS-2 and extreme elevations on the PAI or MMPI-2 provides highly robust findings. For feigned cognitive impairment, substantially below-chance performance may furnish conclusive evidence of feigning. For defensiveness and deception, markedly anomalous findings on urinalysis, hair analysis, guilty knowledge test (GKT), and penile plethysmography can provide objective confirmation of certain response styles. Even with these robust findings, corroborative data should be actively sought and multiple methods employed. The degree of convergence among responsestyle measures is crucial. If an evaluator has ample data based on pencil-and-paper measures, structured interviews, and corroborative sources regarding a particular response style, then the determination is a relatively straightforward matter. However, some examinees may be tempted to feign on pencil-and-paper measures but respond forthrightly on more person-based methods, such as interviews. What should evaluators do if the examinee appears reliable on the basis of clinical and corroborative data, yet presents a “feigned” profile on the MMPI-2? At a minimum, such inconsistencies reduce the degree of certainty in the diagnostic conclusions. Nonetheless, situational factors (e.g., limited literacy or frustration at the test’s length) may be explored. If salient, evaluators might choose the PAI instead of the MMPI-2, because of its lower reading level and briefer length. The absence of such convergence, depending on the particular clinical presentation, may argue



29.  Clinical Assessment of Response Styles 587

for the general designation as unreliable responding if other data (e.g., collateral sources) are mostly convergent. Suffice it to say that highly consistent results from different sources of clinical investigation are invaluable in making definite conclusions regarding a specific response style.

Absence of Alternative Explanations Specific indicators of dissimulation may be ranked by the degree to which they may be open to alternative interpretations. For example, rare symptoms are, by definition, infrequently seen in the clinical population; the presence of several such symptoms, although potentially indicative of feigning, obviously does not preclude other interpretations. Moreover, some scales have lax criteria (e.g., < 20% of genuine responders) for what is considered rare, so the accuracy of this approach must be established for each scale. In comparison to rare symptoms, a large number of improbable symptoms or symptom combinations is extremely unlikely to occur in genuine responders. As an illustration, an overly dramatic presentation has sometimes been described in case studies to signify feigning. Alternative explanations include prominent symptoms of a serious mental disorder (SMD; e.g., grandiose delusions) or personality pathology (e.g., histrionic personality disorder). Other alternatives must be explicitly considered. Assessment reports that explicitly consider other alternatives are likely to convince other mental health professionals regarding an evaluator’s fairness and thoroughness. Standardized measures, such as the SIRS-2, can furnish specific probability estimates that a particular presentation is feigned. For example, the SIRS-2 Decision Model can provide a high level of confidence for classifying feigning and genuine responding. Extreme elevations on validity scales—while uncommon even for feigners—may provide strong evidence of feigning. Often the lack of elevated scores can be used to rule out feigning and enable evaluators to establish an accurate classification of genuine responding. Using the PAI NIM scale as an example, extreme elevations (> 110T) almost always signify feigning when PAI consistency scales remain unelevated (Hawes & Boccaccini, 2009). Although less thoroughly studied, unelevated NIM scales (i.e., NIM ≤ 65T) can indicate a high probability of genuine responding (Rogers, Gillard, Wooley, & Ross, 2012). Significantly below Chance Performance (SBCP) provides the most compelling evidence

of feigned cognitive impairment to the virtual exclusion of other explanations. When a sufficient number of trials are given and the scores are markedly below persons with no ability (i.e., chance performance), the likelihood of feigning is very high. Although other factors may be present (e.g., fatigue and depression), they are insufficient to produce these abysmal results.

Hybrid Styles of Dissimulation An important task in the clinical determination of dissimulation is whether a examinee is presenting with a hybrid style or an ambiguous clinical presentation (see also Witt & Neller, Chapter 21, this volume). Evaluators must be careful not to overinterpret contradictory findings, particularly when the indicators are gathered from distinct sources of clinical data. Two conditions under which diagnostic conclusions regarding hybrid styles may be justified include (1) circumscribed issues and (2) specified time periods. An example of circumscribed issues would be the assessment of a suspected child molester, who appeared open and honest in the description of his or her dayto-day functioning, with the notable exception of any discussion of pedophilia or other paraphilias. Depending on the quality of the clinical data, evaluators might well conclude that the examinee exhibits a hybrid honest–defensive response style and provide a thorough description of what is meant by this term. The second condition would relate to specific periods. In occasional personal-injury evaluations, the examinee is seen as defensive regarding his or her functioning prior to the injury (i.e., no problems at all) and malingering following the injury. Such a clinical presentation would be characterized as a defensive/malingering response style requiring careful explanation to the referral source. However, many nonfeigning examinees involved in personal injury cases appear to polarize, perhaps unintentionally, their time perspectives (i.e., the untroubled past vs. the problem-ridden present; Rogers, 1995).

Documentation of Response Styles Response styles, such as malingering and defensiveness, can be conceptualized at three basic levels: mild, moderate, and severe. In synthesizing the clinical data, evaluators should attempt to describe the examinee’s malingering or defensiveness in terms of these levels. Table 29.7 presents the three

588

V I .  S u m m a r y

TABLE 29.7.  Reporting Dissimulation: A Sampling of Descriptive Statements

Mild dissimulation (malingering or defensiveness)

Moderate defensiveness

1.  “Although minor distortions were observed in the patient’s [examinee’s] presentation, these are expected given the context of the evaluation.” 2.  “Although the patient [examinee] manifested a slight tendency to minimize [or amplify] his or her selfreport, no major distortions were observed.” 3.  “Although some variations were noted in the patient’s [examinee’s] self-report, they have no (or little) bearing on diagnosis and disposition.”

1.  “Clinical findings clearly indicate that the patient [examinee] was minimizing [and/or denying] his [or her] psychological impairment. The defensiveness was observed in . . . [descriptive examples].” 2.  “The patient [examinee] has denied several important symptoms including . . . [descriptive examples]; these denied symptoms have direct bearing on the patient’s [examinee’s] diagnosis, which may be more severe than currently documented.” 3.  “The patient [examinee] has evidenced a moderate degree of defensiveness as observed in . . . [descriptive examples]. Such defensiveness is fairly common in patients being assessed for mandatory treatment.”

Moderate malingering 1.  “Clinical findings clearly indicate that the patient was exaggerating [and/or fabricating] his [or her] psychological impairment. This was observed in . . . [descriptive examples].” 2.  “The patient [examinee] has fabricated several important symptoms including . . . [descriptive examples]; these symptoms have direct bearing on the patient’s [examinee’s] diagnosis.” 3.  “The patient [examinee] has evidenced a moderate degree of malingering as observed in . . . [descriptive examples]. This attempt to distort the assessment results raises some concern about motivation for treatment.” [Add a comment on whether this is a minor or major concern.] Severe malingering 1.  “The patient [examinee] is attempting to present him- or herself as severely disturbed by fabricating many symptoms including . . . [descriptive examples]).” 2.  “The patient [examinee] has evidenced severe malingering by presenting . . . [strategies of malingering, e.g., ‘rare and improbable symptoms, uncorroborated by clinical observation’]. Most notable examples of fabrication are . . . [descriptive examples].”

gradations of malingering and defensiveness, with sample descriptions for their inclusion in clinical reports. Evaluators must decide, for the purposes of synthesizing clinical data into a relevant report, which observations of the examinee’s response style should be included. As noted earlier, the clinical relevance of a examinee’s dissimulative style must be examined with reference to the referral question. With certain referral questions, discussion of the response style may have little direct bearing on the referral question; therefore, evaluators would have the option of briefly reporting or possibly withholding their clinical observations. In the majority of cases, however, the examinee’s

Severe defensiveness 1.  “The patient [examinee] is attempting to present him- [or herself] as well adjusted by denying many observed symptoms including . . . [descriptive examples].” 2.  “The patient [examinee] has evidenced severe defensiveness by presenting . . . [strategies of defensiveness, e.g., ‘denial of everyday problems, endorsement of overly positive attributes, and denial of psychological impairment despite overwhelming clinical data to the contrary’]. Most notable examples are . . . [descriptive examples].” 3.  “The patient’s [examinee’s] self-report includes the denial of any psychiatric difficulties, despite convincing evidence of . . . [DSM-5 diagnosis]. This severe defensiveness raises major concerns about current motivation for treatment, although this apparent reluctance may be reduced once rapport is established.”

dissimulative style is relevant to the referral question, diagnosis, or capacity to form a therapeutic relationship. In such cases, evaluators should describe the examinee’s response style, including its severity. Although practitioners are encouraged to use the terms employed throughout this book, such terms are in no way a substitution for a thorough description of the examinee’s response style and its relevance to the assessment. Evaluators must address the examinee’s deliberateness, type of distortion, and degree of distortion. In supporting their diagnostic conclusions, evaluators should provide specific examples, preferably employing direct quotes from the examinee. In summary, the clinical report should include a detailed descrip-



29.  Clinical Assessment of Response Styles 589

tion of how the examinee is responding, and how this response style relates to both diagnostic issues and the referral question.

CONCLUSIONS The strength of clinical assessment in the evaluation of malingering and defensiveness remains chiefly in the use of well-validated assessment measures. The synthesis of these measures must take into account their detection strategies and concomitant effectiveness. Although multiple measures do not always increase discrimination, the selective use of multiple detection strategies adds conceptual strength to the establishment of a specific response style. A review of clinical methods by the four criteria of susceptibility, detection, detection-coached, and interpretability, highlights the strengths and limitations in our current knowledge of dissimulation. Considerable advances have been realized during the last decade in the clinical assessment of response styles. Further advances in response-style research are needed, as delineated in the final chapter of this book.

REFERENCES Addleson, B. C. (2004). The distorting effect of excessive defensiveness on self-report measures of mental health and on treatment outcome measurement. Dissertation Abstracts International, 65, 2084. Ardolf, B., Denney, R., & Houston, C. (2007). Base rates of negative response bias and malingered neurocognitive dysfunction among criminal defendants referred for neuropsychological evaluation. The Clinical Neuropsychologist, 21(6), 899–916. Bender, S. D., & Rogers, R. (2004). Detection of neurocognitive feigning: Development of a multi-strategy assessment. Archives of Clinical Neuropsychology, 19(1), 49–60. Bianchini, K. J., Greve, K. W., & Glynn, G. (2005). On the diagnosis of malingered pain-related disability: Lessons from cognitive malingering research. The Spine Journal, 5(4), 404–417. Boone, K. B. (2011). Clarification or confusion?: A review of Rogers, Bender, and Johnson’s a critical analysis of the MND criteria for feigned cognitive impairment: Implications for forensic practice and research. Psychological Injury and Law, 4(2), 157–162. Boone, K. B., Lu, P., Sherman, D., Palmer, B., Back, C., Shamieh, E., et al. (2000). Validation of a new technique to detect malingering of cognitive symptoms: The b test. Archives of Clinical Neuropsychology, 15, 227–241. Cacioppo, J. T., Adler, A. B., Lester, P. B., McGurk, D.,

Thomas, J. L., Chen, H., et al. (2015). Building social resilience in soldiers: A double dissociative randomized controlled study. Journal of Personality and Social Psychology, 109(1), 90–105. Daubert v. Merrell Dow Pharmaceutical, Inc., 509 U.S. 113 S.Ct. 2786 (1993). DeViva, J. C., & Bloem, W. D. (2003). Symptom exaggeration and compensation seeking among combat veterans with posttraumatic stress disorder. Journal of Traumatic Stress, 16(5), 503–507. Gelder, B. C., Titus, J. B., & Dean, R. S. (2002). The efficacy of neuropsychological symptom inventory in the differential diagnosis of medical, psychiatric, and malingering patients. International Journal of Neuroscience, 112(11), 1377–1394. Gillard, N. D., & Rogers, R. (2015). Denial of risk: The effects of positive impression management on risk assessments for psychopathic and nonpsychopathic offenders. International Journal of Law and Psychiatry, 42–43, 106–113. Goldstein, N. E., Zelle, H., & Grisso, T. (2014). Miranda Rights Comprehension Instruments (MRCI): Manual for juvenile and adult evaluations. Sarasota, FL: Professional Resource Press. Gudjonsson, G. H. (2003). The psychology of interrogations and confessions handbook. London: Wiley. Hawes, S., & Boccaccini, M. T. (2009). Detection of overreporting of psychopathology on the Personality Assessment Inventory: A meta-analytic review. Psychological Assessment, 21, 112–124. Hopwood, C. J., Morey, L. C., Rogers, R., & Sewell, K. (2007). Malingering on the Personality Assessment Inventory: Identification of specific feigned disorders. Journal of Personality Assessment, 88(1), 43–48. Jackson, R. L., Rogers, R., & Sewell, K. W. (2005). Miller Forensic Assessment of Symptoms Test (M-FAST): Forensic applications as a screen for feigned incompetence to stand trial. Law and Human Behavior, 29, 199–210. Jans, L. H., Kaye, H. S., & Jones, E. C. (2012). Getting hired: Successfully employed people with disabilities offer advice on disclosure, interviewing, and job search. Journal of Occupational Rehabilitation, 22(2), 155–165. Kelsey, K. R., Rogers, R., & Robinson, E. V. (2015). Selfreport measures of psychopathy: What is their role in forensic assessments? Journal of Psychopathology and Behavioral Assessment, 37(3), 380–391. Larrabee, G. J. (2008). Aggregation across multiple indicators improves the detection of malingering: Relationship to likelihood ratios. The Clinical Neuropsychologist, 22(4), 666–679. Levenson, M. R., Kiehl, K. A., & Fitzpatrick, C. M. (1995). Assessing psychopathic attributes in a noninstitutionalized population. Journal of Personality and Social Psychology, 68, 151–158. Lilienfeld, S. O., & Widows, M. (2005). Manual for the Psychopathic Personality Inventory—Revised (PPI-R). Lutz, FL: Psychological Assessment Resources.

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McGarry, A. L. (1973). Competency to stand trial and mental illness. Washington, DC: National Institute of Mental Health. Merten, T., & Rogers, R. (2017). An international perspective on feigned mental disabilities: Conceptual issues and continuing controversies. Behavioral Sciences and the Law, 35(2), 97–112. Millon, T., Grossman, S., & Millon, C. (2015). Millon Clinical Multiaxial Inventory-IV (MCMI-IV) manual. Bloomington, MN: NCS Pearson. Morey, L. C. (1996). An interpretive guide to the Personality Assessment Inventory (PAI). Lutz, FL: Psychological Assessment Resources. Morey, L. C. (2007). Personality Assessment Inventory (PAI) professional manual (2nd ed.). Lutz, FL: Psychological Assessment Resources. Norton, K. A., & Ryba, N. L. (2010). An investigation of the ECST-R as a measure of competence and feigning. Journal of Forensic Psychology Practice, 10(2), 91–106. Paulhus, D. L., Neumann, C. S., & Hare, R. D. (2016). Manual for the Self-Report Psychopathy Scale 4th edition. Toronto: Multi-Health Systems. Pedroza, G. L. (2003, March). Attitudes and beliefs of psychologists toward the workers’ compensation system. Dissertation Abstracts International: Section B: The Sciences and Engineering, 63(8-B), 3933. Pellecchia, G., Moroni, F., Carcione, A., Colle, L., Dimaggio, G., Nicolò, G., et al. (2015). Metacognition Assessment Interview: Instrument description and factor structure. Clinical Neuropsychiatry: Journal of Treatment Evaluation, 12(6), 157–165. Poythress, N., G., Nicholson, R., Otto, R. K., Edens, J. F., Bonnie, R. J., Monahan, J., et al. (1999). Professional manual for the MacArthur Competence Assessment Tool—Criminal Adjudication. Lutz, FL: Psychological Assessment Resources. Robinson, E. V., & Rogers, R. (in press). Detection of feigned ADHD across two domains: The MMPI-2-RF and CAARS for faked symptoms and TOVA for simulated attention deficits. Journal of Psychopathology and Behavioral Assessment. Rogers, R. (Ed.). (1988). Clinical assessment of malingering and deception. New York: Guilford Press. Rogers, R. (1995). Diagnostic and structured interviewing: A handbook for psychologists. Lutz, FL: Psychological Assessment Resources. Rogers, R. (Ed.). (1997). Clinical assessment of malingering and deception (2nd ed.). New York: Guilford Press. Rogers, R. (Ed.). (2008). Clinical assessment of malingering and deception (3rd ed.). New York: Guilford Press. Rogers, R., Bender, S. D., & Johnson, S. F. (2011a). A critical analysis of the MND criteria for feigned cognitive impairment: Implications for forensic practice and research. Psychological Injury and Law, 4(2), 147–156. Rogers, R., Bender, S. D., & Johnson, S. F. (2011b). A commentary on the MND model and the Boone

critique: “Saying it doesn’t make it so.” Psychological Injury and Law, 4(2), 163–167. Rogers, R., & Fiduccia, C. E. (2015). Forensic assessment instruments. In B. L. Cutler & P. A. Zapf (Eds.), APA handbook of forensic psychology: Vol. 1. Individual and situational influences in criminal and civil contexts (pp. 19–34). Washington, DC: American Psychological Association. Rogers, R., Gillard, N. D., Wooley, C. N., & Ross, C. A. (2012). The detection of feigned disabilities: The effectiveness of the Personality Assessment Inventory in a traumatized inpatient sample. Assessment, 19(1), 77–88. Rogers, R., & Robinson, E. V. (2016). Psychopathy and response styles. In C. B. Gacono (Ed.), The clinical and forensic assessment of psychopathy: A practitioner’s guide (2nd ed., pp. 217–230). New York: Routledge/ Taylor & Francis Group. Rogers, R., Sewell, K. W., Drogin, E. Y., & Fiduccia, C. E. (2012). Standardized Assessment of Miranda Abilities (SAMA) professional manual. Lutz, FL: Psychological Assessment Resources. Rogers, R., Sewell, K. W., & Gillard, N. D. (2010). Structured Interview of Reported Symptoms-2 (SIRS-2) and professional manual. Lutz, FL: Psychological Assessment Resources. Rogers, R., Sewell, K. W., Morey, L. C., & Ustad, K. L. (1996). Detection of feigned mental disorders on the Personality Assessment Inventory: A discriminant analysis. Journal of Personality Assessment, 67(3), 629–640. Rogers, R., & Shuman, D. W. (2005). Fundamentals of forensic practice: Mental health and criminal law. New York: Springer. Rogers, R., Tillbrook, C. E., & Sewell, K. W. (2004). Evaluation of Competency to Stand Trial—Revised (ECST-R) and professional manual. Lutz, FL: Psychological Assessment Resources. Rogers, R., Vitacco, M. J., Jackson, R. L., Martin, M., Collins, M., & Sewell, K. W. (2002). Faking psychopathy?: An examination of response styles with antisocial youth. Journal of Personality Assessment, 78(1), 31–46. Schinka, J. A., Kinder, B. N., & Kremer, T. (1997). Research validity scales for the NEO-PI-R: Development and initial validation. Journal of Personality Assessment, 68(1), 127–138. Sharf, A. J., Rogers, R., Williams, M. M., & Henry, S. A. (2017). The effectiveness of the MMPI-2-RF in detecting feigned mental disorders and cognitive deficits: A meta-analysis. Journal of Psychopathology and Behavioral Assessment, 39(3), 441–455. Singh, J. P., Grann, M., & Fazel, S. (2011). A comparative study of violence risk assessment tools: A systematic review and meta-regression analysis of 68 studies involving 25,980 participants. Clinical Psychology Review, 31(3), 499–513. Slick, D. J., & Sherman, E. M. S. (2013). Differential diagnosis of malingering. In D. A. Carone & S. S.



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Bush (Eds.), Mild traumatic brain injury: System validity assessment and malingering (pp. 57–72). New York: Springer. Slick, D. J., Sherman, E. M. S., & Iverson, G. L. (1999). Diagnostic criteria for malingered neurocognitive dysfunction: Proposed standards for clinical practice and research. The Clinical Neuropsychologist, 13(4), 545–561. Walters, G. D. (2001). The Psychological Inventory of Criminal Thinking Styles (PICTS) professional manual. Allentown, PA: Center for Lifestyle Studies. Webster, C. D., Douglas, K. S., Eaves, D., & Hart, S. D. (1997). HCR-20: Assessing risk for violence, version 2. Burnaby, BC, Canada: Simon Fraser University, Mental Health, Law, and Policy Institute.

Wildman, R., Batchelor, E., Thompson, L., Nelson, F., Moore, J., Patterson, M. E., et al. (1978). The Georgia Court Competency Test: An attempt to develop a rapid, quantitative measure for fitness to stand trial. Unpublished manuscript, Forensic Services Division, Central State Hospital, Milledgeville, GA. Williams, M. M., Rogers, R., & Sharf, A. (2017). Faking good: An investigation of social desirability and defensiveness in an inpatient sample with personality disorder traits. Manuscript submitted for publication. Wygant, D. B., Ben-Porath, Y. S., & Arbisi, P. A. (2004). Development and initial validation of a scale to detect infrequent somatic complaints. Poster presented at the 39th annual symposium on Recent Developments of the MMPI-2/MMPI-A, Minneapolis, MN.

CHAPTER 30

Researching Response Styles Richard Rogers, PhD

Research methodology for the assessment of response styles has continued to evolve since the publication of the third edition about a decade ago in 2008. However, progress has remained very uneven. Rapid and noteworthy advances are balanced by areas of virtual stagnation. Too often, research on response styles deemphasizes its conceptual underpinnings. Without explicitly operationalized detection strategies, assessment research on response-style measures remains mostly an atheoretical enterprise that does not substantially contribute to our knowledge base. As a concrete example, previous chapters had variable success in determining the detection strategies. They varied from post hoc extrapolations to well-defined, empirically validated strategies. A major component of this chapter involves the systematic examination of five basic research designs that are often applied to the study of response styles. For the sake of simplicity, the term dissimulators is used to designate those individuals adopting any specific response style (e.g., malingering or defensiveness). In contrast, the term deceivers is reserved for nonspecific deception (e.g., lying and general deception). Before beginning this examination, I would like to highlight the important linkages between theory and practice. The basic paradigm consists of three phases. First, competing models of deception can be explicitly

tested. Second, these results will inform responsestyle researchers regarding basic principles and potential strategies. Third, the discriminant validity of these potential strategies can be rigorously evaluated. This paradigm is illustrated in the next section, concentrating on the valuable contributions of Sporer and Schwandt (2006, 2007) and subsequently by Sporer (2016).

LINKAGES BETWEEN THEORY AND PRACTICE Response-style researchers can broaden their understanding of theory and practice by reading literature outside their own specialty areas. For example, Sporer and Schwandt (2007) provided a sophisticated analysis of general deception, with testable hypotheses regarding different theoretical approaches. While valuable in its own right, their analytic approach could be used as a model for the study of malingering, defensiveness, and other specific response styles. Sporer and Schwandt (2006, 2007) identified four alternative models for understanding deception. They are described in an outline format with their concomitant predictions. 1.  Arousal theory. Most persons engaging in deception experience psychophysiological arousal.

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It is postulated that increased arousal will be observable in both nonverbal and paralinguistic cues (e.g., faster speech, more filled pauses, and more nodding). 2.  Affective theory. Persons engaging in deception may experience a range of emotions, from fear and guilt to “duping delight” (Ekman, 1988). Fear and guilt are postulated to have commonalities (e.g., decreased eye contact and greater limb movements) and differences (e.g., slower speech and decreased head movements with guilt). 3.  Attempted control theory. Persons engaging in deception have beliefs about how deceivers are “caught” by others. They attempt to inhibit these telltale signs; unwittingly, these inhibitory efforts may become their own cues to deception. It is postulated that many nonverbal and paralinguistic cues will be reduced in an effort to reduce detection. 4.  Cognitive theory. Persons engaging in deception experience an increased cognitive load when constructing a coherent story that fits with past accounts. It is hypothesized that verbal output will be decreased (e.g., fewer words, slower speech, and increased pauses) as a result of this increased cognitive demand. A core value of Sporer and Schwandt’s (2006, 2007) work involves its combination of competing and complementary hypotheses that can be empirically tested. For instance, the attempted control model provides a wealth of interesting hypotheses. It attempts to identify deceivers’ own misconceptions about deception and apply them to the detection of deception. This model for detection strategies is elegantly sophisticated in its use of deceivers’ own countermeasures. As a first step, Minnesota Multiphasic Personality Inventory–2 (MMPI-2) feigning research on the Dissimulation scale (Gough, 1954) capitalizes on inaccurate neurotic stereotypes to identify likely malingerers. As the next step, the attempted control model would ask feigners to describe their views of how feigners are identified. The elegance of this model involves its focus on the relative absence of supposed feigning characteristics as an indirect method of classifying feigners. While illustrated with feigning/malingering, the attempted control model can potentially be applied to other specific response styles. More recently, Sporer (2016) provided a cogent analysis of competing theories of cognitive

processing that may be applied to the categorization of both deceivers and truth-tellers. While these models are complex, with widely variable effect sizes, his contribution should be considered a model on how to build theory-driven methods for the detection of deception. Research on response styles often emphasizes assessment methods, without sufficient attention to detection strategies or their overarching theoretical framework. For a more integrated body of knowledge, I propose that future studies should address at least two of the three phases of responsestyle research: theory, detection strategies, and empirical findings (e.g., scales or indicators). The next section examines the basic research designs used in response-style research.

RESEARCH DESIGNS In the first chapter, Table 1.1 presents a succinct review of basic research designs, describing their comparative strengths and limitations. The large majority of studies is devoted to the simulation design, which uses analogue research to maximize internal validity. The remaining designs use quasi-experimental methods and examine different aspects of external validity in varying degrees of success. Across all designs, a critical issue is the ability to establish discriminant validity using the most clinically relevant or setting-relevant (e.g., job applications) comparisons. To avoid cumbersome terms, I use clinically relevant to encompass other professional settings, such as employment and health care. Two competing dimensions, important in reviewing these designs, are clinical relevance and experimental rigor. In certain respects, these dimensions are inversely related, so that it often infeasible to achieve high levels of clinical relevance and experimental rigor within the same study. However, exceptions may occur, particularly when known-groups comparisons are augmented with sophisticated laboratory procedures (e.g., the use of hair analysis to verify denied substance abuse). Despite the likely trade-off between relevance and rigor, most research on dissimulation can be improved on one or both of these dimensions. Thus, an objective of this section is to review current research designs and to offer recommendations with respect to both clinical relevance and experimental rigor.

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Simulation Research The vast majority of response-style research relies on simulation designs. Therefore, in this chapter I focus on simulation studies, beginning with basic design issues. Basic Design Issues

Simulation research, a subset of analogue research, includes relevant comparison samples. For example, feigning research in psychological settings must include clinical samples composed of individuals with genuine mental disorders and no appreciable evidence of feigning. The bulk of feigning studies uses nonclinical participants who are randomly assigned to control (i.e., honest or standard) conditions that are compared to (1) one or more experimental groups with directions to simulate, and (2) clinical samples of convenience. Within this design, simulators and controls are compared; however, these results have only marginal relevance to malingering, because they do not differentiate feigned from genuine disorders (Rogers & Gillard, 2011). In contrast, the quasiexperimental comparison of feigners to genuine patients provides the critically relevant data. Analogue research that does not include the relevant comparison groups must be considered fatally flawed and should not have been published (see Box 30.1). Assume, for the moment, that a researcher is trying to detect feigned mental disorders and uses the following question with only student samples (simulators and controls): “Do you hear voices that no one else seems to hear?” Unlike simulators, controls almost never respond affirmatively. However, it would be a fundamental error to conclude that this item is effective at detecting feigned mental disorders. When tested with the clinically relevant sample (patients with severe psychotic symptoms), it will likely fail specBOX 30.1.  Fundamentally Flawed Simulation Design Simulation research without clinically relevant (or setting relevant) comparison groups cannot be interpreted. Findings in the predicted direction could be the result: 1. Accurate detection of the response style, or 2. Inaccurate detection of genuine responders with the clinically relevant condition.

tacularly. Even though methodological flaws have been known for decades (e.g., Rogers, 1997), researchers continue to publish defective studies and are still relied upon by unsophisticated practitioners to reach indefensible conclusions. An instructive example regarding the dangers of omitting clinical samples is illustrated by the development of the original MMPI F scale. Selection of its items was based on infrequency of endorsement by normative and not clinical samples (i.e., a quasi-rare strategy; see Rogers, Chapter 2, this volume). Predictably, many F items are commonly endorsed by both feigners and genuine patients. For genuine diagnostic groups, Rogers, Sewell, Martin, and Vitacco (2003) found average F scale elevations of 80.10T for schizophrenia, and 86.31T for posttraumatic stress disorder (PTSD). For genuine patients one SD above average, these elevations easily exceed 100T. Moreover, the problems with the F persist with the MMPI-2 Restructured Form (MMPI-2-RF) F-r scale. Using an inpatient sample commonly diagnosed with major depressive disorder (46.5%), schizophrenia (35.4%), or bipolar disorder (18.1%), Sellbom and Bagby (2010) found F-r to be markedly elevated, averaging 82.43T in presumably genuine patients. Researchers are tempted to rely only on college samples, simply because of the ease of data collection. Ease of research at the expense of clinically relevant research is almost always unwarranted. Recently, some investigators attempted to identify “clinical” groups in college populations. Again, the justification appears to be more expediency than experimental rigor. Although many students may experience mood (e.g., depression) and trauma-related (e.g., PTSD) symptoms, critical questions remain about diagnoses. For example, DSM-5 (American Psychiatric Association, 2013) may require symptoms of substantial psychological distress or impairment as criteria for mental disorders. Such clinically significant distress or psychological impairment must be established by mental health professionals rather than facilely assumed. Elements of Simulation Research

Simulation studies share common elements that include instructions, preparation, incentives, standardization of measures, and manipulation checks. Each element is examined separately, with an emphasis on improving the quality of response-style research, and summarized in a convenient checklist (see Figure 30.1).



30. Research Response Styles 595 1.  Relevant comparisons

INSTRUCTIONS

††Feigning vs. genuinely disordered?

The clinical utility of simulation studies depends on the effectiveness of the experimental conditions to communicate and actively involve participants to “act as if” this condition were intrinsically motivated with real-world consequences. In general, I submit that simulation instructions can often be improved. Instructions should explicitly work to invest participants with a sense of personal involvement. Rogers and Cruise (1998) identified six basic elements that must be considered in construction of experimental instructions with respect to dissimulation. Additional elements (e.g., affective investment and stereotypes) should also be considered. This section examines each element separately in enumerated paragraphs.

††Defensiveness vs. absence of mental disorders? ††Defensiveness vs. social desirability ††Defensiveness vs. denial (e.g., substance abuse or paraphilia) vs. relevant comparison groups 2.  Instructions for experimental conditions ††Clear and comprehensible? ††Sufficiently specific to mirror real-world applications? ††Familiar context with which participants may have had some experience? ††Challenge participants as a method of motivation? ††Underscore relevance as a method of motivation? ††Caution about believable presentation? 3. Coaching ††Inclusion of symptom-based coaching? ††Inclusion of detection-based coaching? ††Single disorder vs. comorbidity? 4.  Motivation and incentives ††Positive internal motivation (e.g., “beating the test”) for successful simulation? ††Negative internal motivation (e.g., “failing the test”) for successful simulation? ††Positive incentives (e.g., receiving research credit) for successful simulation? ††Negative incentives (e.g., losing research credit) for failed simulation? 5.  Other considerations ††Use of stereotypes about simulators (e.g., characteristics avoided by most simulators) ††Use of manipulation checks (see Table 30.1)

FIGURE 30.1.  Checklist for simulation designs in the evaluation of response styles.

1. Comprehensibility. The overriding issue is whether participants have a clear understanding of what is expected of them. Microsoft Word and Internet sites provide estimates of reading comprehension via the Flesch–Kincaid Scale grade estimates (see DuBay, 2004). However, even basic information about psychological issues can easily exceed the comprehension level of research participants. As an instructive example, popular Web-based sources for information (e.g., WebMD) about mental disorders often exceed a high school reading level (Grohol, Slimowicz, & Granda, 2014). Beyond verbal or written recall, can participants firmly grasp the true nature of the task and their role? 2. Specificity. The instructions must be sufficiently explicit that researchers will be able to make a meaningful interpretation of the resulting data. Of course, circumstances do occur in which a proportion of persons engage in a global response style (e.g., denial of all personal faults in contested child custody cases). In such cases, more global instructions may parallel real-world applications. With highly specific instructions, care must be taken that participants understand complex instructions and competently apply them in their research participation. 3. Scenario or context. As part of an experimental design, most studies randomly assign participants to specific experimental conditions, without considering the relevance of the assigned context to the individual participants. Pragmatically, relevance to participants should be evaluated as part of the manipulation check. For example, it is

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valuable to know about participants’ own or their family’s experiences with traumas when studying feigned PTSD. One alternative to random assignment asks participants to choose which context or scenario they would prefer in an effort to increase their investment in the simulation (see Elhai et al., 2007). As noted, this option violates the random assignment, a requirement of experimental designs. Rogers and Gillard (2011) provide a compelling argument that simulators should have some familiarity with the context being simulated. As a farfetched analogy, researchers can never expect participants to effectively simulate a role in an entirely different culture than their own. It may be equally implausible to ask presumably law-abiding college students to simulate an incarcerated murderer attempting to feign insanity as a legal defense. Researchers may wish to pilot-test their scenarios or contexts to ensure that participants have some familiarity. Alternatively, post hoc analyses may assist in testing systematically whether knowledge or experience has any appreciable effect on the ability to simulate effectively. 4. Motivation and incentives. Data from research participants may have limited generalizability simply because these individuals may have trivialized their involvement as a necessary chore (e.g., routine participation for extra credit). Many simulation studies adopt a rather formulaic approach to motivation by simply offering nominal incentives and naively assuming that participants will be truly motivated to perform their best. I would argue that investigators should use multiple methods in an effort to approximate real-world motivations. In simulation studies, methods for motivation include challenging, relevance, and positive and negative incentives (see Figure 30.1). 5. Relevance. Participants may also be motivated if they understand the relevance of the research to their lives and the lives of their families. For example, spiraling insurances costs with decreased health care benefits are fueled, in part, by bogus and unnecessary claims. As a result, the parents of participants may receive marginal or even substandard health care. As participants, do they want to try to help researchers reduce bogus claims? In an inpatient study, Wooley and Rogers (2015) asked participants in the clinical comparison sample to take the research seriously to improve the accuracy of feigning tests. For this clinical group, they appropriately stressed that accurate feigning tests need to be improved so that genuine patients with

traumas—like themselves—will not be turned away from therapeutic services. 6. Credibility. The motivation to appear credible is posited as a powerful incentive for actual simulators. Not only is the loss of credibility likely to be associated with failure to achieve the goals of dissimulation, but this loss may also result in very negative social, economic, and possibly legal consequences. For example, a patient with borderline personality disorder who feigns a bogus suicide attempt (e.g., no drugs were ingested in an “overdose”) loses not only the immediate goal (e.g., hospitalization and medical attention) but may also jeopardize future treatment, funding associated with disability status, and credibility among health care professionals and fellow patients. As a further example, criminal defendants feigning insanity place their long-term future, if not their lives, at risk, since an insanity plea includes an admission to the crime. To approximate real-world applications, simulation research often includes an admonition to simulators to make their presentations believable. In many studies, participants are also cautioned that the test has indicators of faked performance. This caution is intended to make participants understand that their believability is not an abstract construct, but rather something that is directly appraised. Ironically, studies without admonitions and cautions run the risk of producing distorted and possibly bogus results because uninformed simulators did not monitor their own credibility. In large-scale feigning research on the Trauma Symptom Inventory (TSI), Elhai et al. (2007) found that the lack of cautionary instructions produced more marked elevations, suggesting that uninformed participants may not take the issue of believability as seriously as do others. This trend has also been observed in measures of feigned cognitive impairment (Gorny & Merten, 2007). COACHING AND SELF‑PREPARATION

Coaching continues to be a generic term for using information about psychological measures in order to produce test results that appear to be convincingly genuine by successfully covering the response style. In most cases, the coaching is selfinitiated reliance on abundant internet resources. A Google search, accessed on November 29, 2016, utilized the following terms: coaching, faking, and disability claims. It produced 57,600 hits. While



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most appear to be documents, YouTube videos are readily available. For my purposes in this section, the terms coaching and self-preparation are considered equivalent. Two distinct procedures have been employed in coaching studies: disorder-based coaching and strategy-based coaching. Disorder-based coaching provides examinees with detailed information about a particular disorder or condition. Its usefulness to simulators depends largely on the assessment methods. For diagnoses, such as PTSD, knowledge of symptoms plus an informed portrayal of purported trauma may enhance its convincing presentation. In many instances, however, efforts at disorder-based coaching appear misguided, because they are largely irrelevant to the assessment of response styles: 1. Many measures, such as multiscale inventories, are imprecise for diagnostic purposes. For example, feigners do not need to simulate a specific MMPI-2 two-point code to be considered a genuine patient with major depression. Hence, this type of coaching is mainly irrelevant. 2. Comorbidity is very common in clinical settings, which can complicate the assessment of response styles (Elhai et al., 2007). Informing simulators about a single disorder completely overlooks real-world issues of comorbidity. 3. Disorder-based coaching may actually mislead research participants. They may become preoccupied with creating a genuine disorder (e.g., clinical scales), rather than focusing on the primary task of avoiding detection as feigning on response-style scales (e.g., validity scales). Predictably, studies (e.g., Jelicic, Ceunen, Peters, & Merckelbach, 2011) have commonly found that disorder-based coaching has little or no effect on participants’ ability to successfully feign psychological or cognitive impairment. Researchers should include the more challenging detectionbased coaching. As the first published study, Rogers, Bagby, and Chakraborty (1993) compared both approaches and demonstrated the relevance and superiority of detection-based coaching. Detection-based coaching informs participants in straightforward language about the use of detection strategies for the evaluation of response styles. This form of coaching assumes that actual malingerers will examine closely the purpose of psychological measures and attend to response-style measures, such as validity scales. As an illustration of detection-based coaching, participants might be

informed by the following: “The test has several indicators to identify persons faking disorders. For example, fakers sometimes fail very simple items that most brain-injured persons can successfully complete” (i.e., the floor effect). Coaching is also observed in terms of social desirability, particularly in the workplace. Miller and Barrett (2008) reported how police departments may openly coach applicants on how to appear highly conscientious. Such efforts can portray police candidates as ideal candidates, while instructing them to avoid unlikely virtues that are used to detect social desirability. Foiling Detection Strategies

A critical issue is whether detection strategies can remain professional “secrets.” This issue is especially important for those detection strategies that are easily neutralized by countermeasures. For example, the improbable symptom strategy might be neutralized by simple instructions to avoid farfetched or fantastic symptoms. In contrast, the symptom combination strategy would be difficult to foil based on the complexity of the task and need for specialized knowledge. In the development of response-style measures, researchers should avoid easily foiled detection strategies. An important alternative to fragile simple secrets (e.g., no errors on easy items) and their vulnerability to coaching is the development of intricate strategies. Intricate strategies involve the combination of multiple strategies. Rogers (1997, p. 403) presented the following example: Please do your very best job on this test. For the test to be accurate, you have to try your best. Warning: Every now and then, someone tries to fool the test. They pretend to have problems in thinking that they really don’t have. The test has safeguards against faking. First, the test keeps track of the answers you get wrong to see if they are easy items or hard items. Second, the test keeps track of how many seconds you use on items. If you take the same amount of time on easy and hard items, you are not trying your best. Third, the test looks at your wrong answers to see if you make the same kind of mistakes as people that really have problems.

This caution also address three additional safeguards to detect feigned cognitive impairment (see Bender & Rogers, 2004). What happens when simulators are informed proactively about intricate detection strategies? Bender and Rogers (2004) tested this question

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using the Test of Cognitive Abilities (TOCA; Rogers, 1996). Unlike simple detection-based coaching, simulators appear to be unable to capitalize on this information and lower their scores on response-style measures to avoid detection. On the contrary, coached simulators did slightly worse than uncoached simulators. For example, coached simulators scored 21% higher (i.e., more detectable) than uncoached simulators on the magnitude-of-error strategy. The multiple tasks ask the simulator to consider two (or possibly more) detection strategies simultaneously. These strategies require the simulator to attempt to accommodate seemingly incompatible concepts. Attempts to focus on item difficulty (i.e., performance curve strategy) and plausibly incorrect responses (i.e., magnitude of error) may sacrifice response time, which is potentially an additional indicator of feigning, especially when focused on easy items. The idea of publicized intricate strategies has been described for more than a decade. Unfortunately, research has been slow to test the effectiveness of this approach. Instead, simulation research continues to favor simple detection strategies based on the implicit, and likely naive, hope that feigners will remain uninformed. With burgeoning Internet resources, studies of publicized intricate strategies should become a high priority for malingering and other response styles. Affective Involvement

Sporer and Schwandt (2006) suggest that different affective responses are likely to influence simulators’ verbal and nonverbal presentations. They found differences between fear and guilt for deceptive persons. To my knowledge, affective involvement has not been considered in studies of specific response styles. In the consideration of malingering, studies need to focus on at least two general emotional responses: greed-based pleasure and fear. Analogous to gambling (e.g., decision affect theory; Mellers, Schwartz, Ho, & Ritov, 1997) and the deceptive goal of “duping delight” (Spidel, Hervé, Greaves, & Yuille, 2011), some malingerers are likely to experience pleasure at their anticipated and unwarranted gains. Alternatively, some malingerers may be motivated primarily by fear. In the criminal domain, this fear may correspond to a classic avoidance– avoidance decision, with malingerers fearing their likely consequences whether they do not feign (e.g., lengthy incarceration) or feign unsuccessfully (e.g., punitive actions as a result of being caught). Affec-

tive involvement represents a potentially important consideration in assessing response-style measures via simulation designs. It may help to develop and refine detection strategies. Response‑Style Stereotypes

Simulation studies using response-style research typically ask participants to comply with experimental instructions and provide their own convincing performances. A closely related method would be to ask participants to respond as a typical person in that experimental condition. Using feigned mental disorders as an example, participants could be asked to respond as “most malingerers” would do. The purpose of these latter instructions would be to identify malingering stereotypes. Research using within-subjects design could compare participants’ own performances to their stereotypical patterns. If successful, a new detection strategy might be developed based on response-style stereotypes. Motivations and Incentives

Rogers and Granacher (2011) argue that, in forensic practice, motivations to malinger are often complex and cannot be facilely assumed from the circumstances. By the same token, response-style researchers should not inaccurately presuppose that all participants are equally motivated by the study’s design. Therefore, simulation designs can be substantially strengthened by including multiple methods for motivating participants, as well as assessing the effectiveness of external incentives. MOTIVATION

The goal of internal motivation is ego involvement. In real-world applications, successful persons are fully invested in the process and its outcome. One potentially useful approach of involving participants is to challenge them. Examples of positive internal motivation include the following: • “Can you beat the test?” • “Do you think you are good enough to beat the test?” • “With only one job opening, it is important to make the best impression, while appearing very sincere and convincing.” The first two questions are generic challenges that have been used in simulation studies (e.g.,



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Wooley & Rogers, 2015) to internally motivate participants to be “winners.” The third question (Williams, Rogers, & Sharf, 2017) represents a specific challenge for social desirability. However, an alternative involves negative internal motivation. Here are two examples: • “Unfortunately, past research shows that most persons like yourself do not have the skill to ‘beat the test’; please give it your best effort to avoid being classified in the failure group.” • “At the end of the testing, you will be told whether you succeeded or failed. Please do your best to avoid failing.” To my knowledge, negative internal motivation has not been applied to simulation research. While research typically stresses the positive motivation, unconvincing presentations often carry far-reaching, real-world implications. For instance, insincere job applicants are likely to be unsuccessful in furthering their careers. INCENTIVES

Incentives must be distinguished from motivations. Simply because participants are offered nominal positive incentives for successful faking does not mean that they are strongly motivated by them. That said, some participants in institutional settings may find that even small amounts of money (e.g., $10) represent a substantial incentive in terms of commissary items. Moreover, participants in the community may still be motivated, especially if the nominal reward is coupled with the concept of winning (internal motivation). Two recommendations are provided: 1. Couple the nominal incentive with a positive motivation, such as to succeed. Even token amounts of money (e.g., penny ante poker) combined with recognition (e.g., the best player) can be an effective motivator. 2. Use manipulation checks to evaluate differing levels of motivation as a result of the external incentive. Research on the magnitude of positive incentives has produced equivocal results. Shum, O’Gorman, and Alpar (2004) found that incentives ($20) produced more credible performances than no incentives, although classification rates remained largely unaffected. Elhai et al. (2007) attempted to provide higher incentives ($40–50)

but made them available to only the top three performers in each group. When given instructions to be believable, the addition of the incentive appeared to influence motivation; however, its corresponding effect on the TSI Atypical Presentation (ATP) scale was very modest. The daunting challenge for simulation designs is how to approximate real-world incentives. Researchers have practical and ethical constraints on their abilities to provide large monetary incentives. NEGATIVE INCENTIVES

Can research provide negative incentives for bungled dissimulation? Designs might include some form of public disapproval (e.g., publishing failed attempts by research participants; see Patrick & Iacono, 1986). As an example of a simple design, researchers could offer credit simply for participation in several parts of a study. The credit would be “taken away” from all parts of the study if participants failed at dissimulation. Naturally, informed consent would have to delineate these conditions. A variation of this design with fewer ethical constraints would be research in which participants select for themselves different conditions ranging from high-risk/high-gain to low-risk/low-gain. Rogers and Cruise (1998) studied the specific effects of negative incentives. They motivated student simulators by informing them that the names of failed simulators would be posted on the departmental bulletin boards. Simulators in the negative-incentive condition were found to be more focused on their task of feigned depression and produced fewer irrelevant symptoms. Clearly, negative incentives should be considered as a core element for all response-style research. Using the Rogers and Cruise paradigm, possible ethical problems can be largely circumvented by posting fictitious names, thereby minimizing the potential discomfort to participants in the failed simulation group. Manipulation Checks

Manipulation checks represent an essential, but sometimes overlooked, component of simulation research on response styles. For example, Rogers, Payne, Correa, Ross, and Gillard (2009) found that 7.3% of inpatients failed to recall the experimental instructions (genuine or feigned), and 1.2% openly acknowledge putting forth poor effort. The combined 8.5% represents total “noise”; their inclusion would obscure the accurate find-

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ings. Manipulation checks can also be applied to measures, such as the MMPI-2-RF, to exclude careless or inconsistent effort from distorting the results. Robinson and Rogers (in press) found—even among college students—that a small percentage (4.4%) was not consistent in their efforts. Researchers with simulation designs should include the simple step of evaluating participants’ efforts via manipulation checks. Three related elements should be considered in manipulation checks: recall, comprehension, and reported effort. Recall (“Please tell me what the instructions were”) can easily be augmented by comprehension (“Please tell me in your own words what you were asked to do”). For reported effort, one option is to ask participants for a numerical rating. A simple example would be: “On a scale from 1 to 10, how hard did you try?” This approach avoids a potentially pejorative question, such as asking about “poor effort,” which may encourage a socially desirable response. RELEVANCE

Researchers are likely interested in the extent to which participants can relate to the scenario and simulated circumstances. If the instructions involve circumstances or psychological conditions completely alien to the participant, his or her performance is likely compromised. Some researchers prefer to check with participants regarding the relevance of the simulation before beginning this phase of the research. EFFECTIVENESS OF THE INCENTIVES

An interesting question is whether the incentives served their purpose and helped to motivate participants in their simulation. The results of nominal rewards may be nominal motivation for a substantial subset of participants. PERCEIVED SUCCESS

Researchers have also surveyed participants’ beliefs about their success at dissimulation. Results have varied across studies. For example, Kropp (1994) found that 90% of simulators on the original Structured Interview of Reported Symptoms (SIRS) believed they were successful; this belief was unrelated to their ability to elude detection. More recently, Kelsey (2014, p. 54) found the 75% of inmates with psychopathic traits could appear to be a “safe, calm inmate who deserved an easier

sentence.” Their scores on self-report psychopathy scales below those observed in college students suggested this was correct (Kelsey, Rogers, & Robinson, 2015). Much more attention should be paid to the debriefing phase of dissimulation studies. Table 30.1 provides a detailed model of manipulation checks that might serve to standardize specific inquiries. Researchers need to be selective in their choice of what elements to include, although comprehension and reported effort are essential. Moreover, some attention should be paid to participants’ motivation to succeed and the limits on their involvement. Simulation Studies Distinguishing Defensiveness from Social Desirability

Contested child custody evaluations exemplify the importance of distinguishing between defensiveness and social desirability. Because most involved parents present themselves in a favorable light (see Gould, Rappaport, & Flens, Chapter 25, this volume), the key question is whether they are suppressing their clinical scales in denying serious psychopathology (i.e., defensiveness) or in attempting to make a good impression. Two important cautions must be taken into account (see Box 30.2). As noted in Box 30.2, research on overly positive presentations has often been imprecise in its methodology. Regarding the second point, the scenario and simulation instructions determine the response style. For instance, the seeking of desirable employment (social desirability) differs substantially from denying severe substance abuse in order to maintain employment (defensiveness). Recent research by Williams et al. (2017) suggests that total denial of problematic traits is understandably more common for social desirability than for defensiveness. BOX 30.2.  Two Common Misconceptions about Scales for Defensiveness and Social Desirability 1. Scale names are sometimes misleading (e.g., Wiggin’s Social Desirability [Wsd] was devel‑ oped as a measure of defensiveness). 2. Descriptions of studies are sometimes misleading: Researchers need to look at the description of the scenario and the sample to determine whether the study involves defen‑ siveness or social desirability.



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TABLE 30.1.  An Overview of Manipulation Checks in Simulation Research

Facets of the research

Example questions

Instructional sets 1.  Recall of instructions

“What were you told to do? What were your instructions?”

2.  Comprehension of instructions

“What do these instructions mean to you?”

3.  Compliance with instructions

“Some people always do what they are told, while others may be less motivated. Which better describes you with this research?”

4.  Relevance of the instructions

“Have you ever been faced with a situation like the one described in the instructions? Could you relate to this scenario? Of course, this was just an experiment. Could you imagine yourself doing what you were asked to do, if the situation warranted it?”

Preparation 1.  Preparation effort

“What did you do during the preparation time?”

Investment 1.  Internal motivations

(positive) “Were you motivated you by the challenge of ‘beating’ the test?” (negative) “Were you motivated you by the fear of failing to ‘beat’ the test?”

2.  External incentives

(positive) “Did you try harder because of the reward [e.g., research credit]?” (negative) “Did you try harder because of fear of failure [e.g., posting of failed dissimulators]?”

Dissimulation efforts 1.  Portrayal of the condition

“How much effort did you put into being convincing in the research role?”

2. Believability

“Did you try to portray ‘enough’ of the condition [e.g., talented job applicant] to meet the goal [e.g., being offered the position]?” “Did you try to avoid ‘going over the top’ for the condition [e.g., the best job applicant ever] to the meet the goal?”

3.  Avoidance of detection items

“Were there ‘bogus items’ designed to catch fakers?” (If yes) “What made bogus items look bogus?”

Limits to participation 1. Self-disclosure

“Despite your efforts to appear different than you actually are, how much of the real you showed through?”

2.  Emotional interference

“Sometimes when people are going through a difficult time, they find it hard to really participate; did this happen to you?”

3.  Purpose of the study

“Were you worried that the purpose of the study was something different than you were told?”

Self-appraisal of response styles 1.  Perceived success

“How successful were you at fooling the test?”

2.  Perceived ability to dissimulate

“How good are you at fooling others?”

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Known‑Groups Comparisons and Partial‑Criterion Design Known-groups comparisons (KGCs) comprise two discrete and independent phases: (1) establishment of criterion groups (e.g., patients with genuine disorders and malingerers) and (2) systematic analysis of similarities and dissimilarities between criterion groups. The primary challenge of KGC research involves the first phase, namely, the reliable and accurate classification of criterion groups. Because of this challenge, such research is relatively infrequent in dissimulation research. A KGC must be distinguished from a partialcriterion design (PCD), previously referred to as a “bootstrapping comparison” (see Rogers & Bender, 2013). KGC is characterized by the following two essential requirements: 1. Use of the best validated methods available for the purposes of accurate classification of criterion groups. 2. Removal of the too-close-to call cases that are very prone to both measurement and classification errors. The SIRS and the SIRS-2 provide an instructive example. When the SIRS was originally developed, experts on malingering represented the best method available; experts were asked to identify only clear cases of malingering to compare with patients in whom feigning was not suspected. With the extensive validation of the SIRS/ SIRS-2, it has become the best validated measure for feigned mental disorders, especially when used with indeterminate cases removed. Thus, it has replaced experts as the best validated measure.

In contrast to the KGC, the PCD typically relies on one or more measures that have moderate validity and classification accuracy. Depending on the base rate of a particular response style, even a sensitivity of .75 may result in only about 50% accuracy, similar to the likelihood of a coin toss (Merten & Rogers, 2017). Often the PCD applies the criterion only to the response style of interest (e.g., feigning) but not to other relevant groups (e.g., patients with genuine disorders). Table 30.2 highlights the differences between to two designs. A pair of studies by Glassmire and Tarescavage clearly illustrates the core differences between KGC and PCD research designs. In a KGC study, Glassmire, Tarescavage, and Gottfried (2016) utilized the SIRS-2, described as a “more comprehensive measure of feigning” (p. 1586) as the criterion to validate the Miller Forensic Assessment of Symptoms Test (M-FAST), a screening measure. For the PCD, Tarescavage and Glassmire (2016) flipped the measures: The M-FAST screen became the partial criterion design (PCD) and the SIRS-2 became the test to be validated. As I noted in Chapter 22 (this volume), these authors were painfully aware of the limitations of this approach. Relying predominantly on the M-FAST for classification, the questionable criterion groups were labeled “presumed genuine and “suspected feigners.” Their PCD design, and thus, their conclusions, are severely limited by the following: 1. The classification—based mostly on the MFAST—was “deemed inadequate for deriving a presumed genuine responding group” (p. 496). 2. It was further constrained by “primary use of M-FAST scores to derive the presumed feigning group, particularly because the M-FAST

TABLE 30.2.  Typical Differences between KGCs and PCDs for Assessing Response Styles

Typical differences

KGC

PCD

Criterion

Uses the best validated measure.

Uses a moderately validated scale often select out of convenience.

Applies stringent criteria to all involved groups (e.g., malingering and genuine responding).

Applies criteria only to the response style of interest (e.g., malingering).

Removes an indeterminate group (i.e., too close to call).

Uses all participants.

Utility estimates

Warranted

Inadvisable

Conclusions

Definite

Tentative



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is intended for use as a screening measure of feigning” (p. 501). Consistent with Table 30.2, it would have been prudent to avoid utility estimates for the Tarescavage and Glassmire (2016) study because of its unknown accuracy stemming from the PCD methodology. At best, only tentative conclusions are warranted. The perils of PCD studies are well-established.1 PCD studies can be seriously flawed by the use of questionable external criteria. This PCD problem is exemplified by the use of questionable criteria or questionable experts: • Questionable criteria. For example, Hankins, Barnard, and Robbins (1993) adopted a very lax standard for the determination of malingering; any bogus symptom was deemed sufficient. • Questionable experts. Edens, Poythress, and Watkins-Clay (2007) provided prison psychiatrists with rudimentary training and deemed them to be malingering experts. The application of KGCs to individual response styles deserves brief comment. For easy reference, I have captioned each response style in the paragraphs below. Malingering

An easily avoidable problem with malingering research involves the use of DSM indices to establish criterion groups. As noted in DSM-5 (American Psychiatric Association, 2013, p. 727), these indices are intended as only a threshold model (i.e., “strongly suspected”) and not a clinical decision model. The sole effort (Rogers, 1990) to test the DSM-5 model resulted in very poor classification rates. Denied Substance Abuse

A “gold standard” does exist for denied drug use, namely, hair analysis using radioimmunoassay (RIA; see Stein, Rogers, & Henry, Chapter 8, this volume). Highly reliable classification can be employed for both current (i.e., last month) and longitudinal (e.g., last year) substance abuse. It has been employed for the Timeline Followback (TLFB) method to systematically study denied drugs at 3-month intervals (Sharma, Oden, VanVeldhuisen, & Bogenschutz, 2016).

General Deception

Limitations of the polygraph—especially the control question test (CQT)—militate against its use in KGC research (see Iacono & Patrick, Chapter 19, this volume). Although the guilty knowledge test (GKT) has much greater potential, its reliance on case-specific information constitutes a major obstacle to nomothetic research. Defensiveness among Sex Offenders

Studies have demonstrated that a high proportion of patients with paraphilias consciously minimize their aberrant sexual interests and activities (see also Rogers & Dickey, 1991). As summarized by Iacono and Patrick (Chapter 19, this volume), the penile plethysmograph (PPG) is much more accurate with normative (nondeviant) groups than with persons denying paraphilias. Nonetheless, it could be used in KGC research for establishing a group with denied paraphilic interests to compare with normative samples with no evidence of paraphilias (i.e., nonarrested community participants).

Differential Prevalence Design Rogers, Harrell, and Liff (1993) first described the differential prevalence design and carefully distinguished it from the KGC. In a differential prevalence design, the researcher assumes that two samples will have different proportions of dissimulating persons. The assumption is generally based on perceived incentives for deception; most commonly, persons involved in civil litigation are presumed to be more likely to dissemble than persons from other contexts. The differential prevalence design has no practical value in establishing criterion-related validity. It is unhelpful and potentially harmful for clinical classification. Why such a harsh criticism? 1.  Differences in prevalence rates are inferred, not measured. Inferences are founded on commonsensical beliefs or general survey data rather than on estimates derived from the specific samples under investigation. 2.  Prevalence rates for individual response styles usually represent a minority of cases. Looking across survey data for different referrals, typically less than one-third of the group membership can be classified with an individual response style. For example, Rogers (2008) summarized extensive data

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from more than 500 forensic experts on the prevalence of malingering. Overall averages ranged from 15 to 17% in forensic settings. Even when the stakes are very high (e.g., insanity evaluations; see Rogers, 1986), only a minority appear to feign or otherwise engage in deception. 3.  Elevated scores do not necessarily signify response styles. This design prevents researchers from knowing which cases produced elevated scores. It is theoretically possible that every elevated score was produced by persons not engaged in the particular response style. Accuracy cannot be determined. Practitioners are often confused on this point. A prudent caveat might include the following: “No useful conclusions can be drawn from this study about the accuracy of the response-style measures.” To recap these three points, we learn very little from differential prevalence designs about response styles. By design, we do not know who is dissimulating in each group. Logically, we do not know how many are dissimulating in each group. Even when groups yield predicted differences, we do not know what meaning should be assigned to deviant or atypical scores. For all we know, every “deviant” or “atypical” score could be indicative of honest responding. We also do not know how comparable the different samples are on many important dimensions, beyond conjectured incentives. Rogers (1997) enunciated the previously stated reasons for why the differential prevalence design should be abandoned for the classification of response styles. Now, more than two decades later, this deficient design continues to be observed. For example, Arce, Fariña, Seijo, and Novo (2015) tried to study impression management by comparing parents in child custody cases to other parents. Without any other criteria, they implicitly assumed that a differential prevalence between the two groups could be used to empirically evaluate impression management.

Mixed‑Group Validation Frederick (2000) provided an interesting argument for the use of mixed-group validation to address the fundamental problems with differential prevalence design that I raised in the previous section. He argued that knowing which individuals were malingering was irrelevant; instead, knowledge of the proportion of malingerers could be used to calculate sampling error based on computer simu-

lations. Others (e.g., Jewsbury & Bowden, 2014) have elaborated on this approach and championed its broader applications. While mathematically elegant, mixed-group validation presents formidable, if not unattainable, methodological challenges. At its very core, it assumes that base rates such as malingering are both knowable and consistent. However, the base rate of malingering is highly variable. In surveying forensic experts, Rogers, Salekin, Sewell, Goldstein, and Leonard (1998) found a remarkable range in the estimated prevalence of malingering (SD = 14.4%). Using a broaden category for malingering (i.e., encompassing suspected malingering and even symptom exaggeration), the widely cited research by Mittenberg, Patton, Canyock, and Condit (2002) reported highly variable base rates2 depending on both the referral question (i.e., 8.1 to 30.1%) and clinical presentation (e.g., 8.8% for moderate to severe head injury vs. 38.5% for mild head injury). In addition, mixed-group validation assumes the base rates are stable within the sample. Yet individual circumstances change, affecting the motivation to feign. Third, it appears to assume what needs to be tested, specifically, the accuracy of the measure. As an absurd example, we could use head circumference as a measure of social desirability. We could reliably measure the circumferences and posit that the largest heads are feigning. Mixed-group validation would allow us to consider the sampling error and would generate utility estimates. The absurdity of this example demonstrates the conceptual weakness of this approach. Crawford, Greene, Dupart, Bongar, and Childs (2006) attempted to apply mixed-group validation to feigning on the MMPI-2. They made the unwarranted assumption that 7% of their inpatient sample was feigning. This percentage was derived from an entirely different reference point (i.e., forensic referrals to neuropsychologists). However, even if the samples shared general characteristics, this critical leap of faith is impermissible given the known variability across samples. Apparently, without the use of manipulation checks, they simply conjectured that 90% of highly atypical simulators (i.e., graduate students) were able to feign a presumably unfamiliar scenario (i.e., permanent and significant facial disfigurement). Tolin et al. (2010) attempted to entirely sidestep the issue of base rates by using widely divergent base rates found in the literature. In their conclusion, they provide a fundamental and devastating criticism of mixed-groups validation: “Estimates of



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the base rates of symptom exaggeration among CS and NCS3 veterans vary widely, and the present study makes no assertion of the accuracy of any of these estimates” (p. 734; emphasis added).

Combined Research Models Rogers (1997) advocated the combination of simulation design and KGC in the validation of assessment methods for dissimulation. The respective strengths of these designs are complementary. Well-designed simulation studies address satisfactorily the need for experimental rigor (internal validity). Specific response styles can be investigated, as they relate to assessment methods, characteristics/abilities of simulators, and incentives. Moreover, within-subject designs allow for the development of response-style scales based on differentiating patterns between standard and simulation conditions. For example, Rogers, Robinson, and Gillard (2014) used the detection strategy of symptom combinations to identify item pairs found mostly with feigners rather than genuine responders. Early research on the M Test (Beaber, Marston, Michelli, & Mills, 1985) illustrates the need for combined research models (i.e., simulation design plus KGC). The original simulation study appeared very promising, with initial classification rates approaching 90% (Beaber et al., 1985). A second study by different investigators (Gillis, Rogers, & Bagby, 1991) also yielded very positive results for the simulation design but not for the KGC. As a result, Rogers, Bagby, and Gillis (1992) revised the decision rules based on a combined research design. More recently, Personality Assessment Inventory (PAI) feigning research underscores the importance of combining simulation designs with KGCs. Rogers, Sewell, Morey, and Ustad (1996) compared 166 naive and 80 sophisticated simulators to 221 genuine patients with specific mental disorders. The Rogers Discriminant Function (RDF) was carefully developed and cross-validated. Despite its subsequent cross-validation, the RDF results from the simulation design were not confirmed in KGC study with forensic patients (Rogers, Sewell, Cruise, Wang, & Ustad, 1998). This disparity was further established in a more recent meta-analysis (Hawes & Boccaccini, 2009). Within the cognitive domain, Brennan and Gouvier (2006) compared results on the Slick (Slick, Sherman, & Iverson, 1999) criteria for sim-

ulators and suspected malingerers. Interestingly, their data (see Figures 2 and 3) underscore the importance of combined research models. “Definite” malingering was categorized much more frequently among simulators (i.e., 12 of 48, or 25.0%) than among suspected malingerers (i.e., 1 of 54, or 1.9%). These findings strongly support the need for combined research capitalizing on the respective strengths of simulation design and KGC. The logical sequence is the development of an assessment method, with simulation studies followed by KGC. Simulation designs possess two advantages in the validation of response-style measures: (1) the comparative availability of research participants for simulation research, and (2) the ability to refine measures systematically based on simulation designs. In contrast, KGC studies are challenging to implement for two reasons. First, some response styles occur infrequently, which means extensive data collection is required. Second, experts or state-of-the-art measures are often not available. The substitution of marginally adequate measures undermines the fundamental value of KGC and yields only marginally relevant PCD studies. The sine qua non KGC involves its high standards for independent classification. Use of measures with high levels of accuracy, such as the SIRS-2 with indeterminate cases removed, standardizes this classification. First, the implementation of empirically validated detection strategies improves accuracy. Second, the removal of an indeterminant group (too close to call) reduces both measurement and classification errors.

The Malingered Neurocognitive Dysfunction Model Bender and Frederick (Chapter 3, this volume) observed the various criticisms levied at the original malingered neurocognitive dysfunction (MND; Slick et al., 1999) model. In response to some of these criticisms, Slick and Sherman (2013) substantially revised the MND criteria (for a thorough review, see Young, 2014) For example, they modified the original “discrepancies” to “marked and implausible discrepancies.” Despite being published for several years, subsequent research does not appear to have investigated the utility of the revised MND model. A PsycINFO search (September 29, 2017) revealed that the original 1999 MND was utilized with no refereed articles applying the modified MND criteria.

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Specification of the MND Models

Rogers, Bender, and Johnson (2011) raised issues regarding the incredible complexity of the original MND, which apply equally well to the revised MND model. Rigorous research is needed regarding the specification of the MND models. This point is illustrated by the following four research questions: 1. Can practitioners and researchers agree on what constitutes “well-validated psychometric measures” (Slick & Sherman, 2013, p. 63) of presumably feigned cognitive impairment? 2. Can practitioners and researchers agree on what constitutes “marked and implausible discrepancies” (p. 63), when applied to actual assessment cases? 3. Can practitioners and researchers agree on how to apply the term “unambiguously incompatible” (p. 63) in describing disparities between self-reported symptoms and test performance? 4. Can practitioners and researchers agree on data that are more “on balance with noncompliance than compliance” (p. 63)? As an initial step, these questions can easily be addressed via separate surveys of practitioners and researchers. It would be valuable for respondents to (1) generate their own list for Question 1 and (2) produce their own definitions and examples for the remaining questions. However, the more comprehensive approach would be to provide practitioners with actual case material and ask for their determinations. The phrase, actual case material, refers to video recordings (examinee and informants) and test data. Researchers can fully appreciate how distilled case summaries may yield spuriously positive results based on how observations are characterized (e.g., implausible presentation vs. credible presentation). Measurement of the MND Models

The value of any diagnostic model, such as DSM-5 or MND, rests on its ability to produce reliable and consistent measurement. The Standards for Educational and Psychological Testing (American Educational Research Association, American Psychological Association, & National Council on Measurement in Education, 2014) underscore the critical importance of reliability with the following: “The reliability/precision of measurement is always important. However, the need for precision

increases as the consequences of decisions and interpretations grow in importance” (p. 33). Obviously, conclusions about malingering are highly consequential and may change the entire course of an examinee’s life in terms of treatment, compensation, and negative perceptions by health care providers. Therefore, the establishment of reliability represents the highest priority for MND research. Unfortunately, not a single MND reliability study was found in PsycINFO search (September 29, 2017). The critically important issues are summarized in Box 30.3. These core questions about MND reliability urgently need to be tested with different clinical populations and referral questions. They need to be tested on individual criteria, as well as in aggregate scores (e.g., the sum of MND criteria 3.1 through 3.7). The urgency is stressed by the total absence of reliability data coupled with the highly consequential decisions based on the MND model.

Receiver Operating Characteristic Analysis Applied to Response‑Style Research Receiver operating characteristic (ROC) analysis was developed in the 1950s in research on signal detection across a range of applications (e.g., radar and memory vigilance tasks; see Swets, 1973). Although the potential applications of ROC analysis are relatively complex, a primary use in behavioral sciences is a plotting of sensitivity by the false-positive rate (Murphy et al., 1987). This graphical display allows the practitioner to immediately assess the accuracy of the measure in terms of sensitivity and specificity. As noted by Swets (1988), one early application of ROC analysis was to determine the accuracy of polygraph examinations. Swets provided a penetrating analysis of the measured values for the polygraph, with marked differences reported by the source of the study (e.g., commercial polygraphers vs. university researchers). Nicholson and his colleagues (Nicholson et al., 1997) championed the use of ROC analysis to BOX 30.3.  Reliability on the MND Models 1. What is the interrater reliability of the MND specific criteria? 2. What is the test–retest reliability of the MND specific criteria? 3. What is the measurement error of the MND specific criteria?



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evaluate feigned and defensive MMPI-2 profiles. They attempted to use the “area under the curve” (AUC) to compare the relative usefulness of different validity scales. The limitation of this approach is that some scales are much more effective at lower or higher ranges (see Nicholson et al., 1997, Figures 1–3), which is obscured by AUC comparisons. Although Robert Nicholson was a superb methodologist, the usefulness of ROC analysis is questionable in evaluating the utility of response-style measures. A basic assumption of ROC analysis is that classifications should be rendered at every single possible score; otherwise, the AUC is irrelevant. This assumption is controverted by clinical practice. Practitioners (and most researchers) use the best validated cutoff scores. Therefore, AUC comparisons are inapplicable. Moreover, clinical decisions may not seek the optimal classification (i.e., “elbow of the curve”), but may instead wish to maximize sensitivity or specificity. Utility estimates for response-style measures should not be limited to group accuracy (i.e., sensitivity and specificity) but also should take into account likelihood estimates for individual scores. For this purpose, utility estimates should also be considered in examining the probability for the presence (positive predictive power [PPP]) or absence (negative predictive power [NPP]) of a response style. For instance, practitioners need to know the likelihood that a particular cutoff score signifies feigning or genuine responding.

FUNDAMENTAL QUESTIONS An extensive review of the response-style literature reveals many important areas with little or no research. For example, little is known about how culture, language, and background affect responses styles and the detection of response styles (see also Correa, Chapter 4, this volume). In addition, the motivation of dissimulators is often inferred rather than researched, even though the multidetermined nature of behavior belies the simplicity of these inferences. The following sections briefly outline fundamental questions in the study of dissimulation.

Cultural Issues and the Study of Dissimulators Research on culture and response styles has been addressed unevenly, leaving many major gaps. As a particular strength, impression management by

job applicants has been systematically studied, focusing on cross-cultural differences across 43 countries (Fell & König, 2016). These researchers (see also Fell, König, & Kammerhoff, 2016) measured respondents’ views toward “severe faking” (e.g., inventing credentials) and “mild faking” (e.g., strategic omissions) using the Interview Faking Behavior scale (IFB; Levashina & Campion, 2007). While they focused on general attitudes, this line of research could also take into account specific cultural issues (e.g., acculturation) and personality factors as they relate to faking. Li and Reb (2009) investigated self-deception and impression management directly in comparing participants from two different cultures: the United States and Singapore. For social desirability condition (job applicants), scores on impression management were much lower for Singaporean than American participants. These results raise three interesting questions: First, are persons from Singapore more restrained than Americans in expressing social desirability? Second, is the measure of social desirability less effective in Singapore for cultural or linguistic reasons? Third, do other social or societal factors (e.g., very low unemployment levels in Singapore) contribute to these disparities? A methodological limitation is that participants are assigned to the dissimulation condition, which may constrain the interpretation of their findings with respect to dissimulators in general and cultural issues in particular. An interesting alternative would involve a modified simulation design. Using a within-subjects design, all participants would complete the standard administration (i.e., genuine condition) first. They could then be given three options for Phase 2: 1. Genuine retake: Readministration under standard instructions with a small but guaranteed incentive (e.g., $1.00). 2. Embellishment: Readministration under instructions to engage in minor enhancements with the hope of securing a higher reward (e.g., $5.00) but with a nonstringent standard for detection (i.e., only extreme elevations are excluded from the reward). 3. Dissimulation: Readministration under instructions to engage fully in the particular response style with the hope of securing a much higher reward (e.g., $10.00) but with a stringent standard for detection (i.e., moderate elevations do not receive the reward).

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The strength of this design involves its ecological validity. Participants are provided the option of responding genuinely or faking. Corresponding to real-world applications, small embellishments may be overlooked, but major issues about dissimulation are carefully examined. This “self-selected (SS) simulation” design could be used to investigate the characteristics of dissimulators, including important cultural and clinical factors.

Detection of Feigned Specific Disorders Merten and Rogers (2017) outlined the methodological challenges facing researchers who want to identify specific-disorder feigning (SD-feigning). For example, ambitious attempts with the MMPI2 attempted to identify feigned PTSD (i.e., Fptsd; Elhai, Ruggiero, Frueh, Beckham, & Gold, 2002). However, the same research group (Elhai et al., 2004) suggested that the Fptsd may have limited application to civilian PTSD. Moreover, later research (Marshall & Bagby, 2006) also found that the Fptsd lacked incremental validity for feigned PTSD. Methodologically, a basic deficiency in the research design partially contributed to the disappointing results. Box 30.4 summarizes the criterion groups for SD-feigning using feigned PTSD as an example. The twin issues involve whether feigned PTSD can be distinguished from genuine PTSD (Groups 1 and 3) and from other feigned disorders (Groups 1 and 2). Without both comparisons, the determination of SD-feigning simply cannot be established. Depending on the construction the SDfeigning scale, a comparison of Groups 1 and 4 is strongly recommended. For example, the traumafeigning scale for the original TSI used “unusual phenomena or seemingly psychotic experiences” (Briere, 1995, p. 11), in this case, comparing Group 4 with psychotic disorders and Group 1.

BOX 30.4.  A PTSD Example of Criterion Groups for SD Feigning

Otherwise, the SD-feigning scale may be routinely misclassifying psychotically disordered patients as PTSD feigners.

Imprecision of Cutoff Scores Rogers and Bender (2013) underscore the importance of reducing errors in the clinical classification of response styles. At least implicitly, some researchers appear willing to accept a single point on only one scale in rendering an important determination, such as genuine responding versus feigning. For a single-point cutoff score, the untenable position appears to be based on the laser-accuracy myth of cutoff scores, which represents one of the most dangerous and indefensible fictions in response-style classifications. Its two misassumptions are summarized in Box 30.5. In addressing these false assumptions, all cutoff scores have measurement errors, which are easily calculated. Regrettably, the standard error of measurement (SEM) has virtually been ignored for years in diagnostic and response-style research. Take the MMPI-2-RF, for example: SEMs for clinical samples vary from 10 (F-r and Fp-r) to 12 (Fs) raw scores. Thus, any small difference (e.g., 1–3 points) is very susceptible to measurement error. Simply put, the use of single-point cutoff scores is clinically unwarranted. The second false assumption may come from indiscriminately lumping together all data into a single estimate of likelihood. Let’s address this problem with a simple example: The goal is to develop a cutoff score for what height is needed to “slam dunk” a basketball. Keeping it simple, our sample varies in height from 5 to 7 feet, and our optimum cutoff score is 6 feet. If we lump them altogether, then we are falsely assuming that persons at 5 feet, 0 inches and 5 feet, 11 inches have identical likelihoods. This false assumption has been convincingly demonstrated. Rogers, Gillard, Wooley, and Ross (2012) examined the PAI NIM scale and found errors averaging over 50% within a narrow band of the cutoff scores (± 5T). It is simply common sense. Using the earlier example,

A. Simulation groups 1. PTSD feigning 2. General psychiatric feigning

BOX 30.5.  Two False Assumptions of Single-Point Cutoff Scores

B. Genuine patient groups 3. Genuine PTSD 4. Genuine with mixed diagnoses but not PTSD

1. There is no measurement error. 2. All scores, above and below the cutoff score, have an equal likelihood of being accurate.



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most basketball players at 6 feet, 1 inch cannot dunk. Methodologically, it is imperative that future research examine both single-point and welldefined cutoff scores, which remove a narrow indeterminate range. In some instances, the differences may be modest (Wooley & Rogers, 2015). However, in my opinion, it is simply inexcusable to conduct response-style research and neglect this simple yet effective step for reducing measurement and classification errors.

Evaluators and Response Styles Rogers, Sewell, and Goldstein (1994) found that estimated prevalences of malingering ranged more than 50% for a sample of mostly forensic psychologists. The key question involves the following: How much of this variability involves potential biases/characteristics of the evaluators? Several studies of malingering on evaluations of competency to stand trial found remarkable disparities that reflect more about evaluators than about examinees. Two studies found relatively low prevalence rates for malingering: 12.7% (Gothard, Rogers, & Sewell, 1995) and 16.3% (Rogers, Jackson, Sewell, & Harrison, 2004). In stark comparison, Otto, Musick, and Sherrod (2010) reported 50.0% of competency examinees were malingering, or an increase of about 300%. Such marked disparities underscore the need to study evaluators’ attitudes and beliefs about malingering.

Dissimulation and Treatability The role of specific response styles in assessing patients’ treatability has been largely neglected. Krittman (2004) studied treatment compliance of patients in a residential treatment program, which included individuals deemed to be malingering. Unexpectedly, malingerers evidence a greater participation in treatment (i.e., more sessions attended) than their nonfeigning counterparts. However, their participation was not reflected in case managers’ ratings, which indicated their perceptions that feigners were less involved. These findings raise several rival hypotheses. Do feigners attend more sessions but participate less? Alternatively, are case managers biased by their knowledge of feigners? An important issue is whether exaggeration of symptoms may be interpreted as a “cry for help.” Early research (e.g., Hale, Zimostrad, Duckworth, & Nicholas, 1986) suggested that this interpreta-

tion does not have empirical support. Persons with marked elevations on MMPI validity scales often did not remain in treatment, raising questions about their treatment motivation. Berry et al. (1996) conducted a simulation study to examine specifically the effects of a “cry for help” condition on MMPI-2 validity and clinical scales. Simulators wanting immediate access to outpatient care reported very extreme scores (Ms > 100T) on F, Fb, Fp, and DS. In light of Berry et al.’s results, further studies are needed to separate those desperately seeking treatment (e.g., likely factitious disorders) from other motivations. Alternatively, evaluators’ biases against the professional determination of malingering need to be examined; it is quite possible that the “cry for help” is being invoked as a way of “explaining away” feigning (Merten, & Merckelbach, 2013, p. 131). Factitious disorders are also clearly linked to treatment seeking based on false presentations. In an analysis of online support group, Lawlor and Kirakowski (2014) found the desire to receive affection was the most common motivation for factitious presentations. Interestingly, these individuals acknowledged that their presentations were intentional, but that they often felt internally compelled to carry out treatment-seeking behaviors (e.g., claimed suicidal ideation) as a way of meeting their psychological needs. In trying to understand factitious presentations, Rogers, Jackson, and Kaminski (2004) studied PAI validity scales for two groups of simulators who role-played factitious patients urgently seeking multiple therapy sessions each week. These sophisticated simulators (i.e., advanced doctoral students in psychology) assumed either a factitious–demanding or factitious–dependent role. Factious-demanding differed from factious–dependent on several PAI validity scales. Of most interest, low scores on the PAI Defensiveness Index distinguished both factitious groups from both malingering and control conditions. An important implication of Rogers et al. is that treatment-seeking factitious patients should not be simply lumped together. Instead, meaningful subtypes of factitious and other response styles need to be investigated. Denial and defensiveness are typically seen as obstacles to effective treatment. In rehabilitation settings, the denial of illness can be the strongest predictor of poor treatment engagement (Ramanathan-Elion, McWhorter, Wegener, & Bechtold, 2016). For substance use, denial and selfdeception can effectively predict shorter periods of abstinence (Martínez-González, López, Iglesias,

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& Verdejo-García, 2016). Finally, non-admitting sex offenders are typically seen as poor treatment candidates. For example, Beyko and Wong (2005) found that denial and low motivation among sex offenders contributed to poor treatment completion. Interestingly, they saw these effects as limitations of the treatment program rather than shortcomings of the offenders. Researchers may wish to consider the transtheoretical model (TTM; Prochaska & DiClemente, 1982) and its stages of change in examining the role of denial in the determination of treatment readiness. At the precontemplation stage, clients are not willing to acknowledge significant problems requiring treatment. A singular advantage of the TTM is that such denial can be construed as an integral part of treatment with its interventions and objectives (Jordan, Rogers, Neumann, & Norlander, 2013). In their systematic review, Norcross, Krebs, and Prochaska (2011) provide two important insights relevant to therapeutic interventions related to the precontemplation stage: (1) Approximately 40% of clients are not ready to consider change; and (2) well-intentioned but misguided interventions for precontemplation clients may “drive them away, while attributing their lack of progress to clients’ resistance” (p. 152). Two research priorities are summarized in Box 30.6. In keeping with Norcross et al. (2011), the first question recognizes that that response styles (e.g., defensiveness) may be accessible to change, particularly if clients do not feel pressured and blamed. Likewise, the second question, while uncomfortable to consider, acknowledges that professionals often take for granted their clients’ motivation— even when their involvement is externally coerced (e.g., spousal pressure, condition of employment, or court mandate). The notion of reactance (i.e., negative responses to the practitioner’s interventions), extensively studied with psychotherapy (Beutler, Harwood, Michelson, Song, & Holman, 2011), has yet to be systematically investigated with respect to examinees’ response styles.

BOX 30.6.  Two Research Priorities at the Precontemplation Stage 1. Do TTM interventions reduce denial and defensiveness on psychological measures? 2. How does mental health professionals’ lack of awareness of this prechange status con‑ tribute to negative treatment outcomes?

CONCLUSIONS As noted by Rogers and Correa (2008), research on dissimulation can be roughly categorized in four phases: (1) early case studies on which much of clinical practice is still based, (2) sustained interest in psychometric and social-psychological research emphasizing group differences, (3) development of empirically validated detection strategies, and (4) emphasis on individual classifications using combined research designs (e.g., simulation designs and KGCs). Valuable research has pursued two divergent paths. First, specialized measures (e.g., the SIRS-2) have been carefully constructed to evaluate specific response styles using sophisticated detection strategies. Second, embedded measures continue to be explored, with detection strategies being developed and validated within comprehensive measures (e.g., the Wechsler Adult Intelligence Scale–IV [WAIS-IV]). Response-style research forms a complex matrix composed of specific response styles, detection strategies, and clinical domains. Important advances during the last decade have been acknowledged throughout this edition. Although the refinement of assessment methods and consolidation of response-style knowledge are critically important, pioneering research is also needed for uncharted areas, such as detection strategies for feigned medical complaints.

NOTES 1.  These perils were listed with KGC in the previous edition of this volume. However, they are best considered here with the addition of PCD to the chapter. 2. Research by Mittenberg et al. (2002) is sometimes erroneously reported to have rates of malingering exceeding 40%. This is patently not true; they used a much broader category that included suspected malingering and symptom exaggeration. 3. For clarification, CS and NCS represent “compensation-seeking” and “non-compensation-seeking,” respectively.

REFERENCES American Educational Research Association, American Psychological Association, National Council on Measurement in Education (AERA/APA/NCME). (2014). Standards for educational and psychological testing. Washington, DC: American Educational Research Association.



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Rogers, R., Payne, J. W., Correa, A. A., Ross, C. A., & Gillard, N. D. (2009). A study of the SIRS with severely traumatized patients. Journal of Personality Assessment, 91, 429–438. Rogers, R., Robinson, E. V., & Gillard, N. D. (2014). The SIMS screen for feigned mental disorders: The development of detection-based scales. Behavioral Sciences and the Law, 32(4), 455–466. Rogers, R., Salekin, R. T., Sewell, K. W., Goldstein, A., & Leonard, K. (1998). A comparison of forensic and non-forensic malingerers: A prototypical analysis of explanatory models. Law and Human Behavior, 22(4), 353–367. Rogers, R., Sewell, K. W., Cruise, K. R., Wang, E. W., & Ustad, K. L. (1998). The PAI and feigning: A cautionary note on its use in forensic-correctional settings. Assessment, 5, 399–405. Rogers, R., Sewell, K. W., & Goldstein, A. (1994). Explanatory models of malingering: A prototypical analysis. Law and Human Behavior, 18, 543–552. Rogers, R., Sewell, K. W., Martin, M. A., & Vitacco, M. J. (2003). Detection of feigned mental disorders: A meta-analysis of the MMPI-2 and malingering. Assessment, 10, 160–177. Rogers, R., Sewell, K. W., Morey, L. C., & Ustad, K. L. (1996). Detection of feigned mental disorders on the Personality Assessment Inventory: A discriminant analysis. Journal of Personality Assessment, 67, 629–640. Sellbom, M., & Bagby, R. M. (2010). Detection of overreported psychopathology with the MMPI-2 RF form validity scales. Psychological Assessment, 22(4), 757–767. Sharma, G., Oden, N., VanVeldhuisen, P. C., & Bogenschutz, M. P. (2016). Hair analysis and its concordance with self-report for drug users presenting in emergency department. Drug and Alcohol Dependence, 167, 149–155. Shum, H. K., O’Gorman, J. G., & Alpar, A. (2004). Effects of incentive and preparation time on performance and classification accuracy of standard and malingeringspecific memory tests. Archives of Clinical Neuropsychology, 19, 817–823. Slick, D. J., & Sherman, E. M. S. (2013). Differential diagnosis of malingering. In D. A. Carone & S. S. Bush (Eds.), Mild traumatic brain injury: System validity assessment and malingering (pp. 57–72). New York: Springer. Slick, D. J., Sherman, E. M. S., & Iverson, G. L. (1999). Diagnostic criteria for malingered neurocognitive dysfunction: Proposed standards for clinical practice and research, The Clinical Neuropsychologist, 13, 345–561. Spidel, A., Hervé, H., Greaves, C., & Yuille, J. C. (2011). “Wasn’t me!”: A field study of the relationship between deceptive motivations and psychopathic traits in young offenders. Legal and Criminological Psychology, 16(2), 335–347. Sporer, S. L. (2016). Deception and cognitive load: Ex-

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panding our horizon with a working memory model. Frontiers in Psychology, 7, 420. Sporer, S. L., & Schwandt, B. (2006). Paraverbal indicators of deception: A meta-analytic synthesis. Applied Cognitive Psychology, 20(4), 421–446. Sporer, S. L., & Schwandt, B. (2007). Moderators of nonverbal indicators of deception: A meta-analytic synthesis. Psychology, Public Policy, and Law, 13(1), 1–34. Swets, J. A. (1973). The relative operating characteristic in psychology. Science, 182, 990–999. Swets, J. A. (1988). Measuring the accuracy of diagnostic systems. Science, 240, 1285–1293. Tarescavage, A. M., & Glassmire, D. M. (2016). Differences between Structured Interview of Reported Symptoms (SIRS) and SIRS-2 sensitivity estimates among forensic inpatients: A criterion groups comparison. Law and Human Behavior, 40(5), 488–502.

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Author Index

Aamodt, M. G., 405, 413, 559, 560, 562 Aasland, O. G., 156 Abbe, A., 411, 412 Abdel-Motleb, M., 221 Abel, G. G., 408 Abeles, N., 331, 499 Abidin, R. R., 499, 507, 508 Abracen, J., 403, 407 Abrahams, J. P., 488 Abrahamse, A., 246 Abram, K. M., 152 Abrams, S., 365 Acar, A. T., 214 Acar, F., 221 Acartürk, T. O., 221 Achenbach, T. M., 476, 509 Ackerman, M. C., 92, 498 Ackerman, M. J., 92, 498, 503, 507, 508, 509 Acklin, M. W., 308 Adamou, M. C., 188 Adams, B. G., 405 Adams, G. C., 368 Adams, K. M., 52, 134, 135, 140, 340, 353 Adams, P., 103 Adams, R. A., 238 Addleson, B. C., 584 Adhikari, B. P., 157 Adler, A., 105 Adler, J. S., 189 Adshead, G., 224 Aduan, R. P., 219 Afzali, M. H., 455 Aggeler, P. M., 218 Aguerrevere, L. E., 293 Aguila-Puentes, G., 335 Aguinas, H., 381 Ahern, L., 240 Ahmadi, K., 455, 456

Ajzen, I., 535 Akehurst, L., 482 Alam, F., 199 Albanese, M. A., 321 Albert, S., 305 Al-Dajani, N., 296 Alden, L. E., 66 Alderson, P., 102 Alexander, M., 412 Alexander, M. P., 125 Alfano, K., 325 Alfonso, C. A., 105 Alhilali, L. M., 131 Ali, I., 129 Ali, R. L., 156, 157 Ali, S. S., 105, 163, 199 Alinejad, N. A., 214 Alison, E., 412 Alison, L, 412 Alison, L. J.,, 412 Allalouf, A., 62 Allen, D. N., 132 Allen, J. J. B., 364 Allen, J. P., 156 Allen, L. M., 28, 134, 135, 247, 331, 334, 340, 490 Allensworth-Davles, D., 155 Alliger, G. M., 378, 538, 540, 544 Almer, E., 303 Al-Ozairi, A., 130 Alpar, A., 599 Alpern, H. P., 129 Alpert, M., 105, 109, 180, 305 Alqahtani, S., 214 Alterman, A. I., 155, 158 Altshuler, L., 108 Altshuler, M., 526 Alway, Y., 129 Alwes, Y. R., 455, 456, 458, 459, 460, 461

615

Amador, M., 412 Ambady, N., 404 Ambrosini, P. J., 482 Ambwani, S., 72 Amir, N., 394 Amoroso, D. M., 408 Amos, S., 413 Ampudia-Rueda, A., 275 Anastasi, A., 62, 436, 538 Andermann, F., 106 Andersen, J. M., 103, 104 Anderson, B. J., 160 Anderson, C., 222 Anderson, C. D., 533, 539, 540, 544 Anderson, D. S., 264, 353 Anderson, E. W., 100, 101, 110 Anderson, N., 160 Anderson, P. B., 410 Andreasen, N. C., 103 Andreski, P., 189 Andrews, B. P., 179, 377 Andrews, D. A., 181, 407 Andreyev, L., 99 Andrikopoulos, J., 128 Annalakshmi, N., 304, 307 Anthony, J. C., 57, 133, 389 Appelbaum, P. S., 192, 434 Appleby, G. S., 371 Applegate, K. C., 264 Arana, G. W., 281 Arbisi, P. A., 36, 135, 158, 159, 194, 248, 260, 263, 264, 273, 574 Arce, R., 604 Archer, E. A., 499 Archer, E. M., 275 Archer, P. A., 159 Archer, R. P., 257, 263, 275, 280, 405, 439, 483, 484, 502 Archer, R. R., 159

616 Arciniegas, T. B., 129 Ardila, A., 67 Ardolf, B. R., 246, 247, 331, 576 Arentsen, T. J., 331 Arguedas, D., 102, 106 Arkes, H. R., 478 Arkowitz, H., 457 Armistead-Jehle, P., 290, 463 Armitage, T. E., 368 Armstrong, D., 237, 238 Arnold, B., 69 Arnold, G., 132, 135, 324, 325, 339, 353 Arnold, I. A., 218 Aronson, J. A., 128 Arredondo, R., 159 Arrigo, J. M., 389 Arsal, G., 377 Arthur, W., 533 Arvey, R. D., 370, 377 Asaad, G., 105 Ash, T., 476 Ashawesh, K., 214 Ashendorf, L., 134, 138, 321 Asher, R., 213, 220 Ashton, M. C., 376, 546 Ashton, V. L., 128 Aslan, S., 214 Aster, K., 490 Astner, K., 28, 340 Atkins, D. G., 288, 289 Atkinson, R. M., 203, 204, 205 Atlis, M. M., 260 Auble, P. M., 116 Ausborn, D. L., 353 Austin, W. G.,, 499 Auwarter, V., 164 Auxemery, Y., 222 Avasthi, A., 462 Avenido, J., 105 Axelrod, B. N., 45, 51, 132, 134, 324, 336, 351 Axelson, D., 157 Axinn, W. G., 157 Aybek, S., 238, 240 Aydinli, A., 405 Ayoub, C. C., 88, 222, 223 Baade, L. E., 270, 322 Babchishin, K. M., 408 Babiak, P., 91 Babikian, T., 132, 135, 324, 339, 340 Babor, T. F., 156 Bacchiochi, J. R., 289 Bachman, J. E., 198 Backhaus, S. L., 141, 353 Back-Madruga, C., 198 Baer, R. A., 21, 31, 32, 33, 67, 259, 262, 263, 405, 483, 484, 499, 500, 507 Baethge, C., 103 Bagby, M., 36, 478, 479, 480, 481, 482, 490, 491 Bagby, R. M., 69, 84, 100, 176, 193, 194, 197, 258, 264, 265, 266, 267, 269, 274, 275, 289, 291, 293, 296, 434, 450, 451, 452, 453, 457, 487, 500, 559, 594, 597, 605, 608

Author Index Bahali, K., 214 Bahn, C., 553 Bailey, J., 305 Bains, D. S., 192 Baity, M. R., 288, 504, 507 Baker, A. L., 86 Baker, D. A., 487, 489 Baker, E., 91 Baker, S. P., 34 Baker, W. J., 43, 134, 139, 322, 329, 353 Bakri, R., 164, 165 Bala, N., 476 Baldessarini, R. J., 281 Baldwin, K. C., 405 Balikova, M. A., 164 Ballas, S. K., 219 Ballenger, J., 483, 484 Balliu, N., 156 Bamford, K. W., 65 Banchero, R., 64 Bancroft, J., 407 Bangert-Drowns, R. L., 33 Bangerter, A., 531, 535 Banks, S., 426 Banks, S. J., 46 Barbaree, H. E., 86, 407 Barber-Rioja, V., 439 Barkemeyer, C. A., 463, 465 Barkley, W., 410 Barnard, G. W., 451, 452, 603 Barnes, C. M., 541 Barnes, P., 237, 238 Barnett, G., 411 Barnett, W., 214 Barnum, R., 155 Barr, M. A., 104, 105 Barr, S. R., 437 Barrash, J., 329, 344, 345, 351, 353 Barrett, G. V., 597 Barrett, H. M., 391, 393, 395 Barrett, L. F., 3 Barrick, M. R., 375, 376, 531, 535, 539, 540, 542, 554 Barsky, A. J., 217 Bartel, P., 181 Barth, J. T., 124, 125 Bartol, C. R., 552, 560 Bash, I., 305 Bash, I. Y., 109 Bashem, J. R., 353 Bashford, C., 98 Basis, F., 214 Bass, A., 139 Bass, C., 34, 99, 216, 217, 220, 221, 222, 228 Basso, M. R., 34 Batchelder, K., 304, 306, 310 Batchelor, J., 488, 489 Bates, D. W., 217 Bates, J. A., 381 Bates, J. E. W., 24 Bathurst, K., 499, 500, 501 Bauer, K., 224 Bauer, L., 134, 137, 138, 526 Bauer, M., 214, 220 Bauer, R. M., 364

Baughman, B. C., 134, 338 Baum, L. J., 159, 275 Baumann, E. C., 3 Baumgart, M. R., 280, 281 Baumgartl, V. O., 377, 380 Beaber, R. J., 450, 451, 452, 605 Beasley, J. F., 478 Beatty, R. W., 553 Beaty, J. C., 381 Bechtold, K. T., 609 Beck, A. T., 464 Beck, J. T., 105, 424 Becker, J. V., 406, 407, 409 Becker, T. E., 546 Beckham, J. C., 20, 195, 196, 608 Beckson, M., 214 Beeson, P. B., 218 Bekdas, M., 222 Belanger, H. G., 56, 127 Belfi, B., 264, 441, 443 Bell, B. A., 214 Bell, K. R., 130 Bell, M. J., 222 Bell, T., 196, 282, 439 Bell, V., 155 Bell-Sprinkel, T. L., 325 Belza, M., 203, 204, 215, 220 Bender, H. A., 489 Bender, S. D., 4, 6, 8, 10, 13, 18, 20, 21, 22, 23, 24, 27, 28, 29, 30, 42, 43, 44, 45, 46, 56, 57, 99, 122, 123, 124, 126, 127, 128, 135, 137, 139, 140, 142, 197, 247, 303, 306, 309, 330, 333, 439, 449, 488, 575, 576, 577, 597, 602, 605, 606, 608 Benedek, D. M., 116 Benge, J. F., 458, 459, 460 Bengston, P., 160 Benke, T., 106 Benn, A. F., 303, 308, 405 Bennett, B., 406 Benning, S. D., 376, 377 Ben-Porath, Y. S., 36, 49, 136, 158, 159, 194, 195, 227, 248, 249, 257, 258, 260, 262, 263, 264, 267, 270, 273, 275, 425, 483, 484, 553, 556, 560, 561, 564, 574 Ben-Shakhar, G., 363, 364, 368, 410 Benson, D. F., 238 Bentler, P. M., 151 Bentovim, A., 224 Berenberg, A. N., 108 Beretta, V., 411 Berg, I. A., 478, 545 Berger, L. R., 152 Berger, O., 192 Bergman, A., 426 Bergman, E. T., 392–393 Bergman, M. A., 122 Berkanovic, E., 63 Berkowitz, D., 112 Berlin, F. S., 214 Berlin, J. A., 318 Berman, J., 130 Bernard, L. C., 341, 342, 343, 351, 353 Bernardino, C. R., 221 Bernat, E. M., 381 Bernet, W., 116



Author Index 617 Berney, T., 99 Bernstein, D. P., 43, 426 Bernstein, E. M., 393 Bernstein, I. H., 559 Berntsen, D., 389 Berrios, G., 103 Berry, C. M., 378, 379 Berry, D. T. R., 36, 45, 67, 85, 128, 135, 137, 138, 243, 246, 247, 249, 257, 259, 262, 281, 293, 329, 330, 334, 353, 405, 436, 437, 439, 442, 449, 455, 458, 459, 483, 486, 487, 490, 499, 575, 609 Berry, J. W., 66, 68 Bersoff, D. N., 4, 62 Berthelson, L., 54, 55, 84, 141 Bertinetti, J. F., 483 Bertram, D. M., 340 Bertrand, R., 62, 63 Best, S. R., 189 Betts, G. L., 369 Beume, L., 106 Beutler, L. E., 610 Beyko, M. J., 610 Bhargava, D., 220 Bhugra, D., 219 Bianchetti, A., 107 Bianchini, K. J., 44, 45, 55, 124, 126, 130, 131, 132, 134, 140, 262, 270, 273, 274, 293, 320, 321, 329, 331, 337, 339, 343, 353, 517, 577 Biddle, J. C., 156 Bigler, E. D., 49, 51, 55, 56, 125, 326, 329 Bilder, R. B., 141 Billings, F., 180 Binder, L. M., 30, 56, 57, 125, 128, 129, 132, 133, 316, 353 Binder, R. L., 192 Binder-Brynes, K., 190 Bing, M. N., 544 Binggeli, S., 535 Birchwood, M., 103, 104 Bird, C. M., 34 Birkeland, S. A., 532 Birmaher, B., 157 Birt, A., 178 Bisson, J. I., 213, 221 Biswas, J., 527 Bitzer, R., 203 Bjork, R. A., 393 Blaauw, E., 216 Black, F. W., 110, 320 Blaha, R. Z., 488 Blais, M. A., 284, 288, 504 Blanchard, D. D., 100, 288 Blanchard, E. B., 201, 465, 518 Blanchard, K. A., 160 Blanchard, M., 3 Blanchard, R., 407 Blank, J., 48, 123 Blankenship, K. L., 90 Blasingame, G. D., 91, 410 Blaskewitz, N., 488, 489 Bleijenberg, G., 218 Blitz, R., 200 Bloch, J. D., 221 Bloem, W. D., 192, 584

Blom, N., 214 Blonigen, D. M., 376, 377, 380 Bluglass, K., 224 Blum, N., 426 Blumberger, D., 214 Blumstein, A., 402 Blyth, C. C., 222 Boals, A., 139 Boast, N., 218 Boat, B. W., 216 Boccaccini, M. T., 72, 76, 135, 136, 249, 280, 286, 287, 288, 289, 292, 439, 503, 555, 563, 571, 578, 587 Boegner, F., 214, 220 Boellinghaus, I., 201 Boer, D. P., 178 Boettcher, A. C., 133 Bogdan, R., 395 Bogenschutz, M. P., 603 Bohni, M. K., 389 Boisvenu, G. A., 363 Bolino, M., 533, 535 Boll, T., 125 Bolter, T., 135 Bombel, G., 310 Bond, C. F., 405 Bond, J. P., 214 Bond, M., 126 Bongar, B., 604 Bonn-Miller, M. O., 409 Bonta, J., 181 Book, A., 413 Book, A. S., 183 Bools, C., 478, 480 Boon, S. D., 3 Boone, K. B., 31, 43, 44, 52, 123, 126, 132, 133, 134, 135, 137, 141, 198, 249, 324, 325, 339, 345, 347, 351, 353, 478, 576, 577 Bor, R., 289 Borges, G., 154 Born, M. P., 544 Borneman, M. J., 544 Bortolaso, P, 214 Borum, R., 19, 181, 412, 451, 452, 453, 507, 557, 558, 560, 561, 565 Bosch, C. M., 137, 247, 329, 449, 575 Bosley, J. T., 402 Bott, J. P., 532 Bottoms, B. L., 479 Bourke, M. L., 410 Bouvy, H., 181 Bow, J. N., 498, 508 Bowden, S., 410 Bowden, S. C., 51, 55, 604 Bowen, C., 290 Bowyer, J. R., 163 Boyd, A., 535, 536, 537 Boyer, D., 341, 342, 343, 351, 353 Boyle, K., 152 Braam, A. W., 214 Brabender, V. M., 307, 309 Bracken, M., 489 Bradbury, S. F., 34 Bradley, L. A., 34 Bradley, P., 370

Bragason, O. O., 412 Braginsky, B. M., 425 Braginsky, D. D., 425 Braham, L. G., 104 Branaman, T. F., 91, 409 Brand, B. L., 437, 438, 442 Brandon, S. E., 405, 411, 412 Brandt, J., 138, 199 Brannick, M. T., 532, 544 Branson, C. E., 159 Brantley, P. J., 465 Brasseux, R., 343 Braver, E. R., 130 Braverman, M., 191, 199 Braxton, L. E., 195 Brennan, A. M., 131, 262, 321, 331, 353, 605 Breslau, N., 188, 189, 389 Bressler, M., 369 Breuer, J., 237, 239, 387, 391 Brewin, C. R., 188 Brickell, T. A., 466 Briddick, H., 294 Bridges, S. A., 275 Briere, J., 196, 390, 608 Brinkmeyer, K., 375 Brislin, R. W., 63, 64 Brittain, R., 112 Brittlebank, A. D., 394 Brodey, B. B., 158 Brodey, I. S., 158 Brodie, J. D., 111 Brooks, B. L., 56, 57, 128, 133, 488, 489 Brooks, D. N., 76 Broome, K. M., 151 Brothers, S. L., 135, 249, 257, 281, 405, 483 Broughton, N., 110 Brown, D. J., 220, 387, 388, 389, 540 Brown, G. K., 464 Brown, H., 238 Brown, I., 478 Brown, J. P., 214, 218 Brown, L. M., 126 Brown, M., 137, 408 Brown, R. J., 238 Brown, R. M., 224 Brown, R. S., 37, 127 Brown, T. A., 190 Brown, W. L., 320 Bruce, M. N., 540, 544 Bruck, M., 479 Bruner, G. C., 7 Bruning, J. R., 412 Bruns, B., 478 Bruns, D., 37 Bryant, E. M., 543 Bryant, R., 201 Bryant, R. A., 130, 188, 200, 290 Buchan, B. J., 151 Buchsbaum, M. S., 190 Buck, J. N., 301 Buckley, J. P., 412 Buckley, T. C., 465 Buckley, T. J., 163 Buddin, W. H., Jr., 137

618 Buehler, J., 141 Buelow, M., 139 Buffington, J. K., 108, 176, 179, 199 Buffington-Vollum, J. K., 257, 280, 405, 439, 502, 503 Bujaki, M., 66 Bukstein, O., 158 Bull, R., 410, 412, 413, 478, 482 Bullard, P. L., 37 Buller, D. B., 543 Bunnting, B. G., 463, 466 Bupp, L. L., 291, 504 Burchett, D. L., 8, 159, 197, 258, 264, 265 Burck, A. M., 161 Burger, G. K., 49, 73, 457, 458, 487 Burger, G. O., 100 Burges, C., 191 Burgoon, J. K., 543 Burgoon, M., 37 Burke, M. J., 293 Burke R. S., 157 Burkett, B., 203, 204 Burman, D., 222 Burnam, M. A., 64 Burnell, R. H., 223 Burns, G. N., 544, 545, 546 Burns, M. W., 224 Burns, R. C., 301 Burr, W., 200 Burris, L. R., 371 Bury, A. S., 193, 265, 289 Busby, R. M., 192 Busch, C. R., 129 Bush, S. S., 47, 123, 139, 248 Bussey, K., 477 Bussière, M., 403, 407, 411 Bustamante, J., 99 Butcher, J. N., 33, 92, 135, 158, 159, 192, 194, 249, 257, 259, 260, 262, 275, 280, 406, 425, 483, 500, 501, 502, 506, 553, 555, 556, 564 Butler, S., 108 Bux, D. A., 160 Byard, R. W., 221, 223 Byrd, D., 68 Cacciola, J. C., 158 Cacciola, J. S., 155 Cacioppo, J. T., 585 Cahill, B. A., 412 Cahill, D. J., 543 Cahn, G., 515, 516 Caillouet, B. A., 518 Caine, S. L., 304, 305 Caldwell, A. B., 557 Caldwell, M. F., 86 Calhoun, P. S., 189, 195, 196 Callahan, C., 371 Callahan, J. L., 458, 459 Callaway, S., 490 Callegari, C., 214 Callister, M., 37 Callon, E. B., 463, 465 Calsyn, R. J., 544 Camara, W. J., 257, 405 Campagna, A. C., 221

Author Index Campbell, L. A., 190 Campbell, T. C., 160 Campion, M. A., 531, 532, 533, 534, 535, 536, 537, 545, 607 Cann, S., 406 Canyock, E. M., 47, 83, 123, 191, 245, 331, 335, 604 Capilla Ramírez, P., 61 Caplan, J. P., 221 Caputo, A., 555 Caradoc-Davies, G., 214 Carbonell, J., 98 Cardenal, V., 73 Caretti, V., 87 Carise, D., 158 Carle, A. C., 462 Carlisle, A. L., 303, 457 Carlson, C., 489 Carlson, H. C., 489, 540 Carlson, S. R., 381 Carmody, D. P., 304 Carney, M. W. P., 214, 218 Carone, D. A., 123, 489 Carp, A., 303 Carpenter, W. T., 106 Carr, G. D., 504, 505 Carroll, S., 246 Carsky, M., 7 Carson, S. H., 395 Carta, F., 221 Carter, D. M., 103, 104 Carter, K. E., 222 Carter, M., 314 Caruso, J. C., 156 Caruso, K. A., 116 Carvalho, J., 90 Casas, J. M., 64, 67 Casey, J., 126 Cashel, M. L., 73, 291, 428, 504 Casoni, D., 424 Cassar, J. R., 478 Cassell, P. G., 413 Cassidy, C. M., 156 Castagna, N., 498 Cattell, R. B., 505 Catts, S. V., 106 Cavanaugh, J. L., 179, 184, 405, 502, 564 C’de Baca, J., 152 Ceci, S. J., 388, 479 Cellar, D. F., 539 Cerulo, K. A., 542 Ceunen, E., 597 Cha, E., 64 Chadwick, P., 103 Chafetz, M. D., 478, 488, 489 Chakraborty, D., 597 Chalder, T., 218 Chambers, A., 288, 504 Chambliss, J., 192 Chan, K., 539 Chan, Y., 157 Chance, J., 262 Chandler, R. K., 155 Chang, G., 155, 156 Chang, I., 152 Chang, K., 151

Chang, L., 134, 324 Chaplin, T., 408 Chaplin, T. C., 407 Chapman, M. J., 188 Charcot, J.-M., 236 Chardoul, S., 157 Charter, R. A., 294 Charuvastra, A., 160 Chauhan, P., 114 Chavira, D., 69 Chelminski, I., 422 Chelune, G. J., 335 Chen, D. K., 320 Cheng, D. M., 155 Chermack, S. T., 158 Chernyshenko, O. S., 539 Cherpitel, C. J., 154 Chesterman, P., 110 Chew, B. H., 221 Chi, T., 509 Chibnall, J. T., 139, 556, 560, 561, 562 Childs, H., 604 Chintapatla, S., 216 Chmielewski, M., 265, 266, 268 Chmielowski, T., 533, 552 Choca, J. P., 159 Chojnacki, J. T., 556 Chong, C. D., 125 Chouinard, M. J., 353 Chriki, L., 284 Christiansen, N. D., 539, 540, 544, 545, 546, 561, 562, 565 Christiansen, P., 412 Christophe, D., 407 Chung, H., 164 Chung, T., 156 Cicerone, K. D., 465 Cigrang, J. A., 500 Cima, M., 460, 517 Ciorciari, J., 394 Ciota, M. A., 131, 132, 353 Cipolotti, L., 34 Cirimele, V., 164 Cisler, J. M., 129 Clancy, C. P., 195 Clancy, K. J., 544 Clancy, S. A., 390, 391, 392, 393, 394, 395, 396 Clark, A. P., 381, 475, 581 Clark, C. R., 174 Clark, D. A., 406 Clark, D. B., 156 Clark, J. A., 249, 455 Clark, M. E., 284 Clark, T. S., 289 Clarke, P., 129 Cleckley, H., 174, 175, 176, 177, 178, 180, 184 Clegg, C., 406, 458, 459, 464, 486 Clemens, F., 413 Clements, C., 479 Clements, R., 159 Clevenger, S. V., 188 Cochrane, R. E., 314, 553, 554, 555, 561 Cofer, C. N., 262, 263 Cogburn, R., 180



Author Index 619 Cohen, A., 130 Cohen, B. M., 218, 219 Cohen, J. D., 21, 128 Cohen, M., 105 Coid, J., 181 Cole, A., 406 Cole, J. A., 132, 321 Coles, E. M., 184 Collie, C. F., 195 Collie, J., 190 Collins, R. L., 320 Collinson, G., 112 Colon, H. M., 152 Colquitt, A. L., 546 Combs, D., 34 Commons, M. L., 521 Comtois, K. A., 157 Conder, R. L., 490 Condit, D. C., 47, 83, 123, 191, 245, 331, 604 Conners, C. K., 509 Connery, A. K., 487, 489 Connor, J. P., 154 Conover, E., 132, 321 Conroy, M. A., 401, 402, 403 Constantinou, M., 138, 321, 488, 489, 490 Contardi, S., 105 Conte, J., 390 Conti, R. P., 478, 480 Converse, P. D., 546 Cook, D. J., 525 Cook, D. M., 221 Cook, P. E., 501 Cook, R., 410 Cook, R. L., 156 Cook, R. R., 192 Cooke, D. J., 176 Cooley-Towell, S., 403 Coons, P. M., 214 Cooper, B. S., 178 Cooper, D. B., 458, 463, 465, 466 Cooper, G. A., 164 Copeland-Disorbio, J., 37 Copoloy, D., 102, 103 Corey, D. M., 554, 561, 564 Coric, V., 413 Corley, R. P., 376 Cornelius, J., 105 Cornell, D. G., 98, 101, 123, 159 Coronado, V., 124 Correa, A. A., 28, 61, 62, 64, 65, 67, 68, 69, 70, 72, 73, 141, 192, 287, 289, 291, 292, 333, 438, 442, 443, 468, 599, 607, 610 Corrigan, S., 331 Costa, P. T., Jr., 540, 544 Costa Santos, J., 424 Costello, R. M., 559 Cottler, L. B., 157 Coulton, G. F., 553 Court, J. H., 490 Courtney, J. C., 321, 490 Courtois, C. A., 387 Covinsky, K. E., 318 Cox, B. J., 189

Cox, D. N., 180, 434 Cox, D. R., 490 Cox, J., 519 Cozolino, L., 325 Craddock, M. M., 353 Craig, J. R., 370 Craig, R. J., 160, 293 Crawford, E., 604 Creamer, M., 201 Creekpaum, J. K., 526 Cremonte, M., 154 Crews, F., 387 Crewson, P. E., 410 Crighton, A. H., 49, 136, 264, 275 Crim, D., 402 Crimlisk, H. L., 240 Croft, P. R., 221 Croft, R. D., 222 Crombez, G., 365, 410 Cronbach, L., 303 Cronin, E., 214 Crosby, R. D., 353 Cross, T. P., 409, 413 Crossman, A., 476, 477 Crossman, A. M., 304 Crouch, J. A., 353 Croughan, J., 157 Crowhurst, B., 184 Crowne, D., 542, 544, 559 Crowne, D. P., 182 Cruise, K. R., 37, 91, 108, 176, 288, 317, 439, 503, 595, 599, 605 Cucina, J. M., 543 Cue, B. J. H., 504 Cuellar, I., 65, 69 Cuijpers, P., 318 Cullum, C. M., 335, 353 Cumella, E., 135, 260 Cumming, G. F., 409 Cummings, J. L., 105, 108 Cunnien, A. J., 88, 89, 93, 212 Cunningham, J. A., 8 Cunningham, K. S., 10 Curran, P., 303 Curtis, K. L., 130, 131, 321, 329, 331, 337, 343 Curtiss, G., 134, 335, 342, 353 Custer, H., 405, 413 Cuthbert, B. N., 381 Cutler, R. B., 21, 273 da Silva Guerreiro, J., 424 Dacey, C. M., 159 Dahale, A. B., 219 Dahlstrom, L. E., 18, 544 Dahlstrom, W. G., 18, 92, 158, 249, 257, 259, 280, 544, 553 Dalby, J. T., 220 Dalessandro, M., 402 Dalla-Déa, H. R. F., 156 Dalzell, H. J., 89 Dams-O’Connor, K., 155 Dana, R. H., 65, 66, 67, 69 Dandachi-FitzGerald, B., 28, 30, 240, 460 Dando, C. J., 413

Daniel, A. E., 107, 108 Dasgupta, A., 164 Daubert, S. D., 295, 296 Daum, C., 238, 239 Davey, C. A., 216 David, A. S., 103, 104 Davidoff, D. A., 130 Davidson, H. A., 108, 198, 199 Davidson, J., 189 Davidson, M., 292 Davies, N. P., 222 Davies, R., 126 Davis, D., 93, 136, 411 Davis, G. C., 189 Davis, H. G., 159 Davis, J. J., 54, 55, 340, 345 Davis, J. M., 163 Davis, K. D., 197 Davis, K. M., 405 Davis, M. I., 90 Davis, N., 125 Davis, P., 222 Davis, R. D., 38, 280, 381, 485, 486, 502, 559 Davison, H. K., 544 Dawson, E., 413 Day, D. M., 407 de Carteret, J. C., 191 de Dios Francos, A., 101 de Fátima Ávila, L., 214 de Graaf, R., 318 De La Fuente, J. R., 156 de la Gándara Martín, J. J., 101 De Lorenzo, M. S., 3 De Los Reyes, A., 509 De Marce, J. M., 157 De Micheli, D., 156 de Ridder, L., 222 de Roten, Y., 411 de Vries, R. E., 376 de Waal, M. W., 218 de Wit, H., 152 Deacon, B. J., 129 Deal, W. P., 441 Deale, S., 203 Dean, A. C., 339, 345, 346 Dean, R. S., 30, 576 Dear, G. E., 293, 296 Dearth, C. S., 135, 437 Deb, S., 129 Deelman, B. G., 246 Deese, J., 395 Deitsch, S. E., 192 Delaney-Black, V., 151, 166 Delgado, K., 544 D’Elia, L. F., 347 Delis, D. C., 44, 124, 134 DeLisi, M., 410 DellaPietra, L., 344, 351 Delouche, A., 125 Delplanche, M. L., 222 Delprino, R. P., 553 Demakis, G. J., 109, 130, 135, 136, 192, 197, 260 DeMier, R. L., 314 Demircioglu, F., 222

620 Denney, R. L., 84, 246, 314, 316, 320, 322, 331, 439, 576 Denning, J. H., 56, 320, 349, 353 Dennis, M. L., 151, 157, 158 Denny, R. L., 249 Denny, S. J., 225 DePaulo, B. M., 175, 178, 184, 405, 541, 543 DePaulo, P. J., 541, 543 DePrince, A. P., 391, 393, 394 Derlega, V. J., 541 Derogatis, L. R., 36 Derringer, J., 281, 296 Deshpande, S., 544 Despland, J.-N., 411 Detrick, P., 556, 557, 560, 561, 562 deTurck, M. A., 178 Deutsch, H., 221 Devan, J. L., 556, 557, 558, 559, 560, 565 Devilly, G. J., 394 DeViva, J. C., 584 DeVylder, J. E., 32 Dewan, M. J., 214 Dhingra, D., 222 Di Carlo, G., 87 Diamond, B., 116 Diamond, G. S., 151 Diaz-Arrastia, R., 126 Dickens, S. E., 69, 84, 100, 176, 434, 453, 457, 487 Dickey, R., 401, 411, 413, 603 Dickinson, C., 86 Dickson, A. L., 132, 321 Dickson, M. W., 540 DiClemente, C. C., 610 Diefendorff, J., 541 Diep, A., 130, 353 Dietz, P., 114 DiFazio, R., 407 DiGuilio, D., 139 Dikmen, S. S., 126, 127, 130, 133 Dilchert, S., 381 Dimitrova, R., 405 Ding, J. M., 240 Dinkins, J. P., 490 Dion, K. L., 176 DiPasquale, T., 104 Dirks, J. F., 36 Disorbio, J. M., 37 DiThomas, M., 151 Dixon, L. J., 129 Doane, B. M., 320, 460 Dobmeier, P., 218 Dobmeier, S. G., 222, 226 Doghramji, K., 200 Dohn, H. H., 216 Dolan, K., 162, 163, 164 Dolan, M., 155 Dolezal-Wood, S., 127 Dollinger, S. J., 389, 390 Dolmetsch, R., 179 Donald, T. G., 224 Donders, J., 343, 353, 488, 489 Donnell, A. J., 127 Donovan, D. M., 156, 218 Donovan, J. J., 533, 539, 543, 544, 545

Author Index Dorfman, W. I., 192 Dorr, D., 293, 295 Douglas, E. F., 533, 537, 539, 540 Douglas, K. S., 181, 192, 582 Doverspike, D., 532, 539 Doyle, S. R., 156 Dragon, W. R., 262, 275 Draijer, N., 214 Drake, E. B., 331 Drapeau, M., 411 Drasgow, F., 539 Dresler, C. M., 291, 504 Drew, C. A., 99 Drislane, L. E., 377, 380 Drob, S. L., 43, 86, 110, 123 Drogin, E. Y., 8, 514, 516, 518, 519, 520, 527, 582 Drozdick, L. W., 323 Druce, H., 216 Drummer, O. H., 164 du Vall, G. A., 221 Dualan, I., 113 DuBay, W. H., 595 Duckworth, J., 609 Duffey, P., 240 Duke, A. D., 34, 248 Dulcan, M. K., 157 Dumitrascu, N., 310 Duncan, A., 321 Duncan, J. C., 8, 105, 176 Duncan, S. A., 9, 91, 176, 195, 264, 288, 353, 439 Dundar-Mayer, P., 290 Dunn, J. T., 191 Dunn, L. A., 364 Dunnette, M. D., 540 Dupart, T., 604 Dura, P. L., 460 Duran, C., 483, 484 Durham, T. W., 488, 489 Duval, N. J., 214 Duvdevani, T., 190 Dwight, S. A., 378, 533, 538, 540, 544, 545 Dwyer, J. H., 31, 152 Dwyer, M., 406 Dwyer, T. J., 196, 458 Eakin, D. E., 195 Eames, P., 238 Earleywine, M., 152 Earnest, K., 127 Earnst, K. S., 196 Easton, K., 303, 304, 305 Eastwood, S., 213, 221 Eaton, N. K., 540 Eaves, D., 181, 582 Echemendia, R. J., 67, 68 Eckert, T. A., 198 Edelstein, B., 434 Edelstein, R. S., 475 Edens, J. F., 108, 109, 176, 179, 182, 183, 196, 199, 289, 292, 434, 439, 458, 459, 460, 503, 519, 603 Edmed, S. L., 127, 458 Edson, C. F., 365

Edwards, A. L., 275, 462 Edwards, C. L., 442 Edwards, M. J., 183, 238, 239, 240, 336, 339 Eekhof, J. A., 218 Egan, G., 412 Egan, S. S., 9, 91, 176, 439 Egberink, I. J. L., 381 Ehlers, A., 201 Ehlers, W., 220 Ehrlich, E. J., 64 Ehrlich, S., 219, 220 Eidson, C. E., 378 Einarsson, E., 412 Einzig, S., 264, 443 Eisenberg, H. M., 199 Eisendrath, S. J., 221, 222 Eisenstadt, M., 86 Eissler, K., 99 Ekim, N., 214 Ekman, P., 175, 475, 477, 479, 481, 541, 593 Elaad, E., 363, 410 Elber, L., 363 Elhai, J. D., 20, 189, 192, 193, 196, 197, 305, 596, 597, 599, 608 Elkington, K. S., 152 Ellingson, J. E., 535, 536, 537, 539, 540 Ellinwood, E., 106 Elliott, C. D., 264 Elliott, E., 54 Elliott, P., 201 Ellison, M. L., 3, 4 Elntib, S., 412 Elwood, R. W., 47 Emens, M., 479 Emery, R. E., 92, 497 Emsley, R., 105 Endicott, J., 428, 431 Enelow, A., 191 Engel, L. R., 320 Engelberg, T., 413 Engle, J., 91 English, A., 535 English, K., 403, 409 English, L. T., 135, 259 Epstein, J. A., 175 Epstein, M., 508 Epstein, R. S., 37 Erard, R. E., 304, 310 Erber, J. T., 24 Erbes, C. R., 194 Erdberg, P., 304 Erdodi, L., 489 Erickson, B. J., 89 Erickson, S. K., 92 Erkek, N., 222 Erlen, J., 64 Eron, L., 304 Ertürk, S. G., 457 Escobar, J. I., 64 Espay, A. J., 238 Espinosa, A. D., 405 Esselman, P. C., 130 Esteller, À., 377 Estevis, E., 34



Author Index 621 Estoup, A., 525 Etherton, J. L., 131, 132, 262, 339, 353 Eubanks, L., 223 Evans, A. D., 476, 477 Evans, C. D. H., 89 Evans, J. R., 405, 411, 412 Evans, L., 478, 480 Evanski, P. M., 248 Everhart, D. E., 488, 489 Everson, M. D., 216 Evyapan, A., 108 Ewart, C. K., 65 Exner, J., 305 Fabry, J. J., 483 Fairbank, J. A., 204 Fakhran, S., 131 Falkenbach, D. M., 9, 91, 176, 439 Fallon, B. A., 221 Falloon, I. R. H., 104 Fals-Stewart, W., 159, 162, 291, 296, 507 Fan, J., 546 Fang, J., 526 Fann, J., 126 Fantoni-Salvador, P., 62, 72, 159 Farber, B. A., 3, 412 Farina, A., 307 Fariña, F., 604 Farkas, M. A., 402 Farkas, M. R., 437, 442 Farnham, S. D., 381 Farr, J. L., 34 Farrell, H. M., 520 Farrier, J. N., 214 Farwell, L. A., 363 Faschingbauer, T. R., 380 Faul, M., 124 Faust, D., 478 Fausti, A., 316 Fausti, S. A., 28 Fay, P. J., 449 Fazari, G. M., 164, 165 Fazel, S., 181, 582 Fazio, R. L., 320, 322 Fedoroff, J. P., 214 Feeley, T. H., 178 Feeney, G. F., 154 Feeney, T., 157 Fehon, D. C., 156, 159 Feigenbaum, K., 303, 304, 305 Feild, H. S., 553 Feily, A., 214 Feinberg, S. E., 410 Feinn, R., 155 Feinstein, A., 130 Feix, J., 132, 321 Feldman, K. W., 222, 224 Feldman, M., 303, 304 Feldman, M. D., 6, 84, 212, 214, 215, 216, 218, 221, 222, 223, 224, 227, 228, 244, 245, 246, 481 Feldman, M. E., 195 Feldman-Schorrig, S., 216 Feldstein, S. W., 161 Fell, C. B., 607 Femina, D. D., 390

Ferchiou, A., 102 Ferguson, C. J., 21 Fernandez, K., 72, 73, 76, 288, 291, 292 Fernández, P. S., 460 Fernandez, S., 69 Fernandez, Y. M., 407, 408 Ferrara, P., 219, 220, 223, 225 Ferrari, J. R., 90 Ferrier, I. N., 394 Ferris, G. R., 530, 533 Ferro, T., 427 Fiad, T. M., 214 Fichtenberg, N. L., 132, 134, 141, 353 Fiduccia, C. E., 8, 156, 296, 297, 580, 582 Fiedler, K., 368 Fila, K., 195, 288, 439 Filippi, C. G., 125 Findlay, G., 34 Fink, P., 218 Fink, R., 215 Finklestein, S., 281 Finney, J. W., 159 First, M. B., 157, 391 Firstman, R., 224 Fischer, C., 103 Fischer, D., 381 Fischler, G. L., 560, 565 Fishbain, D. A., 21, 34, 273 Fisher, J. M., 138, 353 Fisher, P., 157 Fisher, R. P., 412 Fitzgerald-Smith, J., 130 Fitzpatrick, C. M., 582 Fitzpatrick, P. J., 130 Flaherty, V. L., 558 Flaro, L., 321, 490 Flato, C. G., 72 Fleming, J. A., 363 Flens, J. R., 92, 497, 499, 501, 506, 507, 600 Fliege, H., 214, 218, 219, 220, 221, 227 Flores, J., 72 Floyd, K., 543 Foa, E. B., 192, 391 Fodstad, L., 309 Fogel, M. H., 113 Foley, S., 222 Folks, D., 99 Folstein, M., 101 Folstein, S., 101 Forbey, J. D., 158, 159 Ford, C., 99 Ford, C. V., 220, 221 Ford, J. D., 189 Fordtran, J. S., 221, 228 Foreman, D. M., 222 Forest, A., 108 Forman, M. D., 221 Formigoni, M. L., 156 Forneris, C. A., 465 Forrest, A., 215 Forrest, T. J., 132 Forsell, Y., 156 Forsyth, A. B., 64 Forth, A., 86, 181 Forth, A. E., 91, 434

Fosberg, I., 303 Foster, H. G., 140 Foster, J., 478 Fouts, A., 156 Fowers, B. J., 37 Fowler, K. A., 91 Fowles, D. C., 380 Fox, D. D., 127, 135 Fox, H. M., 305 Fox, J. R. E., 103 Fragoso, J. M., 64 Frame, C., 102 Francis, J. P., 214 Francis, L. J., 481 Franco, J. N., 64, 65 Franco, K., 222 Frank, M. G., 175 Franklin, C. L., 192, 196 Franzen, M. D., 137, 353 Fraser, I., 409 Fraser, J., 412 Fraser, T. N., 221 Frederick, R. I., 18, 28, 29, 30, 31, 42, 44, 51, 55, 75, 124, 137, 140, 247, 314, 315, 316, 329, 333, 335, 353, 410, 440, 442, 449, 488, 489, 490, 525, 575, 576, 577, 604, 605 Freedman, D. B., 49, 213 Freeman, A., 99 Freeman, M. A., 37 Freeman, N. J., 402 Freeman, T., 439, 458, 459 Freitas, T. T., 162 Fremouw, W., 91, 191, 192, 406, 454, 458, 459, 462, 464, 486 French, L. M., 458, 466 Freud, S., 236, 237, 238, 239, 240, 241, 387, 391 Freund, K., 407, 408 Freyd, J. J., 391, 393, 394 Frick, P. J., 86, 380 Friedl, M. C., 214 Friedland, S. I., 515, 516 Friedman, H. S., 221 Friedman, M. J., 188, 189 Friston, K. J., 238 Frosch, W., 104 Frosina, P., 413 Frueh, B. C., 20, 189, 204, 304, 305, 306, 608 Fuchs, D., 480 Funk, R. R., 157 Furnham, A., 36, 37, 537 Gabel, J., 83, 113, 433, 439, 440, 449, 456, 459 Gabriel, A. S., 541 Gacono, C. B., 91, 115, 176, 310 Gaines, M. V., 282, 286, 288, 289 Galatzer-Levy, I. R., 130 Galbreath, J., 137 Gallagher, S. N., 91, 409 Gallo, D. A., 395 Gamer, M., 201 Gamez, W., 480 Gammada, E., 290

622 Ganellen, R. J., 303, 304, 305 Ganis, G., 407, 409, 413 Gannon, T. A., 409, 410 Ganser, S., 31, 101 Gansler, D. A., 132, 320 Gantner, A. B., 159 Garb, H., 305 Garcia, H. A., 192 Garcia-Willingham, N. E., 137, 247, 329, 449, 575 Gardiner, D. H., 545 Gardner, R. A., 479 Garfield, P., 200 Garland, B. H., 72 Garland, D., 159 Garland, S. N., 463 Garner, B. A., 514 Garrido, M. M., 61 Garriott, J. C., 163 Garry, M., 395 Gartner, C. G., 66, 68, 69 Gass, C. S., 135, 260 Gassen, M. D., 439 Gastfriend, D. R., 158 Gates, E. C., 156 Gaunlett-Gilbert, J., 105 Gavin, D. R., 154 Geary, B. B., 161, 162 Geiselman, R. E., 412, 413 Geisser, M. E., 198 Gelder, B. C., 29, 30, 36, 576 Gelkopf, M., 224 Gendreau, P., 181 Geneste, J., 154 Ger, M. C., 457 Geraerts, E., 458, 461, 462 Gerard, A. B., 508 Gerardi, R. J., 201 Germain, A., 200 Gershuny, B., 192 Gerson, J. M., 542 Gervais, J., 86 Gervais, R. O., 49, 55, 130, 136, 247, 258, 260, 263, 264, 270, 271, 273, 320 Gfeller, J. D., 47, 48, 247, 331, 353 Ghimire, D. J., 157 Gibbon, K. L., 216 Gibbon, M., 391 Gibbons, J. L., 65 Gierok, S. D., 132, 321 Giger, P., 67, 75, 460, 461, 462 Gilbert, J. D., 221 Giles, C. L., 282 Giles, S., 412 Gill, M. K., 157 Gillard, N., 259 Gillard, N. D., 11, 12, 19, 20, 22, 23, 24, 45, 49, 56, 64, 69, 84, 91, 100, 174, 177, 181, 182, 196, 197, 246, 249, 262, 287, 289, 423, 433, 439, 442, 455, 458, 480, 522, 573, 582, 583, 587, 594, 596, 599, 605, 608 Gillard, N. G., 192 Gillaspy, J. A., 160 Gillespie, C. F., 408 Gillespie, M. A., 545

Author Index Gillis, J. R., 450, 451, 452, 453, 605 Gilmore, D. C., 530, 533 Gilmore, J., 102 Ginton, A., 367 Giordani, B., 125 Gironda, R. J., 284 Giuliano, A. J., 137 Giza, C. C., 125 Glancy, G., 112 Glaser, D., 220 Glass, A., 331 Glassmire, D. M., 13, 55, 264, 339, 340, 436, 438, 439, 441, 444, 454, 455, 602, 603 Glasziou, P., 53, 318 Glaze, R. M., 533 Gleaves, D. H., 391 Glenn, W. J., 135, 259 Glinski, A., 102 Glucksman, E., 201 Glynn, G., 45, 130, 262, 339, 577 Goddard, K. M., 264 Godding, V., 225 Godinho, A., 8 Goebel, R. A., 353 Goffin, R. D., 535, 536, 537, 539, 546 Goffman, E., 530 Goggin, C., 181 Gogineni, A., 160 Goksugur, S. B., 222 Gola, T., 43, 134, 322, 329, 353 Gold, J. A., 7 Gold, J. M., 38 Gold, P., 608 Gold, P. B., 20 Gold, S. N., 192 Goldberg, J. O., 138 Goldberg, L. R., 371, 373, 378, 379 Goldberg, M. A., 238 Goldberg, R. A., 221 Goldberg, S. A., 245 Goldfarb, J., 222 Golding, J. M., 392, 393 Golding, S., 19, 507 Goldinger, L. A., 371 Goldman, D. J., 132 Goldman, J. D. G., 401 Goldstein, A., 8, 10, 123, 451, 604, 609 Goldstein, A. B., 218, 220 Goldstein, A. M., 110 Goldstein, C. C., 413 Goldstein, G., 132 Goldstein, H., 28 Goldstein, I. C., 532 Goldstein, M. L., 308 Goldstein, N. E., 582 Gonzalez Marquez, J., 75 González Ordi, H., 61 Gonzalez-Duarte, A., 221 Goodenough, F. L., 301 Goodloe, E., 98 Goodman, G. S., 475, 479 Goodnow, J. J., 481 Goodwin, B. E., 194, 195, 264, 266, 267, 268 Goodwin, D. W., 102, 103, 104, 105, 106

Goodwin, J., 216 Gordon, D. K., 214, 218 Gordon, H. M., 477 Gordon, M. M., 66 Gordon, R. A., 483 Gordon, S. N., 130 Gorny, I., 596 Gorsuch, R., 43, 137 Gostisha, A. J., 87 Gothard, S., 432, 438, 439, 609 Gottfried, A. E., 499 Gottfried, A. W., 499 Gottfried, E. D., 98, 439, 454, 602 Gottfried, L. A., 88, 218, 220 Gough, H. G., 259, 370, 377, 553, 554, 555, 593 Gould, J. W., 92, 497, 600 Gould, K. R., 129 Gould, R., 238 Gouvier, W. D., 54, 126, 605 Grabe, H. J., 218 Grabowski, J. G., 34 Grady, R., 224 Graham, J. R., 67, 92, 158, 159, 249, 257, 259, 260, 280, 478, 483, 484, 499, 500, 501, 553, 557 Graham, W. R., 546 Graley, J., 303, 304 Gralnick, T. M., 296 Grambow, S. C., 157 Granacher, R. J., 85, 439, 598 Granacher, R. P., Jr., 45, 49, 136, 192, 197, 243, 245, 246, 247, 257, 259, 264, 436, 449, 455, 523 Granda, R., 595 Grandjean, N. R., 432 Grandpre, J., 543 Granhag, P. A., 411, 412, 413 Grann, M., 181, 582 Grant, I., 67 Grant, M., 156 Graue, L. O., 336, 338, 455, 458 Gray, B. T., 308 Gray, J., 224, 310 Gray, M. J., 196 Gray, N. S., 103 Gray, S. R., 408, 409 Greaves, C., 480, 598 Green, C., 106 Green, D., 249, 264, 280, 437, 439, 441, 442, 443 Green, H., 221 Green, P., 28, 49, 75, 128, 134, 136, 247, 249, 263, 273, 321, 322, 331, 334, 340, 353, 489, 490 Green, R., 34 Greenberg, B., 559 Greene, C., 290 Greene, F. L., 275 Greene, R. L., 7, 21, 32, 55, 92, 159, 192, 257, 258, 275, 434, 461, 500, 501, 511, 557, 559, 565, 604 Greenfeld, D., 478 Greenfield, D., 100 Greenfield, S. F., 156 Greenleaf, E. A., 545



Author Index 623 Greenwald, A. G., 381 Greenwald, S., 334 Greer, S., 248 Gregory, R. J., 88, 214, 219 Greguras, G., 541 Greiffenstein, M. F., 43, 44, 124, 128, 132, 134, 139, 262, 322, 329, 335, 338, 351, 353 Greiner, M. V., 223 Grenier, J. R., 373 Greub, B., 345, 353 Greve, K. W., 44, 45, 55, 124, 126, 130, 131, 132, 140, 246, 262, 293, 320, 321, 322, 329, 331, 337, 339, 340, 343, 353, 517, 577 Grey, B. J., 192 Grier, F., 214 Grier, M., 154 Griffioen, B. T., 460 Griffith, A., 508 Griffith, J. L., 214, 223 Griffith, R. L., 533, 534, 535, 536, 537, 539, 540, 546, 552 Griffiths, E. J., 227 Grilo, C. M., 156, 159 Gripshover, D. L., 159 Grisham, J. R., 190 Grisso, T., 110, 113, 155, 192, 280, 314, 434, 582 Groh, D. R., 90 Grohol, J. M., 595 Gross, A., 460 Grossi, L. M., 443 Grossman, L. S., 303, 307, 308, 405, 502, 564 Grossman, R., 190 Grossman, S., 38, 159, 280, 281, 580 Grote, C. L., 54, 141, 326 Grover, S., 462 Grubenhoff, J. A., 489 Gruber, C. P., 485 Grubin, D., 409, 410 Gryczynski, J., 156, 164 Gu, W., 264 Guastello, S. J., 372 Gudjonsson, G. H., 8, 404, 412, 490, 579 Gudonis, L., 86 Guidotti-Breting, L. M., 132 Guilmette, T. J., 137, 353, 478 Guion, R. M., 373 Gujar, A., 535 Guldager, S., 159 Guller, I. B., 563 Guller, M., 563 Gully, J., 213 Gunduz-Bruce, H., 102, 106 Gunes, C., 222 Gunn, D., 488, 489 Gunner, J. H., 137 Gunstad, J., 127, 128, 345, 353 Gupta, A., 222 Gur, R., 462 Gur, R. C., 460 Guriel, J., 191, 192, 196, 453, 454, 468 Gurley, J. R., 310 Gutheil, T. G., 479, 521

Gutiérrez, J. M., 460 Guttfreund, D. G., 65 Guy, L. S., 439, 453, 454, 455, 456 Guy, S. M., 151 Guyatt, G., 317, 318, 321, 324 Guyton, M. R., 74, 76, 455, 457 Guzman-Cavazos, L., 483 Gyoerkoe, K. L., 308, 405 Haaland, D., 561, 562, 565 Haase, M., 224 Haber, J., 159 Habing, B., 155 Hagan, L. D., 275 Hagan, M. A., 498 Hager, J. C., 477 Haines, M. E., 29, 353 Hakstian, A., 176 Hakstian, A. R., 374 Hale, A. S., 188 Hale, G., 609 Hales, E. S., 478 Halevy, R., 175, 178 Hall, D. E., 223 Hall, G. C. N., 406 Hall, H., 112, 114 Hall, J., 376, 380 Hall, J. A., 6 Hall, J. E., 154 Hall, R. C., 56 Hall, R. C. W., 128, 129, 188 Hall, V. L., 76 Hallgren, M., 156 Halligan, P., 34, 99, 216, 217, 221, 222, 228 Hambleton, R. K., 62, 63, 64, 74 Hamill, C., 221 Hamilton, J. C., 6, 84, 212, 218, 221, 222, 228 Hamilton, J. D., 204, 215 Hamilton, J. M., 137, 290 Hammond, D. C., 387 Hamre, M. C., 221 Hamsher, J., 307 Han, B., 131 Han, E., 164 Han, K., 33, 275, 502 Hand, C. G., 408 Handel, R. W., 159, 257, 262, 263, 265, 275, 280, 405, 439, 483, 502 Hanges, P. J., 540 Hankins, G. C., 451, 452, 453, 603 Hanks, R. A., 141, 353 Hanley, C. N., 248 Hanson, A., 214 Hanson, R. K., 403, 407, 410, 411 Haque, M. M., 105 Harding, K. K., 221 Hare, R. D., 86, 91, 174, 175, 176, 177, 178, 179, 180, 184, 376, 427, 433, 434, 444, 582 Hargrave, G., 555 Harkins, L., 411 Harms, P. D., 544 Haro, J. M., 61, 157 Harold, C. M., 540, 546 Harp, J. P., 271, 272

Harper, H., 161 Harpur, T. J., 176, 180 Harrell, E. H., 20, 141, 333, 603 Harrigan, P., 406 Harrington, M. G., 214 Harris, A. J., 402 Harris, D. B., 301 Harris, G. T., 181, 407, 408 Harris, K. M., 344, 353 Harris, M., 105 Harris, M. J., 128, 259, 490 Harris, M. M., 372 Harris, P. B., 292 Harris, P. L., 476 Harris, S. K., 155 Harris, T. B., 531 Harrison, A. G., 336, 339 Harrison, K., 113 Harrison, K. S., 7, 552, 583, 609 Harrison, N., 475, 581 Harrow, M., 106, 107 Hart, J. R., 135, 136, 249, 280, 503, 563, 578 Hart, K., 478 Hart, K. J., 137 Hart, S. D., 180, 181, 434, 582 Harter, J., 64 Hartmann, E., 200, 307, 309, 310 Hartmann, T., 307, 309, 310 Hartshorne, H., 18, 530 Hartwig, M., 405, 411, 413 Harvey, A. G., 130, 200 Harwood, T. M., 610 Hatcher, L. L., 344, 353 Hathaway, M. J., 224, 225 Hathaway, S. R., 18, 257, 457, 553 Hauck, E., 532 Hausknecht, J. P., 34 Hausotter, W., 188 Havens, J. R., 13 Hawes, S., 280, 286, 287, 288, 292, 571, 587 Hawes, S. W., 249 Hawk, G. L., 98, 101, 123 Hay, G., 99 Hayes, M. A., 155 Hayman, B. S., 413 Haynes, B., 317 Haynes, R. B., 53, 318 Haynes, S. D., 321 Hays J., 106, 454 Hay-Smith, E. J. C., 127 Haywood, T. W., 303, 405, 502, 564 He, J., 405 Heaton, R. K., 42, 67, 68, 335, 341, 353 Hegedus, A. M., 156 Heggestad, E. D., 535, 539 Heilbronner, R. L., 31, 43, 44, 47, 122, 139, 335, 336, 353 Heilbrun, K., 499, 500 Heine, S. J., 66 Heinly, M. T., 131, 132, 262, 339, 353 Heinrichs, R. J., 270 Heintz, J. M., 159 Heinze, M. C., 439, 451, 452, 453, 455, 458, 459

624 Heizer, J. F., 157 Helander, A., 163 Helle, A. C., 301, 579 Hellemann, G. S., 141 Hellerstein, D., 104 Helzer, J. E., 157 Hemel, D., 130 Hemmati, T., 463 Hemphill, J. F., 462 Henderson, M., 36, 37, 406 Henderson, R. G., 203 Henk, C. M., 291, 504 Henle, C. A., 381 Henly, G. A., 152, 160, 161 Hennessy, E. V., 37 Henrich, J., 66 Henry, S., 11, 151, 578, 603 Henry, S. A., 9, 249, 574 Henry-Edwards, S., 156 Herman, J. H., 387 Heron, M. P., 37 Héroux, D., 86 Herron, E. W., 301 Hervé, H., 480, 598 Heseltine, K., 181 Hess, D. T., 410 Hesse, M., 159 Hessels, A., 458 Hewitt, J. K., 376 Hewlett, R., 105 Heym, H. H., 220 Hiatt, D., 555 Hibbs, N., 544 Hibler, N. S., 552 Hickey, S. E., 223 Hickling, E. J., 518 Hickling, M. T., 518 Hickman, R. O., 222 Hicks, B. M., 376 Hilimire, M. R., 32 Hill, B. D., 54, 133 Hill, C. E., 3 Hill, C. R., 86 Hill, M. E., 68 Hiller, M. L., 151 Hilsabeck, R. C., 130, 353 Hilts, D., 413 Hindman, J., 410 Hinds, J. D., 73, 458, 476, 484, 486 Hinshaw, S., 509 Hirayama, Y., 214, 221 Hirsch, S., 218 Hirshberg, B., 221 Hiscock, C. K., 316, 329 Hiscock, M., 316, 329 Hitschfeld, M. J., 155 Hjorthoj, A. R., 162 Hjorthoj, C. R., 162 Ho, K., 598 Hobbs, S. E., 125 Hoberman, H. M., 90 Hochschild, A. R., 541 Hodes, R. L., 365 Hoekstra, J. H., 222 Hoelzle, J. B., 258, 260 Hoersting, R., 438

Author Index Hoffman, J. M., 130 Hoffman, L., 406 Hoffman, N. M., 353 Hoffman, R., 102 Hofling, C. K., 198 Hogan, J., 375, 534, 540 Hogan, R., 534, 540, 555 Hoke, S. E., 409 Holden, C. E., 103 Holden, R. R., 183, 544, 564 Holdnack, J. A., 49, 57, 133, 323 Holdren, R. R., 500 Holland, B., 52, 534, 540 Holland, C. L., 408 Holland, H. L., 412 Holland, L. A., 408 Hollander, E., 199 Holman, J., 610 Holstege, C. P., 222, 226 Holt, K. R., 264 Holtzman, W. H., 301, 307 Honey, R. J. D. A., 221 Honts, C. R., 365, 368, 411 Hook, J. N., 490 Hoover, C. F., 248 Hopp, G., 135, 137, 249, 316, 329, 442, 455 Hoppe, C., 92, 434, 500, 559 Hopwood, C. J., 72, 73, 196, 283, 284, 285, 289, 296, 503, 581 Horacek, T., 406 Horner, M. D., 56 Horowitz, S. W., 409 Hoskins, B., 165 Hotopf, M., 217 Hough, L., 258 Hough, L. M., 540, 544 Hough, R. L., 64 House, A., 127 Housley, D., 213 Houston, C. M., 246, 331, 576 Houston, R. J., 353 Houston, W., 341, 353 Hovda, D. A., 125 Howard, P., 411 Howe, L. L. S., 321 Howell, A. J., 462 Howell, C. T., 509 Howell, R. J., 303, 457 Howell, R. T., 161 Howieson, D. B., 51, 125, 483 Hoy, M., 442 Hoyert, D. L., 37 Hser, Y., 152 Hsiang-Ching, K., 37 Hsu, T. C., 156 Hu, X., 363 Hu, Z. Q., 367 Hubley, A. M., 321 Hubschmid, M., 238 Hudson, B., 98 Hudson, J. I., 388 Hudson, S. M., 86 Huffman, J., 408 Hughes, J. N., 86 Hui, C., 65, 67

Huizinga, T., 413 Hulin, C. L., 540 Hultsch, D. F., 48 Humeniuk, R. E., 156, 157 Humes, D., 216 Hungerford, L., 557 Hunsaker, F. G., 37 Hunsley, J., 305 Hunt, J., 541, 542 Hunt, W. C., 152 Hunter, J. E., 375 Hurley, K. D., 508 Hurley, K. E., 441 Hurren, E., 412 Hurt, S. W., 7 Hurtz, G. M., 533 Huscroft-D’Angelo, J., 508 Hutaff-Lee, C. F., 487 Hyler, S. E., 237 Hynan, D. J., 499, 503, 504, 505, 508 Iacono, W. G., 174, 179, 361, 363, 364, 366, 368, 369, 376, 381, 409, 480, 578, 599, 603 Iglesias, E. B., 609 Inbau, F. E., 202, 412 Incesu, A. I., 248 Inglis, Jr., A. F., 224 Ingram, M. V., 271, 272 Ingram, P. B., 249, 264, 265, 267, 276 Inman, D. J., 200 Inman, T. H., 128, 249, 353, 442, 455, 490 Inwald, R., 553, 561 Inwald, R. E., 562 Ipek, H., 214 Irby, J., 124 Isaacson, J. A., 533 Israelit, S. H., 214 Iverson, G. L., 42, 46, 49, 56, 57, 123, 125, 127, 128, 129, 130, 132, 133, 134, 135, 137, 246, 247, 262, 323, 330, 353, 423, 577, 605 Ivey, M. A., 155 Izsak, E., 222 Jabeen, S., 105, 199 Jaceldo, K. B., 64 Jackson, D. N., 375, 546 Jackson, R. L., 6, 7, 23, 85, 90, 113, 292, 410, 428, 431, 436, 439, 454, 455, 456, 552, 572, 583, 609 Jacobson, A., 108 Jaffe, M. E., 113 Jaffee, W. B., 162, 163, 164 James, R. A., 221 Jamieson, D. W., 410 Jans, L. H., 579 Jantschek, G., 214 Janus, E. S., 402 Jason, L. A., 90 Jayne, B. C., 412 Jeanrie, C., 62, 63 Jeffay, E., 290 Jelicic, M., 458, 461, 597 Jelinek, L., 102



Author Index 625 Jellinek, M. S., 222 Jenicek, M., 212 Jennett, B., 125, 126 Jenny, C., 222 Jervis, M., 223 Jewsbury, P. A., 604 Jhawar, A., 264 Jia, H., 151 Jick, R., 192 Jiménez Gómez, B., 101 Jiménez-Gómez, F., 275 Jindal, S., 88, 214, 219 Johansen, S. S., 164 Johanson, C. E., 152 Johnson, J., 152, 292 Johnson, J. A., 370, 534 Johnson, M., 553, 562 Johnson, S. C., 223 Johnson, S. F., 44, 46, 123, 140, 330, 577, 606 Johnson-Greene, D., 139 Johnston, L., 129 Johnston, N. G., 539 Johnston, T., 92, 434, 500, 559 Jonas, J. M., 88, 99, 215 Jones, A., 108, 137, 163, 271, 272, 318, 319, 320 Jones, B., 88, 99, 215 Jones, C., 214 Jones, D. N., 91 Jones, E. C., 579 Jones, E. E., 410 Jones, E. T., 544 Jones, G. N., 465 Jones, H., 407 Jones, K. P., 4 Jones, L., 403, 413 Jones, M., 488, 489 Jones, N. F., 36 Jones, P. S., 64 Jones, T. W., 222 Jones-Alexander, J., 465 Jordaan, G., 105 Jordan, A. D., 408 Jordan, M. J., 610 Jordan, S., 413 Jordon, R. G., 192 Joseph, J., 544 Jourard, S. M., 6 Ju, D., 134 Judge, T. A., 554 Judson, A. J., 262 Jung, S., 411 Junginger, J., 102, 103 Junkuy, A., 164 Jureidini, J. N., 222, 224 Justice, A. C., 318 Kaemmer, B., 92, 158, 249, 257, 259, 280, 553 Kagan, J., 479 Kahn, D. A., 214 Kahn, M. W., 305 Kahn, R. E., 380 Kalambokis, G., 108 Kalishman, S. C., 406

Kally, Z., 135, 260 Kalmar, K., 465 Kaminer, Y., 155, 158 Kaminski, P. L., 6, 454, 609 Kammerhoff, J., 607 Kamp, J. D., 540 Kampa, R., 227 Kamphaus, R. W., 486, 487, 510 Kan, L. Y., 280 Kanaan, R. A., 99, 236, 237, 238, 239, 240 Kanas, N., 104, 105 Kanbay, A., 214 Kang, H. K., 130 Kanjee, A., 63 Kansagara, D., 221 Kapfhammer, H. P., 218, 219, 220 Kaplan, B. J., 198 Kaplan, E., 129, 134 Kaplan, H. B., 154 Kaplan, M., 304 Kapur, B., 163 Karantzoulis, S., 489 Karg, R. S., 157 Karlstrom, E. M., 408 Karno, M., 64 Kasapis, C., 426 Kasdan, M. L., 223 Kashdan, T. B., 196 Kashluba, S., 126 Kashubeck, S., 64 Kashy, D. A., 175, 178, 184 Kasper, E., 103 Kaspi, S. P., 394 Kassin, S. M., 404, 411, 413 Kaster-Bundgaard, J., 133 Kathmann, N., 488, 489 Kauder, B. S., 562 Kauffman, C., 92 Kaufman, A. S., 68 Kaufman, J., 157 Kaufman, S. H., 301 Kaufmann, H., 221 Kaufmann, P. M., 56, 127 Kay, G. G., 335 Kay, T., 129, 130 Kaye, H. S., 579 Kazdin, A. E., 509 Keane, T. M., 198, 201, 202, 204 Kearney, C. A., 478 Kearns, S. C., 223 Kebbell, M. R., 412, 413 Keenan, A., 533 Keenan, T., 476 Keeshin, B. R., 223 Kehl-Fie, K., 284 Keiser, L., 191 Keiski, M. A., 137, 290 Kelemen, W. L., 544 Keller, C., 222 Kelly, C. E., 412 Kelly, K. S., 302 Kelly, M. P., 353 Kelly, R., 198 Kelly, T. M., 156 Kelsey, K. R., 11, 156, 183, 184, 196, 197, 287, 582, 600

Kemp, S., 127 Kenaszchuk, C., 158 Kenedi, C. A., 213 Kenna, J., 100 Kenney, R. D., 223 Kent, G., 102, 103 Keselman, H. J., 52 Kessler, H. R., 130 Kessler, R. C., 157, 188, 192 Kessous, N., 408 Ketcham, K., 93 Kewman, D. G., 131 Keyvan, A., 457 Khadivi, A., 100, 288 Khan, S. A., 216 Khattar, N. A., 178 Kieckhaefer, J., 412 Kiehl, K. A., 582 Kiester, P. D., 34, 248 Kihlstrom, J. F., 389 Killgore, W. D. S., 344, 351 Kilpatrick, D. G., 189 Kim, B. H., 258, 545 Kim, C. H., 218 Kim, K., 64 Kim, M. S., 127, 325, 347 Kim, S., 156 Kimber, J., 162 Kimbrell, T., 439, 458, 459 Kin, U., 66 Kinder, B. N., 304, 305, 306, 581 King, B. H., 220 King, E. B., 4 King, J. H., 122, 288, 342, 353 King, M. F., 7 Kingery, L. R., 28 Kinney, D. I., 339 Kinns, H., 213 Kinsman, R., 36 Kintz, P., 164 Kirakowski, J., 227, 240, 609 Kirby, A. C., 196 Kircher, J. C., 365, 368, 409, 411 Kirchner, F. H., 483 Kirchner, W. K., 540 Kirisci, L., 156 Kirk, J. W., 487, 488, 489 Kirkendol, S. E., 175 Kirkhart, K. J., 91 Kirkley, S. M., 15 Kirkwood, M. W., 123, 487, 488, 489 Kisamore, J. L., 532 Kissack, J. C., 163 Kivisto, A. J., 310 Kivlahan, D. R., 156 Klapp, B. F., 220 Klausky, J. D., 539 Klaver, J. R., 180, 181 Klein, D. N., 427 Klein, E. M., 130 Klein, H., 221 Klein, S., 90, 221 Kleiner, J. S., 320 Kleinman, S. B., 189 Kleinman, S. M., 405, 412, 413 Kleinmuntz, B., 370

626 Klingler, A., 106 Klopfer, J., 48, 123 Kluemper, D., 544 Knatz, H., 553, 561 Knight, J. R., 155, 156 Knight, K., 151, 463 Knoll, J. L., 98, 115, 116, 202, 247 Knowles, R., 106 Koch, W., 192, 200, 424 Kocur, D., 91 Koehler, J. J., 520 Koenigsberg, H., 104 Koerner, A., 411 Kohlmaier, J., 488 Kohnken, G., 412 Kokish, R., 91, 410, 411 Kokturk, N., 214 Kolb, L. C., 201 Kolos, A. C., 437 Komar, J. A., 540 Komar, S. G., 540 Kompanje, E. J., 481 König, C. J., 535, 545, 607 Konitsiotis, S., 108 Konopasek, J. E., 410 Konopasky, R., 406 Kordinak, S. T., 559 Koren, D., 130 Kornfeld, A. D., 556, 557 Kortte, K. B., 37 Kosson, D., 86 Kosson, D. S., 87, 91, 434 Koster, E. H., 129 Kotov, R., 480 Koubeissi, M. Z., 214 Koutzoukis, C., 129 Kowalski, R. M., 7, 531 Kozak, M. J., 381 Kozinn, W. P., 214 Kozlowska, K., 222 Kraemer, H. C., 332 Kraepelin, E., 174 Krafft, L., 138 Krahn, L. E., 218, 220 Kramer, J. H., 134 Kramer, M. D., 200, 376, 377 Kramer, R., 334 Kranzler, H. R., 155 Kraus, A., 106 Kraut, R. E., 481 Krauter, K. S., 376 Kravitz, H. M., 405 Krebs, P. M., 610 Kremer, T., 581 Krieger, M., 307 Krings, F., 535 Krishnan, R., 106 Kristof, A. L., 530 Krittman, S. W., 609 Kroger, R. O., 8 Krol, A. L., 488, 489 Kroll, L. S., 483, 484 Kroner, D. G., 411, 463 Kronkvist, O., 413 Kronstrand, R., 164 Kropp, P. R., 91, 98, 179, 191, 600

Author Index Krueger, R. F., 281, 296, 376, 377, 380, 480 Krukowski, R. A., 52, 135, 353 Kruth, M., 225 Kubak, F. A., 475, 581 Kuch, K., 189 Kucharski, L., 176 Kucharski, L. T., 9, 91, 98, 113, 195, 264, 288, 289, 439 Kuehnle, K., 479 Kuipers, E., 105 Kuisma, I., 309 Kulik, C. C., 33 Kulik, J. A., 33 Kummel, E., 34, 273 Kunce, C., 545 Kuncel, N. R., 544 Kuncel, R., 160 Kung, M., 533, 542, 544 Kupers, T., 115 Kurke, M. I., 552 Kuroski, K., 490 Kurtines, W., 555 Kurtz, A. B., 214 Kurtz, J. E., 136, 291, 292, 504 Kurus, S., 110 Kushnir, V., 8 Kwartner, P. P., 454 La Greca, A. M., 481 Labkovsky, E., 363 Labott, S., 305 Labouvie, E. W., 154, 155, 160 Lacey, J. H., 89 Lachar, D., 485 Lacoursiere, R. B., 215 LaFond, J. Q., 402 Laibstain, D. F., 130 Lally, S., 439 Lally, S. J., 280, 281 Lalumière, M. L., 178 Lamb, C. R., 221 Lamb, D. G., 442 Lambert, M. E., 321 Lammy, A. B., 54, 141, 326 Landers, R. N., 545 Landfield, K., 380 Landre, N., 125 Lang, A. E., 238 Lang, A. R., 159, 376, 502 Lang, P. J., 201 Langdon, J., 302 Langdon, R., 102, 106 Lange, R. T., 127, 128, 129, 135, 264, 289, 290, 353, 458, 466 Langenbucher, J., 162 Langer, F., 499 Langton, C. M., 403 Lanier, V. W., 288, 291, 504, 507 Lankshear, D. W., 481 Lanyon, R. I., 7, 10, 27, 36, 303, 404, 405, 408, 462, 463 Laphan, S. C., 152 Laravuso, J. J., 364 Lareau, C. R., 257 Larimer, M. E., 155

Larøi, F., 102, 103 Larrabee, G. J., 31, 36, 44, 45, 46, 47, 49, 51, 52, 53, 54, 84, 123, 124, 128, 132, 134, 135, 140, 141, 245, 246, 247, 248, 249, 265, 323, 329, 330, 331, 333, 334, 335, 350, 353, 488 Lash, S. J., 157 Lashani, Z., 455 Lasher, L., 226 Lasich, A. J., 463 Lasko, N. B., 390, 394, 395 Lassiter, G. D., 543 Latimer, W. W., 160, 161 Laurenceau, J.-P., 3 Lautenschlager, G. J., 558 Laux, J. M., 161 Lavin, T. J., 527 Lawlor, A., 227, 240, 609 Lawrence, J. M., 216 Lawry, S. S., 408 Laws, D. R., 408 Layden, M., 201 Lazarus, A., 214 Lazowski, L. E., 161, 162 Leach, L., 129 Leary, M. R., 7, 531 Leavitt, F., 246 Leborgne, B., 181 LeBourgeois, H. W., 110 Lechner, D. E., 34 Ledesma, R. D., 154 Leduc, K., 477 Lee, C. M., 155 Lee, J. W, 64 Lee, K., 163, 376, 476, 477 Lee, L. M., 533 Lee, T. T., 159, 260 Lee, W. C., 539 Lee, Z., 180, 181, 475, 581 Leed, E. J., 237 Leenders, K. L., 460 Lees-Haley, P. R., 37, 127, 128, 135, 136, 191, 194, 259, 260, 331, 334 Lehman, C. L., 190 Lehman, R. A., 42 Lehman, R. A. W., 335, 353 Lehmkuhl, U., 220 Lehr, E. Y. C., 287 Leichtman, M. D., 479 Leighton, A., 331 Leininger, B. E., 137 Leistico, A. R., 479, 485 Lennon, R., 222 Lennox, C., 155 Lenny, P., 293, 296 Lenz, K., 220 Lenzenweger, M. F., 395 Leo, G. I., 162 Leonard, K., 8, 123, 451, 604 LePage, J. P., 463, 464 Lepage, J. S., 196, 282, 439 Lesikar, S., 451, 452 Lesser, I. M., 198 Letourneau, E. J., 408, 409 Leudar, I., 102, 103, 104 Leung, A., 371



Author Index 627 Levashina, J., 432, 530, 531, 532, 533, 534, 535, 536, 537, 544, 545, 546, 552, 607 LeVasseur, M. E., 155 Levenson, J. S., 91, 402, 404, 410 Levenson, M. R., 582 Levin, A. P., 189, 203 Levin, F., 221 Levin, H. S., 125, 199 Levin, R. A., 533, 534, 539, 540, 543, 552 Levin, S., 307 Levine, P. L., 222 Levitt, H. M., 3 Levitz, S. M., 214 Levy, S., 162 Lewandowski, M. J., 34 Lewinsohn, P. M., 101 Lewis, D. J., 105 Lewis, D. O., 390 Lewis, H., 103 Lewis, J. L., 439, 458, 459, 461, 486, 487 Lewis, M., 477, 479 Lezak, M. D., 51, 125, 126, 483, 490 Li, A., 607 Li, E. Y., 521 Li, G., 34 Li, H., 218 Li, J. C., 38 Liang, Y. D., 367 Libow, J. A., 88, 220, 226 Lichtenberger, E. O., 68 Lickel, J. J., 129 Lidz, C. W., 426 Lievens, F., 543, 545 Liff, C. A., 28 Liff, C. D., 20, 141, 333, 603 Light, M. J., 225 Lilienfeld, S. O., 91, 92, 176, 179, 305, 377, 380, 582 Liljequist, L., 195 Lilly, M. A., 199 Limosin, F., 215 Lin, D. L., 164 Lin, J. L., 221 Lincoln, T. M., 102 Lindsay, R. C. L., 476 Lineberry, T. W., 155 Links, P. S., 218, 220 Linneberg, I. H., 159 Lipke, H. J., 394 Lipman, F. D., 123, 198 Lippert, T., 413 Lipsey, T. L., 189 Liston, T. E., 222 Litz, B. T., 394 Liu, C., 70, 71, 436, 438, 444 Liu, H. C., 164 Liu, P., 69 Liu, R. H., 164 Llewellyn, J., 108 Lloyd, D. M., 34 Lobo, D. N., 216 Lock, T. G., 397 Loeber, R., 479, 509 Loewenstein, R. J., 394, 437 Loftus, E. F., 93, 199, 388, 389, 395

Loftus, G. R., 389 Logue, M., 413 Long, D., 533 Longabaugh, R., 155, 156, 160 Longhurst, J. G., 478 Looman, J., 403, 407 Lopez, M. N., 294 López, R., 377 López, R. V., 609 Loring, D. W., 321, 483 Loughan, A. R., 488 Loughran, T., 237 Louth, S. M., 180 Love, J. M., 126, 262, 353 Loving, J. L., 309 Lovley, A., 7 Lowe, B., 479 Lowery, A. E., 463 Lowmaster, S. E., 563 Lowy, D. G., 545 Loza, W., 181 Loza-Fanous, A., 181 Loze, J. Y., 215 Lozhkina, O., 156 Lu, P., 52, 126, 132, 133, 134, 135, 324, 325, 339, 345, 353 Lu, P. H., 324, 325, 478 Luborsky, L., 158 Lucas, C. P., 157 Lucente, S., 291 Lucio, E., 483, 484 Luebbert, M. C., 560 Lueke, S. B., 535 Lund, C. A., 403 Lunde, K. B., 309 Lundell, R. G., 365 Lundin, A., 156 Luten, T. L., 475 Lutz, R. W., 405 Lyass, A., 3 Lykken, D. T., 174, 363, 368 Lynam, D. R., 86 Lynch, J. K., 137, 318, 320, 488 Lynch, K. G., 155 Lynett, E., 176 Lynn, E. J., 203, 204, 215, 220 Lynn, R., 68 Lynn, S. J., 397 Lyon, T. D., 216 Lyons, I., 129 Lysy, D. C., 544 MacAllister, W. S., 489 MacAndrew, C., 159 Macciocchi, S., 125 MacDonald-Wilson, K. L., 3 Macgowan, M. J., 404 Machamer, J. E., 126 Mackinnon, A., 102, 103 MacKinnon, D. P., 412 Macklin, M. L., 394 MacLaren, V. V., 409 MacLeod, C., 129, 394 MacNeil, B., 183 Madea, B., 163, 164 Madsen, L., 410

Maglione, M., 152 Magura, S., 151 Mailis-Gagnon, A., 214, 219 Maillet, G., 407 Main, C. J., 36 Makaremi, A., 481 Malcarne, V., 69 Malcolm, P. B., 407 Maldonado, R., 69 Malec, J. F., 137, 290 Malin, H. M., 214 Malina, A. C., 122 Malla, A., 156 Malle, B. F., 394 Malloy, L. C., 216 Malm, T., 535 Malone, S. M., 381 Mamani-Pampa, W., 483 Mamounas, J. M., 103 Mancill, R. B., 190 Mandal, E., 91 Manford, M., 106 Mangan, D. J., 368 Mangos, P. M., 533 Manly, J. J., 49, 67, 68 Mann, R. E., 404 Manning, C. G., 395 Manning, P. K., 541, 542 Mansel, R. E., 214 Manson, T. M., 532 Mantan, M., 222 Manzel, K., 345, 353 Mao, Y. Y., 367 Marcella, A. J., 65 Marchie, A., 103 Marcopulos, B. A., 518 Marcotte, D., 331 Marcus, B., 376 Marek, R. J., 275 Margulis, S. T., 541 Marín, B. V., 62, 63, 64, 65 Marín, G., 62, 63, 64, 65 Marín, P. F., 460 Marion, B. E., 194, 195, 264, 266, 267, 268, 439 Mark, V. H., 130 Markon, K. E., 281, 296, 376, 377 Marks, S., 213 Marlow, J., 222 Marlowe, D., 182, 542, 544, 559 Marshall, M. B., 193, 500, 608 Marshall, P. S., 322, 339 Marshall, W. L., 86, 407, 408 Marston, A., 450, 605 Martelli, M. F., 123, 248 Martens, M. P., 155 Martin, C. S., 152 Martin, M. A., 11, 12, 21, 140, 193, 249, 259, 280, 461, 594 Martin, P. K., 42, 51, 52, 54, 122, 141, 257, 270, 322, 326 Martin, T. A., 353 Martin-Cannici, C., 73, 291, 504 Martindale, D. A., 497, 507 Martinez, G., 63, 64 Martinez, J., 221

628 Martínez-González, J. M., 609 Martino, S., 156, 159 Martino, V., 116 Martins, W. D., 214 Maruta, J., 56 Marzullo, D. M., 37 Masand, P. S., 214 Mash, E. J., 479 Mason, J., 275 Mason, L. H., 194, 266, 267, 268 Mathews, A., 394 Mathias, C. W., 353, 517 Mathieu, C., 91 Mathis, M. B., 395 Matthews, J. A., 214 Matusewicz, M., 56, 123, 126, 127, 128, 139, 142 Maurice, P. D. L., 214 Mavreas, V. G., 103 Maxwell, W. L., 125 May, L., 412, 413 May, M. A., 18, 530 May, V. R., 248 Mayer, C., 218 Mazerolle, P., 412 Mazur-Mosiewicz, A., 489 McAllister, T. W., 129, 204 McBride, C. K., 87 McBride, W. F., 49, 136, 264, 270 McCabe, M., 406 McCaffery, R. J., 138 McCaffrey, R. J., 134, 137, 204, 318, 320, 321, 353, 488, 489, 490, 526 McCalannahan, K., 102 McCammon, S. L., 488, 489 McCann, J. T., 92, 478, 485, 490, 499 McCarter, R. J., 76 McCarthy, G., 129 McCarthy-Jones, S., 102, 103, 104, 106 McCartney, J., 540 McClelland, G. M., 152 McCloy, R. A., 540 McClure, R. J., 225 McConaughy, S. H., 509 McCracken, L. M., 127, 130 McCrae, R. R., 540, 544 McCullagh, S., 130 McCulloch, J. A., 34, 273 McCullough, S. A., 221 McCusker, P. J., 436, 437, 439 McDaid, D. D., 61 McDaniel, M. A., 533, 537, 541 McDermott, B. E., 99, 113, 244, 245, 246 McDermott, K. B., 395 McDevitt-Murphy, M. E., 196, 285 McDonald, R., 224 McDonald, S., 216 McDonell, M, G., 157 McDougall, A., 306, 310 McElhinny, J., 36 McElreath, J. M., 543 McEvoy, G. M., 553 McEwen, B. S., 190 McEwen, D. R., 214 McFadden, V., 457, 458 McFarland, L. A., 535, 536, 537, 538, 540

Author Index McFarlane, A. C., 200 McFarlin, S. K., 162 McGaugh, J. L., 388 McGee Ng, S. A., 296 McGrath, M., 406 McGrath, M. J., 341, 353 McGrath, R., 563 McGrath, R. E., 100, 258, 288, 308, 484 McGrath, R. J., 409 McGue, M., 381, 480 McGuigan, K. A., 37 McGuire, B. E., 36 McGuire, G., 412 McGuire, J., 106 McGuire, M., 214 McHugh, P., 101 McKee, J., 545 McKenna, T., 159 McKenzie, D., 129 McKillip, J., 152 McKinley, J. C., 18, 257, 457, 553 McKinney, W. T., 163 McKinzey, R. K., 335, 353, 490 McLaughlin, J. L., 280 McLellan, A. T., 158 McLeod, B. A., 3 McMillan, T., 126, 191 McNally, D., 102 McNally, R. J., 92, 93, 189, 201, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397 McNamara, J. J., 216 McNary, S. W., 437 McNiel, D. E., 192, 221 McNulty, J. L., 159, 194, 260 McQuiston, D. E., 520 McReynolds, L. S., 155 McWhorter, J. W., 609 Meadow, R. S., 213, 224 Meares, S., 127, 131 Medoff, D., 92, 310 Meehan, K., 110 Meehan, K. B., 43, 86, 123 Meehl, P. E., 18, 55 Mega, M. S., 105 Megargee, E. I., 159, 501, 502 Mehnert-Kay, S., 218 Meier, T. B., 125 Meijer, E., 364, 368 Meijer, E. H., 363, 365, 368, 409, 410 Meikle, A. W., 221 Meisner, S., 304, 305 Meissner, C. A., 405, 412 Meixner, J., 363 Meldrum, R. C., 152 Mellers, B. A., 598 Meloy, J. R., 91, 115, 176, 432 Melton, G. B., 293, 401 Melton, R., 204 Memon, A., 412 Mendelsohn, G. A., 501 Mendelson, G., 198 Mendham, M. C., 289 Mendoza-Romero, J., 64 Merckelbach, H., 43, 73, 74, 239, 240, 365, 368, 410, 458, 460, 461, 517, 597, 609

Merten, T., 30, 61, 67, 75, 76, 239, 449, 458, 460, 461, 462, 468, 488, 489, 573, 574, 596, 602, 608, 609 Mesmer-Magnus, J., 544 Messer, J., 464 Messer, J. M., 454, 462 Messo, J., 389 Metham, L., 136 Metts, S., 541 Metzger, L. J., 390 Metzler, A. E., 295, 296 Meyer, G. J., 304, 310, 317, 497 Meyers, J. E., 128, 130, 133, 246, 262, 335, 353 Meyersburg, C. A., 395 Meyerson, A. T., 238 Meyyazhagan, S., 223 Michelli, J., 450, 605 Michelson, A., 610 Michie, C., 176 Middleton, W. C., 449 Mieczkowski, T., 151 Miele, A. S., 137, 320 Mihura, J., 310 Mihura, J. L., 304 Mikkelsen, E. J., 479 Miles, C., 411 Milia, P., 221 Miller, B. A., 154 Miller, B. L., 105, 238 Miller, C. E., 597 Miller, C. S., 161 Miller, C. W., 52 Miller, F. G., 161 Miller, G. A., 90, 161 Miller, H., 129, 198 Miller, H. A., 13, 74, 110, 249, 437, 439, 453, 454, 455, 456, 521 Miller, H. R., 138 Miller, J., 21, 31, 32, 33, 262, 263, 405 Miller, J. B., 46 Miller, J. C., 353, 412 Miller, J. L., 539, 552 Miller, K. B., 192 Miller, L., 104 Miller, L. J., 54, 84, 141 Miller, M. L., 539 Miller, M. W., 195 Miller, P. M., 521 Miller, S. W., 67 Miller, W. R., 155, 160, 161, 412 Millis, S. R., 46, 47, 49, 52, 54, 55, 126, 128, 132, 134, 135, 246, 323, 333, 335, 344, 346, 352, 353 Millon, C., 38, 159, 280, 281, 485, 486, 502, 580 Millon, T., 19, 38, 92, 159, 280, 281, 292, 293, 294, 485, 486, 502, 580 Mills, J. F., 463 Mills, M., 108 Mills, M. J., 450, 605 Millsap, R. E., 463 Milne, R., 412 Minanni, C. A., 221 Mindt, M. R., 68 Miner, M. H., 407, 408, 414



Author Index 629 Mirzaee, J., 455 Mishara, A., 102 Mitchell, C. N., 151 Mitchell, D., 214 Mitchell, G., 325 Mitchell, J., 105, 106 Mitchell, M., 258 Mitchell, S. G., 164 Mitrushina, M., 347 Mittenberg, W., 31, 47, 48, 54, 83, 84, 123, 130, 139, 141, 191, 245, 246, 326, 331, 335, 336, 337, 350, 351, 353, 604, 610 Mitton, J., 218 Mizrahi, R., 107 Moczynski, N. P., 132, 320 Modderman, J., 381 Modlin, H., 198 Moeller, K. E., 163, 164 Moffitt, T. E., 86 Mogge, N. L., 196, 282, 285, 287, 289, 439, 458, 459, 463, 464, 486 Molina, A. M., 156 Molina, R. A., 64, 65 Molinder, I., 406 Moltó, J., 377 Monaci, L., 281, 297 Mondon, K. R., 139 Monson, B., 89 Montecinos, S. C., 412 Monteiro, M. G., 156 Montes, J. M., 156 Montes, O., 74, 76, 455, 457 Montesano, C., 164 Montgomery, G. E., 545 Montoya, E., 45 Mooney, G., 129 Mooney, S., 290 Moons, K. G. M., 318 Moore, C., 221 Moore, R. A., 154 Moore, R. J., 507 Moos, R. H., 159 Moosmann, B., 164 Moran, M. J., 436 More, H. W., 552 Morel, K. R., 461, 462 Moreland, K. L., 63 Moretti, M. M., 504 Morey, L. C., 33, 71, 72, 73, 88, 136, 179, 195, 227, 249, 280, 282, 283, 284, 285, 286, 288, 291, 292, 454, 484, 485, 486, 502, 503, 504, 507, 563, 573, 578, 581, 605 Morfitt, R., 564 Morfitt, R. C., 500 Morgan, A. H., 157 Morgan, C. A., 413 Morgan, C. D., 293, 295, 301, 304 Morgan, J. P., 163 Morgan, R. D., 282 Morgan, T. J., 160 Morgenstern, J., 160 Morgeson, F. P., 533, 545 Moritz, S., 102 Morra, L. F., 38

Morris, J., 114 Morris, S., 390 Morrison, A. L., 353 Morson, R., 197 Morton-Bourgon, K., 403 Morton-Bourgon, K. E., 403, 410 Moskalewicz, J., 154 Moskowitz, A., 192 Mossman, D., 55, 203, 309, 320 Moston, S., 411, 413 Mott, R. H., 104, 105 Mouchantaf, F. G., 221 Mount, M. K., 375, 376, 535, 539, 540, 542, 554 Mozley, S. L., 195 Mueller-Hanson, R., 535, 536, 537, 539 Muhammad, M., 214 Mulchan, S. S., 54, 84, 141 Mulla, M. M., 6, 84, 212 Mullen, P. E., 216 Müller, G. A., 221 Mullins, J., 485 Mulvey, E. P., 426 Mumley, D., 113 Munizza, J., 83, 113, 433, 439, 449, 456, 459 Munley, P. H., 192, 556 Munro, C. A., 57, 133 Mura, M., 545 Murphy, J. M., 606 Murphy, K. R., 378 Murphy, S. L., 37 Murphy, W. D., 404 Murray, H. H., 301, 304 Murrie, D. C., 114, 139, 288, 292, 439 Murthy, N. P., 214 Musick, J. E., 521, 609 Musshoff, F., 163, 164 Musty, R. E., 152 Myerholtz, L. E., 161 Myers, B., 397 Nabarro, J. D. N., 214 Nadelson, T., 213, 220 Nadolny, E., 454, 457, 458 Nagle, A. M., 488, 489 Nakhutina, L., 489 Nampiaparampil, D. E., 130, 218 Narchet, F. M., 412 Nascimento, E., 377 Nasreddine, Z. S., 75 Nasser, N. J., 214 Nathan, J. S., 257, 405 Nayani, T. H., 103, 104 Neal, B., 559 Neal, J., 451, 452 Neal, T. M. S., 280 Neighbors, C., 155 Nel, D., 105 Neller, D. J., 401, 410, 432, 579, 587 Nelson, E. A., 129 Nelson, L., 463 Nelson, L. D., 381 Nelson, N. W., 135, 141, 247, 260, 330 Nelson, R., 410 Nentjes, L., 43

Netter, B., 304, 305 Nettleton, S., 240 Neumann, C. S., 6, 37, 91, 431, 582, 610 Newcombe, D. A., 157 Newman, A., 98 Newton, P., 478 Nezworski, M., 305 Nguyen, C. T., 271, 272 Nguyen, P. H., 221 Nicholas, D., 609 Nichols, D. S., 193, 275, 500, 501, 511 Nichols, H. R., 406 Nicholson, K., 123, 214, 248 Nicholson, R. A., 289, 606, 607 Nicholson, T. R., 236, 238 Nielsen, M. K., 164 Nielsen, T. A., 200 Niesen, W., 106 Niesten, I. J. M., 43 Nikolayev, M., 479 Nimnuan, C., 217 Nitch, S., 325, 339 Nobre, P. J., 90 Nochajski, T. H., 154 Noland, R. M., 72, 76, 288 Noone, G., 412 Norbech, P. C. B., 307, 309, 310 Norcliffe-Kaufmann, L., 221 Norcross, J. C., 610 Nordengoft, M., 162 Nordquist, C. R., 98, 476 Norenzayan, A., 66 Noriega, R. S., 164, 165 Norlander, B., 610 Norman, D., 130 Norris, M., 103 Norris, M. P., 29, 353 Norton, K., 113 Norton, K. A., 4, 433, 580, 582 Novicevic, M., 544 Novo, M., 604 Nowatzki, N., 151, 152 Nowicki, M., 543 Nugent, P. M., 411, 463 Nulsen, C., 331 Nunes, K. L., 403, 408 Nunley, T. V., 192 Nurmikko, T., 34 Nystrom, L. E., 128 Oakley, D. A., 34 O’Bannon, R. M., 371, 372 Ober, B. A., 134 O’Biren, T. E., 164 O’Brien, C. P., 158 O’Bryant, S. E., 134, 320, 353 O’Connell, M. S., 532, 533, 542, 543, 544 O’Connor, E., 104 O’Connor, M. K., 218 O’Dell, R. T., 454, 457, 458 Oden, N., 603 Odland, A. P., 42, 51, 54, 84, 122, 141, 257, 270, 322, 326 Odle-Dusseau, H. N., 373 O’Donnell, M. L., 201 O’Donohue, W. T., 91, 368, 408, 411, 497

630 Oesterle, T. S., 155 Oettel, D. J., 214 O’Farrell, T. J., 162 O’Gorman, J. G., 599 O’Grady, K. E., 164 Oka, M., 13 Okazaki, S., 68 Okifuji, A., 198 Oksol, E. M., 368 Oldershaw, L., 478, 479, 480, 481, 482, 490, 491 Olesen, F., 218 Olesen, N., 92, 434, 500, 559 Oleszkiewicz, S., 412, 413 Olio, K. A., 387 Oliva, P. S., 388 Oliveira, R. K. D., 221 Olson, B., 221 Olson, D. R., 476 Olson, R. E., 160 Olson, W., 191 Olver, M. E., 406 O’Malley, K., 155 O’Malley, L., 240 Ondersma, S. J., 164 O’Neill, M., 192 Ones, D. S., 7, 371, 372, 373, 374, 375, 376, 378, 380, 381, 531, 532, 539, 540, 544 Orav, E. J., 217 Ord, J. S., 130, 131, 293, 321, 329 Ordi, H. G., 460 O’Reilly, R. A., 218 Orey, S. A., 128, 490 Orlando, M. J., 157, 285, 289 Ormerod, T. C., 413 Ornduff, S. R., 288 Orr, S. P., 201 Orr, T., 132, 321 Ortiz, N. R., 151 Ortiz-Tallo, M., 73 Osberg, T. M., 406 Osborn, C. A., 408 Ossipov, P. V., 199 Ossipov, V. P., 108 Ostfeld, B. M., 224 Ostrosky-Solís, F., 70, 75 Ostrov, E., 564 Osugi, A., 363 O’Sullivan, E., 541 Oswald, F. L., 540, 545 Oswald, M., 460 Otis, J. D., 198 Otto, R. K., 19, 154, 159, 196, 434, 458, 497, 502, 507, 521, 525, 609 Oulis, P. G., 103 Ozer, E. J., 189 Pace, K. T., 221 Padayachi, U. K., 401 Pai, S., 90 Pai, S. M., 155, 462, 463 Pallmeyer, T. P., 201 Palmer, C. H., 221 Palmer, R. F., 152 Palusci, V. J., 223

Author Index Paniagua, F. A., 65 Paniak, C., 126 Pankratz, L., 28, 45, 204, 215, 316 Pannone, R. D., 533, 540, 544 Papadopoulos, M. C., 214 Papadopoulou, K., 34 Papanicolau, A., 199 Pappas, D., 108 Pareés, I., 238, 239 Parker, H. A., 393, 395 Parker, J. D., 157 Parker, N., 198 Parker, P. E., 214, 215, 218 Partridge, G. E., 174 Pasini-Hill, D., 403 Passanisi, A., 87 Patel, M., 105 Pathé, M., 216 Patrick, C. J., 91, 174, 179, 182, 361, 366, 368, 369, 376, 377, 380, 381, 409, 578, 599, 603 Patrick, D., 403 Patrick, I., 403 Patton, C., 47, 83, 123, 191, 245, 331, 335, 350, 604 Paulhus, D. L., 10, 66, 262, 405, 462, 463, 500, 521, 531, 540, 542, 544, 558, 559, 582 Payne, D. G., 397 Payne, J. W., 4, 5, 10, 34, 85, 131, 192, 424, 436, 437, 442, 599 Payne, S. L., 543 Peace, K. A., 388 Pearce, C., 227 Pearlson, G., 106 Pearlson, G. D., 57, 133 Pearse, J., 404 Pearson, P. R., 481 Peck, C. P., 49, 135 Pedroza, G. L., 585 Peebles, J., 507 Peebles, R., 222 Peed, S., 28, 316 Peeters, H., 543, 545 Pella, R. D., 54 Pellecchia, G., 585 Pellizzer, G., 102 Pena, M., 163 Pendergrast, M., 388 Pennuto, T. O., 484 Perala, J., 105 Pérez García, M., 75 Perez-Stable, E. J., 64 Perl, D. P., 126 Perlman, C. A., 391, 393, 395 Perna, R. B., 488 Perrin, S., 139 Perrine, M. W., 152 Perry, G. G., 304, 305 Persinger, M. A., 93 Peskin, J., 477 Peters, J. M., 404, 410 Peters, M. V., 597 Peters, R. H., 155 Petersdorf, R. G., 218 Peterson, E., 189

Peterson, G. W., 406 Peterson, K. H., 436 Peterson, M. H., 533, 537, 540, 543, 544, 546 Peterson, R. L., 489 Petrila, J., 293, 401 Petronio, S., 541 Pettigrew, C. G., 302 Petty, J. A., 136, 260 Petzel, T., 152 Pezdek, K., 389 Pfeiffer, E., 220 Pfohl, B., 426, 427 Phenix, A., 90 Phillips, D. L., 544 Phillips, M. R., 216 Phipps, A., 408 Piazza, N. J., 161 Picano, J. J., 135 Pickering, J. W., 302 Pickett, T. C., 248 Piechowski, L. D., 310 Pierri, J., 152 Piesse, A., 394 Pietz, C. A., 439 Pilkey, D. W., 408 Pillay, B., 463 Pillips, L. R., 64 Pina, A., 409 Pincus, D. B., 198 Pinna, A., 221 Pinsoneault, T. B., 485, 486 Pinto, M., 159 Piquero, A. R., 152 Pirelli, G., 525 Pitman, R. K., 192, 200, 201, 203, 390, 394, 395 Plassmann, R., 220, 221 Plaud, J. J., 409 Pleil, J. D., 163 Plummer, B., 158 Pogge, D., 485, 486, 509 Pogge, D. L., 100, 288 Pokorny, A. D., 154 Polak, A., 476 Pole, N., 202 Pollack, S., 191 Pollock, P., 98, 115 Polusny, M. A., 194 Ponds, R. M., 30 Ponsford, J., 127, 129 Pope, H. G., 88, 99, 215, 219, 388 Pope, K. S., 406 Popli, A. P., 214 Popliger, M., 476 Poppe, C. J., 125 Porter, S., 178, 388 Porter, T. M., 214 Posthuma, A., 498, 499, 507, 508–509 Pouget, E. R., 180 Powel, J., 314 Powell, G. E., 76 Powell, K. E., 101, 105, 108, 109, 199 Powell, M., 439, 458, 459 Powell, M. B., 412 Powell, R., 218



Author Index 631 Power, S., 66 Powers, D. E., 38 Poy, R., 377, 380 Poythress, N. G., 108, 176, 179, 183, 289, 293, 401, 433, 439, 458, 459, 460, 461, 582, 603 Poznyak, V., 156 Pradhan, B., 156 Prager, I. G., 24 Pragst, F., 164 Prassas, A., 394 Prentkowski, E., 478 Preusse, M., 408 Price, J. D., 481 Prieler, J., 490 Prins, J. B., 218 Pritzl, T. B., 498, 503, 507, 508, 509 Prochaska, J. O., 610 Protter, B., 112 Puente, A. E., 75, 257, 405 Pullan, G., 407 Pullen, S., 403 Pulman, A., 227 Pulos, S., 222 Purcell, R., 216 Purdon, S. E., 264 Purisch, A. D., 439, 452, 458, 459 Purser, S. M., 264 Putka, D. J., 378 Putnam, F. W., 393 Putnam, S. H., 52, 134, 135, 136, 140, 353 Putnam, S. L., 151 Putnam, W. H., 353 Qu, C., 131 Quas, J. A., 216, 479 Quellhorst, E., 221 Quigley, B., 98 Quine, W. V. O., 238 Quinn, K. M., 477 Quinnell, F. A., 498, 508 Quinsey, V. L., 178, 181, 407, 408 Quintero, J., 101 Rabb, L., 222 Rabbone, I., 223 Rabinowitz, Y. G., 413 Racz, M. I., 221 Rada, R. T., 216 Radley, A., 34 Rafferty, J., 301 Ragatz, L., 196, 282, 439 Ragatz, L. L., 91 Rahilly, P. M., 224, 225 Raitt, F. E., 93 Ramakrishnan, M., 532 Ramanathan-Elion, D. M., 609 Ramchad, R., 157 Ramsay, L. J., 545 Rand, D. C., 223 Ranga, K., 106 Rappaport, S., 92, 497, 499, 506, 600 Raskin, D. C., 174, 179, 365, 368, 409, 411 Raskin, M., 238 Ratcliff, K. S., 157

Rauschmann, M., 188 Raven, J. C., 490 Rawlings, D. B., 336, 351 Ray, J. V., 91, 182, 380 Raymark, P. H., 373, 378, 538 Razani, J., 325, 347, 353 Read, J. M., 412, 413 Reb, J., 607 Rebok, G. W., 34 Reboussin, B. A., 389 Reddy, H. B., 264 Reddy, M. K., 194 Reddy, V., 478 Redlich, A. D., 412 Reed, J. S., 321 Reedy, S. D., 324, 325 Rees, L. M., 132, 320 Reese, C. S., 353 Reeve, C., 109 Reich, P., 88, 218, 220 Reich, W., 482 Reid, D. B., 405, 558 Reid, J. E., 202, 370, 412 Reid, S., 31 Reid, W. H., 61 Reidy, T. J., 292 Reinert, D. F., 156 Reinhard, M., 106 Reinhardt, V., 7, 45, 85 Reiss, A. D., 531 Reitan, R. M., 335, 341, 343, 347 Repasky, S. A., 192 Repper, J., 224 Resick, P. A., 188 Resnick, P. J., 10, 67, 85, 98, 99, 103, 107, 108, 109, 112, 131, 188, 189, 191, 192, 202, 215, 243, 247, 425, 457, 465 Rey, A., 137, 490 Reynolds, C., 487 Reynolds, C. R., 486, 510 Reynolds, W. M., 559 Ricciardelli, P., 34 Rice, M. E., 181, 407, 408 Richards, H., 90 Richards, H. J., 155, 462, 463 Richardson, A. M., 140 Richardson, D. C., 363 Richardson, G., 363 Richardson, J. T., 92 Richardson, W. S., 53, 318 Richman, J., 164, 165 Rickarby, G. A., 198 Ricker, J., 134 Ricker, J. H., 353 Riddle, T. L., 353 Rieke, M. L., 372 Riemann, B. C., 394 Rienstra, A., 75, 488, 489 Rienzo, D., 408 Ries, R. K., 157, 216 Rigdon, S. E., 367 Riggs, D., 192 Rigler, J., 198 Rikoon, S. H., 158 Riley, B. D., 179 Riley, W., 105

Rilling, J. K., 128 Rim, S. B., 479 Rimé, B., 181 Rimel, R., 125 Rimland, C., 109 Rinaldo, J. C., 67, 483, 484 Rios, F., 156 Rios, J., 485, 486 Ristuccia, C., 393 Ristuccia, C. S., 391 Ritchie, M., 161 Ritov, I., 598 Ritson, B., 108, 215 Rittner, L., 222 Rivers, J. K., 214 Robbins, L., 451, 452, 603 Robbins, R. N., 134, 324 Roberson, T., 132 Roberts, M. D., 478, 553, 562, 563 Roberts, N., 34 Roberts, S., 102 Robie, C., 540 Robins, L. N., 157 Robinson, D. R., 365 Robinson, E. V., 11, 23, 24, 49, 91, 178, 433, 458, 573, 574, 582, 600, 605 Robinson, S. M., 162 Robles, R. R., 152 Roby-Brami, A., 248 Rock, D. A., 38 Rockett, I. R. H., 151 Rodin, G. M., 217, 219 Roe, A., 303 Roe, A. V., 457 Roebuck-Spencer, T., 126 Roediger, H. L., III, 392, 395 Roese, N. J., 410 Roesler, T. A., 222 Rogers, R., 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 31, 34, 37, 43, 44, 45, 46, 47, 49, 55, 56, 57, 61, 62, 64, 65, 67, 68, 69, 70, 71, 72, 73, 83, 84, 85, 87, 88, 89, 91, 93, 98, 99, 100, 103, 108, 110, 113, 114, 116, 123, 124, 134, 135, 136, 137, 140, 141, 151, 156, 157, 159, 176, 178, 179, 181, 182, 183, 184, 190, 191, 192, 193, 194, 196, 197, 213, 223, 238, 239, 243, 244, 245, 246, 247, 249, 257, 258, 259, 260, 262, 263, 264, 265, 266, 267, 268, 271, 272, 280, 281, 285, 286, 287, 288, 289, 290, 291, 302, 303, 306, 317, 329, 330, 332, 333, 401, 404, 411, 413, 414, 422, 423, 424, 425, 428, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 464, 467, 468, 476, 480, 481, 483, 484, 486, 487, 488, 499, 503, 504, 506, 516, 522, 523, 526, 571, 572, 573, 574, 576, 577, 578, 579, 580, 581, 582, 583, 587, 592, 594, 595, 596, 597, 598, 599, 600, 602, 603, 604, 605, 606, 608, 609, 610 Rogers, R. R., 115, 287, 289, 290 Roggensack, K. E., 3

632 Rohling, M. L., 128, 129, 130, 132, 133, 334, 488 Rollnick, S., 412 Romain, P. M., 71, 72 Romano, A., 214 Romeo, A., 89 Root, J. C., 132, 134, 324 Roper, B. L., 134, 331, 338 Roper, G., 477 Roper, H., 222 Rorschach, H., 301 Rose, A., 127, 129 Rose, C., 91 Rose, C. H., 222 Rose, M., 220 Roseman, C. P., 161 Rosen, C. S., 158 Rosen, G. M., 130, 191, 196, 199 Rosenberg, C., 112 Rosenberg, D. A., 224, 226 Rosenberg, H., 161 Rosenblatt, A. I., 159, 502 Rosendal, M., 218 Rosenfeld, B., 249, 280, 437, 439, 441, 442 Rosenfeld, J. P., 363 Rosenhan, D., 100 Rosenquist, P. B., 214 Rosenthal, R., 102, 404 Rosenzweig, S., 408 Roske, A., 91, 115, 176 Roskos, P. T., 247 Rosomoff, H. L., 21, 273 Rosomoff, R. S., 21, 273 Ross, C. A., 22, 56, 192, 196, 289, 442, 455, 587, 599, 608 Ross, H. E., 154 Ross, H. S., 476 Ross, P. T., 339 Ross, S. A., 34, 36 Ross, S. J., 412 Ross, S. R., 52, 135, 140, 353, 376 Rosse, J., 543 Rosse, J. G., 539, 540, 552 Rosso, M., 556 Rossor, M. N., 34 Rostow, C. D., 381, 559 Roth, N., 164 Roth, P. L., 373, 538 Roth, R. S., 198 Rothenhäusler, H. B., 218 Rotholc, A., 335, 353 Rothstein, M., 375 Rothstein, M. G., 539, 546 Rotter, J. B., 301 Rouen, D., 162 Rouillon, F., 181, 215 Rouleau, I., 353 Roulin, N., 531, 532, 535, 536, 537, 538 Rourke, B. P., 136 Rouse, S. V., 500, 564 Rovine, M. J., 3 Rowan, C., 112 Rowse, G., 106 Ruane, J. M., 542 Rubenzer, S., 293, 380, 516, 517, 520

Author Index Rubin, D. C., 389 Rubin, H. B., 408 Ruch, F. L., 542 Ruch, W. W., 542 Ruddick, W., 541 Rudek, D. J., 65 Ruff, R. M., 48, 123, 140 Ruffman, T., 476 Ruffolo, L. F., 353 Rufino, K. A., 292 Ruggero, C. J., 422 Ruggiero, K. J., 20, 189, 608 Ruiz, M. A., 292, 331 Ruiz Sanchez de Leon, J. M., 75 Rulka, G., 90 Rumans, L. W., 219 Ruocco, A. C., 295 Rush, B. R., 158 Rush, E. B., 479 Russano, M. B., 405, 412 Russell, A. S., 247 Russell, E. W., 335, 353 Russinova, Z., 3 Rutigliano, P., 162 Ryan, A. M., 378, 535, 536, 537, 538, 540 Ryan, J. J., 137 Ryan, L. M., 134 Ryan, W., 98 Ryba, N., 113 Ryba, N. L., 4, 433, 580, 582 Ryckman, R. M., 7 Ryder, A. G., 66, 289 Sabella, C., 222 Sachs, M. H., 106 Sackeim, H., 462 Sackett, D., 317 Sackett, P. R., 371, 372, 374, 375, 376, 378, 538, 540, 544, 545, 546 Sadock, V. A., 198 Saez, P., 68 Sahai, H., 152 Sahd, D., 216 Saitz, R., 155 Saiyasombat, M. I., 214 Saiz, B. J., 460 Sakellariou, A., 227 Saks, M. J., 520 Salazar, X., 325 Salbach, H., 220 Salekin, K. L., 23, 85, 431, 436, 460, 479 Salekin, R. T., 8, 123, 176, 317, 411, 413, 451, 475, 479, 480, 481, 482, 485, 581, 604 Salvador-Carulla, L. L., 61 Salvo, M., 221 Salyer, K., 161 Samenow, S., 113 Samet, J. H., 155 Samra, J., 424 Sanchez, M. P., 73 Sánchez-Crespo, G., 275 Sandal, G. M., 7 Sanders, J. F., 322 Sandham, A. L., 413 Sandler, J. C., 402

Sanfey, A. G., 128 Sansone, L. A., 218 Sansone, R. A., 214, 218, 227 Santamaria, P., 73 Santamaria Fernández, P., 61 Sanz Granado, O., 101 Sasaki, A. W., 220 Satyanarayan, M., 214 Saunders, J. B., 156 Saunders, L. S., 200, 201 Savage, B., 222 Savino, A. C., 221, 228 Savitsky, K., 413 Savodnik, I., 43, 137 Sawyer, R. J., 134, 338 Saxe, L., 409 Schacter, D. L., 389, 390, 392, 394, 395, 479 Schatzow, E., 387 Scheflin, A. W., 220, 387, 388 Scher, A. I., 126 Scherr, A., 524 Schiffman, W., 113 Schimmenti, A., 87 Schinka, J. A., 452, 581 Schipper, L. J., 247, 249, 293, 334 Schlenker, B. R., 531 Schmand, B., 75, 246, 488, 489 Schmaus, B., 125 Schmeidler, J., 190 Schmidt, F., 221 Schmidt, F. L., 371, 373, 374, 375, 540 Schmidt, J., 545 Schmidt, M., 333, 351, 353 Schmit, M. J., 540 Schmitt, N., 370, 374, 381, 540, 545 Schmitter-Edgecombe, M., 525 Schmitz, N., 156 Schmod, J., 368 Schneck, J., 99 Schneekloth, T. D., 155 Schneider, D., 533 Schneider, K., 174, 175, 184, 449 Schneider, S. D., 102 Schneiderman, A. I., 130 Schoenberg, M. R., 293, 295 Schoenfeld, L. S., 559 Scholler, G., 220 Schoua-Glusberg, A., 63 Schraft, C. V., 87 Schrantz, K., 292 Schredl, M., 200 Schreiber Compo, N., 412 Schreier, H. A., 88, 222 Schretlen, D., 305, 451, 452, 453, 457 Schretlen, D. J., 28, 57, 109, 128, 133, 138 Schroeder, R. W., 42, 51, 122, 136, 257, 270, 271, 272, 320, 321, 322 Schrum, C. L., 485 Schubert, C., 426 Schultz, D. S., 307, 309 Schultz-Ross, R. A., 98, 476 Schutte, C., 45, 51, 324 Schwab-Stone, M. E., 157 Schwandt, B., 592, 593, 598 Schwartz, A., 598



Author Index 633 Schwartz, R., 406 Schwartz, R. H., 152 Schwartz, R. P., 164 Schwedt, T. J., 125 Schweitzer, N. J., 520 Scogin, F., 555 Scott, B. A., 541 Scott, C., 113, 289 Scott, J., 394 Scott, J. C., 129 Scott, R. L., 483 Scott, W., 139 Scragg, P., 195, 289 Seals, R. W., 508 Seamons, D., 303, 304 Seamons, D. T., 457 Sechrest, L. B., 373 Seelen, J., 406 Segarra, P., 377 Seifer, R., 158 Seigel, R., 101 Seijo, D., 604 Sellbom, M., 36, 49, 194, 260, 264, 265, 266, 267, 269, 274, 275, 291, 293, 296, 439, 560, 594 Selle, N. K., 363 Sellers, A. H., 192 Seltman, M. F., 224 Selzer, M. A., 7 Selzer, M. L., 154 Semmer, N., 152 Serafino, G., 562 Serfass, L., 436 Serlie, A., 381 Serpa, J. G., 141 Servat, J. J., 221 Sethi, G. R., 222 Seto, M. C., 178 Sewell, K., 110, 113, 123, 291, 504, 581 Sewell, K. W., 4, 8, 10, 11, 12, 21, 22, 23, 37, 64, 69, 72, 73, 84, 85, 100, 123, 136, 140, 176, 193, 249, 259, 262, 280, 286, 288, 301, 302, 310, 317, 411, 413, 423, 432, 433, 436, 439, 451, 454, 458, 460, 461, 476, 484, 486, 503, 522, 572, 573, 579, 580, 582, 594, 604, 605, 609 Shackleton, H., 490 Shafer, A. T., 224 Shaffer, D., 157 Shah, K. A., 221 Shalvi, S., 175 Shandera, A. L., 249 Shannon, R. M., 224 Shapiro, A. M., 128 Shapiro, A. P., 222 Shapiro, B., 105 Sharf, A., 577, 599 Sharf, A. J., 9, 249, 574 Sharland, M. J., 47, 48, 331 Sharma, G., 603 Sharma, K. K., 113 Sharpe, M. C., 240 Shavzin, A., 303 Shaw, J., 155 Shaw, T. G., 458, 459 Shawyer, F., 103

Shealy, S. E., 136 Sheehan, B. L., 310 Sheetz, B. M., 158 Shelton, J. T., 54 Shelton, S. A., 192 Shen, M., 164 Shepard, P. H., 340, 490 Shepard, S. E., 221 Shepherd, B. E., 462 Sheppard, K., 91, 115, 176 Sheppard, S., 129 Sherer, M., 126, 342, 353 Sheridan, L. P., 216 Sheridan, M. S., 212, 224, 225, 226 Sherman, D. S., 353 Sherman, E. M., 330, 338, 341, 349, 350, 488, 489, 490 Sherman, E. M. S., 42, 44, 45, 46, 54, 57, 123, 124, 131, 140, 246, 262, 270, 330, 577, 605, 606 Sherman, E. S., 423, 488 Sherman, J. J., 198 Sherman, M., 109, 425, 432 Sherman, S. J., 395 Sherritt, L., 155, 156 Sherrod, C. B., 521, 609 Sherry, S., 200 Shields, A. L., 156, 161 Shin, H. C., 218 Shin, L. M., 390, 394 Shirtcliff, E. A., 87 Shively, S., 126 Shmerling, R. H., 218 Shoemaker, D. P., 412, 413 Shore, D. L., 137, 290 Shores, E. A., 36 Shorter, E., 237 Showalter, E., 237 Shoykhet, M., 222 Shrier, L., 155 Shum, H. K., 599 Shuman, D. W., 6, 10, 67, 68, 413, 422, 425, 432, 444, 526, 580 Shusman, E. J., 553, 561, 562 Sias, P. M., 543 Siefert, C. J., 284, 288, 504 Siegel, S., 153 Siegert, R. J., 127 Sigal, M., 224 Sigall, H., 410 Sigurdsson, J. F., 412 Silk-Eglit, G., 320 Silk-Eglit, G. M., 137, 318, 319 Sillars, A., 3 Silva, M. A., 127 Silver, J. M., 56, 128, 129, 139, 334, 349 Silver, S. M., 200 Silverberg, N. D., 125, 127, 142 Silvers, K., 105 Simcox, A. M., 439, 458, 459, 486, 487 Simeon, D., 199 Simmons, A., 122 Simmons, L. A., 13 Simon, L., 426 Simon, R. I., 526 Simonet, D. V., 535, 536, 537

Simpler, A., 292 Simpson, D. D., 151, 463 Simpson, G., 163 Sinal, S. H., 223 Singh, J., 462 Singh, J. P., 181, 582 Sireci, S. G., 64 Sirinoglu, M. B., 222 Skinner, H. A., 154, 156 Skodol, A. E., 281, 296 Slater, E., 238 Sleep, C. E., 136, 260 Slick, D. J., 42, 43, 44, 45, 46, 48, 54, 57, 123, 124, 130, 131, 134, 135, 137, 140, 246, 247, 249, 262, 270, 316, 317, 329, 330, 334, 336, 337, 338, 339, 340, 341, 349, 350, 423, 442, 455, 488, 577, 605, 606 Slimowicz, J., 595 Slobogin, C., 293, 401 Slovenko, R., 191 Slovik, L. S., 223 Small, I. F., 103, 104 Small, J. G., 103 Smit, F., 318 Smith, B. H., 198 Smith, B. N., 198 Smith, D. B., 13, 539, 540 Smith, G., 249, 262, 435, 437, 449, 487 Smith, G. M., 151 Smith, G. P., 49, 73, 74, 100, 179, 437, 450, 451, 452, 453, 457, 458, 461, 487, 517 Smith, G. S., 151 Smith, G. T., 442 Smith, H. H., 42, 335, 353 Smith, J. G., 297 Smith, K., 339, 345, 346, 347, 348 Smith, M. A., 532 Smith, M. D., 476 Smith, M. S., 314 Smith, P., 181 Smith, P. C., 155 Smits, N., 318 Snell, A. F., 533, 535, 537 Snell, D. L., 127 Snowdon, J., 216 Sobaci, G., 248 Sobel, J. B., 248 Sobell, L. C., 162, 165 Sobell, M. B., 162, 165 Soble, J. R., 130 Socia, K. M., 402 Soderbergh, S., 111 Sokol, M. C., 37 Solan, M. C., 227 Soliman, S., 109 Sollman, M. J., 138, 249 Solomons, R., 216 Sommer, I. E., 101 Sommers-Flanagan, J., 412 Sommers-Flanagan, R., 412 Song, J. M., 164 Song, X., 610 Sorenson, J. R., 292 Soukara, S., 413, 482

634 Southall, D. P., 223 Spaan, P. E., 488, 489 Spaan, P. E. J., 75 Spanierman, L. B., 130 Sparr, L., 188, 192, 215 Sparr, L. F., 200, 203 Spector, P. E., 544 Speed, F. M., 44, 316 Speed, J., 129 Spellacy, F. J., 135 Spence, S. A., 239 Spencer, C. C., 533 Spencer, R. J., 134, 353 Speth, E., 91, 115, 176 Spidel, A., 180, 480, 598 Spiegel, D., 215, 387, 388 Spilberg, S. W., 554, 564 Spitzer, M., 106 Spitzer, R. L., 157, 237, 391, 428, 431 Spohn, C., 151 Sporer, S. L., 592, 593, 598 Sprafkin, R., 109 Spray, B. J., 439 Spreen, O., 335, 345, 490 Springman, R. E., 432 Spuijbroek, E. J., 214 Sribanditmongkol, P., 164 Staal, M. A., 500 Stahl, T., 368 Stallings, M. C., 376 Stanush, P. L., 538 Stark, S., 539 Starkstein, S. E., 107 Starzyk, K. B., 183 Steadman, H. J., 181 Stecher, M. D., 539, 552 Steel, R. M., 216, 218 Steer, R. A., 464 Stefanis, C. N., 103 Stein, J. A., 151 Stein, L. A. R., 11, 151, 152, 159, 478, 483, 484, 578, 603 Stein, M. D., 160 Stein, M. T., 478 Steinberg, J. S., 464 Steiner, A. R., 490 Steiner, M., 218 Steinfeld, R. R., 158 Stenclik, J. H., 320 Stephane, M., 102 Stephenson, G. M., 411 Stermac, L., 305 Stern, P. C., 410 Stern, T. A., 221 Steuerwald, B. L., 91 Stevens, A., 449 Stevens, C. K., 530 Stevens, D., 222 Stevenson, M., 264 Stevenson, R. J., 102, 106 Stinchfield, R. D., 152, 160, 161 Stinson, J. D., 406, 407, 409 Stock, H., 557, 558, 560, 561, 565 Stockdale, K. C., 406 Stokes, J., 485, 486, 509 Stolberg, R., 92

Author Index Stone, D. C., 198 Stone, J., 237, 239, 240 Stone, J. I., 543 Storr, C. L., 389 Stout, J. W., 224 Stouthamer-Loeber, M., 476, 479, 480, 509 Stoverink, A. C., 531 Strafkin, R., 425 Straus, S. E., 53, 318 Strauss, E., 48, 135, 137, 249, 316, 329, 335, 345, 442, 455, 490 Strauss, G. P., 38 Strauss, J. S., 106 Stredny, R. V., 257, 280, 405, 439, 502 Streiner, D. L., 20, 23 Stromwall, L. A., 412, 413 Strong, D. R., 55, 92, 434, 500, 559 Strong, S. R., 424 Strutz, F., 221 Stuart, G. L., 103 Stucky, B. D., 157 Stump, J., 555 Sturgeon, J., 264 Stutts, J. T., 223 Su, E., 222 Suchday, S., 65 Sue, S., 68 Suendermann, O., 201 Sugar, C. A., 141 Suhr, J., 345, 353 Suhr, J. A., 127, 128, 329, 341, 342, 343, 345, 351, 353 Sullivan, J. P., 249 Sullivan, K., 29, 288, 353 Sullivan, K. A., 127, 264, 289, 458 Sullivan, M. J. L., 139 Sullivan, S. K., 38 Sumanti, M., 43, 137 Surgenor, L. J., 127 Surrette, M. A., 562 Sutherland, A. J., 217, 219 Suwannachom, N., 164 Swaim, G. W., 541 Swanson, L. M., 218, 227 Swartz, J. D., 301 Sweet, J. J., 122, 132, 133, 135, 246, 247, 260, 342, 353 Swets, J. A., 606 Swiatkiewicz, G., 154 Swider, B. W., 531 Swift, R. M., 152 Swihart, A. A., 344, 345, 353 Sy, J. T., 129 Sydell, E. J., 535 Sykes, R., 238 Tabacoff, R., 86 Tabernik, H. E., 93 Tackett, J. L., 480 Tahir, N. M., 164, 165 Tait, R. C., 139 Takahashi, S., 65 Talan, J., 224 Talbot, R. E., 104 Talbott, J. A., 238

Talley, J. L., 335 Talwar, V., 476, 477 Tamay, Z., 222 Tan, J. E., 48 Tan, O. T., 214 Tanay, E., 519 Tang, J., 541 Tarescavage, A. M., 13, 264, 271, 272, 436, 438, 439, 441, 454, 602, 603 Tarter, R., 156 Tarter, R. E., 156, 158 Taskaynatan, M. A., 215 Tausche, A. K., 214 Tavalaie, S. A., 455 Taylor, J., 227 Taylor, J. E., 533 Taylor, M., 68, 221 Taylor, M. J., 67 Taylor, S., 130, 544 Teal, F. F., 248 Tearnan, B. H., 34, 36 Teasell, R. W., 222 Tekin, S., 413 Tellegen, A., 92, 136, 158, 159, 194, 227, 249, 257, 259, 262, 263, 264, 267, 275, 280, 377, 483, 484, 553, 560 Temkin, N. R., 126, 130 Tenhula, W. N., 135 Tennen, H., 155 Tenore, P. L., 163 Teplin, L. A., 152 Terdal, L. G., 479 Terkelson, K. G., 7 Ternes, M. S., 249, 264, 265, 267, 276 Terr, L. C., 392 Tetrault, J., 221 Tett, R. P., 375, 376, 535, 536, 537, 553 Tewksbury, R., 402 Thakur, M., 106 Thananchai, T., 164 Thelen, M., 480 Theroux-Fichera, S., 31, 336, 353 Thies, E., 449 Thomann, K. D., 188 Thomas, A., 214 Thomas, J. C., 562 Thomas, K. M., 195, 196, 285, 287, 289, 290 Thomas, M. L., 264, 404, 405, 408, 463 Thomas, P., 102 Thomas, R. M., 92 Thompkins, B. A. O., 134 Thompson, A., 376 Thompson, C. R., 214 Thompson, G., 249, 329, 442, 455 Thompson, G. B., 137, 316, 317 Thompson, G. N., 198 Thompson, J. A., 553, 562 Thompson, J. S., 103, 115 Thompson, J. W., 110 Thompson, K. E., 192 Thompson, L. L., 335, 353 Thornton, B., 7 Thornton, G. C., III, 535, 539 Thornton, L. C., 380 Thorpe, J. S., 301



Author Index 635 Thuras, P., 194 Thurston, A., 376 Tibault, P., 139 Tierney, D., 406 Tiet, Q. Q., 159 Tiffany, W. R., 248 Tillbrook, C. E., 4, 113, 433, 440, 460, 580, 582 Timbrook, R. E., 499, 557 Timmons, L., 303, 304, 307 Tims, F. M., 151 Tippins, N. T., 381 Titus, J. B., 30, 576 Titus, J. C., 157, 158 Tobin, N. L., 508 Todd, R. D., 482 Todd, W., 66, 71 Toft, T., 217 Tohen, M., 218, 219 Tolin, D. F., 391, 604 Toliver, R. F., 412, 413 Tombaugh, T. N., 28, 38, 48, 75, 132, 140, 249, 319, 320, 329, 334, 455, 516 Tomicic, T. L., 108, 176, 179, 199 Tong, D., 406 Tonigan, J. S., 155, 160 Toomey, A., 264 Toomey, J. A., 439 Toomey, J. P., 195, 288, 439 Tornusciolo, G., 86 Tourangeau, R., 151 Towers, K., 314 Trabucchi, M., 107 Trafimow, D., 125 Trandel, D. V., 390 Tranel, D., 51, 125, 353 Trapnell, P. D., 540 Trauer, T., 102 Travers, R. M. W., 545 Travin, S., 112 Tremblay, R. E., 86 Trethowan, W. H., 100 Trevor, C. O., 34 Triandis, H., 65, 67 Trichter, J. G., 163 Trief, P., 109, 425 Trigwell, P. J., 216 Trimble, M., 191, 198 Tristan, E., 533, 542, 544 Trost, Z., 139 Trout, A., 508 Trower, P., 104 Trucco, E., 162 Trueblood, W., 333, 351, 353 Trull, T. J., 480 Tsianos, E. V., 108 Tsiouris, A. J., 125 Tucker, D. D., 196 Tuckett, A. C., 541, 542 Tuerk, P. W., 201 Tulsky, D. S., 134 Tuma, J. M., 302 Turk, D. C., 198 Türkcan, A., 457 Turnbull, J., 137 Turnbull, W., 8

Turner, J. A., 524 Turner, T. H., 56 Turnley, W., 533 Tursich, M., 437 Tussey, C. M., 518 Tuzinski, K. A., 545 Twumasi-Ankrah, P., 322 Tyner, E. A., 440, 525 Tysse, J. D., 517 Tzall, D., 437 Uddo, M., 192 Uddo-Crane, M., 126 Uhlenhuth, E. H., 152 Ulstad, K. L., 503 Umlauf, R. L., 264, 273 Underwager, R., 93 Unsicker, J., 158 Unsinger, P. C., 552 Uomoto, J. M., 130 Urbanoski, K., 158 Urbina, S., 538 Ustad, K. L., 72, 136, 286, 439, 573, 605 Ustun, T. B., 157 Vaduganathan, M., 221 Vaiana, M., 246 Vaishampayan, N., 131 Valdimarsdottir, E. B., 412 Vallabhajosula, B., 490 Vallano, J. P., 412 van Beilen, M., 460 van de Vijver, F. J. R., 405 van den Broek, M. D., 281, 297 van der Kolk, B., 200 van der Meer, J. W., 218 van Gorp, W. G., 134, 138, 324, 331, 490 Van Hemert, A. M., 218 Van Hooft, E. A. J., 544 Van Hout, M. S. E., 246 Van Iddekinge, C. H., 373, 374, 378, 538 van Impelen, A., 458, 460 van Nimwegen, C., 381 van Oostendorp, H., 381 van Rooijen, M. A., 154 Vandecreek, L., 553 Vandenberg, B. R., 432 Vanderploeg, R. D., 56, 127, 134, 342, 353 Vanderspek, R., 165 VanKirk, K. K., 56 VanVeldhuisen, P. C., 603 Varanko, M., 102 Varela, J. G., 292, 555 Varney, N., 134 Vasilopoulos, N. L., 543, 545, 546 Vasquez, E., 409 Vasterling, J. J., 320 Veazey, C. H., 454, 455, 456 Veith, I., 236 Veldkamp, B. P., 381 Velikonja, D., 136 Veltri, C. O. C., 290 Venables, K., 76 Venables, N. C., 376, 377, 380, 381 Vender, S., 214

Vennemann, B., 222 Venner, R. M., 34 Verbrugge, R. R., 37 Verdejo-García, A., 610 Verity, C. M., 222 Vernon, P. E., 530 Verschuere, B., 175, 364, 365, 368, 409, 410 Veysey, B., 402 Vickery, C. D., 128, 132, 135, 137, 442, 490 Victor, T. L., 141, 331, 339, 499 Vieira, S., 214 Vieregge, P., 214 Vierkant, A. D., 105, 106 Viglione, D. J., 304, 310, 432, 438 Viglione, V., 304, 305 Vilar López, R., 75 Villado, A. J., 533 Villain, M., 164 Villaneuva, M. R., 353 Vincent, J. P., 508 Vinokur, A., 154 Viswesvaran, C., 7, 371, 373, 374, 375, 378, 531, 532, 540, 544 Vitacco, M. J., 6, 9, 11, 12, 21, 37, 83, 84, 85, 91, 92, 93, 98, 110, 113, 115, 140, 193, 249, 259, 280, 432, 433, 434, 439, 440, 449, 455, 456, 458, 459, 460, 461, 594 Vivian, J. E., 559 Vogt, A. T., 42, 335, 353 Vogt, J., 98 Volbrecht, M. E., 133, 138, 139 von Hahn, L., 223 Voss, W. D., 157 Vosti, K. L., 219 Vrij, A., 175, 368, 407, 409, 411, 413, 449, 482 Vroom, V. H., 535 Wachpress, M., 108 Waddell, G., 34, 38, 248, 273, 274 Wagenheim, B. R., 64 Wagner, A. L., 454 Wagner, E., 158 Wagner, J., 66, 68, 69 Wahass, S., 102, 103 Wainberg, M., 190 Wakefield, H., 93 Wakeling, H., 411 Wald, M. M., 124 Waldman, I. D., 380 Walfish, S., 564 Walker, M., 159, 488, 489 Wall, T. D., 266, 267, 269 Wallace, B., 413 Wallace, P., 218 Wallach, H., 305 Waller, D. A., 88 Waller, N. D., 370 Waller, N. G., 377 Walls, B. D., 135, 137, 247, 249, 257, 281, 329, 405, 449, 483, 575 Walsh, D., 413 Walsh, K. W., 133

636 Walsh, W., 413 Walsh, W. B., 556 Walters, G. D., 8, 55, 56, 85, 182, 333, 466, 582 Walton, N. H., 76 Wanek, J. E., 374, 375, 376, 378 Wang, E. W., 288, 289, 439, 605 Wangelin, B. C., 201 Warberg, B., 408 Ward, J., 109 Ward, N. G., 216 Ware, E. E., 408 Ware, J., 404 Warner, J. L., 533 Warner, J. O., 224, 225 Warner-Chacon, K., 325 Warren, J., 113, 114 Warriner, E. M., 136 Warrington, E. K., 335, 346 Wasserman, G. A., 155 Wasyliw, O. E., 303, 307, 308, 405, 502, 564 Wasylkiw, L., 183 Waters, D. P., 290 Watkins, M. M., 108, 176, 179, 439, 458, 459 Watkins-Clay, M. M., 289, 439, 458, 459, 603 Watson, C. G., 425 Watson, D., 281, 296, 480 Watson, I. D., 163 Watson, R., 408 Watt, I., 240 Watts, D., 499, 557 Waxman, S. E., 43, 86, 110, 123 Weakley, A., 525 Weathers, F. W., 195, 196, 285, 394 Weaver, C. M., 197 Webb, L. M., 295 Webster, C. D., 181, 582 Wechsler, D., 22, 335, 336, 343, 344, 457 Wecksell, B., 485, 486, 509 Weed, N. C., 159 Weekes, J. R., 463 Weekley, J. A., 532, 540, 545 Wefel, J., 353 Wegener, S. T., 37, 609 Wei, C., 127, 128 Weigold, M. F., 531 Weinborn, M., 132, 321, 331 Weis, J., 213 Weiss, D. S., 189 Weiss, G. S., 478 Weiss, P., 559 Weiss, P. A., 559, 560 Weiss, R. A., 442, 443 Weiss, R. D., 162 Weiss, W. U., 559 Weissman, H. B., 128, 129 Weissman, M., 334 Wekking, E. M., 246 Weld, E., 426 Weller, C. E., 413 Wells, R. D., 478 Welsh, G. S., 18, 544 Wen, J., 324, 325, 345, 353

Author Index Wender, E. H., 478 Wertham, F., 99 Wertheimer, J. C., 132, 134 Weshba, R., 264 Wessels, W. H., 463 Wessely, S., 99, 106, 217, 218, 237 Wessely, S. C., 237, 240 West, L., 101 West, M. A., 407 West, S., 10, 85, 131, 188, 247, 425 Weston, W. A., 220 Wetter, M. W., 33, 67, 192, 193, 259, 331, 499, 507 Wetter, S. R., 44, 124 Wheeler, K. G., 370 Whelton, J., 302 White, H. R., 154, 155 White, M., 158 White, R. J., 222 Whiteside, D. M., 46, 137, 290 Whitfield, C. L., 93, 388 Whiting, J. B., 13 Whitley, B. J., 90 Whitley, G., 203, 204 Whitney, K. A., 340, 345, 490 Whitney, K. W., 264 Whittaker, R., 127, 139 Widiger, T. A., 480 Widom, C. S., 390 Widows, M., 179, 437, 487, 582 Widows, M. R., 450, 457 Wieczorek, W. F., 154 Wiederman, M. W., 218 Wiedmaier, P., 461 Wiegel, M., 408 Wiemann, S., 378 Wiggins, C. W., 258 Wiggins, J. S., 33, 262, 263, 275 Wild, T. C., 158 Wildman, R., 432 Wilhelm, K., 137 Wilkinson, C. B., 388 Wilkinson, L., 20 Willen, R. M., 413 Willenberg, H., 220 Williams, C. L., 135, 159, 260, 478, 484 Williams, C. S., 514 Williams, J. A., 525 Williams, J. B. W., 157, 391 Williams, J. E., 290 Williams, J. M. G., 394 Williams, L. M., 93, 390 Williams, M. A., 37 Williams, M. M., 9, 249, 574, 577, 599, 600 Williams, R. J., 151, 152 Williams, S., 477 Williamson, S., 180 Willis, C. G., 370 Willis, T., 222 Willis, W. G., 353 Willoughgy, S., 34 Wilson, A. E., 476, 478 Wilson, M. S., 91 Wilson, P., 488 Wilson, R. J., 407, 408, 414

Wilson, S. L., 87 Winckworth, C., 222 Wing, H., 373 Winn, H. R., 126 Winograd, M. R., 364 Winslow, D. L., 220 Winslow, M. M., 353 Winter, J. P., 544 Winters, K. C., 152, 155, 160, 161 Wisdom, N. M., 320, 458, 459, 460, 467 Witt, P. H., 401, 402, 403, 432, 579, 587 Wittman, L., 200 Wolf, I., 136 Wolfe, P. L., 132 Wolfson, D., 335, 341, 343, 347 Won, C. W., 218 Wong, S. C. P., 610 Wong, S. P., 181 Wood, H., 557 Wood, J., 305, 308, 409 Wood, R., 128 Woodland, M. H., 159 Woods, D. M., 546 Woods, S. P., 132, 321, 331 Woodson, J., 161 Woody, G. E., 158 Wooley, C. N., 22, 56, 85, 99, 156, 161, 166, 188, 190, 196, 197, 247, 287, 289, 290, 425, 455, 587, 596, 599, 608, 609 Woolley, R. M., 374 Worley, K., 98 Wormith, J. S., 406 Worthington, A., 76 Wortzel, H. S., 125 Wright, D., 218 Wrobel, T. A., 485, 486, 487 Wroblewski, V. R., 546 Wu, C., 151 Wunder, J., 36 Wupperman, P., 422 Wyer, M. M., 175 Wygant, D. B., 36, 49, 55, 135, 136, 248, 249, 257, 258, 260, 263, 264, 265, 270, 271, 273, 274, 281, 297, 320, 405, 439, 483, 484, 574 Wymer, J. H., 465 Wynkoop, T. F., 353, 442 Xhihani, A., 413 Xiang, P., 164 Xu, F., 476 Xu, L., 124 Yakobov, E., 139 Yalndag-Öztürk, N., 222 Yamada, A. M., 65 Yan, T., 151 Yancey, J. R., 376, 377 Yang, M., 181 Yang, Y., 64 Yanke, A., 89 Yano, K., 197 Yantz, C. L., 134 Yates, B. D., 98, 476 Yates, G. P., 6, 84, 212, 214, 216, 218, 221 Yates, P. M., 403



Author Index 637 Ye, Y., 154 Yeager, C. A., 390 Yehuda, R., 190 Yeung, E., 127 Yildirim, O., 222 Yim, I. S., 479 Ying, Y., 151 Yonge, O., 224 Yorker, B. C., 224 Yoshita, Y., 533, 535, 552 Youden, W. J., 318 Young, D., 422 Young, G., 45, 48, 123, 605 Young, J. C., 134, 331, 338, 340 Young, J. Q., 221 Young, M., 66 Young, R. M., 154 Young, R. W., 284 Young, S., 8 Young, S. E., 376, 381

Youngjohn, J. R., 136, 264, 271, 331 Youngstrom, E., 509 Yudko, E., 156 Yuille, J. C., 178, 179, 480, 482, 598 Yutzy, S., 198 Zaccario, M., 485, 486, 509 Zahner, G. E., 476 Zakzanis, K. K., 127, 129, 290 Zald, D., 131 Zaldivar, F., 156 Zapf, P. A., 110 Zasler, N. D., 248 Zax, M., 65, 545 Zeedyk, M. S., 93 Zeitlin, S. B., 394 Zelazny, J., 157 Zelicoff, A., 367 Zelle, H., 582 Zellner, J. A., 65

Zenisek, R., 46 Zerbe, W. J., 531 Zevitz, R. G., 402 Zgoba, K., 402 Zgoba, K. M., 402, 404 Zhang, X. E., 64 Zhu, J., 265 Zickar, M. J., 533, 534, 540 Ziegler, E. A., 141 Zielinski, R. E., 31, 336, 353 Zilberstein, K., 87 Zillmer, E., 302 Zimmerman, M., 422, 426 Zimmermann, P., 462, 468 Zimostrad, S., 609 Zitelli, B. J., 224 Zottoli, T. M., 439 Zuckerman, M., 376 Zurowski, M., 214 Zych, K., 135

Subject Index

Note. f or t following a page number indicates a figure or a table. Abel Assessment of Sexual Interest (AASI and AASI-2), 408–409 Absence of alternative explanations, 587 Abuse, 479 Accommodation patterns, 34 Acculturation, 65–66, 68–69. See also Cultural factors Acculturation Rating Scale for Mexican Americans–II (ARSMA-II), 68–69 Accusatorial approaches, 402–404, 411–412, 414 Achenbach System of Empirically Based Assessment (ASEBA), 509 Acquiescent responding, 8, 505–506, 506t, 579. See also Response styles Addiction Acknowledgement scale (AAS), 159 Addiction Potential scale (APS), 159 Addiction Severity Index (ASI), 158 Addictive behaviors, 89–90 Adjustment problem/disorder, 45 Admission of guilt, 404–405 Adolescence assessment procedures and, 482–490, 484t, 486t, 488t diagnoses associated with deception and, 86–87 family factors and, 481 overview, 475–481, 477t, 479t, 482f, 491 perception and, 479–480 personality and, 480–481 substance abuse and, 151 threshold and clinical decision models, 490–491, 491t types of deception, 477–478, 477t Adulthood, 87–92, 220

Advanced Clinical Solutions (ACS), 49, 50t, 57t, 323 Affective functioning, 177t, 598 Affective theory, 593 African State Presidents’ Detainee Scale (ASPDS), 463, 466 Aggression, 175, 176, 192 Agreeableness, 375–377, 480, 537–538 Alcohol, Smoking and Substance Involvement Screening Test (ASSIST), 154t, 156–157 Alcohol Use Disorders Identification Test (AUDIT), 154, 154t, 156, 165 Alcohol/Drug Problem Acknowledgement and Proneness scales (ACK, PRO), 159 Alcohol-induced psychotic disorder (AIPD), 105–106. See also Psychosis Alternative explanations, 587 Alzheimer’s dementia, 107 Ambivalence, 5 American Congress of Rehabilitation of Medicine criteria (ACRM), 125, 125t American with Disabilities Act (ADA), 379–380 Amnesia, 389–390. See also Memory functioning Amplified detection strategies for feigned cognitive impairment, 27–28, 29t, 57t for feigned mental disorders, 25t–26t, 27 medical presentations and, 36 Anorexia nervosa, 83–84, 89. See also Eating disorders Antipsychotics, 102

638

Antisocial behavioral traits, 176, 177t, 199, 376 Antisocial personality, 55 Antisocial personality disorder (ASPD) children and adolescents and, 480–481 deception and, 84 detection of positive distortion and, 292 diagnosis of, 19 malingering and, 9, 43 overview, 91–92, 93 psychopathy and, 175 Anxiety alcohol-induced psychotic disorder and, 105 malingering and, 123 mild traumatic brain injury (mTBI) and, 131–132 Personality Assessment Inventory (PAI) and, 290 Anxiety disorders, 287–288 Arousal theory, 592–593 Assessment. See also Detectionv strategies; Reporting and testifying regarding assessment results; Screening; individual measures accurate assessment of response styles and, 22 biochemical methods of evaluating substance abuse and, 162–165 children and adolescents and, 479–480, 482–490, 484t, 486t, 488t deceptive impression management in personnel selection and, 532–533 detection strategies for feigning, 132–133, 571–577, 572t, 575t



Subject Index 639

documentation of response styles and, 587–589, 588t evaluating response-style measures, 579–580, 581t factitious disorders and, 216–217, 223–224 law enforcement personnel and, 565 malingered insanity and, 114–115 malingered psychosis and, 110–114, 111t malingering of PTSD and, 192–197, 193t, 194t, 195t, 199–202 measure selection and, 524–525 overview, 571, 589 psychopathy and, 181–183 random responding in the PAI, 283–284 of simulated adjustment, 577–579, 578t Assessment of Depression Inventory (ADI), 463, 464, 467, 467t Assimilation, 66. See also Cultural factors Attempted control theory, 593 Attentional functioning, 129–130, 338–343 Attention-deficit/hyperactivity disorder (ADHD) assessment procedures and, 485 detection strategies for feigning and, 573, 574 neuropsychological testing and, 336, 339 overview, 478 Attorneys, 514–519. See also Litigation factors Atypical Response (ATR) scale, 196 Atypical test pattern (ATP), 30t, 31, 333–334 Auditory hallucinations, 101–104, 102t, 107t. See also Hallucinations Auditory Verbal Learning Test (AVLT), 335, 345–346, 351 Autobiographical memory, 390. See also Memory functioning Back random responding, 284. See also Random responding Back-translation, 63–64. See also Test translations Balance Desirability (BD) scale, 296–297 Balanced approach, 519 Balanced Inventory of Desirable Responding (BIDR), 542–543, 558–559. See also Paulhus Deception Scale (PDS) Base rates. See also Prevalence of malingering Advanced Clinical Solutions (ACS) and, 323 deceptive impression management in personnel selection and, 532–533 Millon Adolescent Clinical Inventory (MACI) and, 485 mixed-group validation and, 604 neurocognitive malingering and, 47–48, 57, 333

Battery for Health Improvement–2 (BHI2), 37–38 Beck Depression Inventory (BDI), 24 Behavior, 107, 199–200, 212 Behavior Assessment System for Children—Third Edition (BASC3), 486t, 487, 491, 509, 510, 511 Behavioral monitoring, 409 Behaviors associated with malingering, 84–85 Bernard DFA, 341–343, 342t. See also Discriminant function analyses (DFAs) Betrayal trauma theory, 391–392 Beyond-reasonable-doubt standard, 4 Bias, 378–379 Big Five model, 375–377, 379–380, 480–481 Biochemical methods of evaluation, 153, 153t, 162–166, 586. See also Evaluation Biographical Case History scale (BCH), 369–370 Biphasic Alcohol Effects Scale, 152–153 Bipolar disorder, 103 Blatant extreme responding (BER), 545 Blatant symptoms, 435 Blended approach, 577–578 Blended strategy with affirmation of virtuous behavior and denial of personal faults, 32t Bogus-item technique, 533, 544 Borderline personality disorder, 91 Breathalyzer, 162, 163, 165 Brief assessment and screening measures. See also Assessment; individual measures clinical applications of, 466–467, 467t future directions, 467–468 overview, 449–450 Brief MAST (BMAST), 154. See also Michigan Alcoholism Screening Test (MAST) British WMT, 76. See also Word Memory Test (WMT) California Psychological Inventory (CPI), 553, 554–555 California Verbal Learning Test (CVLT and CVLT-II) cutoff scores and, 324t detection strategies for feigned traumatic brain injury, 134 feigned amnesia and, 324–325 mild traumatic brain injury (mTBI) and, 132t overview, 335 Cancer Needs Distress Inventory (CaNDI), 463 Candidate and Officer Personnel Survey (COPS), 563 Cannot Say Scale (CNS), 259 Careless responding, 283–284, 293–294 Cashel Discriminant Function (CDF) child custody evaluations and, 504 detection of positive distortion and, 291–292

detection strategies for simulated adjustment, 33 overview, 291 simulated adjustment and, 578t Catatonia, 107–108 Category Test, 132t, 135 Chained likelihood ratios, 53–55, 54t, 140 Change, stages of, 610 Cheating behavior, 176, 178 Child Behavior Checklist (CBCL), 476, 509 Child custody evaluations assessment of children in, 508–511 cutoff scores and, 506–507 explanatory model of motivations related to, 85t measures of parenting ability, 507–508 MMPI-2 validity scales and, 262 overview, 92, 497–498, 510–511 psychological testing in, 498–499 response styles and, 499–506, 501t, 506t Child protection, 225–226 Childhood. See also Child custody evaluations; Childhood sexual abuse (CSA) assessment procedures and, 482–490, 484t, 486t, 488t diagnoses associated with deception and, 86–87 factitious disorder imposed on another (FDIOA) and, 220 family factors and, 481 overview, 475–481, 477t, 479t, 482f, 491 perception and, 479–480 personality and, 480–481 threshold and clinical decision models, 490–491, 491t types of deception, 477–478, 477t Childhood sexual abuse (CSA). See also Recovered memories of childhood sexual abuse; Sexual abuse children and adolescents and, 479 clinical applications and, 396–397 factitious disorder and, 216 forensic context and, 397 overview, 387–388 research regarding, 390–396 Chinese SIRS-2, 70–71. See also Structured Interview of Reported Symptoms (SIRS and SIRS-2) Chronic school refusal, 478. See also School factors CIA Recognition Index, 134 Classification errors, 51–52 Cleckley traits, 176, 177t Clinical context, 23, 111–112, 111t Clinical decision models, 93–94, 202, 202t Clinical interviews. See also Assessment; Structured interviews; individual measures children and adolescents and, 482 combat-related PTSD and, 204

640 Clinical interviews (cont.) detecting malingered psychosis and, 110–111, 111t impression management and, 183 malingered insanity and, 114–115 malingering of PTSD and, 200–201 recovered memories and, 391 sex offenders and, 402–404, 411–413, 414 substance abuse and, 157–158 Clinical utility child custody evaluations and, 497 integrity testing and, 380 Miller Forensic Assessment of Symptoms Test (M-FAST), 74 Personality Assessment Inventory (PAI), 72–73 polygraph tests and, 369 response styles and, 583–585, 584t Schedule of Affective Disorders and Schizophrenia (SADS) and, 430–431 screening and, 466–467, 467t Structured Interview of Reported Symptoms (SIRS and SIRS-2), 70, 71, 439–443 Structured Inventory of Malingered Symptomatology (SIMS), 74 Clinician factors, 423–424, 480 Close approximation to genuine symptoms, 26t, 27 Coaching attorneys’ knowledge about malingering and, 518 detection strategies for feigning and, 572–573 evaluating response-style measures and, 579 Miller Forensic Assessment of Symptoms Test (M-FAST) and, 454 neurocognitive malingering and, 331–332 projective measures and, 309–310 screening and, 468 simulated adjustment and, 578t simulation design and, 596–597 Structured Inventory of Malingered Symptomatology (SIMS), 460 Cognitive Complaints (COG) scale, 270 Cognitive defensiveness, 33–34, 579. See also Defensiveness Cognitive distortions, 90–91, 406 Cognitive impairment. See also Feigned cognitive impairment; Malingered neurocognitive dysfunction (MND); Neurocognitive malingering detection strategies for feigning and, 574–577, 575t integrity testing and, 381 Personality Assessment Inventory (PAI) and, 290 simulated adjustment and, 579 Structured Interview of Reported Symptoms (SIRS and SIRS-2), 442–443 Cognitive processing, 129–130

Subject Index Cognitive theory, 593 Collateral reports. See also Informant report; Record reviews child custody evaluations and, 509 children and adolescents and, 481 detection strategies for feigned cognitive impairment, 575 diagnostic interviews and, 428 ethical standards and, 525 malingering of PTSD and, 200 neurocognitive malingering and, 45 posttraumatic stress disorder (PTSD) and, 203–204 psychopathy and, 182 structured interviews and, 426–427 Combat-related PTSD, 203–205, 267–269, 268t–269t. See also Posttraumatic stress disorder (PTSD) Combined research models, 605 Command hallucinations, 103–104. See also Hallucinations Comorbidity alcohol-induced psychotic disorder and, 105 detection strategies for feigned cognitive impairment, 28 integrity testing and, 376 medical presentations and, 243 neurocognitive malingering and, 45–46 posttraumatic stress disorder (PTSD) and, 190 simulation design and, 597 trauma and, 198 Comparison Question Technique (CQT), 364–368, 369. See also Polygraph tests Compelling inconsistencies, 45, 57t Competency to Stand Trial Assessment Instrument (CAI), 582t Competency to stand trial. See also Evaluation of Competency to Stand Trial—Revised (ECST-R); Forensic context; Psychosis malingered psychosis and, 113–114 Miller Forensic Assessment of Symptoms Test (M-FAST) and, 455 mutism and, 107–108 Structured Interview of Reported Symptoms (SIRS and SIRS-2), 441 Complete effort, 574. See also Incomplete effort Composite International Diagnostic Interview–3.0 (CIDI-3), 157 Comprehensive System. See Exner Comprehensive System (CS) Compulsive personality, 502 Computed tomography (CT), 124–125, 216, 250. See also Neuroimaging technologies Computerized administration, 468 Computerized Assessment of Response Bias (CARB), 490 Concealed information test, 363–364. See also Polygraph tests

Concussion, 124–126, 125t. See also Mild traumatic brain injury (mTBI); Postconcussion syndrome (PCS) Conduct disorder (CD), 85t, 86–87, 476, 478 Confessions, 93 Configural analysis approach, 502 Confirmatory factor analysis, 85, 436–437. See also Factor analysis Conners 3, 509–510, 511 Conners Rating Scale (CRS), 509 Conning behavior, 176 Conscientiousness, 375–377, 480, 537–538 Consistency across comparable items detection strategy, 29t, 30, 510 Construct drift, 57t Construct validity, 371, 374–377, 539–540. See also Validity Consultation, 518–519 Content equivalence, 63 Contradictory symptoms, 428 Control question test (CQT), 603 Convergent indicators, 153t Convergent validity, 437, 454, 586–587 Conversion disorder malingering and, 123 neurological factors and, 237–240 overview, 236–237, 240–241 posttraumatic stress disorder (PTSD) and, 190, 190t Conversion symptoms, 198 Coping styles, 392 Correction scale, 259 Correctional settings, 115, 116t, 463. See also Forensic context Countermeasures, 365 Covert video surveillance (CVS), 223–224. See also Observed data; Surveillance video CRAFFT, 154t, 155 Criminal Attribution Inventory (CRAI), 463 Criminal justice system, 361–362. See also Forensic context Criminal offenders. See also Forensic context; Reporting and testifying regarding assessment results; Sex offenders, deception in forensic interviews and, 432–434 malingered psychosis and, 112–116, 114f, 116t malingering of PTSD and, 192 psychopathy and, 176 response styles and, 5 Schedule of Affective Disorders and Schizophrenia (SADS) and, 428 Structured Inventory of Malingered Symptomatology (SIMS) and, 458 substance abuse and, 151 Criminological explanatory model, 10. See also Explanatory models Criterion group contamination, 55 Criterion-related validity, 371–374, 438–439. See also Validity



Subject Index 641 Cross-domain, logistically-derived embedded PVT (CLEP), 46–47. See also Performance validity testing (PVT) Cross-validation, 318, 320–321, 321t Cult brainwashing, 214 Cultural competency, 61, 67–68, 76–77 Cultural factors. See also Linguistic factors; Multicultural assessment; Test translations acculturation measures and, 68–69 interpretive norms and, 66–68 measures of feigned mental disorders and, 69–74, 70t Morel Emotional Numbing Test (MENT) and, 461 overview, 61, 76–77 research design and, 607–608 response styles and, 64–65 screening and, 468 Structured Inventory of Malingered Symptomatology (SIMS), 460 Culture of malingering, 526, 527 Custody evaluations. See Child custody evaluations Cutoff scores accurate assessment of response styles and, 22 Auditory Verbal Learning Test (AVLT) and, 345 child custody evaluations and, 506–507 imprecision of, 608–609 malingering of PTSD and, 197 Millon Clinical Multiaxial Inventory (MCMI-III and MCMI-IV) and, 293t, 294 neurocognitive malingering and, 332–333 neuropsychological testing and, 344t Personality Assessment Inventory (PAI), 282t, 287t screening and, 468 Test of Memory Malingering (TOMM) and, 320–321, 320t Victoria Symptom Validity Test (VSVT) and, 318 Wechsler Adult Intelligence Scale–IV (WAIS-IV), 337t

Daubert standards of admissibility, 246, 409, 580 Debasement, 295, 485 Deception. See also individual diagnoses child custody evaluations and, 92, 504–505, 506 children and adolescents and, 476–477, 477t detection strategies for defensiveness and medical presentations and, 37 diagnoses associated with, 86–92 evaluation of, 84–86, 85t false-memory syndrome and, 92–93 frequency of malingering and deception by psychopaths, 178–179 misconceptions regarding malingering and, 9

overview, 6, 83–84, 93–94, 592 psychopathy and, 175–176, 177t, 178–179, 183–184 skilled deception and psychopathic traits, 179–181 treatment outcomes and, 584–585 Deceptive impression management. See Impression management Deceptive ingratiation (DI), 534. See also Impression management Deese–Roediger–McDermott paradigm (DRM), 395 Defensiveness child custody evaluations and, 92, 507, 508 children and adolescents and, 478, 479, 479t, 481, 491 collateral data and, 426–427 descriptive statements and degrees of, 588t detection strategies for, 18–23, 24, 33–34, 37–38 evaluating response-style measures and, 581t forensic interviews and, 434 law enforcement personnel and, 559, 565 malingered insanity and, 116 medical presentations and, 37 MMPI-2 validity scales and, 260, 262 overview, 7, 10 paraphilias and sexual abuse and, 90–91 projective measures and, 306–308, 307t Schedule of Affective Disorders and Schizophrenia (SADS) and, 431–432 sex offenders and, 401, 414–415 simulated adjustment and, 31–32, 31–33, 32t, 577, 578, 578t simulation design and, 600 substance abuse and, 578 treatment outcomes and, 584–585, 609–610 unstandardized interviews and, 425 Defensiveness Index (DEF) child custody evaluations and, 504 detection of positive distortion and, 291–292, 292t detection strategies for simulated adjustment, 33 overview, 161, 291 simulated adjustment and, 578t Delusions hallucinations and, 101, 102t, 103 research on malingered psychosis, 106–107, 107t Dementia, 46 Denial children and adolescents and, 478 malingered insanity and, 116 patient characteristics and, 32t personal faults and, 32t research methodology and, 603 Schedule of Affective Disorders and Schizophrenia (SADS) and, 432

sex offenders and, 401, 403–404, 405, 406, 411–413 simulated adjustment and, 578–579 skilled deception and psychopathic traits, 179–180 substance abuse and, 152, 153, 159 treatment outcomes and, 609–610 Dependency on medical complaints, 35t, 36 Depression alcohol-induced psychotic disorder and, 105 Assessment of Depression Inventory (ADI), 464 auditory hallucinations and, 103 machismo and, 64–65 malingering and, 123 mild traumatic brain injury (mTBI) and, 129, 131–132 mutism and, 108 Personality Assessment Inventory (PAI) and, 290 structured interviews and, 422 trauma and, 198 Desirability child custody evaluations and, 502 Millon Adolescent Clinical Inventory (MACI) and, 485 Millon Clinical Multiaxial Inventory (MCMI-III and MCMI-IV) and, 295 Personality Inventory for DSM-5 (PID5) and, 296–297 Detachment effects, 65 Detailed Assessment of Posttraumatic Stress (DAPS), 196–197 Detection strategies. See also Assessment attorneys’ knowledge about malingering and, 517 children and adolescents and, 482–490, 484t, 486t, 488t clinical interview technique of detecting malingered psychosis and, 110–111, 111t conceptual issues, 18–23 cutoff scores and, 22 deceptive impression management in personnel selection and, 543–546 for defensiveness and medical presentations, 37–38 detection of medical feigning/ malingering and, 247–250, 248t detection strategies for feigned traumatic brain injury, 139–142, 141t diagnostic interviews and, 427–428 effect sizes and, 21 evaluating response-style measures and, 579 factitious disorders and, 226–228 for feigned cognitive impairment, 27–31, 29t, 30t, 137–138, 574–577, 575t for feigned mental disorders, 24–27, 25t–26t, 573–574 for feigned traumatic brain injury, 132–138

642 Detection strategies (cont.) for feigning, 571–577, 572t, 575t focused nature of, 21 malingered neurocognitive dysfunction (MND) and, 349–352, 351t–352t, 353t malingering and deception and, 84–86, 85t for malingering and medical presentations, 34–37, 35t medical presentations and, 243–244 MMPI-2 and MMPI-2-RF and, 258–259, 258t negative distortion and, 288 neurocognitive malingering and, 48–49, 50t, 57t, 332–334 positive distortion and, 290–292 random responding in the PAI, 283–284 research methodology and, 597–598, 610 sex offenders and, 404–411 for simulated adjustment, 31–33, 32t skilled deception and psychopathic traits, 179–181 specific clinical domains, 33–37, 35t for specific response styles, 23–38, 25t–26t, 29t, 30t, 32t, 35t unstandardized interviews and, 425–426 utility estimates and, 23 Developmental processes, 476–477, 477t, 481, 482f Deviant Responding (DR) scale, 179 Diagnosis. See also Diagnostic criteria; Differential diagnosis; individual diagnoses conversion disorder and, 237, 240 criteria for malingered neurocognitive deficits and, 123–124 factitious disorders and, 216–217, 223–224, 227–228 feigned amnesia and, 317–318 malingered neurocognitive dysfunction (MND) and, 606 medical presentations and, 244 mild traumatic brain injury (mTBI) and, 129–132, 132t neurocognitive malingering and, 330–331 postconcussion syndrome (PCS) and, 126–127 posttraumatic stress disorder (PTSD) and, 188–190, 201–202, 202t projective measures and, 306 Schedule of Affective Disorders and Schizophrenia (SADS) and, 430–431 Structured Interview of Reported Symptoms (SIRS and SIRS-2), 440 structured interviews and, 422, 444 Diagnostic and Statistical Manual of Mental Disorders (DSM), 10, 43, 57, 188–190, 213 Diagnostic and Statistical Manual of Mental Disorders (DSM-III), 388–389

Subject Index Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) attorneys’ knowledge about malingering, 515 neurocognitive malingering and, 330 posttraumatic stress disorder (PTSD) and, 188–189 substance abuse and, 157 Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR), 127, 330 Diagnostic and Statistical Manual of Mental Disorders (DSM-5). See also individual diagnoses attorneys’ knowledge about malingering, 514–515 children and adolescents and, 480, 481 diagnoses associated with deception, 86–92 evaluating malingering and deception and, 84 factitious disorder and, 217 malingered neurocognitive dysfunction (MND) and, 606 malingering and, 43, 99, 123 medical presentations and, 244–245 neurocognitive malingering and, 330–331 Personality Inventory for DSM-5 (PID5) and, 296–297 posttraumatic stress disorder (PTSD) and, 188–189 response styles and, 10 retrospective assessments, 527 trauma memories and, 389 Diagnostic criteria. See also Diagnosis; Diagnostic and Statistical Manual of Mental Disorders (DSM-5); individual diagnoses factitious disorder and, 213 malingered neurocognitive dysfunction (MND) and, 42 medical presentations and, 244 posttraumatic stress disorder (PTSD) and, 188–190, 189t Diagnostic Interview Schedule for Children-–V (DISC-IV), 157, 482 Diagnostic Interview Schedule for Children—Revised (DISC-R), 476 Diagnostic interviews, 157–158, 427– 428. See also Clinical interviews; Structured interviews Differential diagnosis. See also Diagnosis conversion disorder and, 237 medical presentations and, 244–245, 245t MMPI-2-RF validity scales and, 274–275 posttraumatic stress disorder (PTSD) and, 190–191 trauma and, 198–199 Differential prevalence design posttraumatic stress disorder (PTSD) and, 190–191 response styles and, 11, 12t, 13 simulated adjustment and, 577

Diffusion tensor imaging (DTI), 125. See also Neuroimaging technologies Digit Span Scaled Score, 324 Digit Span subtests. See also Reliable Digit Span (RDS and RDS-R) Advanced Clinical Solutions (ACS) and, 323 cutoff scores and, 324t detection strategies for feigned cognitive impairment, 31 detection strategies for feigned traumatic brain injury, 134–135 neurocognitive malingering and, 329 validity testing in neuropsychological assessment and, 55 Directed lie technique (DLT), 365 Disability, 61, 289, 331 Disacknowledgement, 152 Disacquiescent responding, 8. See also Response styles Disclosure Millon Adolescent Clinical Inventory (MACI) and, 485 Millon Clinical Multiaxial Inventory (MCMI-III and MCMI-IV) and, 295 sex offenders and, 410, 411–413 Discriminant function analyses (DFAs) Halstead–Reitan Battery (HRB) and, 335 neuropsychological testing and, 333 Wechsler Adult Intelligence Scale–IV (WAIS-IV) and, 336–338, 337t Wisconsin Card Sorting Test (WCST) and, 341–343, 342t Discriminant validity. See also Validity Miller Forensic Assessment of Symptoms Test (M-FAST) and, 454 Structured Interview of Reported Symptoms (SIRS and SIRS-2), 70, 437–439, 438t Disease delusions, 106. See also Delusions Dishonesty, 527 Dissimulation. See also Response styles clinical relevance of, 583–585, 584t criteria for malingered neurocognitive deficits and, 124 detection strategies for feigning and, 573 domains of, 8 evaluating response-style measures and, 579–580, 580, 581t MMPI-2 validity scales and, 260 motivations related to diagnoses and dissimulation, 85, 85t personnel selection and, 530 projective measures and, 302, 311 research design and, 607 treatability, 609–610 Dissimulation Scale (Ds), 259 Dissociation, 101, 108, 295 Dissociative amnesia. See Repression perspective Dissociative Experiences Scale (DES), 393



Subject Index 643 Dissociative identity disorder (DID), 214, 437, 440 Distinctive hallucinations, 105–106. See also Hallucinations Distortion, 152–153 Diverse populations, 61–62. See also Cultural factors; Test translations Documentation of response styles, 587–589, 588t. See also Reporting and testifying regarding assessment results Dot Counting Test (DCT), 576 Drinker Inventory of Consequences (DrInC), 160, 160t Drug Abuse Screening Test (DAST), 154t, 156, 165 Drug Use Screening Inventory (DUSI), 154t, 156 Drug-induced hallucinations, 105. See also Hallucinations Dutch SIMS, 73–74. See also Structured Inventory of Malingered Symptomatology (SIMS) Dutch WMT, 75–76. See also Word Memory Test (WMT) Eating disorders deception and, 83–84 explanatory model of motivations related to, 85t structured interviews and, 426 Educational factors, 198 Effect sizes detection strategies and, 21 negative distortion and, 287–288 neuropsychological testing and, 344t response style measure scores and, 281–282 Effort Score Analysis, 323 Effort testing, 47, 56 Ego strength, 557–558 Electronic medical records (EMRs), 228. See also Record reviews Embedded performance validity testing. See also Performance validity testing (PVT) feigned amnesia and, 324–325, 324t neurocognitive malingering and, 46, 57t validity testing in neuropsychological assessment and, 51 Embedded validity scales (EVSs) classification accuracy statistics and, 332–333 Finger Oscillation Test (FOT) and, 347–348 neurocognitive malingering and, 329–330 neuropsychological testing and, 334–349, 337t, 342t, 344t, 348t Embellishment, 607 Emic qualities, 66. See also Cultural factors Emotional labor, 541 Emotional stability, 375–377 Emotions, deceptions regarding, 477, 541

Empathy, 91–92 Empirical validation, 20 Employment Inventory, 370–371 Employment Productivity Index, 370 Employment screening tests, 362–363, 369–381. See also Personnel decisions; Preemployment evaluations; Workplace Endorsement of excessive virtue, 35t, 36–37, 239 Epidemiology factitious disorder imposed on another (FDIOA) and, 224 factitious disorders imposed on self (FDIOS) and, 217–220, 219t, 220t of feigned medical presentations, 245–247 Equal Employment Opportunities Commission (EEOC), 379–380 Equal weighting, 31 Erotomania delusions, 106. See also Delusions Erroneous Psychiatric Stereotype (EPS), 27 Erroneous stereotypes, 26t, 27 Establishing posterior probabilities, 140 Ethical factors, 5, 61–62, 519–525 “Ethical Principles of Psychologists and Code of Conduct” (APA), 520–522 Etic qualities, 66. See also Cultural factors Etiology, 220–221 Evaluation. See also Assessment; Child custody evaluations; Screening biochemical methods of evaluating substance abuse and, 162–165 deceptive impression management in personnel selection and, 532–533 of detection procedures, 334 sex offenders and, 402–404 substance abuse and, 153–166, 153t, 160t, 165–166 Evaluation of Competency to Stand Trial—Revised (ECST-R) Structured Interview of Reported Symptoms (SIRS and SIRS-2), 439–440 Structured Inventory of Malingered Symptomatology (SIMS), 460 Event-related potential (ERP), 363–364, 381 Exaggeration attorneys’ knowledge about malingering and, 518 clinical interviews and, 200 deceptive impression management in personnel selection and, 534 dissimulation and, 609 factitious disorder and, 212 malingering and, 123 Paulhus Deception Scale (PDS) and, 462 substance abuse and, 152, 153, 166 Excessive impairment, 27–28 Executive functions, 338–343 Exhibitionism, 214

Exner Comprehensive System (CS), 310 Expectancy from others, 480 Explanatory models addictive disorders and, 90 children and adolescents and, 481, 482f motivations related to diagnoses and dissimulation, 85, 85t overview, 9–10 Extensive image creation (EIC), 534. See also Impression management External criteria, 371–372. See also Validity External inconsistencies, 111, 199–200 External motivations, 45, 57t Externalizing disorders, 482 Externalizing proneness, 376–377, 380 Externalizing Spectrum Inventory (ESI), 377 Extraversion, 375–377, 537–538 Extreme Desirability (ED) scale, 296–297 Extreme response style, 65. See also Response styles Eye scanning, 162, 164–165 Eysenck Personality Inventory (EPI), 463 Fabricated and induced illness, 222. See also Factitious disorder imposed on another (FDIOA) Factitious disorder imposed on another (FDIOA). See also Factitious disorders (FD) assessment and diagnosis and, 223–224 childhood victims of, 220 epidemiology of, 224 management of, 224–226 Factitious disorders (FD). See also Factitious disorder imposed on another (FDIOA); Factitious disorders imposed on self (FDIOS) compared to malingering, 99, 123 evaluation and, 84 explanatory model of motivations related to, 85t medical presentations and, 245 posttraumatic stress disorder (PTSD) and, 190–191, 190t research on malingered psychosis, 101 Factitious disorders imposed on self (FDIOS). See also Factitious disorders (FD) assessment and diagnosis and, 216–217 epidemiology of, 217–220, 219t, 220t etiology, 220–221 factitious medical disorders, 213–214 factitious psychological disorders, 214–216 management of, 221–222 prognosis, 221 Factitious presentation, 6. See also Factitious disorders (FD) Factor analysis, 436–437. See also Confirmatory factor analysis

644 Failure to Maintain Set (FMS) score, 51–52 Fake Bad Scale (FBS). See also Symptom Validity Scale (SVS) case examples of, 250 detection strategies for feigned traumatic brain injury, 135, 136 malingering of PTSD and, 194 Fake Bad Scale (FBS-r), 270 Faking, 531, 533–535, 540–543 False imputation, 99, 190, 243–244 False memory perspective, 388, 395, 396–397. See also Memory functioning False positives feigned amnesia and, 318 integrity testing and, 378 projective measures and, 306 screening and, 281 False-memory syndrome (FMS), 85t, 92–93 False-positive rate (FPR), 51–52, 54–55 Falsification, 212 Family factors, 86–87, 89, 481 Familywise error rate (FWER), 52–53 FBS/SVS, 132t Feedback, 585–586 Feigned amnesia, 314–325, 319t, 320t, 321t, 322t, 323t, 324t, 325f. See also Amnesia; Memory functioning; Test of Memory Malingering (TOMM) Feigned cognitive impairment. See also Cognitive impairment; Malingered neurocognitive dysfunction (MND); Neurocognitive malingering case examples of, 250 children and adolescents and, 478, 488–490, 488t clinical indicators of malingered psychosis, 109–110 detection strategies for, 23–24, 27–31, 29t, 30t, 137–138, 574–577, 575t measures of for international populations, 74–76 medical presentations and, 247, 248–249 mild traumatic brain injury (mTBI) and, 131, 132t MMPI-2-RF validity scales and, 269–273, 271t, 272t–273t neuropsychological models of, 42–43, 57, 57t Personality Assessment Inventory (PAI) and, 290 Structured Interview of Reported Symptoms (SIRS and SIRS-2), 442–443 Feigned cognitive measures (FCMs), 329–330, 332–333. See also individual measures Feigned medical complaints/symptoms, 8. See also Medical presentations Feigned mental disorders case examples of, 250–251 detection strategies for, 23–27, 25t–26t

Subject Index measures of for international populations, 69–74, 70t Miller Forensic Assessment of Symptoms Test (M-FAST) and, 453–454 MMPI-2-RF validity scales and, 265–269, 266t, 268t–269t Feigned presentations. See Medical presentations Feigning. See also individual diagnoses case examples of, 250–251 child custody evaluations and, 503 children and adolescents and, 487–488 detection strategies for, 24–27, 25t–26t, 571–577, 572t, 575t posttraumatic stress disorder (PTSD) and, 191 psychopathy and, 183 skilled deception and psychopathic traits, 179 15-Item Test, 55, 137–138 Financial incentives, 191, 262. See also Motivations Finger Oscillation Test (FOT), 335, 347–349, 348t, 352t First-order false belief, 476–477 Fitness-for-duty evaluations, 563–565. See also Law enforcement personnel Five-factor model of personality (FFM), 375–377, 379–380, 480–481 Floor effect detection strategy cultural factors and, 74 for feigned cognitive impairment, 28, 29t, 576 Finger Oscillation Test (FOT) and, 347 medical presentations and, 249 Forced-choice testing (FCT) administering and interpreting, 316 cultural factors and, 75–76 feigned amnesia and, 314–316, 325–326 feigned cognitive impairment and, 28, 30t, 31, 334, 576 neurocognitive malingering and, 48–49 workplace deceptive impression management and, 546 Forensic assessment instruments (FAIs), 580, 582–583, 582t Forensic context. See also Criminal offenders; Law enforcement personnel; Litigation factors; Reporting and testifying regarding assessment results child custody evaluations and, 497 children and adolescents and, 479 clinical indicators of malingered psychosis, 109–110 defensiveness and denial and, 116 detection strategies for feigned traumatic brain injury, 132–138, 140–142, 141t ethical standards and, 519–525

evaluating malingering and deception and, 84 false-memory syndrome and, 93 feigned amnesia and, 314, 322 forensic interviews and, 432–434 M Test and, 451 malingered psychosis and, 112–116, 114f, 116t malingering of PTSD and, 191–192 Miller Forensic Assessment of Symptoms Test (M-FAST) and, 455 MMPI-2-RF validity scales and, 273–274 mutism and, 107–108 Paulhus Deception Scale (PDS) and, 463 Personality Assessment Inventory (PAI) and, 288 polygraph tests and, 361–362, 366–367 posttraumatic stress disorder (PTSD) and, 190–191 prevalence of malingering and, 123 projective measures and, 308–310, 311 psychopathy and, 176 response styles and, 4–5, 580, 582–583, 582t Schedule of Affective Disorders and Schizophrenia (SADS) and, 428 sex offenders and, 401–404 Structured Interview of Reported Symptoms (SIRS and SIRS-2), 444 Structured Inventory of Malingered Symptomatology (SIMS) and, 458 substance abuse and, 151, 152 trauma memories and, 397 Forensically relevant instruments (FRIs), 280, 580, 582–583, 582t Four-Item Rapid Alcohol Problems Screen (RAPS4), 154, 154t Fraudulent responding, 534 Full Scale IQ (FSIQ), 348–349, 489. See also WAIS-III Full Scale IQ (WAISIII FSIQ) Fully structured interviews, 422. See also Structured interviews Functional Assessment of Cancer Therapy—Gastric (FACT-G), 463 Functional disorders, 236–237 Functional somatic syndromes, 217–218 Gambling disorder, 90 Ganser syndrome, 100–101 Gas chromatography/mass spectrometry (GC-MS), 162 Gender, 453, 454, 499–500, 557 Genuine memory impairment profile (GMIP), 321–322 Genuine responding, 55–56, 282 Genuine retake, 607 Georgia Court Competency Test (GCCT), 432–433, 582t German SIMS, 460 Giveaway weakness, 238–239 Glasgow Coma Scale (GCS), 125, 126 Global Appraisal of Individual Needs— 5th edition (GAIN-5), 158



Subject Index 645 Global Appraisal of Individual Needs— Short Screener (GAIN-SS), 154t, 157, 165 Global Assessment of Functioning (GAF), 113 Global level of functioning, 335–338, 337t Good Impression Scale (Gi), 554–555 Grandiosity, 106, 178. See also Delusions Guarded responding, 260, 262, 307 Guilt, 404–405 Guilty knowledge test (GKT), 363–364, 586. See also Polygraph tests Gustatory hallucinations, 107t. See also Hallucinations

Hair analysis, 162, 164, 165, 586 Hallucinations, 101–106, 102t, 107t, 214 Halstead–Reitan Battery (HRB), 335 Hare facets, 176, 177t Health attitudes, 37–38 Health Problem Overstatement (HPO) scale, 36 Health-risk behaviors, 37 Historic Clinical Risk–20 (HCR-20), 181, 182, 582t Histrionic personality, 502 Hit rate, 332–333, 344t Hogan Personnel Selection Series, 370–371 Holden Psychological Screening Inventory (HPSI), 183 Holtzman Inkblot Technique, 307 Homicidal ideation, 214 Honesty, 3 Hoover’s sign, 238–239 Hopemont Capacity Assessment Interview (HCAI), 434 Hormonal functioning, 213–214 Hospitalization, 215, 322, 403, 454–455 Hybrid responding, 8, 587. See also Response styles Hypochondriasis, 245 Hysteria, 215, 236–237. See also Conversion disorder Iceberg phenomenon, 9 Identity disturbances, 91 Image protection (IP), 534. See also Impression management Immunoassay (IA), 162 Impairment, 288–289 Implicit Association Test (IAT), 381 Imposed etic tests, 66. See also Cultural factors Impression management. See also Negative Impression Management (NIM); Positive Impression Management (PIM); Selfpresentation child custody evaluations and, 92, 500–502, 501t, 505, 506t deceptions within the workplace and, 4 law enforcement personnel and, 558–559, 558t, 565

Miller Forensic Assessment of Symptoms Test (M-FAST) and, 454 Paulhus Deception Scale (PDS) and, 462 personnel selection and, 530–546, 536t–537t psychopathy and, 178, 182–183 types and forms of, 533–535 Improbable symptoms. See also Symptoms clinical interview technique of detecting malingered psychosis and, 110 detection strategies for feigning and, 572 medical presentations and, 35, 35t response styles and, 24, 25t Structured Interview of Reported Symptoms (SIRS and SIRS-2) and, 435 Incarceration, 402–404, 428. See also Criminal offenders; Forensic context Incentive, 57, 598–599, 600 Incompetence to stand trial. See Competency to stand trial; Criminal offenders; Forensic context Incomplete effort. See Suboptimal effort Inconsistencies clinical interview technique of detecting malingered psychosis and, 111 conversion disorder and, 238 detection strategies for feigned traumatic brain injury, 132–133 malingering of PTSD and, 199–200 Millon Clinical Multiaxial Inventory (MCMI-III and MCMI-IV) and, 294 Personality Assessment Inventory (PAI) and, 283–284 structured interviews and, 423–426 Inconsistency trap, 425 Incremental validity, 140 Indeterminate classification, 57t Indiscriminant symptom endorsement conversion disorder and, 239 detection strategies and, 25t, 27, 573 medical presentations and, 35t, 36, 37 Schedule of Affective Disorders and Schizophrenia (SADS) and, 429–430 Informant report, 153t, 509. See also Collateral reports Infrequency scales child custody evaluations and, 505, 506t overview, 259 Personality Assessment Inventory (PAI) and, 283 Infrequent Responses scale (F-r), 263 Infrequent Somatic Complaints scale, 36 Infrequent Somatic Responses (fs) scale, 259, 263 Inhibitory control, 477

Insanity defense. See also Forensic context; Not guilty by reason of insanity (NGRI) plea malingering of PTSD and, 192 overview, 112–116, 114f, 116t Personality Assessment Inventory (PAI) and, 288 Insight, 83–84, 424 Integration, 66. See also Cultural factors Integrity testing clinical applications of, 380 controversial aspects of, 378–380 deceptive impression management in personnel selection and, 537–538 future directions, 380–381 law enforcement personnel and, 554, 554t Intellectual disability Finger Oscillation Test (FOT) and, 347–348 neuropsychological testing and, 338–339 Recognition Memory Test (RMT) and, 346 Structured Interview of Reported Symptoms (SIRS and SIRS-2), 441–442 Intelligence testing, 68 Intensity of medical complaints, 35t, 36, 37, 239 Intentionality, 44–45 Interchangeability, 31 Internal inconsistencies, 111, 199–200 Internal motivations, 45, 57t Internalizing disorders, 482 International Statistical Classification of Diseases (ICD-10), 126, 244–245, 245t International Test Commission (ITC), 62 Internet, 227, 331–332 Interpersonal functioning, 175–176, 177t, 183 Interpersonal Measure of Psychopathy, 91 Interpretation of results. See also Reporting and testifying regarding assessment results acculturation measures and, 68–69 cultural factors and, 66–68 documentation of response styles and, 587–589, 588t ethical standards and, 521–522 evaluating response-style measures and, 579 law enforcement personnel and, 565 reporting and testifying and, 525–526 Interventions, 221–222, 228 Interview Faking Behavior (IFB) scale, 534, 607 Invariant response style, 4 Invention type of malingering, 123. See also Malingering Inventory of Drug Use Consequences (InDUC), 160, 160t

646 Inventory of Legal Knowledge (2010), 521–522 Investigations, 227. See also Detection strategies; Forensic context Inwald Personality Inventory (IPI), 370, 553, 556, 561–562, 562t IQ, 198, 323, 381, 479 Irrelevant reporting, 8, 585–586. See also Response styles Item response theory (IRT), 381 Jealousy delusions, 106. See also Delusions Job performance evaluations, 530, 540–543, 546, 559–561. See also Employment screening tests; Workplace Kiddie Schedule for Affective Disorders and Schizophrenia—Present/ Lifetime versions (K-SADS-PL), 157 Known-group comparisons (KGCs) combined research models and, 605 detection strategies for feigning and, 571–572, 572t Miller Forensic Assessment of Symptoms Test (M-FAST) and, 454 neurocognitive malingering and, 332, 334 research methodology and, 602–603, 602t response styles and, 11, 12t screening and, 468 simulated adjustment and, 577 Structured Interview of Reported Symptoms (SIRS and SIRS-2), 438–439, 443 Structured Inventory of Malingered Symptomatology (SIMS) and, 458 Language factors, 460, 468. See also Cultural factors; Linguistic factors; Test translations Latent class modeling (LCM), 55 Law enforcement personnel. See also Forensic context; Personnel decisions fitness-for-duty evaluations, 563–565 personality assessment and, 554–555, 554t postoffer psychological assessment and, 555–563, 556t, 558t Lawyers. See Attorneys Learning disabilities, 323, 336, 339 Learning principles, 28, 576. See also Violation of learning principles Letter Memory Test (LMT) case examples of, 250, 251 detection strategies for feigned cognitive impairment, 138 medical presentations and, 249 Miller Forensic Assessment of Symptoms Test (M-FAST) and, 455 Structured Interview of Reported Symptoms (SIRS and SIRS-2), 442 Level of Service Inventory (LSI), 181

Subject Index Levenson Self-Report Psychopathy Scale (LSRP), 582t Lie scale, 259 Lies, 533 Life Assessment Questionnaire (LAQ), 34–35 Lifestyle functioning, 177t Likelihood ratios (LRs), 53–55, 54t, 140 Linguistic equivalence, 63 Linguistic factors, 61, 62, 65. See also Cultural factors; Multicultural assessment; Test translations Liquid chromatography/mass spectrometry (LC-MS), 162 Litigation factors. See also Forensic context; Reporting and testifying regarding assessment results attorneys’ knowledge about malingering, 514–519 medical presentations and, 243–244 MMPI-2 validity scales and, 262 posttraumatic stress disorder (PTSD) and, 190–192, 203–204 trauma and, 198–199 traumatic brain injury (TBI) and, 142 Logical Memory Recognition subtest, 344 Love delusions, 106. See also Delusions Lying. See also Deception; Untruthfulness children and adolescents and, 476–477, 477t, 481 paraphilias and sexual abuse and, 90–91 psychopathy and, 178–179 M Test, 450–453, 452t, 467–468, 467t MacAndrew Alcoholism scale (MAC and MAC-R), 159, 560 MacArthur Competence Assessment Tool—Criminal Adjudication (MacCAT-CA), 433, 434, 582t Machismo, 64–65 Magnetic resonance imaging (MRI), 124–125, 216, 250. See also Neuroimaging technologies Magnitude of error, 28, 29t, 333–334, 575–576 Malingered neurocognitive dysfunction (MND). See also Feigned cognitive impairment; Neurocognitive malingering; Neuropsychological models detection strategies for, 576–577 Finger Oscillation Test (FOT) and, 347–349, 348t medical presentations and, 247 neuropsychological testing and, 340, 341–342, 344–345 Recognition Memory Test (RMT) and, 346–347 research methodology and, 605–606 Slick criteria for, 43–45, 46t structured interviews and, 423–424 Malingered pain-related disability (MPRD), 130–131, 262, 270

Malingering. See also individual diagnoses case examples of, 250–251 children and adolescents and, 477–478, 477t, 480–481 collateral data and, 427 descriptive statements and degrees of, 588t detection strategies for, 18–23, 24, 34–37, 35t evaluation of, 84–86, 85t, 581t feigned cognitive impairment and, 27–31, 29t, 30t frequency of malingering and deception by psychopaths, 176, 178 misconceptions regarding, 8–9 posttraumatic stress disorder (PTSD) and, 190–191, 190t projective measures and, 302–306, 304t psychopathy and, 183 research methodology and, 603 research on malingered psychosis, 100–108, 102t, 107t Schedule of Affective Disorders and Schizophrenia (SADS) and, 431 skilled deception and psychopathic traits, 179 treatment outcomes and, 584–585 unstandardized interviews and, 425 validity testing in neuropsychological assessment and, 56 Malingering Detection Scale (MDS), 463, 465 Malingering Index (MAL) child custody evaluations and, 503 children and adolescents and, 485 cognitive impairment and, 290 detection strategies and, 136, 571–572 disability and, 289 negative distortion and, 287–288 posttraumatic stress disorder (PTSD) and, 196, 289–290 Maltreatment, 479 Manipulation checks, 599–600, 601t Manipulative behavior, 176, 481 Marginality, 66, 68–69. See also Cultural factors Marlowe–Crowne Social Desirability Scale (MCSDS), 182–183 Massachusetts Youth Screening Inventory–2 (MAYSI-2), 154t, 155–156 Mediating diagnoses, 237. See also Diagnosis Medical child abuse, 222, 225–226. See also Factitious disorder imposed on another (FDIOA) Medical conditions, 37, 105–106, 244 Medical examinations, 247, 248t Medical falsification, 213–214. See also Factitious disorders (FD) Medical presentations case examples of, 250–251 classifications, 243–244 detection strategies for, 34–37, 35t, 37–38, 247–250, 248t



Subject Index 647 differential diagnosis and, 244–245, 245t epidemiology of, 245–247 factitious disorders imposed on self (FDIOS) and, 217–218 MMPI-2 validity scales and, 262 MMPI-2-RF validity scales and, 273–275 trauma and, 198 Medical records, 216, 228. See also Record reviews Medical Symptom Validity Test (MSVT) children and adolescents and, 489–490 detection strategies for feigned cognitive impairment, 138 medical presentations and, 249 Medications, 102, 215, 221–222 Memory Complaints Inventory (MCI), 321–322 Memory functioning. See also Amnesia; Feigned amnesia; Recovered memories of childhood sexual abuse; Repression perspective children and adolescents and, 477 clinical indicators of malingered psychosis, 109–110 detection strategies for feigned cognitive impairment, 575–576 malingering of PTSD and, 199 mild traumatic brain injury (mTBI) and, 130 neurocognitive malingering and, 49 posttraumatic amnesia (PTA), 125, 126, 130 research regarding, 390–396 substance abuse and, 151 trauma and, 388–390 Memory Wars, 388, 393 Mental disorders. See also Psychological factors; Serious mental disorders (SMDs); individual syndromes child custody evaluations and, 502 deceptions within the workplace and, 3–4 detection strategies for feigning and, 573–574 diagnoses associated with deception, 86–92 factitious disorder and, 214–216 malingering as, 98–99 neuropsychological testing and, 336, 338, 340 Personality Assessment Inventory (PAI) and, 289–290 Schedule of Affective Disorders and Schizophrenia (SADS), 428–431, 429t, 430t Structured Interview of Reported Symptoms (SIRS and SIRS-2), 440–441 Mental health professionals. See also Reporting and testifying regarding assessment results children and adolescents and, 478–479 cultural competency and, 61, 67–68, 76–77

ethical standards and, 519–525 forensic context and, 5 report writing and, 527 Mental health system, 61, 475, 478–479 Michigan Alcoholism Screening Test (MAST), 154, 154t Mild Brain Injury Atypical Scale (mBIAS), 463, 465–466 Mild traumatic brain injury (mTBI). See also Neurocognitive malingering; Traumatic brain injury (TBI) case examples of, 138–139, 139t, 250 clinical applications of malingering detection and, 139–142, 141t detection strategies for feigned traumatic brain injury, 132–138 differential diagnosis and, 129–132, 132t Mild Brain Injury Atypical Scale (mBIAS) and, 465–466 neuropsychological testing and, 335, 336 research on, 128–129 Test of Memory Malingering (TOMM) and, 320 Victoria Symptom Validity Test (VSVT) and, 319 Miller Forensic Assessment of Symptoms Test (M-FAST) cultural factors and, 74 detection strategies for feigning and, 572t ethical standards and, 521, 522–523 feigned psychopathology and, 265–267 future directions, 468 medical presentations and, 249 MMPI-2-RF validity scales and, 266t research methodology and, 602 Structured Interview of Reported Symptoms (SIRS and SIRS-2), 437, 439, 441 Millon Adolescent Clinical Inventory (MACI), 159–160, 485, 486t, 491 Millon Clinical Multiaxial Inventory (MCMI-III and MCMI-IV) child custody evaluations and, 92, 498, 499–500, 502 detection strategies for feigning and, 572t evaluating response-style measures and, 581t substance abuse and, 158, 159–160 Minimization child custody evaluations and, 92 cognitive impairment and, 579 risk assessment and, 181–182 sex offenders and, 403–404, 411–413 Minnesota Multiphasic Personality Inventory (MMPI) integrity testing and, 379–380 law enforcement personnel and, 555–558, 556t, 559–561, 564 medical presentations and, 249 response styles and, 18–19 sex offenders and, 405–406 validity scales and, 259

Minnesota Multiphasic Personality Inventory–2 (MMPI-2). See also Minnesota Multiphasic Personality Inventory–2 Restructured Form (MMPI-2-RF) case examples of, 251 child custody evaluations and, 92, 498, 499–502, 501t children and adolescents and, 483 cutoff scores and, 608 detection strategies for feigning and, 572, 572t, 573 dissimulation and, 302, 609 evaluating response-style measures and, 581t feigned mental disorders and, 27 forensic interviews and, 434 law enforcement personnel and, 553, 555–558, 556t, 560, 565 malingered insanity and, 115 malingering of PTSD and, 192–195, 193t, 194t, 197 medical presentations and, 36, 249 Miller Forensic Assessment of Symptoms Test (M-FAST) and, 454 mixed-group validation and, 604 overview, 21, 257–259, 258t, 276, 280–281, 297, 303–304, 586, 593 psychopathy and, 176, 179 psychosis and, 100 response styles and, 8, 24 Schedule of Affective Disorders and Schizophrenia (SADS) and, 431 simulated adjustment and, 32, 578t Structured Interview of Reported Symptoms (SIRS and SIRS-2), 439 Structured Inventory of Malingered Symptomatology (SIMS) and, 455 substance abuse and, 158–159 traumatic brain injury (TBI) and, 135–136, 140 validity and, 437 validity scales and, 259–262, 261f Minnesota Multiphasic Personality Inventory–2 Restructured Form (MMPI-2-RF). See also Minnesota Multiphasic Personality Inventory–2 (MMPI-2) case examples of, 250, 251 child custody evaluations and, 498, 499 evaluating response-style measures and, 581t factitious disorders and, 227 feigned psychopathology and, 265–269, 266t, 268t–269t feigned somatic symptoms and, 273–275 law enforcement personnel and, 553, 555–558, 556t, 560–561, 564 malingering of PTSD and, 194–195, 194t, 197 manipulation checks and, 600 medical presentations and, 249 misconceptions regarding malingering and, 9 neurocognitive malingering and, 269–273, 271t, 272t–273t

648 Minnesota Multiphasic Personality Inventory–2 Restructured Form (MMPI-2-RF) (cont.) substance abuse and, 158–159 underreporting and, 275 validity scales and, 262–275, 266t, 268t–269t, 271t, 272t–273t Minnesota Multiphasic Personality Inventory—Adolescent Version (MMPI-A), 483–484, 484t, 491, 491t Minority populations, 66–68. See also Cultural factors; Linguistic factors; Test translations Miranda Rights Comprehension Instruments (MRCI), 582t Misclassifications, 378 Missouri Assessment of Genetics Interview for Children (MAGIC), 482 Mixed-group validation, 604–605. See also Validity MMPI for Adolescents (MMPI-A), 158– 159. See also Minnesota Multiphasic Personality Inventory–2 (MMPI-2) Modified Somatic Perception Questionnaire (MSPQ), 36, 131 Mood disorders, 250–251, 287–288, 426 Morbidity, 221 Morel Emotional Numbing Test (MENT), 461–462, 467, 467t Motivational interviewing, 412 Motivations addictive disorders and, 90 children and adolescents and, 478–479, 479t dissimulation and, 585 factitious disorders and, 215, 227 law enforcement personnel and, 564 to malinger, 100, 100t malingering of PTSD and, 191 neurocognitive malingering and, 45, 57t, 124 paraphilias and sexual abuse and, 90–91 research methodology and, 598–599 simulation design and, 596 skilled deception and psychopathic traits, 179 Motor functioning, 347–349, 348t Multicultural assessment, 61. See also Cultural factors; Linguistic factors; Test translations Multimethod assessment, 381, 523–524, 574–575 Multiphasic Sex Inventory (MSI and MSI-II), 406–407 Multiple test use, 52–53, 57t, 523–524 Multiscale Feigning Index (MFI), 282–283, 286, 289 Multiscale inventories, 135–137, 280–282, 297. See also individual measures Multistep approach, 140–141, 141t Munchausen by proxy. See Factitious disorders (FD); Factitious disorders imposed on self (FDIOS) Mutism, 107–108

Subject Index Narcissism, 91, 502 National Computer Systems/Pearson (NCS) Extended Score, 500–501 Negative Bias (NB) scale, 196 Negative distortion detection of, 286–287, 295 Millon Clinical Multiaxial Inventory (MCMI-III and MCMI-IV) and, 293t, 294–295, 294t, 295t Personality Assessment Inventory (PAI) and, 285–290, 286t, 287t Negative Distortion Scale (NDS), 282–283, 285, 289–290 Negative Impression Management (NIM) child custody evaluations and, 503, 509–510 children and adolescents and, 485 cognitive impairment and, 290 cutoff scores and, 608–609 detection strategies for feigning and, 136–137, 573 disability and, 289 law enforcement personnel and, 563 malingering of PTSD and, 195–196 Miller Forensic Assessment of Symptoms Test (M-FAST) and, 454 posttraumatic stress disorder (PTSD) and, 289–290 Structured Interview of Reported Symptoms (SIRS and SIRS-2), 437 validity testing in neuropsychological assessment and, 56 Negative predictive power (NPP) children and adolescents and, 487 effect size and utility estimates and, 281 malingered neurocognitive dysfunction (MND) and, 351t–352t, 352 Malingering Detection Scale (MDS) and, 465 medical presentations and, 249 Personality Assessment Inventory (PAI), 287t Recognition Memory Test (RMT) and, 346 response distortion and, 281 screening and, 281 Structured Interview of Reported Symptoms (SIRS and SIRS-2), 440–441 NEO Personality Inventory—Revised (NEO-PI-R), 581t Neurobehavioral Symptom Inventory (NSI), 465–466 Neurocognitive malingering. See also Feigned cognitive impairment; Malingered neurocognitive dysfunction (MND); Traumatic brain injury (TBI) case example of feigned TBI, 138–139, 139t children and adolescents and, 488–490, 488t clinical applications of malingering detection and, 139–142, 141t

detection strategies for, 48–49, 50t, 132–138 diagnostic criteria for, 123–124 medical presentations and, 247 mild traumatic brain injury (mTBI) and, 130–131 MMPI-2-RF validity scales and, 269–273, 271t, 272t–273t Morel Emotional Numbing Test (MENT) and, 461 performance validity testing (PVT) and, 45–48 Personality Assessment Inventory (PAI) and, 290 symptom validity testing (SVT) and, 45 validity testing in neuropsychological assessment and, 51–56, 54t Neuroimaging technologies case examples of, 250 factitious disorder and, 216 neurocognitive malingering and, 49 traumatic brain injury (TBI) and, 124–125 Neurological factors, 105–106, 198, 213–214, 237–240 Neuropsychological assessment. See also Psychological testing case example of feigned TBI, 138–139, 139t children and adolescents and, 483–487, 484t, 486t clinical applications of malingering detection and, 139–142, 141t conversion disorder and, 238–239 deceptive impression management in personnel selection and, 537–538 detection strategies for feigned cognitive impairment, 576–577 embedded validity scales (EVSs), 334–349, 337t, 342t, 344t, 348t feigned cognitive impairment and, 137–138, 333–334 MMPI-2-RF validity scales and, 269–273, 271t, 272t–273t neurocognitive malingering and, 47 overview, 349–352, 351t–352t, 353t projective measures and, 308–309 traumatic brain injury (TBI) and, 123, 133–137 validity testing in, 51–56, 54t Neuropsychological models, 42–43, 57, 57t. See also Malingered neurocognitive dysfunction (MND) Neuropsychological Symptom Inventory (NSI), 30, 576 Neuroticism, 375–377, 537–538 Non-content-based invalid responding, 264–265 Nondisclosure, 6, 390 Nonspecific terms, 5–6. See also Response styles Nonstrategy methods of detection, 30–31, 30t Nonverbal Medical Symptom Validity Test (NVMSVT), 321–322



Subject Index 649 Nonverbal skills, 477 Not guilty by reason of insanity (NGRI) plea, 192. See also Insanity defense Observed data, 165–166, 180–181, 223–224 Obvious symptoms, 26t, 27, 239 Olfactory hallucinations, 106, 107t. See also Hallucinations One-way translations, 62–63. See also Test translations Open-ended inquiries, 424 Openness to experience, 375–377, 480, 537–538 Oppositional defiant disorder (ODD), 86–87, 476 Optimal effort, 574. See also Suboptimal effort Overall correct classification (OCC), 23 Overall Test of Battery Mean (OTBM), 133 Overclaiming technique, 544–545 Overly specified symptoms, 26t, 27 Overreporting culturally specific response styles and, 65 MMPI-2-RF validity scales and, 267, 268t–269t, 270, 274 overview, 7 substance abuse and, 151 Overspecification, 5 Overstated pathology, 6–7, 10, 36. See also Response styles Overt integrity tests, 370. See also Integrity testing PAI Public Safety Selection Report, 562–563. See also Personality Assessment Inventory (PAI) Pain, 130–132, 198–199 Pain Disability Index (PDI), 131 Pain disorders case examples of, 251 differential diagnosis and, 244–245 factitious disorder and, 214 medical presentations and, 246–247 MMPI-2 validity scales and, 262 Paraphilias, 85t, 90–91, 214 Parental alienation syndrome (PAS), 92 Parental psychopathology, 481 Parent–Child Relationship Inventory (PCRI), 508 Parenting. See also Child custody evaluations child custody evaluations and, 497–498 children and adolescents and, 480 measures of parenting ability, 507–508 substance abuse and, 151 Parenting Stress Index—4th Edition (PSI-4), 498, 507–508 Parole, 403–404 Partial criterion design (PCD) detection strategies for feigning and, 134, 571–572, 572t Miller Forensic Assessment of Symptoms Test (M-FAST) and, 455

research methodology and, 602–603, 602t response styles and, 12t, 13 Structured Interview of Reported Symptoms (SIRS and SIRS-2), 438–439 Partial malingering, 99, 190, 243–244 Pathogenic explanatory model, 10. See also Explanatory models Patient characteristics, 218, 220, 224 Pattern analysis, 576 Paulhus Deception Scale (PDS). See also Balanced Inventory of Desirable Responding (BIDR) ethical standards and, 521 future directions, 467 law enforcement personnel and, 558–559 PCL Youth Version (PCL:YV), 434. See also Psychopathy Checklist— Revised (PCL-R) Pediatric condition falsification, 222. See also Factitious disorder imposed on another (FDIOA) Pedophilia, 214 Peduncular hallucinosis, 106. See also Hallucinations Penile plethysmograph (PPG), 407–408, 409, 414, 586 Perceived success, 600 Performance curve detection strategy detection strategies for feigned cognitive impairment, 28, 29t, 575–576 feigned cognitive impairment and, 333–334 Finger Oscillation Test (FOT) and, 347 Performance evaluations, 45 Performance validity testing (PVT) case examples of, 250 children and adolescents and, 488 malingered neurocognitive dysfunction (MND) and, 42 medical presentations and, 248–249 mild traumatic brain injury (mTBI) and, 131 MMPI-2-RF validity scales and, 263 neurocognitive malingering and, 45–48, 49, 270 Performance-based testing, 308–309 Persecution delusions, 106. See also Delusions Perseveration type of malingering, 123. See also Malingering Persisting postconcussion syndrome (PPCS), 126. See also Postconcussion syndrome (PCS); Traumatic brain injury (TBI) Personal Experience Screening Questionnaire (PESQ), 154t, 155 Personal Experiences Inventory (PEI), 160–161, 160t, 578 Personal Problems Questionnaire (PPQ), 297

Personality child custody evaluations and, 502–505 children and adolescents and, 480–481 law enforcement personnel and, 554–555, 554t Personality Assessment Inventory (PAI) accurate assessment of response styles and, 22 child custody evaluations and, 498, 499, 500, 502–505, 506–507 children and adolescents and, 484 combined research models and, 605 cultural factors and, 71–73 cutoff scores and, 608–609 detection strategies for feigning and, 136–137, 572, 572t, 573 detection strategies for simulated adjustment, 33 detection strategies for specific response styles and, 24 evaluating response-style measures and, 581t factitious disorders and, 88, 227 law enforcement personnel and, 562–563 malingering of PTSD and, 195–196, 195t, 197 medical presentations and, 249 Miller Forensic Assessment of Symptoms Test (M-FAST) and, 454, 455 negative distortion and, 285–290, 286t, 287t positive distortion and, 290–292, 292t psychopathy and, 100, 179 random responding and, 283–284 response styles and, 583 simulated adjustment and, 577–578, 578t Structured Interview of Reported Symptoms (SIRS and SIRS-2), 439 treatment outcomes and, 609 validity testing in neuropsychological assessment and, 56 Personality Assessment Inventory— Adolescent (PAI-A), 484–485, 486t, 491t Personality disorders (PDs) deception and, 84 detection strategies for specific response styles and, 24 diagnostic interviews and, 427–428 evaluating response-style measures and, 581t structured interviews and, 426–427 trauma and, 198 Personality factors, 8–9, 227, 308–309, 375–377, 380, 480–481, 537–538 Personality Inventory for DSM-5 (PID5), 296–297, 577 Personality Inventory for Youth (PIY), 485–487, 486t Personality-oriented tests, 370–371. See also Integrity testing

650 Personnel decisions. See also Employment screening tests; Law enforcement personnel; Workplace deceptive impression management and, 530–535 detecting and preventing deceptive impression management and, 543–546 models of faking or deceptive impression management in, 535–538, 536t–537t outcomes of deceptive impression management and, 538–543 Personnel Decisions, Inc. (PDI) Employment Inventory, 370 Personnel Reaction Bank (PRB), 370, 377 Personnel Selection Inventory, 370, 374 Pharmacological interventions, 221–222. See also Medications Phase II Profile, 370 Phencyclidine (PCP) use, 108 Physical abuse, 479 Physiological factors, 201–202, 407–411 Police Candidate Interpretive Report (PCIR), 561 Police officers. See Law enforcement personnel Polygraph tests alternatives to, 368–369 clinical applications of, 369 Comparison Question Technique (CQT), 364–368 Guilty knowledge test (GKT), 363–364 sex offenders and, 407, 409–411, 414 skilled deception and psychopathic traits, 179–180, 183 Portland Digit Recognition Test (PDRT), 131, 316 Positive distortion, 290–292, 292t, 293t, 296 Positive Impression Management (PIM). See also Impression management child custody evaluations and, 503–504, 506–507 children and adolescents and, 485 detection of positive distortion and, 291–292, 292t law enforcement personnel and, 563 Miller Forensic Assessment of Symptoms Test (M-FAST) and, 454 simulated adjustment and, 578t Positive predictive power (PPP) Auditory Verbal Learning Test (AVLT) and, 345 children and adolescents and, 487–488 detection strategies for feigned traumatic brain injury, 134 effect size and utility estimates and, 281 malingered neurocognitive dysfunction (MND) and, 351t–352t, 352 Malingering Detection Scale (MDS) and, 465

Subject Index medical presentations and, 249 Miller Forensic Assessment of Symptoms Test (M-FAST) and, 454, 455 neuropsychological testing and, 340–341 overview, 23 Personality Assessment Inventory (PAI), 287t Recognition Memory Test (RMT) and, 346 response distortion and, 281 screening and, 281 Structured Interview of Reported Symptoms (SIRS and SIRS-2), 440–441, 442 validity testing in neuropsychological assessment and, 51–52 Postconcussion syndrome (PCS). See also Concussion; Traumatic brain injury (TBI) detection strategies for feigned traumatic brain injury, 140, 142 differential diagnosis and, 129–132, 132t validity testing in neuropsychological assessment and, 55–56 Posterior probabilities, 45, 57t Posttest probabilities, 31, 576 Posttrauma Response Set Measure (PRSM), 197 Posttraumatic amnesia (PTA), 125, 126. See also Memory functioning; Traumatic brain injury (TBI) Posttraumatic stress disorder (PTSD) assessment of malingered PTSD, 199–202 combat veterans and, 203–205 diagnosis and, 129–132, 132t, 202, 202t factitious disorder and, 215–216 malingering and, 123, 190–198, 190t, 193t, 194t, 195t medical presentations and, 247 memory functioning and, 388, 392 Mild Brain Injury Atypical Scale (mBIAS) and, 465–466 mild traumatic brain injury (mTBI) and, 129–130, 131–132 Miller Forensic Assessment of Symptoms Test (M-FAST) and, 455 MMPI-2-RF validity scales and, 267–269, 268t–269t Morel Emotional Numbing Test (MENT) and, 461–462 Personality Assessment Inventory (PAI) and, 287, 289–290 postconcussion syndrome (PCS) and, 128 projective measures and, 306 specific-disorder feigning (SDfeigning) and, 608 Structured Interview of Reported Symptoms (SIRS and SIRS-2), 440 Structured Inventory of Malingered Symptomatology (SIMS) and, 460 trauma memories and, 388–390

unstandardized interviews and, 425–426 validity testing in neuropsychological assessment and, 56 Predictive power values, 334 Predictive validity, 540. See also Validity Preemployment evaluations. See also Employment screening tests; Workplace integrity testing and, 369–381 law enforcement personnel and, 559 MMPI-2 validity scales and, 262 Preparation, excessive, 526 Presentation styles, 479, 479t. See also Motivations Prevalence of malingering factitious disorder imposed on another (FDIOA) and, 224 factitious disorders imposed on self (FDIOS) and, 217–220, 219t, 220t neurocognitive malingering and, 47–48 Prevention efforts children and adolescents and, 479 deceptive impression management in personnel selection and, 543–546 workplace deceptive impression management and, 545–546 Primary gain, 45 Principal components analyses (PCA), 436 Privacy, 379. See also Confidentiality Probability theory, 515–516 Probable cognitive feigning, 350 Probation, 403–404 Processing Speed Index (PSI), 131 Projection, 301 Projective measures controversies and developments in, 308–310 defensiveness and, 306–308, 307t detecting malingering on, 302–306, 304t interpretation and, 305–306 new research on, 310–311 Protocol validity, 258. See also Validity Prototypical analysis, 10 Provider-based errors, 127–128 Pseudo malingering, 99 Pseudodementia, 100–101 Psychogenic amnesia, 389 Psychological assessment, 22. See also Assessment Psychological factors. See also Mental disorders; Serious mental disorders (SMDs) factitious disorder and, 214–216 medical presentations and, 247 sex offenders and, 405–407 trauma and, 198 traumatic brain injury (TBI) and, 142 Psychological Inventory of Criminal Thinking Styles (PICTS), 182, 463, 466, 582t, 583 Psychological Screening Inventory (PSI), 27



Subject Index 651 Psychological testing. See also Assessment; Neuropsychological assessment; individual measures attorneys’ knowledge about malingering and, 515–516 child custody evaluations and, 497, 498–499 children and adolescents and, 483–487, 484t, 486t, 488–490, 488t law enforcement personnel and, 553, 555–563, 556t, 558t, 562t, 565 malingering of PTSD and, 192–197, 193t, 194t, 195t structured interviews and, 422 Psychopathic Personality Inventory (PPI and PPI-R) impression management and, 182–183 skilled deception and, 179, 180 Psychopathology child custody evaluations and, 92 Millon Clinical Multiaxial Inventory (MCMI-III and MCMI-IV) and, 295 MMPI-2-RF validity scales and, 265–269, 266t, 268t–269t projective measures and, 302–306, 304t Psychopathy children and adolescents and, 480–481 decision in the definition of, 175–176, 177t early conceptualizations of, 174–175 explanatory model of motivations related to, 85t frequency of malingering and deception by psychopaths, 176, 178–179 impression management and, 182–183 MMPI-2-RF validity scales and, 265–269, 266t, 268t–269t risk assessment and, 181–183 skilled deception and psychopathic traits, 179–181 Psychopathy Checklist (PCL), 174, 182–183 Psychopathy Checklist: Youth Version (PCL:YV), 86–87, 434, 480, 481 Psychopathy Checklist—Revised (PCL-R) impression management and, 182–183 malingered insanity and, 115 skilled deception and, 180 Psychopathy Checklist: Screening Version (PCL:SV), 180, 181 Psychophysiological assessment, 201–202. See also Assessment Psychosis clinical indicators of malingered psychosis, 108–110, 109t, 111t clinical interview technique of detecting malingering and, 110–111, 111t defensiveness and denial and, 116 factitious disorder and, 215 malingered insanity and, 114–116, 114f, 116t

motivations to malinger, 100, 100t research on malingered psychosis, 100–108, 102t, 107t skilled deception and, 179–181 trauma and, 198–199 Psychosocial factors, 123, 227 PTSD Checklist—Military (PCL-M), 465–466 PTSD Symptom Scale—Interview Version, 391 Pure malingering, 99, 190 Qualitative inconsistencies, 45 Quantitative compelling inconsistencies, 45 Quasi-rare symptoms, 24, 25t Questioning, 402–404, 411–413, 414 Race-specific norms, 67–68, 453 Random responding. See also Response styles detection of, 283–284, 294 Millon Clinical Multiaxial Inventory (MCMI-III and MCMI-IV) and, 293–294 overview, 8 Personality Assessment Inventory (PAI) and, 283–284 Rank ordering, 539 Rare symptoms detection strategies for feigning and, 572 medical presentations and, 34–35, 35t response styles and, 24, 25t Structured Interview of Reported Symptoms (SIRS and SIRS-2) and, 435 Rarely Missed Index (RMI), 344 Rationalizations, 90–91 Raven’s Standard Progressive Matrices (RPM), 490 Reactive attachment disorder (RAD), 85t, 87 Reactive responding, 534 Reality monitoring, 395 Receiver operating characteristics (ROC) analysis child custody evaluations and, 507 research methodology and, 606–607 Recognition Memory Test (RMT), 335, 346–347, 352, 352t Record reviews. See also Collateral reports factitious disorder and, 216, 228 posttraumatic stress disorder (PTSD) and, 203–204 psychopathy and, 181 Recovered memories of childhood sexual abuse, 387–388, 388–390, 390–396. See also Childhood sexual abuse (CSA); Memory functioning; Repression perspective; Sexual abuse Registration of sex offenders, 404. See also Sex offenders, deception in

Reid Report, 370, 374 Relapse prevention approach, 403–404, 409 Relevant–irrelevant test (RIT), 362 Reliability Comparison Question Technique (CQT) and, 365–366 detection strategies for feigned cognitive impairment, 577 integrity testing and, 371 malingered neurocognitive dysfunction (MND) and, 606 Miller Forensic Assessment of Symptoms Test (M-FAST), 74 Millon Adolescent Clinical Inventory (MACI) and, 485 Personality Assessment Inventory (PAI), 72–73 personnel selection and, 539 Structured Interview of Reported Symptoms (SIRS and SIRS-2) and, 70, 70t, 71, 435–436, 436t, 443 Structured Inventory of Malingered Symptomatology (SIMS) and, 74, 455 test translations and, 62 Reliability Scale of the Prospective Employee Potential Inventory, 370–371 Reliable Digit Span (RDS and RDS-R). See also Digit Span subtests detection strategies for feigned traumatic brain injury, 134–135 mild traumatic brain injury (mTBI) and, 132t neurocognitive malingering and, 46, 329 validity testing in neuropsychological assessment and, 53–56 Religion delusions, 106. See also Delusions Report writing, 527. See also Reporting and testifying regarding assessment results Reported versus observed symptoms conversion disorder and, 239 detection strategies for feigning and, 26t, 27, 573 medical presentations and, 35t neurocognitive malingering and, 57t Structured Interview of Reported Symptoms (SIRS and SIRS-2) and, 435 Reporting and testifying regarding assessment results. See also Criminal offenders; Forensic context; Mental health professionals attorneys’ knowledge about malingering, 514–519 descriptive statements and degrees of, 587–589, 588t ethical standards and, 519–525 Reports from others, 575. See also Collateral reports; Informant report Repressed memories, 93. See also Memory functioning

652 Repression perspective. See also Memory functioning; Recovered memories of childhood sexual abuse clinical applications and, 396–397 compared to psychogenic or organic amnesia, 389–390 overview, 387–388 research regarding, 390–396 Research design links between theory and practice, 592–593 overview, 592, 593–607, 595f, 601t, 602t, 607–610 response styles and, 10–13, 12t Response bias forced-choice testing (FCT) and, 315–316 Millon Clinical Multiaxial Inventory (MCMI-III and MCMI-IV) and, 293–294, 293t Personality Assessment Inventory (PAI) and, 282–283, 282t postconcussion syndrome (PCS) and, 127–128 Response Bias Scale (RBS) case examples of, 250 detection strategies for feigned traumatic brain injury, 136 neurocognitive malingering and, 49, 270 Response distortion effect size and utility estimates and, 281 Personal Problems Questionnaire (PPQ) and, 297 Personality Inventory for DSM-5 (PID5) and, 296 personnel selection and, 531 Response elaboration technique (RET), 545 Response styles. See also Dissimulation accurate assessment of, 22 child custody evaluations and, 92, 499–506, 501t, 506t children and adolescents and, 480 clinical applications of, 9–13, 12t, 583–585, 584t, 586–589, 588t clinical interviews and, 423–426, 427–432, 429t, 430t, 432t culturally specific response styles and, 64–65, 72 defensiveness and, 18–23 detection strategies for specific response styles, 23–38, 25t–26t, 29t, 30t, 32t, 35t effect size and utility estimates and, 281–282 evaluating response-style measures, 579–580, 581t forensic context and, 432–434, 580, 582–583, 582t future directions, 13–15 motivational basis of, 9–10 Personality Assessment Inventory (PAI) and, 282–292, 282t, 286t, 287t, 292t research design and, 10–13, 12t, 592 specific clinical domains, 33–37, 35t

Subject Index substance abuse and, 151–152 validity testing in neuropsychological assessment and, 56 Response to treatment, 216 Response-style stereotypes, 598 Restructured Clinical (RC) scales, 262 Retrieval strategies, 412–413 Retrospective assessments, 526–527 Rey 15-Item Recognition Test, 55, 325t, 490 Rey 15-Item Test, 55, 137–138 Rey Word Recognition Test, 325, 325t Risk assessment measures, 181–184, 582–583. See also Assessment RMT Faces and Words subtests, 347, 352, 352t Rogers Discriminant Function (RDF) children and adolescents and, 485 cognitive impairment and, 290 detection strategies for feigned traumatic brain injury, 136 disability and, 289 psychopathy and, 176 Role assumption, 8. See also Response styles Rorschach measure controversies and developments in, 308–310 detecting malingering and, 303–305, 304t, 306–307 evaluating response-style measures and, 581t Rorschach Performance Assessment System (R-PAS), 304, 310 Rule-in fallacy, 47 Rule-out fallacy, 47 Rule-Out/Rule-In scales, 450, 451 Rutgers Alcohol Problem Index (RAPI), 154–155, 154t Scale of Ethnic Experience (SEE), 68–69 Schedule of Affective Disorders and Schizophrenia for School-Age Children (K-SADS), 482 Schedule of Affective Disorders and Schizophrenia (SADS) detection strategies for feigning and, 572t evaluating response-style measures and, 581t malingered insanity and, 114–115 Schedules for Clinical Assessment of Neuropsychiatry (SCAN), 425 Schemas, 90–91 Schizophrenia auditory hallucinations and, 101–104, 102t clinical indicators of malingered psychosis, 107–108, 109–110 compared to alcohol-induced psychotic disorder, 105 factitious disorder and, 215 research on malingered psychosis, 100–101 Schedule of Affective Disorders and Schizophrenia (SADS) and, 428

Screening. See also Assessment; individual measures biochemical methods of evaluating substance abuse and, 162–165 clinical applications of, 466–467, 467t effect size and utility estimates and, 281 evaluating malingering and deception and, 84–86, 85t factitious disorders and, 223, 227 future directions, 467–468 law enforcement personnel and, 552–553 malingered insanity and, 115 sex offenders and, 414 Structured Interview of Reported Symptoms (SIRS and SIRS-2), 442–443 substance abuse and, 154–158, 154t Screen-outs, 23 Seashore Rhythm Test (SRT), 335, 342t, 343, 351 Secondary gain, 7, 45, 85–86, 237–238 Secondary malingering, 44–45 Second-order false belief, 476–477 Selection outcomes, 538–540 Selectivity of symptoms, 435 Self-Appraisal Questionnaire (SAQ), 181–182 Self-deception, 462, 607–608, 609–610 Self-Deceptive Enhancement Scale, 10 Self-deceptive enhancements (SDE). See also Impression management child custody evaluations and, 500–502, 501t law enforcement personnel and, 558–559, 558t, 565 Self-deceptive positivity (SDP), 92 Self-disclosure, 3–4, 6, 424 Self-preparation, 309–310, 596–597 Self-presentation, 31–33, 32t, 246, 533–535. See also Impression management Self-report. See also individual measures forensic context and, 582 integrity testing and, 371 medical presentations and, 249 MMPI-2-RF validity scales and, 273 psychopathy and, 182–184 sex offenders and, 406–407, 410, 414–415 substance abuse and, 153, 153t Self-Report Psychopathy Scale–Fourth edition (SRP-4), 582, 582t Self-unfavorable reporting. See Overreporting Semistructured interviews, 422. See also Structured interviews Sensitivity brief measures and, 450 Malingering Detection Scale (MDS) and, 465 Millon Clinical Multiaxial Inventory (MCMI-III and MCMI-IV) and, 294–295, 294t MMPI-2-RF validity scales and, 275

 neurocognitive malingering and, 332–333, 334 neuropsychological testing and, 344t Personality Assessment Inventory (PAI), 286t, 287t response distortion and, 281 Schedule of Affective Disorders and Schizophrenia (SADS) and, 429–430, 431 Structured Inventory of Malingered Symptomatology (SIMS) and, 458 Separation, 66. See also Cultural factors Serious mental disorders (SMDs). See also Mental disorders collateral data and, 426 diagnostic interviews and, 427–428 evaluating response-style measures and, 581t Set shifting, 111 Setting for administration of screening or assessments, 467 Severity indexing, 133, 430 Severity of symptoms, 435 Sex offenders, deception in. See also Childhood sexual abuse (CSA); Criminal offenders detection strategies for, 404–411 history of sex offending laws and, 401–402 increasing disclosure and, 411–413 interviews and, 402–404 research methodology and, 603 simulated adjustment and, 578–579 Sexual abuse. See also Childhood sexual abuse (CSA); Recovered memories of childhood sexual abuse children and adolescents and, 479 factitious disorders and, 216, 223 Sexual offending behavior, 86–87 Shell shock, 237 Short Index of Problems (SIP), 154t, 155 Short-MAST (SMAST), 154. See also Michigan Alcoholism Screening Test (MAST) Signaling theory, 535–536 Significantly below chance performance (SBCP) detection strategy conversion disorder and, 239 for feigned cognitive impairment, 28, 29t, 30, 576 Simulated adjustment. See also Response styles assessment of, 577–579, 578t detection strategies for, 7, 10, 31–33, 32t Simulation design combined research models and, 605 M Test and, 450–451 response styles and, 11, 12t, 13 simulated adjustment and, 577 Structured Interview of Reported Symptoms (SIRS and SIRS-2), 437–438 Structured Inventory of Malingered Symptomatology (SIMS) and, 458 substance abuse and, 153t

Subject Index 653 Single-point cutoff scores. See also Cutoff scores accurate assessment of response styles and, 22 imprecision of, 608–609 screening and, 468 SIRS-2 Rare Symptoms (RS) scale, 24 Sixteen Personality Factor Questionnaire (16PF), 499, 505–506, 506t, 507 Slick criteria combined research models and, 605 neurocognitive malingering and, 44–45, 46t, 124, 330–331, 349–350 Trail Making Test A and B (TMT) and, 341 traumatic brain injury (TBI) and, 140–141 Slight image creation (SIC), 534. See also Impression management Social desirability child custody evaluations and, 503–504, 508 detection strategies for simulated adjustment, 31–33, 32t job performance and, 541–542 Paulhus Deception Scale (PDS) and, 462–463 sex offenders and, 414–415 simulation design and, 600 Social Desirability—Psychopathy (SDP) index, 582 Socially desirable responding (SDR), 531, 544 Societal rules/norms, 175 Somatic delusions, 106. See also Delusions Somatic syndromes, 217–218, 273–275 Somatization disorder, 244 Source monitoring errors, 102–103 Spanish M-FAST, 74. See also Miller Forensic Assessment of Symptoms Test (M-FAST) Spanish PAI, 71–73. See also Personality Assessment Inventory (PAI) Spanish SIMS, 460 Spanish SIRS-2, 69–70, 70t. See also Structured Interview of Reported Symptoms (SIRS and SIRS-2) Specific response style (SRS), 20. See also Response styles Specific-disorder feigning (SD-feigning), 608 Specificity brief measures and, 450 Malingering Detection Scale (MDS) and, 465 Millon Clinical Multiaxial Inventory (MCMI-III and MCMI-IV) and, 294–295, 294t neurocognitive malingering and, 332–333, 334 neuropsychological testing and, 344t Personality Assessment Inventory (PAI), 286t, 287t response distortion and, 281 Schedule of Affective Disorders and Schizophrenia (SADS) and, 429–430, 431

Splitting of vibration sense, 238–239 Spurious patterns of psychological adjustment, 32t Spurious patterns of psychopathology, 24, 25t, 572–573 Standardized Assessment of Miranda Abilities (SAMA), 582t Standardized methods, 20, 432–434. See also Structured interviews Standards for Reporting of Diagnostic Accuracy (STARD), 317–318 Stanton Survey, 370, 374 Static response style, 8–9. See also Response styles Statistical chance, 516 Stepwise approach, 262 Stigma, 216, 240–241 Stockholm syndrome, 214 Structured Assessment of Violence Risk in Youth (SAVRY), 181 Structured Clinical Interview for DSM Disorders (SCID), 422 Structured Clinical Interview for DSMIV Axis I Disorders, 391 Structured Clinical Interview for DSM-5 (SCID-5), 157 Structured Interview for DSM-IV Personality (SIDP), 426–427 Structured Interview of Reported Symptoms (SIRS and SIRS-2) Assessment of Depression Inventory (ADI) and, 464 children and adolescents and, 483, 487–488 clinical applications of malingering detection and, 439–443 cultural factors and, 69–71, 70t detection strategies for feigning and, 572, 572t ethical standards and, 521–522 evaluating response-style measures and, 581t factitious disorder and, 87, 88 factor-analytic studies, 436–437 future directions, 468 malingered insanity and, 115 medical presentations and, 249–250 Miller Forensic Assessment of Symptoms Test (M-FAST) and, 454, 455 MMPI-2 validity scales and, 262 MMPI-2-RF validity scales and, 265–267, 266t psychopathy and, 176, 179 psychosis and, 100 reliability for, 435–436, 436t, 443 research methodology and, 602 response styles and, 24 retrospective assessments, 526–527 Rogers Discriminant Function (RDF) and, 289 Schedule of Affective Disorders and Schizophrenia (SADS) and, 428 strengths and limitations of, 443–444 Structured Inventory of Malingered Symptomatology (SIMS) and, 455

654 Structured Interview of Reported Symptoms (SIRS and SIRS-2) (cont.) substance abuse and, 90 validity and, 55, 437–439, 438t Structured interviews. See also Assessment; Clinical interviews; individual measures children and adolescents and, 482–483 data sources and, 423–427 forensic interviews and, 432–434 response styles and, 427–432, 429t, 430t, 432t substance abuse and, 157–158 Structured Inventory of Malingered Symptomatology (SIMS) attorneys’ knowledge about malingering and, 517 children and adolescents and, 486t, 487 cultural factors and, 73–74 detection strategies for specific response styles and, 24 future directions, 468 neurocognitive malingering and, 49 psychosis and, 100 Structured Interview of Reported Symptoms (SIRS and SIRS-2), 439 Structured professional judgment (SCJ) guides, 181 SUB scale, 159 Submaximal effort. See Suboptimal effort Suboptimal effort, 6, 574 Substance abuse biochemical methods of evaluating, 153, 153t, 162–165 child custody evaluations and, 504 clinical applications of evaluation and, 153–162, 153t, 154t, 160t, 165–166 defensiveness and, 578 explanatory model of motivations related to, 85t factitious disorder and, 214, 215 hallucinations and, 105 impression management and, 4 mutism and, 108 Paulhus Deception Scale (PDS) and, 463 Personality Assessment Inventory (PAI) and, 284 research methodology and, 603 Substance Abuse Proclivity scale (SAP), 159 Substance Abuse Subtle Screening Inventory (SASSI, SASSI-2, SASSI3, and SASSI-4) simulated adjustment and, 578 substance abuse and, 90, 166 Subtle Attributes scale (SAT), 161 Subtle symptoms, 435 Suicidal ideation, 214, 284 Supplementary Scale Index (SS), 440 Surveillance video, 45, 223–224

Subject Index Symptom Checklist 90-Revised (SCL90-R), 36 Symptom combinations detection strategies for feigning and, 24, 25t, 572–573 medical presentations and, 35, 35t Structured Interview of Reported Symptoms (SIRS and SIRS-2) and, 435 Symptom Combinations (SC) scale, 49 Symptom frequency, 29t, 30 Symptom severity, 26t, 27, 573 Symptom Validity Scale (SVS), 49, 259, 260. See also Fake Bad Scale (FBS) Symptom Validity Scale (SVS-r), 263 Symptom validity testing (SVT) attorneys’ knowledge about malingering and, 517 children and adolescents and, 488–490 conversion disorder and, 238–240 detection strategies for feigned traumatic brain injury, 135–136, 136 malingered neurocognitive dysfunction (MND) and, 42 medical presentations and, 248–249 mild traumatic brain injury (mTBI) and, 131 Miller Forensic Assessment of Symptoms Test (M-FAST) and, 455 neurocognitive malingering and, 45, 48, 49 Structured Interview of Reported Symptoms (SIRS and SIRS-2), 442 Symptoms. See also Improbable symptoms clinical interview technique of detecting malingered psychosis and, 110–111 conversion disorder and, 239 factitious disorder and, 212 malingering of PTSD and, 200 mild traumatic brain injury (mTBI), 125–126 Schedule of Affective Disorders and Schizophrenia (SADS) and, 428–430, 429t, 430t Symptoms Combination (SC) scale, 24, 88 Syndrome analysis, 133 Tactile hallucinations, 106. See also Hallucinations Taint hypothesis, 4 Tandem mass spectrometry (TMS), 162 Teen ASI (T-ASI), 158 Test of Cognitive Abilities (TOCA), 28, 597–598 Test of Memory Malingering (TOMM) attorneys’ knowledge about malingering and, 516 case examples of, 250, 251 children and adolescents and, 489 cultural factors and, 75

feigned cognitive impairment and, 28, 137–138, 576 medical presentations and, 249 Miller Forensic Assessment of Symptoms Test (M-FAST) and, 455 neurocognitive malingering and, 48, 329 overview, 319–321, 320t, 321t, 329, 340, 491t Structured Interview of Reported Symptoms (SIRS and SIRS-2), 441, 442 traumatic brain injury (TBI) and, 131, 132t, 134, 140 Test Translation and Adaptation Guidelines (ITC, 1992), 62 Test translations. See also Linguistic factors interpretive norms and, 66–68 methods of, 62–64 Miller Forensic Assessment of Symptoms Test (M-FAST), 74 Personality Assessment Inventory (PAI), 71–73 Structured Interview of Reported Symptoms (SIRS and SIRS-2), 69–71, 70t Structured Inventory of Malingered Symptomatology (SIMS), 73–74, 460 Word Memory Test (WMT), 75–76 Test Validation Summary (TVS), 410 Testifying regarding assessment results. See Reporting and testifying regarding assessment results Texas Christian University Drug Screen (TCUDS), 154t, 155, 463 Thematic Apperception Test (TAT), 301, 304, 307 Therapy, 221–222, 584–585. See also Treatment Third-party information. See Collateral reports Three question approach, 574–575 Threshold decision models, 93–94 Timeline Followback (TLFB), 162 Trail Making Test A and B (TMT), 335, 341, 342t, 351, 352t Trait disinhibition, 380 Transference type of malingering, 123. See also Malingering Translation by committee, 63. See also Test translations Translations of tests. See Test translations Transtheoretical model (TTM), 610 Trauma factitious disorder and, 215–216 memory functioning and, 388–390 research regarding memory and, 390–396 Structured Interview of Reported Symptoms (SIRS and SIRS-2), 442 Trauma Symptom Inventory (TSI), 196 Trauma Symptom Inventory–2 (TSI-2), 196

 Trauma-related disorders, 198–199. See also Posttraumatic stress disorder (PTSD); Traumatic brain injury (TBI) Traumatic amnesia. See Repression perspective Traumatic brain injury (TBI). See also Mild traumatic brain injury (mTBI); Neurocognitive malingering base rates for malingered mild TBI, 47–48 case examples of, 138–139, 139t, 250 clinical applications of malingering detection and, 139–142, 141t detection strategies for feigned traumatic brain injury, 132–138 differential diagnosis and, 129–132, 132t medical presentations and, 247 Mild Brain Injury Atypical Scale (mBIAS) and, 465–466 Millon Clinical Multiaxial Inventory (MCMI-III and MCMI-IV) and, 295 MMPI-2 validity scales and, 262 neurocognitive malingering and, 45–46, 47, 49, 331 neuropsychological testing and, 334–335, 336, 338, 340–341, 342–343, 345 Personality Assessment Inventory (PAI) and, 290 Recognition Memory Test (RMT) and, 347 research on, 128–129 Test of Memory Malingering (TOMM) and, 320 validity testing in neuropsychological assessment and, 56 Victoria Symptom Validity Test (VSVT) and, 319 Traumatic dissociative amnesia. See Repression perspective Treatment dissimulation and, 584–585, 609–610 factitious disorders and, 221–222, 228 obtaining a history of, 153, 153t sex offenders and, 403–404 trauma memories and, 396–397 Triarchic model of psychopathy, 380 True positive rate (TPR), 51–52 True Response Inconsistency scale (TRIN), 260 True Response Inconsistency scale (TRIN-r), 263, 265 Truthfulness, 176. See also Deception 21-Item Test, 132t, 137–138 Underreporting culturally specific response styles and, 65 MMPI-2-RF validity scales and, 263, 275 substance abuse and, 151 Undetected or underestimated physical illness, 244

Subject Index 655 Universal applicability, 31 Unlikely presentation detection strategies feigned cognitive impairment and, 27–28, 29t, 333–334 for feigned mental disorders, 24, 25t neurocognitive malingering and, 57t Unreliability, 5–6 Unstandardized interviews, 423–426. See also Structured interviews Untruthfulness, 176, 178–179. See also Deception; Lying Urinalysis, 162, 163–164, 165, 586 Utility estimates Auditory Verbal Learning Test (AVLT) and, 345 detection strategies and, 21, 23, 139–140 response style measure scores and, 281–282 Wechsler Adult Intelligence Scale–IV (WAIS-IV), 337t Valence–instrumentality–expectancy theory of motivation, 535 Validity Comparison Question Technique (CQT) and, 366 feigned amnesia and, 325, 325t feigned cognitive impairment and, 577 integrity testing and, 371–377 Miller Forensic Assessment of Symptoms Test (M-FAST) and, 74, 454 Millon Clinical Multiaxial Inventory (MCMI-III and MCMI-IV) and, 294 mixed-group validation, 604–605 MMPI and, 258 neurocognitive malingering and, 332 Personality Assessment Inventory (PAI), 72–73, 503 personnel selection and, 539–540 psychopathy and, 183 Structured Interview of Reported Symptoms (SIRS and SIRS-2), 70, 71 Structured Inventory of Malingered Symptomatology (SIMS) and, 74, 458 substance abuse and, 153 test translations and, 62 traumatic brain injury (TBI) and, 140 Validity Indicator Profile (VIP) feigned cognitive impairment and, 28, 137–138, 576 neuropsychological testing and, 55 traumatic brain injury (TBI) and, 134, 140 Validity scales culturally specific response styles and, 64–65 law enforcement personnel and, 564 MMPI, MMPI-2, and MMPI-2-RF and, 259–275, 261f, 266t, 268t–269t, 271t, 272t–273t

Personality Assessment Inventory (PAI), 285 Personality Inventory for DSM-5 (PID5) and, 296–297 Validity testing, 51–56, 54t Variable Response Inconsistency scale (VRIN), 259–260 Variable Response Inconsistency scale (VRIN-r), 263, 265, 267 Verbal behaviors, 180–181 Verbal Comprehension Index (VCI), 441 Verbal skills, 477 Verifiable test items, 546 Victoria Symptom Validity Test (VSVT) case examples of, 250, 251 detection strategies for feigned cognitive impairment, 137–138 detection strategies for feigned traumatic brain injury, 135 feigned amnesia and, 316–319, 319t medical presentations and, 249 Miller Forensic Assessment of Symptoms Test (M-FAST) and, 455 neurocognitive malingering and, 329 Structured Interview of Reported Symptoms (SIRS and SIRS-2), 442 Video surveillance. See Observed data; Surveillance video Viewing time, 408–409, 414 Violation of learning principles, 28, 29t, 333–334, 576. See also Learning principles Violence Risk Assessment Guide (VRAG), 181 Visual hallucinations, 104–105, 107t. See also Hallucinations Vocational assessments, 583 Vulnerability to faking, 378 WAIS-III Full Scale IQ (WAIS-III FSIQ), 336, 338–339, 345–346, 346. See also Full Scale IQ (FSIQ) WAIS-IV Full-Scale IQ (FSIQ), 22 WAIS-IV Processing Speed Index, 33–34 Warrington Recognition Test for Words, 325, 325t Wechsler Adult Intelligence Scale (WAIS-R), 351 Wechsler Adult Intelligence Scale–III (WAIS-III), 131, 336–337, 351 Wechsler Adult Intelligence Scale–IV (WAIS-IV) accurate assessment of response styles and, 22 cultural factors and, 68 detection strategies for feigned cognitive impairment, 31 feigned amnesia and, 323 neurocognitive malingering and, 46, 49, 50t, 57t Wechsler Adult Intelligence Scale— Revised (WAIS-R), 455

656 Wechsler Intelligence Scale for Children— Third Edition (WISC-III), 489 Wechsler Memory Scale–III (WMS-III), 343–345, 344t, 351 Wechsler Memory Scale–IV (WMS-IV) cultural factors and, 68 feigned amnesia and, 323 neurocognitive malingering and, 49, 50t, 57t Wechsler Memory Scale—Revised (WMS-R), 343–345, 344t Weighted Desirability (WD) scale, 296–297 Well-defined cutoff scores, 22, 197, 468. See also Cutoff scores

Subject Index Wiggins’s Social Desirability scale (Wsd), 33, 578t Wisconsin Card Sorting Test (WCST), 335, 341–343, 342t, 351, 352t WMS-III Logical Memory Delayed Recognition Subtest, 344 Word Memory Test (WMT) children and adolescents and, 490 cultural factors and, 75–76 feigned cognitive impairment and, 28, 576 medical presentations and, 249 neurocognitive malingering and, 49 traumatic brain injury (TBI) and, 131, 134

Worker’s compensation, 191 Working Memory Index (WMI), 131 Working memory skills, 477. See also Memory functioning Workplace, 3–4, 362–363, 369–381. See also Employment screening tests; Job performance evaluations; Law enforcement personnel; Personnel decisions Youden index, 318 Youth Psychopathic Traits Inventory (YPI), 178 Zoophilia, 214