THE VULNERABILITY OF SELF-REPORT MEASURES OF PSYCHOPATHY TO
POSITIVE IMPRESSION MANAGEMENT: A SIMULATION
STUDY WIT H INMATES
Katherine R. Kelsey, M.A.
Dissertation Prepared for the Degree of
DOCTOR OF PHILOSOPHY
UNIVERSITY OF NORTH TEXAS
August 2014
APPROVED:
Richard Rogers, Major Professor Jennifer L. Callahan, Committee Member Amy R. Murrell, Committee Member Vicki Campbell, Chair of the Department of Psychology Mark Wardell, Dean of the Toulouse Graduate School Kelsey, Katherine R. The Vulnerability of Self-Report Measures of Psychopathy to
Positive Impression Management: A Simulation Study with Inmates. Doctor of Philosophy
(Clinical Psychology), August 2014, 138 pp., 32 tables, reference list, 117 titles.
Psychopaths have long been characterized as having a remarkable disregard for the truth,
to the extent that deceit is often regarded as a defining characteristic of the syndrome. Scholars
described heightened concerns about how psychopaths’ deceitful and manipulative nature could
significantly obstruct evaluations of psychopathy. The accurate evaluation of psychopathy is
very important in forensic and correctional settings, and in such issues as risk assessment or
dangerousness. Although the PCL-R is considered the quasi-gold standard when it comes to
evaluating psychopathy, self-report measures have become more widely available and
researched. Very few studies specifically evaluated response styles and self-report psychopathy measures despite the significant concerns regarding psychopathy and deception. The current study evaluated the ability of inmates with different levels of psychopathy to successfully engage in positive impression management on the SRP-4, LSRP, and PPI-R. Utilizing a repeated-
measures, within-subjects design, 78 male inmates completed the study under genuine and
simulation conditions. Overall, inmates were able to significantly lower their scores on all three
self-report measures and achieved scores equivalent to and even lower than college and
community samples. Inmates with higher levels of psychopathy were able to achieve larger
decreases in scores on the PPI-R and on several scales for each measure. Another key finding
was the identification of promising PPI-R Virtuous Responding Scale cut scores that can be
utilized within forensic populations. Results indicate self-report measures should not be used to
replace the PCL-R or comprehensive assessment of psychopathy in forensic evaluations;
however, they do provide additional useful information and may be beneficial in other clinical
settings.
Copyright 2014
by
Katherine R. Kelsey
ii TABLE OF CONTENTS
Page
TABLE OF CONTENTS ...... iii
LIST OF TABLES ...... vi
CHAPTER 1 INTRODUCTION ...... 1
Relevant Historical Conceptualizations of Psychopathy ...... 3
Current Conceptualizations of Psychopathy ...... 8
Assessment of Psychopathy ...... 14
Psychopathy and Deception ...... 18
Current Study ...... 28
CHAPTER 2 METHODS ...... 31
Design ...... 31
Participants ...... 32
Measures ...... 33
Inmate Instructions...... 36
Procedure ...... 38
CHAPTER 3 RESULTS ...... 42
Sample Refinement ...... 42
Final Sample ...... 43
Reliability ...... 46
iii Convergent and Discriminant Validity ...... 48
PIM on Self-report Measures of Psychopathy ...... 51
Psychopathy Level and PIM Ability ...... 55
Impression Management Score and PIM Ability ...... 61
VR Scale and Detection of PIM...... 63
CHAPTER 4 DISCUSSION ...... 71
Implications of Risk Assessments ...... 72
Psychopathy and Risk Assessment ...... 73
Self-Report Measures of Psychopathy Compared to the PCL-R ...... 75
Psychopathy and PIM ...... 81
Susceptibility of Self-Report Measures to PIM at Different Levels of Psychopathy ...... 82
Classification of PIM using the PPI-R VR Cut Score ...... 86
High Psychopathy Does Not Equal Successful PIM ...... 90
Professional Implications ...... 93
Limitations ...... 97
Future Directions ...... 98
Conclusion ...... 101
APPENDIX A STANDARD INSTRUCTIONS ...... 103
APPENDIX B PIM INSTRUCTIONS ...... 105
APPENDIX C INFORMED CONSENT ...... 107
iv APPENDIX D DEMOGRAPHIC INFORMATION...... 110
APPENDIX E MANIPULATION CHECK ...... 112
APPENDIX F DIFFERENCES BETWEEN INMATES WITH SUCCESSFUL AND
UNSUCCESSFUL SCORES ON THE PPI-R VR SCALE UTILIZING A SINGLE-POINT CUT
SCORE OF 40...... 114
APPENDIX G ITEM LEVEL DIFFERENCES BETWEEN INMATES WITH SUCCESSFUL
AND UNSUCCESSFUL SCORES ON THE PPI-R VR SCALE UTILIZING A WELL-
DEFINED CUT SCORE OF 40 ...... 116
APPENDIX H PCL-R FACET SCORES AS PREDICTORS OF SELF-REPORT CHANGE
SCORES ...... 118
APPENDIX I CORRELATIONS BETWEEN THE PCL-R, SRP-4, PPI-R, AND LSRP TOTAL
AND SCALE SCORES ...... 120
APPENDIX J POTENTIAL CUT SCORES FOR THE PPI-R VR SCALE ...... 122
REFERENCES ...... 124
v LIST OF TABLES
Table 1 Cleckley’s (1941) Conceptualization of Psychopathy ...... 5
Table 2 Cooke and Michie’s (2001) Three Factor Model of Psychopathy...... 12
Table 3 Hare’s (2003) Two-Factor, Four-Facet Model of the PCL-R ...... 13
Table 4 Key Terms Utilized by Five Simulation Studies to Describe Response Style ...... 20
Table 5 Rogers et al. (2002) Effects of Level of Psychopathy on Psychopathy Scores Across
Genuine and PIM Conditions...... 26
Table 6 Final Sample Percentages and Group Differences between Inmates with Violent vs.
Non-violent Crimes as their Most Serious Charge ...... 44
Table 7 Differences between Inmates in the Moderate and High Psychopathy Groups on Age,
Education, Total Number of Arrests, Total Number of Months Incarcerated ...... 45
Table 8 Types of Current and Most Serious Charges Across Psychopathy Groups ...... 46
Table 9 Reliability Estimates for Self-Report Measures ...... 48
Table 10 Correlations between PCL-R and SRP-4 Total Scores and Underlying Factors ...... 49
Table 11 Correlations between PCL-R and PPI-R Total Scores and Underlying Factors ...... 50
Table 12 Correlations Between the PCL-R and LSRP Total Scores and Underlying Factors .... 51
Table 13 Effects of Condition and Measure on Psychopathy Scores ...... 52
Table 14 Differences for Inmates Between Genuine and PIM Conditions on Self-Report
Psychopathy Measures ...... 53
Table 15 Reported Means and Standard Deviations for the SRP-4 Total and Scale Scores ...... 54
Table 16 Reported Means and Standard Deviations for the LSRP Total and Scale Scores ...... 55
Table 17 Effects of Condition, Measure, and PCL-R Classification on Self-report Measures ... 56
vi Table 18 Differences for High and Moderate PCL-R Classification between Genuine and PIM
Groups ...... 57
Table 19 Differences Between Moderate and High Psychopathy Group in SRP-4 Scale Scores
Between Genuine and PIM Conditions...... 58
Table 20 Differences Between Moderate and High Psychopathy Groups in PPI-R Scale Scores
Between Genuine and PIM Conditions...... 59
Table 21 Differences Between Moderate and High Psychopathy Group in LSRP Scale Scores
Between Genuine and PIM Conditions...... 61
Table 22 Effects of Condition, Measure, and PDS IM Scale Classification on Self-report Scores
...... 62
Table 23 Differences for Valid and Maybe Invalid Impression Management Scale Scores
between Genuine and PIM Conditions ...... 63
Table 24 Effectiveness of PPI-R VR at Single-Point and Well-Defined Cut Scores for Likely
Genuine ...... 66
Table 25 Effectiveness of PPI-R VR at Single-Point and Well-Defined Cut Scores for Likely
PIM ...... 67
Table 26 Errors in the Indeterminate Category for VR Cut Scores: False Positives and False
Negatives...... 68
Table 27 Differences between Inmates with Successful and Unsuccessful Scores on the PPI-R
VR Scale Utilizing a Well-defined Cut Score of 40 ...... 69
vii CHAPTER 1
INTRODUCTION
The highly researched construct of psychopathy, although not classified as a formal
personality diagnosis, remains a complex syndrome that is frequently used to conceptualize a
significantly pathological group. Many early writers attempted to define the sick and deviant
within this construct, weaving an intricate map of overlapping syndromes and inexplicit criteria.
Pertinent conceptualizations of psychopathy started as early as 1806 with Phileppe Pinel, who called it “mania without delirium.” In his book, A Treatise on Insanity, Pinel (1806/1962, p.
156) documented various types of “mental derangement,” as he illuminated the idea of psychopathy: “It may be either continued or intermittent. No sensible change in the functions of the understanding; but perversion of the active faculties, marked by abstract and sanguinary fury, with a blind propensity to acts of violence.” In 1835, Pritchard contended psychopathy, then called moral insanity, was a syndrome that greatly disrupted society because such persons had no ability to self govern their behaviors (Cooke, 1998). Birnbaum was the first doctor to postulate that not all delinquent individuals were “morally defective or constitutionally inclined to criminality” and subsequently suggested the use of the term sociopath to describe those non- psychopathic individuals (Millon, Simonsen, Birket-Smith, 1998, p. 11). These conceptualizations constitute just a few of the early writers who studied mental illness and psychopathy.
The concept of psychopathy must be clearly differentiated from other antisocial patterns such as sociopathy. Partridge communicated, “all individuals who, at any moment, are displaying behavior which is futile or antagonistic from the standpoint of the group may logically be regarded as sociopathic” (1930, p. 55). Attempting to organize the literature, he continued
1 using the term sociopath to describe a distinct group of deviant people separate from other
mental illness. He primarily focused on characteristics that our modern definition of
psychopathy uses but also included persons into the sociopathic group, who better fit the
diagnosis of antisocial personality disorder (APD). Despite the apparent similarities, the
foundation of sociopathy is extreme deviations from prevalent societal norms. Conversely,
features of psychopathy include these behaviors but also formulate essential features of
personality dysfunctions, specifically chronic pathological interpersonal and affective
experiences or deficits. This important distinction represents the foundation for psychopathy.
As an alternative to Partridge, Lykken (1995) conceptualized these two terms, sociopathy and psychopathy, differently. He defined sociopathy as interchangeable with APD and asserted that its propensity toward criminal behavior was environmental, typically due to inadequate parenting. Conversely, psychopathic personalities resulted from a genetic difference; therefore, psychopaths engaged in antisocial behaviors because of a biological abnormality (Lykken,
1995).
Regardless of the precise origin, the continually shifting formulation of psychopathy has ranged from (a) explosive, impulsive, and reckless persons in the 1920s and 1930s to (b) the more recent conceptualization of conning, manipulative, and predatory individuals (Patrick,
2010). Even now in the 21st century, several different schools debate the nature and
measurement of psychopathy. This diversity may prove problematic to forensic applications
because, as Hare (1996, p. 25) pointed out, “the construct of psychopathy is proving to be
particularly useful in the criminal justice system, where it has important implications for
sentencing, diversion, placement, and treatment options and for the assessment of risk for
recidivism and violence.”
2 In light of these controversies, this introduction is organized into four major sections.
First, a brief overview of the historical ideas highlights the waxing and waning among
researchers in their conceptualizations of psychopathy. Second, prominent, current
conceptualizations of psychopathy provide the general framework for this research study. With
that framework, the third major section examines the relationship of psychopathy to deception.
The fourth and final section introduces the current study and its research questions.
Relevant Historical Conceptualizations of Psychopathy
Disparity in early conceptualizations. Starting in the 19th and early 20th century,
prominent scholars attempted to classify and diagnose disorders from their own theoretical perspectives, leading to significant disparities in the literature. Offering his perspective about
early writings on psychopathy, Partridge (1930, p. 63) wrote “the statistical manual requires us to
consider as psychopathic personalities a large group of persons showing abnormality expressed
mainly in the character and intensity of the emotional reactions.” This broad, overly inclusive
standard represented the underdeveloped conceptualizations of mental health and psychopathy during that time period. Numerous catch-all phrases lumping together all mentally disordered individuals that engaged in antisocial behaviors appeared counterproductive to their rigorous
study of psychopathy, and their lack of consensus or unity diluted the early writings on
psychopathy. Partridge (1930, p. 61) specifically identified over 13 different terms in use, such
as “constitutional psychopathic personality” and “constitutional psychopathic inferiority.” The
first term represented a generally functional person with severe pathologies while the latter
characterized an impaired individual with chronic instabilities and neurotic traits.
A major contribution of Partridge was his extensive review of the early literature on
psychopathy. In 1930, Partridge meticulously described the state of the research that included
3 over 25 diverse conceptualizations of psychopathy by various authors on the subject. Through
this comprehensive survey of existing literature, he eloquently expressed the conceptual disarray that researchers faced with psychopathy. Based upon his analysis (Partridge, 1930), two
prominent models emerged to describe psychopathy: the Kraepelin (1915) and Schneider (1923)
models.
Partridge identified Kraepelin (1915) and Schneider (1923) as influential scholars who
clearly articulated these two contrasting theoretical models of psychopathy. Kraepelin (1915)
used behavior as the core and defining characteristic of psychopathy. His formulation closely
resembles the current conceptualization of APD (APA, 2013). Essentially, he described
psychopathy as the continuous violation of rules and rights of others through aggression,
destruction, and deceitfulness. In contrast, Schneider developed organized categories with
consistent criteria for active and passive types of psychopath. Schneider attributed psychopathy
to emotional deficits and characterological pathology. According to Partridge (1930, p. 64),
“Schneider’s estimation of the psychopathic personalities is that they are such abnormal
personalities as by reason of their abnormality suffer or cause other people to suffer.” Schneider
focused on these emotional aspects of the personality and identified various deficits and
abnormalities prominent within psychopaths. He believed psychopathic personalities displayed
blunted affect and inability to experience inhibitory emotions. In his most recent work,
Schneider (1950/1958, p. 126) wrote, “Their character is a pitiless one and they lack capacity for shame, decency, remorse, and conscience. They are ungracious, cold, surly, and brutal in crime.” In summary, Schneider and Kraepelin’s diverse theories demonstrate the inherent conceptual difficulties presented by psychopathy even in modern formulations.
4 Cleckley’s contributions to psychopathy. Harvey Cleckley represents one of the most influential American researchers on theoretical conceptualizations of psychopathy. From 1941 to 1976, Cleckley published five editions of his book, The Mask of Sanity, all dedicated to clarifying the conceptualization of the psychopathic personality. Cleckley modernized psychopathy with the first organized descriptions of the construct, complete with criteria.
Through the case studies of 13 individuals, Cleckley (1941) described the psychopathic individual as a person able to mask fundamental deficits in his or her personality structure and who experiences an internal chaos that results in chronic, goal-oriented, destructive behavior.
Importantly, he delineated 16 characteristics of personality traits and behaviors that characterized the clinical profile of psychopathy. These criteria provided the foundation we utilize today in our descriptions and measurement of psychopathy (see Table 1).
Table 1
Cleckley’s (1941) Conceptualization of Psychopathy
Superficial charm and good “intelligence” Absence of delusions and other signs of irrational thinking Absence of “nervousness” or psychoneurotic manifestations Unreliability Untruthfulness and insincerity Lack of remorse or shame Inadequately motivated antisocial behavior Poor judgment and failure to learn by experience Pathological egocentricity and incapacity for love General poverty in major affective reactions Specific loss of insight Unresponsiveness in general interpersonal relations Fantastic and uninviting behavior with drink and sometimes without Suicide rarely carried out Sex life impersonal, trivial, and poorly integrated Failure to follow any life plan
5 Core features of a Cleckley psychopath highlight broad emotional deficits. Cleckley believed psychopaths did not have the fundamental capacities to experience and understand normal human emotions. Like Schneider, he understood psychopathy to be innate deficits in emotions while criminal behavior was a secondary product of these emotional insufficiencies.
McCord and McCord. McCord and McCord (1964), highly influential researchers, formulated their description of the psychopathic syndrome based on guiltlessness and lovelessness. They theorized these two specific emotional deficits as the core characteristics of psychopathy. These researchers maintained that psychopathic individuals freely engaged in violent, criminal behavior due to an inability to experience guilt, a highly inhibitory response.
McCord and McCord also described similar aspects to Cleckley’s criteria: Both conceptualized psychopaths as missing typical emotional responses (Patrick, 2010), and both separated psychopathy into primary and secondary groups (Millon et al., 1998). Like Hare, McCord and
McCord documented via numerous examples, the types of deception used by psychopaths and the manipulative behaviors they employed to take advantage of unsuspecting victims. These two theories differed mainly in how they postulated the impairments developed. McCord and
McCord concluded it was extreme social disconnect, an inability to experience love, whereas
Cleckley theorized it as a comprehensive insufficiency in emotional capacity.
Lykken and biology. Despite extensive research, these very diverse conceptualizations of psychopathy have yet to establish a clear etiology for the syndrome. Recognizing these discrepancies, Lykken (1957) stated, “The concept of the psychopathic personality includes so heterogeneous a group of behavior disorders as to be at least two steps removed from the level of useful psychiatric diagnosis.” In an attempt to close the gap and address the etiology issue,
Lykken devoted his dissertation research to test Cleckley’s conceptualization of the psychopathic
6 personality. He strove to identify measureable outcomes that would assist psychology in the
conceptualization and diagnosis of psychopathy.
Lykken’s (1957) important contribution to the literature was the development of a biologically based explanation of psychopathy. Before Lykken, a biological etiology of psychopathy was difficult to prove, leading to insufficiently founded theories. To illustrate,
Karpman (1948) postulated that theories attributing psychopathic personalities to biology were inadequate, because researchers could not identify the biological precursors and did not even consider etiology when conceptualizing psychopathy. In marked contrast, Karpman (1948) focused on environmental factors, specifically, the inadequate socialization and poor parenting of children who became psychopaths. He believed psychopathy resulted from overindulgence or rejection by parents. Unlike their earlier formulations, Lykken theorized a specific biological difference contributed to psychopathy. Specifically, he formulated that as individuals lacking anxiety and fear, psychopaths are “relatively incapable of avoidance learning under circumstances where such learning can only be affected through the mediation of the anxiety response” (Lykken, 1957, p. 6).
Through his experimental research, Lykken (1957, p. 9) identified that primary psychopaths did actually exhibit “less anxiety on a questionnaire device, less galvanic skin response (GSR) reactivity to a conditioned stimulus associated with shock, and less avoidance of punished responses on a test of avoidance learning.” Of all of his conclusions, the lower GSR reactivity is most interesting. After receiving shocks paired to the ringing of a bell, non- psychopaths were conditioned to have an emotional reaction to the stimulus, yet psychopaths remained comparatively unaffected. With this work, Lykken added research evidence for a
7 biological component of psychopathy, specifically a physiological difference in emotional reactivity between psychopaths and non-psychopaths.
Only a few broad commonalities have emerged from the range of foundational traits identified by leading theorists. Across theories, diminished inhibitory responses and increased emotional deficits clearly played a key role in psychopathic behavior. These characteristic deficits are observed in (a) relative lack of fear and anxiety (Lykken, 1957), (b) an innate inability to feel guilt and love (McCord & McCord, 1964), and (c) an undeveloped ability of general emotional capacity (Cleckley, 1941). Additionally, research establishes the link between these insufficiencies and the manipulative behavior commonly employed by psychopaths.
Writers during this time consistently identified conning and fraudulent actions as illustrations of
psychopaths’ interactions with others. Regardless of the individual theory, researchers generally
agree that psychopaths choose to engage in purposeful acts of deception and manipulation.
Current Conceptualizations of Psychopathy
Psychopathy, sociopathy, and APD. As previously noted, the construct of sociopathy
approximates our modern definition of APD (Lykken, 1998). Researchers have established the
most likely cause of APD and sociopathy are environmental influences that may look very
different depending on the specific situation. Criteria required to meet the classification of APD
and sociopathy are mostly behaviorally based and markedly variable. For APD, the only
characterological criterion is lack of remorse, which is not necessary to qualify for the diagnosis
because only three of the seven APD criteria are required for the diagnosis of APD (APA, 2013).
Toch (1998) recognized that including persons with sociopathic behaviors into the
conceptualization of psychopathy would dilute the syndrome. He concluded that most criminals
categorized as sociopathy or APD do not meet criteria for psychopathic personality.
8 Current conceptualizations of psychopathy are careful to distinguish psychopaths from
sociopaths. Unlike APD and sociopathy, psychopathy includes both behavioral traits and personality characteristics (Cleckley, 1941; Hare, 1991). Psychopaths also differ fundamentally from sociopaths and APD in their etiology and emotional experience. APD and sociopaths generally experience a typical range of emotions and affect, whereas psychopaths do not. In contrast to APD, psychopathy is characterized by a dysfunction in emotional processing (Blair,
Mitchell, & Blair, 2005) that is biologically based (Lykken, 1995).
Hare and the PCL-R. Robert Hare, an eminent scholar in psychopathy research, has
continued his pioneering work for more than three decades. Hare operationalized Cleckley’s
criteria to evaluate psychopaths, which soon led to the systematic assessment of psychopathy. In
1980, Hare developed the original Psychopathy Checklist (PCL), a semi-structured interview and
rating form to assess psychopathy in adults. By 1991, Hare published the current version, the
Psychopathy Checklist - Revised (PCL-R), the most widely used and studied assessment
measure of psychopathy. Although the PCL-R was primarily based on Cleckley’s clinical
formulation of psychopathy, it also measures criminal and antisocial behaviors in addition to
Cleckley (Edens, Buffington, Tomicic, & Riley, 2001). Through extensive research with the
PCL-R, scholars have continued to refine the construct of psychopathy.
Hare (1991) specifically asserted that the combination of personality traits and antisocial
behaviors is the clear and defining theme for psychopathy. Unlike Cleckley’s traditional view of
psychopathy, Hare described inadequately motivated criminal behavior as a core element (Hare
& Neumann, 2006). Consistent with the view of psychopathy, the PCL-R evenly assesses for
both core personality traits and behaviors. Regarding personality, psychopaths display a grossly
inflated and narcissistic egocentricity, in which they view themselves as superior to society’s
9 rules (Hare, 1993). They lack genuine concern for others, do not experience guilt or shame, and
are particularly deceitful and manipulative (Hare, 2003). In general, psychopaths display
shallow emotions and severe affective deficits. As noted for Hare’s model, the behavioral
characteristics are considered just as salient as the personality traits. Hare (1991) described
psychopaths as impulsive, presenting poor behavioral controls and lacking normal inhibitions. In
1993, Hare defined psychopaths as irresponsible with an excessive need for excitement, regardless of the consequences. These characteristics usually result in early behavioral problems and later, extensive adult antisocial behaviors and significant criminal records (Hare, 2003; Hare
& Neumann, 2006).
The PCL-R provides a standardized assessment for examining the underlying dimensions
of psychopathy. Although Hare (1991) originally formulated a two factor model comprised of
personality and behavioral characteristics, more recent models include three factors and four
factors. In the next paragraphs, these three prominent, competing models are compared and
contrasted.
Two factor model. In 1988, Harpur, Hakstian, and Hare identified two main components
with the PCL-R using exploratory factor analysis to delineate psychopathy: Factor 1, Affective-
Interpersonal Features of Psychopathy and Factor 2, Impulsive Antisocial Behavior. These two
dimensions included most of the PCL-R items, and together, they conceptualize psychopathy.
Factor 1 includes eight items (1, 2, 4, 5, 6, 7, 8, 16) whereas Factor 2 consists of nine items (3, 9,
10, 12, 13, 14, 15, 18, 19). The three remaining PCL-R items (i.e. promiscuous sexual behavior,
many short-term marital relationships, and criminal versatility) did not initially load onto either
factor. Later, Item 20 (criminal versatility) was added to Factor 1 (Hare, 2003). Fowles and
Dindo (2006, p. 14) articulated, “Factor 1 relates to the predatory inclinations and deficient
10 emotional reactivity associated with psychopathy, whereas Factor 2 relates to impulsivity/disinhibition, early and chronic antisocial behavior.” In the past, this model has been supported and called the “gold standard” by several researchers (e.g., Cooke, 1998; Salekin,
Rogers, & Sewell, 1996).
Three factor model. Cooke and Michie (2001) provided an alternative conceptualization of PCL-R psychopathy and its dimensions. In analyzing the results of several studies, Cooke and
Michie (2001) used item response theory to determine three factors as the best fit model of psychopathy. Thirteen original PCL-R items fit into the three factors that load onto the superordinate construct. According to Cooke and Michie (2001), Factor 1 (Items 1, 2, 4, and 5) is described as Arrogant and Deceitful Interpersonal Style; Factor 2 (Items 6, 7, 8, and 16) as
Deficient Affective Experience; and Factor 3 (Items 3, 9, 13, 14, and 15) as Impulsive and
Irresponsible Behavioral Style. Essentially, Cooke and Michie (2001) separated the traditional
Factor 1 into two, more specific factors, and excluded several antisocial/behavioral items.
Cooke and Michie (2001) asserted that the construct of psychopathy should emphasize interpersonal deceit, affective deficits, and impulsivity. The most prominent difference between the two factor and three factor models was the exclusion of antisocial behaviors from the three factor model. With this exclusion, Cooke and Michie (2001) reiterated Cleckley’s original conceptualization and asserted that criminal behaviors were not significant to understanding psychopathy. They emphasized the importance of including deceitful and manipulative interpersonal style into the construct of psychopathy. With this three factor model, deception was now organized as prominent piece of psychopathic personalities, appearing to replace chronic antisocial behaviors. Table 2 illustrates Cooke and Michie’s three factor model of the
PCL-R and psychopathy.
11 Table 2
Cooke and Michie’s (2001) Three Factor Model of Psychopathy
Arrogant and Deceitful Deficient Affective Impulsive and Items not loading Interpersonal Items Experience Items Irresponsible Items onto factors Glibness/ superficial Lack of remorse or Need for stimulation/ Poor behavioral charm guilt proneness to boredom controls
Grandiose sense of self Shallow affect Parasitic lifestyle Promiscuous worth sexual behavior Pathological lying Callous/lack of Lack of realistic, long- Early behavior empathy term goals problems Conning/manipulative Failure to accept Impulsivity Many short-term responsibility for marital own actions relationships Irresponsibility Juvenile delinquency
Revocation of conditional release
Criminal versatility
Two-factor, four-facet model. In 2003, Hare wrote the second edition of the PCL-R technical manual, which added a two factor, four facet model to the literature. This model is very similar to Cooke and Michie’s three factor model. Two main differences emerge between these models: facets vs. factors, and excluded items. First, Hare retained his original idea of two factors and he added four facets, two for each factor. Factor 1 still consists of interpersonal and affective characteristics while Factor 2 encompasses antisocial behaviors. Hare retained five of the seven items that Cooke and Michie had excluded to constitute the antisocial facet. The remaining two items (promiscuous sexual behavior and many short-term marital relationships) fall under Factor 2 but do not specifically load onto either facet. By creating four facets, Hare was able to again emphasize what many researchers identify as a central component to
12 psychopathy, specifically, the link between emotional/ affective traits and antisocial behaviors.
In the second PCL-R manual, Hare (2003) compared and contrasted these three models; he
contended that his latest model is better empirically supported than Cooke and Michie’s three
factor model. Table 3 illustrates Hare’s two-factor, four-facet model of psychopathy.
Table 3
Hare’s (2003) Two-Factor, Four-Facet Model of the PCL-R
Interpersonal/ Affective Factor Social Deviance Factor Interpersonal facet Affective facet Lifestyle facet Antisocial facet Glibness/superficial Lack of remorse or Need for Poor behavioral charm guilt stimulation/proneness to controls boredom Grandiose sense of self Shallow affect Parasitic lifestyle Early behavior worth problems Pathological lying Callous/lack of Lack of realistic, long- Juvenile empathy term goals delinquency Conning/manipulative Failure to accept Impulsivity Revocation of responsibility conditional release Irresponsibility Criminal versatility Note. Two PCL-R items fall under Factor 2 but do not load onto any of the four facets. These items are promiscuous sexual behavior and many short-term marital relationships.
All three PCL-R models were essential in the evolution of our current conceptualizations of psychopathy. The original two factor model played a crucial role in guiding our understanding of psychopathy. The subsequent three factor model and two-factor, four-facet model are both extensively researched and provide more encompassing representations of the psychopathic personality. Like most diagnoses, psychopathy is a heterogeneous syndrome, which may present differently in various populations and cultures, and across gender. Hare
(2003) asserts that while both models provide strong evidence for PCL-R validity, each model
13 may better fit different populations. Individual investigators are therefore faced with the
decision of which PCL-R model best fits their research design and population.
Assessment of Psychopathy
Limitations of the PCL-R. Although several scholars (e.g. Patrick, 2006) assert that the
PCL-R constitutes the “gold standard” for assessing psychopathy, it has several significant limitations that complicate or limit its use in correctional settings (Edens et al., 2001).
Pragmatically, the PCL-R presents itself as a labor intensive instrument, requiring trained
investigators and practitioners for its use. It is also time consuming, typically requiring 90-120 minutes for the interview portion, which may spread over several sessions (Hare, 2003).
Additionally, the collateral review usually takes 60 minutes and possibly longer if the file is extensive (Hare, 2003). In conventional settings, the training and time commitment can strain limited resources.
Hare (1991, 2003) emphasized the importance of extensive record review to address the impact of deception and manipulation, used by incarcerated examinees, on the accuracy of
psychopathy assessments. Without file review, the PCL-R ratings are based solely on the
respondent’s self-report. However, files often lack important information in most correctional settings, which limits the PCL-R’s usefulness. In addition, Hare (1991) believed the adversarial nature of these evaluations would cause most individuals to engage in some type of deceitful response style. Hare (2003) cited one quantitative research study (Rogers, Vitacco, Jackson,
Martin, Collins, & Sewell, 2002) that found the Psychopathy Checklist: Youth Version (PCL:
YV; Forth, Kosson, & Hare, 2003) was susceptible to response styles. Without further research in this area, Hare (2003, p. 33) cautioned that evaluators have “adequate forensic training and experience and a demonstrated ability to carefully integrate interview and file information.”
14 In regards to utilizing the PCL-R to monitor treatment effects, Hare (1998) acknowledged
that the PCL-R is primarily scored on the basis of static and historical factors that will not
change appreciably over time. Therefore, the measure cannot be utilized to evaluate changes
occurring as a response to treatment over time. Given these limitations posed by using the PCL-
R, other methods have become more widely available and researched. In particular, self-report
measures are often substituted for the PCL-R or other validated measures of psychopathy.
Self-report measures of psychopathy. In the past few decades, self-report measures of psychopathy have become more prominent in the realm of forensic assessment. Three scales have emerged with good validity and reliability, which will be the focus of the current research:
(a) the Self-Report Psychopathy Scale-fourth edition (SRP-4; Paulhus, Neumann, & Hare, in press), (b) the Psychopathic Personality Inventory–Revised (PPI–R; Lilienfeld & Widows,
2005), and (c) the Levenson Self-Report Psychopathy Scale (LSRP; Levenson, Kiehl, &
Fitzpatrick, 1995). These three self-reports were designed to address the shortcomings of
previous psychopathy measures and have been extensively researched (Lilienfeld & Fowler,
2006). A short introduction to these measures follows while a more technical description of their
psychometric properties is included in the methods section.
Self-Report Psychopathy Scale-Fourth Edition. The SRP-4 (Paulhus et al., in press)
evolved from the three earlier versions of the SRP and was designed to assess the four facets of
psychopathy that correspond to the PCL-R two-factor, four-facet model (Hare, 2003; Hare &
Neumann, 2008). The original version of the SRP (29-items) was developed as a self-report
equivalent to the PCL (Mahmut, Menictas, Stevenson, & Homewood, 2011) with the total scores
demonstrating moderate correlations (.35; Hare, 1985). The 60-item second version, SRP-II
(Hare, Harpur, & Hemphill, 1989), demonstrated moderate correlations with the PCL-R (.38 to
15 .54; Hare, 1991; Zágon & Jackson, 1994). Interestingly, Williams and Paulhus (2004) found the
SRP-II did not evidence a similar two-factor structure as the PCL-R. Regarding recent versions, the SRP-III (Williams, Paulhus, & Hare, 2007) has been around longer, but the SRP-4 is the
most current version; however, they both utilize the same items (64 items) and same four scales
(C. S. Neumann, personal communication, August, 2, 2011).
The SRP-4 includes four subscales: (a) Interpersonal Manipulation (IPM), (b) Callous
Affect (CA), (c) Erratic Lifestyle (ELS), and (d) Criminal Tendencies (CT). It is the only self-
report inventory whose subscales correspond to the PCL-R two-factor, four-facet model.
Psychopathic Personality Inventory–Revised. The PPI (Lilienfeld & Andrews, 1996) was also initially developed and validated for use with non-offender samples. However, Lilienfeld and Widows (2005) updated the PPI with a revised version (PPI-R) that is more applicable to forensic populations. To accommodate limited literacy and attention span, they decreased the reading level for the PPI-R. Additionally, they reworded culturally specific expressions and reduced the overall length of the PPI-R items (Lilienfeld & Widows, 2005). Like the PPI, the
PPI-R has validity indices, designed to evaluate response styles, including impression management, malingering, and inconsistent responding.
Levenson Self-Report Psychopathy Scale. The LSRP was originally developed to detect self-reported psychopathic features in non-institutional samples. Primary and secondary scales were designed “to assess a protopsychopathic interpersonal philosophy” (Levenson et al., 1995).
However, several studies (Brinkley, Diamond, Magaletta, & Heigel, 2008; Brinkley, Schmitt,
Smith, & Newman, 2001; Sellbom, 2011; Walters, Brinkley, Magaletta, & Diamond, 2008a)
have found it is effective at measuring psychopathy in incarcerated populations. It is now widely
used in both research and the assessment with institutionalized samples.
16 Utilizing psychopathy self-reports. Self-report measures of psychopathy provide an
alternative assessment approach that does not pose the same limitations as the PCL-R. Reliable and valid self-report measures of psychopathy can be advantageous in several ways; they would
(a) allow for group administration and time efficient screening, (b) eliminate reliance on collateral data, and (c) provide a more dynamic personality measure that may be sensitive to changes in functioning from successful treatment and interventions (Edens et al., 2001). At present, these advantages cannot be achieved with the PCL-R.
By definition, psychopathic individuals have little insight into their own behaviors as well as a significant inability to describe themselves accurately (Hare, 1993). Therefore, self- reports can provide valuable information about how the respondents view themselves and the world (Lilienfeld & Fowler, 2006). Lilienfeld and Fowler (2006) use an item from the
Psychopathic Personality Inventory (PPI; Lilienfeld & Andrews, 1996) to demonstrate this observation. For example, when an individual endorses “I often get blamed for things that aren’t my fault,” a “true” response is unlikely to be an accurate reflection of reality; however, it is an accurate illustration of blame externalization engaged in by persons with psychopathic personalities (Lilienfeld & Fowler, 2006, p. 111). Often expressed indirectly, these self-report responses can offer valuable information about the presence of key characteristics of psychopathy.
Studies (Book, Holden, Starzyk, Wasylkiw, & Edwards, 2006; Edens et al., 2001; Rogers et al., 2002) illustrate that individuals with higher levels of psychopathy can successfully engage in socially desirable responding on self-report measures of psychopathy when compared to those low on psychopathy. For instance, adolescent psychopaths can effectively engage in socially desirable responding during forensic evaluations, effectively decreasing their total PCL: YV
17 scores (Rogers et al., 2002). Research has also shown that persons with psychopathic
personalities are more likely to engage in socially desirable responding than non-psychopathic
individuals in an effort to self enhance their own image (Blair et al., 2005; Hare, 1991, 2003;
Hare & Neumann, 2006; Lykken, 1995; Millon et al., 1998; Vitacco & Neumann, 2008).
Despite general abilities at deception, psychopathic offenders are not more adept than
other offenders at malingering. In particular, research has found that psychopaths have the same
likelihood of being detected when feigning mental disorders as their nonpsychopathic
counterparts (Book et al., 2006; Edens, Buffington, & Tomicic, 2000; MacNeil & Holden, 2006;
Rogers et al., 2002). One explanation is that psychopaths may not understand psychopathology
any better than others and, therefore, are not more adept at malingering.
Psychopathy and Deception
Prominent writers throughout history identified numerous deceptive acts as principal
characteristics of psychopathic personalities (for a review, see Millon et al., 1998). Kraepelin explicitly described psychopaths as deceitful, continuously manipulating and conning other people for their own personal gain (Patrick, 2010). Cleckley (1941) illustrated psychopathic personalities as untruthful, insincere, and unreliable. Four of his 16 criteria describe dishonesty and characterological traits that may facilitate deceitful acts. Specifically, they include (a) superficial charm, (b) unreliability, (c) untruthfulness and insincerity, and (d) lack of remorse or shame. Importantly, his descriptions focus on the psychopath’s ability to successfully mask these insufficiencies in order to appear normal and take advantage of those around them.
Deception and manipulation continue to be important fundamental traits identified by most recent writers of psychopathy, clearly providing a link from the past to the present. Porter and Woodworth (2006, p. 488) aptly summarized the connection from past to present,
18 “Psychopaths long have been characterized as having a remarkable disregard for the truth (e.g.
Cleckley, 1976; Hare, 1998; Meloy, 1988; Porter, Birt, Yuille, & Herve, 2001), to the extent that
deceit often is regarded as a defining characteristic of the disorder.” In 1998, Richards illustrated
Cleckley’s concept of manipulative deception as “beneficial” to psychopaths; they learn to fit in and gain “social advantage.” Richards (1998, p. 75) further elaborated on the developmental process of psychopathy as the maturation of deceptive processes: “At the interpersonal level, conscious deception and the related attitude of contempt for the deceived other begin to predominate over other kinds of interactions.” Hare (1991) discussed his concerns about the deceptive nature of the psychopath and how this could significantly affect evaluations; explaining that conning and manipulation directly relates to criminal versatility. He was concerned that the majority of psychopathic individuals would put forth considerable effort to manipulate and minimize their deviant responses on psychopathy measures (Hare, 2003).
Experimental studies manipulating deception and psychopathy. The current body of
research encompasses very few studies specifically evaluating response styles and psychopathy
and even fewer measuring psychopaths’ ability to deceive on self-report measures. This section
provides a review of the five studies that explicitly examined self-report psychopathy measures
and deceptive response styles.
Among the five main research studies discussed in the following sections, five separate
but related terms were used to describe the response style of Positive Impression Management
(PIM). Book et al. (2006) described inmate response styles as “faking good.” Similarly,
MacNeil and Holden (2006) also classified inmates as “fakers” and “faking good.” While Edens
et al. (2001) used PIM and “faking good” interchangeably. Table 4 demonstrates the number of
terms used to describe PIM and Negative Impression Management (NIM). For the current study,
19 PIM was used to describe all types of favorable deceptive responding, including faking good, socially desirable responding, defensiveness, and underreporting. Conversely, NIM was used to describe all unfavorable response styles, such as faking bad, social nonconformity, and feigning.
Table 4
Key Terms Utilized by Five Simulation Studies to Describe Response Style
Study Key terms used to describe PIM Key terms used to describe NIM Edens et al., Positive impression management 2001 Faking good Socially desirable responding Edens et al., Negative impression management 2000 Faking bad Feign Dissimulation MacNeil & Fakers Fakers Holden, 2006 Faking good Faking bad Feign Malinger Book et al., Faking good Faking bad 2006 Positive impression Malingering Socially desirable responding Negative dissimulation Positive dissimulation Rogers et al., Social desirability Social nonconformity 2002 Defensiveness Note. PIM = Positive Impression Management; NIM = Negative Impression Management.
Response styles on the PPI in college samples. Edens et al. (2001) conducted a repeated- measures simulation study to evaluate the effects of PIM on the PPI and the Marlowe-Crowne
Social Desirability Scale (MCSDS; Crowne & Marlowe, 1960). Specifically, they challenged undergraduates to significantly lower their PPI and SDS scores by engaging in PIM. They also assessed the effectiveness of the PPI Unlikely Virtues scale, which is intended to detect PIM.
20 Overall, Edens et al. (2001) found that simulators, regardless of psychopathy level or
simulation instructions, produced significant decreases in PPI total scores. Simulation conditions
included one of three hypothetical scenarios (Edens et al., 2001): (a) applying for position of a
police officer; (b) applying for employment as an airline pilot; or (c) being screened for a
presentence evaluation after being convicted of a crime. All three scenario instructions yielded
significant decreases in PPI total scores from the genuine condition with effect sizes ranging
from 0.37 to 0.48. However, the type of scenario instructions did not significantly affect
undergraduates’ ability to engage in PIM.
Edens et al. (2001) found that undergraduates with higher psychopathy levels had much larger decreases in their PPI scores compared to those with lower levels of psychopathy. The high psychopathy group achieved significant effect sizes across all three simulation instructions, with Cohen’s d values of 0.86, 0.97, and 1.82 (police recruit, pilot applicant, and criminal scenario, respectively). Conversely, the low psychopathy group had minimal decreases in their
PPI scores, with a mean effect size of 0.17 between the three simulation scenarios.
Regarding the PPI Unlikely Virtues scale, Edens et al. (2001) found that undergraduates significantly increased their scores across the genuine and PIM conditions (d = 0.91). Similarly,
SDS scores were also greatly increased from genuine to PIM (d = 1.39), suggesting that the two
validity scales functioned in the expected direction. However, results were not as successful
regarding the classification accuracy of the two scales. Edens et al. (2001) successfully
classified many undergraduates engaging in PIM; although, a significant number of respondents
were able to avoid detection on this scale. Therefore, the PPI Unlikely Virtues scale could not be
used as a reliable scale for detecting PIM. Similarly, disappointing results were found for the
SDS scale.
21 Edens et al. (2000) employed a repeated-measures simulation design to evaluate the effects of NIM on the PPI. In the simulation condition, undergraduates were asked to convincingly feign psychosis on the PPI and the Minnesota Multiphasic Personality Inventory, second edition, Psychoticism Scale (MMPI-2; Harkness, McNulty, & Ben-Porath, 1995). The authors also evaluated the effectiveness of the PPI Deviant Responding scale, which was designed to detect malingering and NIM.
Edens et al. (2000) found that level of psychopathy was unrelated to undergraduates’ ability to successfully engage in NIM when feigning psychosis or psychopathy on the PPI. More specifically, the authors found no significant relation between genuine-condition psychopathy level and ability to increase psychopathy and psychosis scores in the simulation condition.
Additionally, Edens et al. (2000, p. 289) identified that the PPI Deviant Responding scale was successful at detecting most undergraduates engaging in NIM, with a “high overall diagnostic efficiency, AUC = .98 (SE = .01), p ≤ .001, and a 95% confidence interval ranging from .96 to
1.00.”
Together, both studies (Edens et al., 2001 and Edens et al., 2000) evaluated the effects of response styles on the PPI and established the efficacy of two PPI scales for detecting deception in an undergraduate sample. Edens et al. (2001) established the PPI Unlikely Virtues scale was not effective at successfully identifying undergraduates engaging in PIM. While Edens et al.
(2000) identified that the PPI Deviant Responding scale has an excellent classification rate for those undergraduate respondents engaging in NIM.
These two studies also evaluated the effects of psychopathy levels and ability to simulate either PIM or NIM on the PPI. Edens et al. (2001) found that undergraduates with higher levels of psychopathy, measured by PPI total scores, were able to significantly lower their scores in the
22 simulation condition (PIM) when compared to those with lower levels of psychopathy (d = 1.12 and d = 0.07, respectively). In contrast, Edens et al. (2000) identified that level of psychopathy did not improve undergraduates’ ability to engage in NIM without detection. Edens et al. (2000)
found that only 12 undergraduates successfully evaded detection on the PPI Deviant Responding
scale, and there were no significant differences in the mean genuine PPI total scores between
successful and unsuccessful undergraduate responders (351.33 vs. 353.40, respectively).
Additionally, Edens et al. (2000) found that genuine PPI total scores did not significantly
correlate with the Deviant Responding scale scores (r = -.10) or psychoticism scores (r = -.11) in
the simulation condition. This finding suggests that higher psychopathy levels are not related to
an increased endorsement of genuine or improbable symptoms of psychosis when engaging in
PIM (Edens et al., 2000). Contrary to popular belief, these findings suggest that undergraduates
higher in psychopathy are not more adept at malingering than those low in psychopathy.
However, results from Edens et al. (2001) support the historical notion that psychopathic
personalities are better able to successfully engage in PIM.
Response styles on the HPSI in college samples. Book et al. (2006) utilized an
undergraduate sample to evaluate psychopathy and deception on the Holden Psychological
Screening Inventory (HPSI; Holden, 1996). They established psychopathy levels with the LSRP
and measured effective deception with the HPSI, instructing undergraduates to complete the
HPSI while engaging in PIM or NIM. Specifically, they asked undergraduates to try to appear
like someone “without psychological problems or personality flaws” or to make themselves
appear “mentally disturbed, ” and having “serious psychological problems” (Book et al., 2006,
pp. 606-607).
23 Book et al. (2006) found that individuals with higher psychopathy scores were more successful than others at engaging in PIM on the HPSI. Specifically, simulators in the PIM condition, who were classified as successful (i.e., undetected), had significantly higher Primary,
Secondary, and Total psychopathy scale scores with Cohen’s d values of 0.46, 0.36, and 0.50, respectively. In regards to the NIM condition, they found no significant differences between psychopathy scores and successful or unsuccessful undergraduate simulators (Cohen’s d values for Primary, Secondary, and Total psychopathy scale scores were 0.18, 0.27, and 0.25, respectively). Undergraduates were twice as likely to be caught when engaging in NIM (66%) versus PIM (33%). These findings indicate that simulators with higher psychopathy scores, as measured by the LSRP, have no effect on NIM but can improve their ability to engage in PIM.
MacNeil and Holden (2006) investigated the effects of PIM and NIM on the HPSI with undergraduates using a simulation design. They also utilized the Impression Management (IM) scale from the Balanced Inventory of Desirable Responding (BIDR; Paulhus, 1998) and the
Personality Research Form-Desirability Scale (PRF-D; Jackson, 1984) to establish additional classification methods of successful and unsuccessful deception. Undergraduates in the PIM condition were instructed to appear well adjusted, without psychological and personality problems. The NIM condition had undergraduates appear poorly adjusted, with psychological and personality problems. Each participant completed the PPI under the genuine condition. In the simulation condition, each participant completed the HPSI, IM, and PRF-D under genuine,
PIM, or NIM instructions.
MacNeil and Holden (2006) compared the success rates of PIM simulators with high versus low genuine-condition psychopathy levels on the PPI. The authors utilized three methods to establish successful and unsuccessful responders (MacNeil & Holden, 2006): (a) HPSI manual
24 cut scores, (b) BIDR manual cut scores for the IM scale, and (c) discriminant function analyses, using the HPSI total, IM, and PRF-D scores. According to MacNeil and Holden (2006, p. 648)
“All methods for classifying successful versus unsuccessful deceivers yielded no significant
differences in total psychopathy.” In spite of these initial findings, they employed secondary
analyses and found that three PPI scales did relate to successful PIM, (a) Machiavellian
Egocentricity, (b) Blame Externalization, and (d) Stress Immunity. These findings aligned with
earlier assertions by Paulhus and Williams (2002), who hypothesized that Machiavellianism,
narcissism, and psychopathy portray an emotionally cold, aggressive, deceitful, person willing to
engage in social depravity for self-enhancement.
Regarding NIM, MacNeil and Holden (2006) found that level of psychopathy did not
influence undergraduates’ ability to engage in NIM, aligning with earlier research findings
(Book et al., 2006; Edens et al., 2000; Poythress, Edens, & Watkins, 2001). MacNeil and
Holden (2006) also determined that their results supported the effectiveness of validity scales for
detecting response styles. They supported that self-report inventories (with validity indices) and
validity indicators were still a viable option for accurately measuring high levels of psychopathy
despite popular beliefs that psychopaths are more successful at deception and more likely to
engage in deceitful responding. Results from Book et al. (2006) also support the assertion that
validity indicators are imperative and specifically that NIM can be successfully detected with
these methods.
Response styles and psychopathy in juvenile offenders. Rogers et al. (2002) was the only
study utilizing juvenile offenders to investigate psychopathy and response styles, specifically
PIM and NIM. To do this, they employed a within-subjects simulation design and evaluated
three different measures: (a) the PCL: YV; (b) the Antisocial Process Screening Device (APSD;
25 Frick & Hare, 2001); and (c) the SRP-II. Each juvenile offender completed the psychopathy measures under genuine instructions and one of the two simulation conditions.
Rogers et al. (2002) found that both the PCL: YV and the self-report inventories could be influenced by response styles. PIM was highly effective at decreasing psychopathy scores on all three measures (PCL: YV total score, d = 0.79; APSD total score, d = 0.94; and SRP-II total score, d = 0.82). Moreover, the NIM condition was extremely effective at increasing the total scores on the PCL: YV, APSD, and SRP-II (Cohen’s d values were 1.10, 1.48, and 1.33, respectively).
Regarding an increased ability to engage in PIM, Rogers et al. (2002) found that level of psychopathy was strongly influential. Specifically, they compared moderate and low psychopathy levels, classified by genuine PCL: YV total cut-scores of 14, and found large effect sizes for the moderate group engaging in PIM compared to the low psychopathy group. They established that higher levels of psychopathy significantly increased juvenile offenders’ abilities to engage in PIM, see Table 5.
Table 5
Rogers et al. (2002) Effects of Level of Psychopathy on Psychopathy Scores Across Genuine and PIM Conditions Levels of Psychopathy Moderate Low Psychopathy Scale F Cohen’s d F Cohen’s d PCL: YV Total 27.00*** 1.14 0.82 0.21 APSD Total 20.60*** 0.99 5.44* 0.55 SRP-II Total 19.92*** 0.97 2.01 0.34 Note: F ratios and effect sizes (Cohen’s d) were calculated for each group (i.e. moderate and low) across genuine and PIM conditions (Rogers et al., 2002). PCL: YV = Psychopathy Checklist: Youth Version; APSD = Antisocial Process Screening Device; SRP-II = Self-report of Psychopathy-Second Edition. *p ≤ .05. ***p ≤ .001.
26 However, the authors did not report the effects of psychopathy level on increased ability to engage in NIM. Either they did not evaluate psychopathy level and NIM ability or they did not find significant effects. Table 5 illustrates the effects of psychopathy levels on ability to engage in PIM from the Rogers et al. (2002) study.
Rogers et al. (2002) developed a Social Desirability-Psychopathy (SDP) index from SRP-
II items for detecting juvenile offenders engaging in PIM. Using a SDP cut score of < 18, they were able to correctly identify 36 of the 39 respondents engaging in PIM. The authors achieved moderate sensitivity (.68) and excellent specificity (.88). Despite the important implications of this study and Hare’s (1991) concerns about frequent deception, no further research has examined the effects of response distortion on psychopathy measures.
Summary. Collectively, these five research studies established three important findings.
First, when asked to simulate PIM, participants with moderate levels of psychopathy can more successfully lower their scores on self-report psychopathy measures and PIM validity indicators
(Book et al., 2006; Edens et al., 2001; Rogers et al., 2002). As the only exception, MacNeil and
Holden (2006) found that the successful simulation of PIM was not predicted by the total level of psychopathy but by certain psychopathic traits (i.e. machiavellian egocentricity, blame externalization, and stress immunity).
Second, in contrast to PIM, levels of psychopathy do not appear to affect successful simulation of NIM (Book et al., 2006; Edens et al., 2000; MacNeil & Holden, 2006).
Consistently, results indicated that higher psychopathy levels do not improve participants’ ability to engage in NIM.
Third, these research studies suggest that self-report psychopathy measures with validity indices as well as self-report response style measures can be effective at identifying both PIM
27 and NIM (Book et al., 2006; Edens et al., 2000; MacNeil & Holden, 2006; Rogers et al., 2002).
Importantly, validity indicators were effective at classifying NIM irrespective of the levels of psychopathy. However, these studies have yet to reach a consensus on PIM, which is a primary objective of this dissertation. The current study investigated the effects of PIM on the three most prominently utilized self-report measures of psychopathy in the literature (Seibert, Miller, Few,
Zeichner, & Lynam, 2010). These three measures, the SRP-4, LSRP, and PPI-R, will be evaluated using a within-subjects simulation design to assess the effects of psychopathy levels on successful PIM presentations.
Current Study
Addressing past limitations. As previously described, the effective assessment and identification of deception among psychopathic individuals is infrequently investigated in the current literature. Even fewer studies examine inmates’ ability to successfully minimize their psychopathy scores on self-report measures. Such research is imperative due to the increased use of self-report measures of psychopathy in forensic populations. Several limitations were noted in the previously discussed studies of psychopathy and deception on self-report measures.
The current study attempted to address these limitations.
Need for adult offender population. The most critical limitation of the past studies was their ecological validity. Four of the five studies utilized undergraduate students from universities, representing presumably pro-social individuals. Only one study generalized to offenders (Rogers et al., 2002), but it was limited to juveniles in detention. PIM research on psychopathy has yet to investigate in adult offenders. To improve ecological validity, the current study utilized both pretrial and convicted detainees with criminal histories.
28 Simulation design. The current research study employed a within-subjects simulation design with realistic and relevant scenario instructions. Previous research studies used hypothetical scenarios that were likely to be unfamiliar to undergraduates. Researchers should use instructions and scenarios that participants can easily identify with and imagine themselves in that situation to increase external validity (Rogers & Gillard, 2011). The current study utilized
a realistic scenario, specifically instructing offenders to imagine they have been found guilty of
aggravated assault and must successfully engage in PIM in order to receive a lighter sentence.
Research Questions and Hypotheses. The current study evaluated inmates’ abilities to
engage in PIM on self-report measures of psychopathy. Specifically, it assessed the effects of
PIM on the SRP-4, LSRP, and PPI-R. As summarized in Table 4, five studies utilized broad
conceptualizations that were aimed at general impression management. The current study had a
specific focus, the simulation of a non-psychopathic and non-dangerous person.
Research Question 1: Are self-report psychopathy measures susceptible to PIM? The
first research question investigated the susceptibility of the SRP-4, LSRP, and PPI-R to PIM. It
evaluated the extent that inmates could achieve lower total psychopathy scores. Currently
research is limited regarding psychopaths’ ability to engage in PIM on self-report measures of
psychopathy. Several studies (Book et al., 2006; Edens et al., 2001; Rogers et al. 2002) found
that the high psychopathy groups were more successful at avoiding detection than their low-
psychopathic counterparts.
Hypothesis 1. Inmates will endorse fewer psychopathic characteristics in the PIM
condition on the SRP-4, LSRP, and PPI-R total scores than in the genuine condition.
Hypothesis 2. There will be no significant differences across measures (i.e., SRP-4,
LSRP, PPI-R) regarding changes in total scores between the genuine and PIM conditions.
29 Hypothesis 3. The high psychopathy group will have larger effect sizes when
suppressing their total psychopathy scores on the SRP-4, LSRP, and PPI-R total scores as compared to those in the moderate psychopathy group.
Hypothesis 4. Inmates classified as having a valid Impression Management score on the
Paulhus Deception Scales (PDS; Paulhus, 1998) will achieve larger effect sizes on the SRP-4,
LSRP, and PPI-R from genuine to PIM conditions compared to those classified as maybe invalid.
Research Question 2: Does the PPI-R Virtuous Responding (VR) validity scale
successfully detect PIM? The second research question assessed the effectiveness of the PPI-R
Virtuous Responding (VR) scale to detect PIM. Edens et al. (2001) found that the PPI validity
scales were only modestly successful at detecting deceptive responders. Conversely, two studies
(MacNeil & Holden, 2006; Rogers et al., 2002) suggest validity indicators can be successful.
Hypothesis 5. Overall, the PPI-R VR validity scale will successfully detect those
engaging in PIM.
Hypothesis 6. Inmates that were able to successfully evade detection on the PPI-R VR
validity scale should have higher PCL-R total psychopathy scores than those that are
unsuccessful.
Supplementary Question: Which PCL-R facets will produce the largest effect sizes
between control (standard) and PIM conditions for on SRP-4, LSRP, and PPI-R total
psychopathy scores? No research has specifically addressed this hypothesis. Because Facet 1 is
most closely associated with deception (see Table 3), it would be theoretically linked to higher
effect sizes.
30 CHAPTER 2
METHODS
Design
This study utilized a mixed, repeated-measures simulation design to investigate the differences between offenders’ standard (i.e., control) and minimized (i.e., positive impression management, PIM) psychopathy scores on self-report measures. The control condition established base-line psychopathy scores for each inmate on all the measures. Forthrightness was encouraged in the control condition because (a) results were used for research purposes only and (b) the anonymity of inmates was assured. During the simulation condition, the three self- report measures were re-administered to each inmate under PIM instructions and a relevant scenario. This mixed research design allowed for within-subjects and between-subjects comparisons. Within-subject comparisons analyzed differences between genuine and experimental condition psychopathy scores, specifically assessing ability to minimize psychopathic characteristics on self-report measures. Between-subject comparisons using PCL-
R scores classified offenders into high and moderate psychopathy groups, and examined their relative effectiveness at minimizing psychopathy on self-report measures.
Both internal validity and external validity were considered in the current research design. This simulation design was employed to increase internal validity (Rogers, 2008) by having inmates’ complete measures in a control condition as well as in a simulation condition.
During Phase I of the study, inmates completed four measures (PCL-R, SRP-4, PPI-R, and the
LSRP) under genuine instructions. During Phase II, inmates completed the three self-report measures under PIM instructions. This within-subjects factor allowed for the direct analysis of inmates’ individual ability to lower psychopathy levels on self-report measures.
31 External validity was addressed by using a sample of pretrial and convicted detainees
from Tarrant County Jail. This sample is directly relevant to the forensic applications of these measures. To increase motivation, scenario instructions described a familiar situation (i.e., being involved in the internal justice system) inmates easily related to (Rogers & Gillard, 2011).
Two authorizations were necessary for data collection to begin: (a) approval from the
Institutional Review Board at the University of North Texas, and (b) administrative approval from Tarrant County Jail. Once these two authorizations were received, data collection commenced.
Participants
The current study sample consisted of 86 male offenders recruited from the general population units at Tarrant County Jail in Fort Worth, Texas. Inmates’ participation was strictly voluntary for those providing informed consent. Those procedures were discussed in the subsequent section under Inmate Instructions.
Inclusion criteria. For this study, moderate and high levels of psychopathy were relevant for the successful assessment of the research questions. Specifically, substantial levels of psychopathy were needed in order to adequately evaluate inmates’ abilities to lower their psychopathic personality traits on self-report measures.
Three inclusion criteria were used in the current study to increase the proportion of inmates with moderate and high levels of psychopathy. First, inmates with one or more felony convictions significantly increased the likelihood of including offenders with higher levels of psychopathy than is typically found in the average county jail general population (Rogers,
Jordan, & Harrison, 2007). Second, because of this study’s focus on adult offenders, all inmates
32 had to be at least 18 years old. Third, inmates had to have a fifth grade reading level to ensure
adequate comprehension of the study’s measures and procedures.
Exclusion criteria. Exclusion criteria were minimal for the current study to increase its
representativeness. Inmates considered to be an imminent risk (i.e. within 24 hours) for
interpersonal violence or an elopement danger were excluded during recruitment. Because the
study shared PCL-R data with two other studies, only male inmates were utilized to address the
research objective of another study.
Measures
Psychopathy Checklist – Revised, 2nd Edition (PCL-R). The PCL-R (Hare, 2003) is a semi-structured interview used for the assessment of psychopathy in research, clinical, and forensic settings. The PCL-R consists of 20 items that provide a total psychopathy score, two factor scores, and four facet scores based on Hare (2003). Each item is scored on a three-point likert scale, from (a) a score of “0” indicates the characteristic does not apply to the individual,
(b) score of “1” indicates the characteristic applies to the individual to a certain extent but not to the degree required for a score of “2,” and (c) a score of “2” classifies the characteristic as definitely present in the individual.
The PCL-R has extensive research history demonstrating good reliability and validity
(Hare, 2003). The PCL-R total score has excellent internal consistency (α = .84) combining across male and female offenders and male forensic psychiatric patients; it has a pooled interrater reliability of .87 (Hare, 2003). An extensive body of evidence supports excellent construct validity (see Hare 1991, 2003; Hare & Neumann, 2006; Salekin et al., 1996).
Self-Report Psychopathy Scale-4th edition (SRP-4). The SRP-4 (Paulhus et al., in press) is a 64-item self-report multiscale inventory designed to measure psychopathy. Each item is
33 scored on a five-point likert scale: (1) disagree strongly, (2) disagree, (3) neutral, (4) agree, and
(5) agree strongly. As described in the Introduction, the SRP-4 consists of four non-overlapping,
16-item subscales designed to measure a four-facet structure similar to the PCL-R: Interpersonal
Manipulation (IPM), Callous Affect (CA), Erratic Life Style (ELS), and Criminal Tendencies
(CT). A total SRP-4 score sums the four subscales and provides an overall psychopathy score ranging from 64 to 320. The entire measure is easily readable with a Flesch-Kincaid reading level of grade 4.9.
Reliability and validity for the SRP-4 scales have been established through rigorous research. Internal consistency is acceptable with alpha reliabilities ranging from .67 to .91 for
the subscales and .88 for the total score (Williams, Paulhus, & Hare, 2007). Construct validity is good with moderate to high correlations with other self-report measures of psychopathy, including the PPI-R and LSRP (Seibert et al., 2010).
Psychopathic Personality Inventory–R (PPI–R). The PPI-R (Lilienfeld & Widows, 2005) is a 154-item self-report measure of both global psychopathy and its component traits. Each
question is answered on a 4-point scale from (1) false, (2) mostly false, to (3) mostly true, and
(4) true. The PPI-R total raw score (i.e., the total score is a sum of the eight content scales)
ranges from 131 to 524, which are converted to T scores (Lilienfeld & Widows, 2005). Three
validity scales, VR, Deviant Responding (DR), and Inconsistent Responding (IR), can detect
response styles relevant to psychopathy and self-report measures in general. VR, a primary
focus for this study, is a 13-item scale, with a reliability of .65 and detects PIM responding. The
PPI-R has three main factors with eight subscales (Lilienfeld & Widows, 2005). The first factor,
Fearless Dominance (FD), is composed of the following Content scales: Social Influence,
Fearlessness, and Stress Immunity. The Self-Centered Impulsivity (SI) factor contains four
34 Content scales: Machiavellian Egocentricity, Rebellious Nonconformity, Carefree
Nonplanfulness, and Blame Externalization. The third factor, Coldheartedness (C), consists of only the Coldheartedness scale.
Internal consistency is adequate for the PPI-R Total and the PPI-R Content subscales, with coefficient alpha ranging from .72 to .84 in an offender sample (Lilienfeld & Widows,
2005). Moderate correlations of total score with the SRP-II (.70; Hare, 1991) and LSRP (.56;
Lilienfeld & Widows, 2005) indicate acceptable convergent validity. In their revision, Lilienfeld and Widows (2005) successfully decreased the Flesch-Kincaid reading level from 8th grade on
the PPI to 4th grade on the PPI-R, making it much more appropriate for offender populations. In
summary, the PPI-R is useful for assessing psychopathy in a variety of settings, particularly
correctional facilities, forensic evaluations, and research.
Levenson Self-Report Psychopathy Scale (LSRP).1 The LSRP (Levenson et al., 1995) is a 26-item self-report measure designed to evaluate both the behavioral characteristics and personality traits commonly associated with psychopathy. Each item is endorsed on a four point scale: (1) disagree strongly, (2) disagree somewhat, (3) agree somewhat, and (4) agree strongly.
LSRP total scale scores range from 26 to 104, and has two subscales, the Primary Scale and the
Secondary Scale. The LSRP Flesch-Kincaid reading level at a grade of 4.0 is comparable to the
PPI-R.
Confirmatory factor analysis by Lynam, Whiteside, and Jones (1999) supported the
Levenson et al. (1995) two-factor model. In addition, Lynam et al. (1999) found excellent internal consistency for the Primary Scale (α = .84) but only adequate for the Secondary Scale (α
1The Levenson Self-Report Psychopathy scale (LSRP) is sometimes called the Levenson Primary and Secondary Psychopathy scale (LPSP) or the Self-Report Psychopathy Scale (SRPS) in various research studies.
35 = .68). For convergent validity, Brinkley et al. (2001) found a low correlation (r = .40) between the LSRP and PCL-R total scores. Brinkley et al., (2001) also reported low correlations between the Primary Scale and PCL-R Factor 1 (r = .35) as well as the Secondary Scale and PCL-R
Factor 2 (r = .45).
Paulhus Deception Scales (PDS). The PDS (Paulhus, 1998) is a 40-item self-report questionnaire developed to identify favorable responding. The 5-point likert scale is reduced to dichotomous scoring only zero (i.e. the four least extreme responses receive no score) and one
(i.e., only the most extreme response receives a point). The PDS has two scales: the Impression
Management scale and the Self-Deceptive Enhancement scale. Paulhus (1998) found strong reliability coefficients for IM (.81 to .86) and SDE (.70 to .75). Lanyon and Carle (2007) identified moderate convergent validity, in forensic samples, when compared with validity scales found on multiscale inventories.
Inmate Instructions
Instructional sets for simulation research should increase participants’ involvement by challenging them to successfully evade detection (Rogers, 2008). Experimental instructions can motivate participants with real world consequences they have experienced or can easily imagine themselves experiencing (i.e., acting as if they are in a real life situation; see Rogers & Gillard,
2011).
Genuine instructions. All inmates in Phase I of the research study were instructed to respond genuinely. Reminders of confidentiality were given to increase their comfort level and likelihood that they would respond in a forthright manner. To ensure the comprehension of conditions, inmates silently read the instructions and then paraphrased them. As noted, the genuine condition instructions emphasized the anonymity of their responses and the importance
36 of responding truthfully. Key instructions are included below with complete instructions reproduced in Appendix A.
Please respond to all of the following questions openly and honestly.
Remember, this information will not have your name on it and will not be seen by
correctional officers. It is only used for this research study. It is important that
you present yourself as you really are.
Positive impression management instructions. During Phase II of the study, inmates completed three self-report measures in the PIM condition with the specific goal of lowering their levels of psychopathy. Paralleling Phase I, inmates were asked to read and paraphrase the instructions. In addition, they were provided with a scenario that depicted a realistic situation for most detained inmates, an assault charge. A physical fight was chosen as the context for the scenario because it is commonly experienced by many inmates, directly or indirectly (e.g., observing others fighting). By providing specific response instructions and a realistic scenario, the likelihood of inmates completing Phase II was increased, as well as increasing external validity (Rogers, 2008). PIM instructions are summarized below and included in Appendix B.
Scenario. Imagine that you hurt someone badly in a fight. You have
already been found guilty of aggravated assault. Now the court will decide your
sentence. A presentence investigation report will be written to help decide how
long your sentence will be. If the report says you are a dangerous person who
may be violent again, you will receive a prison sentence of 5-10 years. You want
to appear to be a safe, caring person, who is sorry. That way you can get a short
sentence or even probation.
37 Your Task. Please pretend the rest of the questions in this study are for
your presentence investigation report. Think about what you should say about
yourself. How can you make yourself seem like a peaceful, calm person? Can
you only show your very best side? You want to make others think you are not a
risk for future crime.
Caution. Are you smart enough to convince the psychologist that you
deserve a short sentence, even though you are guilty of a violent crime? Can you
beat the tests? Keep in mind that if you seem “too good to be true” you will look
like you are lying. Please try to be believable when answering the questions, even
though you will have to bend the truth.
Procedure
Researchers. Researchers were advanced doctoral students in the clinical psychology
program at the University of North Texas. They were experienced with working with
incarcerated populations and had also completed formal training in the administration of
psychological measures. Regarding ethics and confidentiality, the researchers have had
extensive experience working with various protected populations. They remained responsible for all aspects of data collection, entry, and analysis.
Recruitment. Male inmates were recruited from the general population at Tarrant County
Jail in Fort Worth, Texas. Upon entering the jail, researchers obtained a list of inmates with past
felony convictions each day. Then researchers summoned the inmates one at a time or in small
groups and briefly explained the study. Inmates that did not wish to participate were excluded
while those who were interested individually completed the study after giving informed consent.
38 Informed consent and confidentiality. Participating inmates were given a thorough
explanation of study procedures and confidentiality. In accordance with UNT Institutional
Review Board requirements, both written and oral informed consent was obtained from all
inmates (see Appendix C). During the consent process, inmates were also informed of the benefits, risks, and their rights as a research participant. To ensure their accurate understanding, inmates paraphrased their understanding of the consent form. This method allowed the researchers to correct any misunderstandings. Copies of the consent form were maintained separately from all other study protocols to ensure anonymity. In addition, a copy of the consent
form was provided if requested by the inmate.
Confidentiality was rigorously protected, and no identifying information was listed on research protocols. Research numbers were assigned to each individual, ensuring the anonymity
of their responses and eliminate any risk of breaking confidentiality. These procedures were
explained during the consent process to reduce any concerns of the inmates.
Brief screening process. Following consent, inmates received the standard instructions,
advising them to respond genuinely and reminding them that their responses were protected by
confidentiality. They then underwent a short screening process to ensure the inclusion and
exclusion requirements were fully met. Specifically, they were briefly asked about general
demographic information and past criminal history through an interview-based questionnaire
(see Appendix D). This process increased the likelihood that most inmates possessed substantial
levels of psychopathy.
Phase I of data collection. Phase I of the study included data collection on all of the measures while the inmate followed the standard instructions to respond genuinely. Following
39 the demographic interview the measures were administered in this order, PCL-R, SRP-4, PPI-R,
LSRP, and PDS.
Order of conditions. An important methodological consideration was the order of the
two conditions (i.e. genuine vs. PIM). Edens et al. (2000) concluded that participants had more
difficulty responding genuinely when asked to simulate first in the PPI study. Rogers et al.
(2002) shared this concern of simulation confounding genuine responding; they also deemed it
was more appropriate to administer the genuine condition first. Therefore, all inmates in this
study, were administered the psychopathy measures under genuine instructions first and then
received PIM instructions. Masking interviewers to the condition was not a concern, because the
current study only utilized self-report measures in the simulation condition.
Order of measures. A second and important consideration was the order of test
administration. While counterbalancing generally helps protect against the possible ordering
effects or extraneous variables (e.g., participant fatigue), all of the measures were administered in the same order. The purpose of this was two-fold. First, in the genuine condition, the PCL-R,
an interview-based measure, helped to build rapport that was essential for creating a positive
environment and elicit genuine disclosure. Secondly, maintaining the same administration order
from genuine to PIM condition ensured that the same measure was not given twice in succession.
Additionally, varying the measures likely reduced inmate fatigue.
Phase II of data collection. Phase II began following a short five minute break. Inmates
received the PIM instructions, read them and paraphrased what they were being asked to do. As
with Phase I, any misconceptions were corrected. They were provided one to two minutes of
preparation time, and then the three self-report measures were administered in the following
order: SRP-4, PPI-R, LSRP, and PDS.
40 Manipulation check. After all measures were administered, the inmate completed a manipulation check (see Appendix E). Manipulation checks are essential when using simulation
design studies and used to evaluate sufficient recall of instructions, adequate comprehension of
study procedures, and satisfactory compliance (Rogers, 2008). Free recall of the participant
instructions checked their memory and understanding of the simulation condition. Their level of
motivation was evaluated via likert scale inquiring about their effort. Compliance and perceived
success was also addressed. Once the manipulation check was completed, the inmates were thanked for their time and allowed to return to their designated areas.
Background check. Prior to inmates participating, jail records confirmed inmates were convicted felons and they were subsequently allowed to complete the study. After completing the study, online background checks were utilized to verify the number, type, and severity of inmates’ past charges and convictions. This information was then utilized to complete scoring of the PCL-R Items 19 and 20.
41 CHAPTER 3
RESULTS
Sample Refinement
The initial sample consisted of 86 male inmates from Tarrant County Jail. As part of the sample refinement, a total of six inmates were removed from the initial sample for three reasons.
First, three inmates were unable to complete both phases of the study and were therefore excluded from the final sample. Second, criminal background checks were performed on all inmates to ensure the accuracy of their reported criminal histories, which is needed for the completion of the PCL-R ratings. Background checks were inconclusive for two inmates resulting in their removal from the study. Third, one additional inmate was removed based on
the inclusion procedures (i.e., the requirement of a prior felony conviction). That inmate was
awaiting trial for felony charges when he completed the study, but had not been convicted at the
time of data analyses.
Further sample refinement was achieved by checking for response styles in the genuine
condition and manipulation checks in the PIM condition. For this refinement, the PDS was
utilized in the genuine condition to ensure inmates presented themselves in a forthright manner
with no systematic efforts at PIM. Two inmates scored above the cut score of 13 for the
Impression Management scale; they were therefore excluded from the final sample. Finally, a
manipulation check was performed with each detainee at the completion of the study. All
inmates were able to successfully demonstrate their knowledge of both the genuine and
simulation instructions. Specifically, 100% of the inmates reported that they followed the
genuine and simulation instructions and 75% believed they were successful at “appearing like a
42 safe calm inmate who deserved an easier sentence” (i.e. in the simulation condition). Following
this sequence of sample refinements, the final sample consisted of 78 inmates.
Final Sample
The average age of the final sample was 34.06 (SD = 10.86) with approximately 25% of
the sample under 25 years old. It consists primarily of African American (44.9%) and European
American (39.7%) individuals with a small portion of Hispanic Americans (15.4%). In contrast to the educational levels in typical jail populations, (which averages only 49% with a high school diploma or equivalent; Harlow, 2003), this sample has a high percentage of (80.8%) high school graduates and those who successfully completed the General Educational Development (GED) requirements.
Hare’s (2003) five psychopathy descriptors2 were collapsed into three categories to
establish the psychopathy groups within this sample. The high psychopathy group (64.1%)
includes inmates who scored between 25 and 40 (i.e., ≥ 25) on the PCL-R. Substantially smaller
percentages (29.5%) were classified in the moderate range (i.e., scores between 17 to 24) with
almost none (6.4%) falling into the low range (i.e., scores between 0 to 16). For all analyses, the
low and moderate groups were combined and categorized as the moderate group.
Criminal histories are important to understanding this population and the generalizability
of its results to other samples of jail inmates. Overall, 84.6% of the sample had violent crimes
(i.e., violent crimes defined by Bureau of Justice Statistics, 2011) and over half (51.3%) had been
incarcerated for six or more years during their adult life. The average age of this subgroup was
37.33 years (SD = 9.18). Likely, as a result of the inclusion criteria (i.e. inmates must have a
2 Hare’s (2003, p. 31) original five psychopathy descriptors were “Very High, High, Moderate, Low, and Very Low.” For the purpose of this study, those groups were collapsed into two groups: a high group (i.e., combining the Very High and High) and moderate group (i.e., combining the Moderate, Low, and Very Low).
43 past felony conviction), the overall sample had a large proportion of individuals (64.1%) classified in the high psychopathy range (Hare, 2003).
Table 6
Final Sample Percentages and Group Differences between Inmates with Violent vs. Non- violent Crimes as their Most Serious Charge Total Violent Non-violent (N = 78) (n = 66) (n = 12) N % n % n % χ2 p Ethnicity 7.41 .03 African American 35 44.9 33 94.3 2 5.7 European American 31 39.7 22 71.0 9 29.0 Hispanic American 12 15.4 11 91.7 1 8.3 Marital Status 1.29 .52 Single 39 50.0 32 82.1 7 17.9 Married 23 29.4 19 82.6 4 17.4 Divorced/Widowed 16 20.6 15 93.8 1 6.2 High School Diploma/GED 1.08 .30 Yes 63 80.8 52 82.5 11 17.5 No 15 19.2 14 93.3 1 6.7 Psychopathy Level 3.10 .08 Moderate 28 35.9 21 75.0 7 25.0 High 50 64.1 45 90.0 5 10.0 Total Time Incarcerateda 0.53 .47 Less than 6 years 38 48.7 31 81.6 7 18.4 6 or more years 40 51.3 35 87.5 5 12.5 Note: Power for these analyses was limited by some cells having fewer than 5 observed cases; to preserve power, categories were combined when applicable. Specifically, divorced and widowed were combined as well as psychopathy levels (low and moderate). a The median split was used to establish two groups.
Most African Americans (94.3%) and Hispanic Americans (91.7%) in this sample had violent crimes as their most serious charge (Table 6). Conversely, European American inmates had far fewer violent crimes as their most serious charge (71.0%). These differences in
44 percentages are not easily understood; they could reflect other differences in the types of arrests and the processing of cases.
Comparisons of demographic and correctional data across psychopathy groups revealed that the high psychopathy group tended to be less educated (d = 0.43) than those in the moderate psychopathy group (see Table 7). It is particularly interesting that the high psychopathy group had double the arrests in their lifetime than the moderate psychopathy group. In light of their arrest histories, offenders in the high psychopathy group tend to be incarcerated about 20 months longer than those in the moderate group. This finding is not statistically significant given the high variability of incarceration periods.
Table 7
Differences between Inmates in the Moderate and High Psychopathy Groups on Age, Education, Total Number of Arrests, Total Number of Months Incarcerated Moderate (n = 28) High (n = 50) M SD M SD F p d Age 35.68 11.53 33.16 10.48 0.97 .33 0.23 Education 11.36 1.66 10.56 1.98 3.25 .08 0.43 Total # of Arrests 10.18 7.54 20.50 20.15 10.49 .002 -0.61 Months Incarcerated 80.68 106.67 106.68 73.60 1.61 .21 -0.30 Note. Homogeneity of variances was violated for total number of arrests, therefore, a more robust test, Welch’s ANOVA, was utilized.
Over the years and across the literature, psychopathy has consistently been linked to criminality (Cooke, 1998) and violent crimes (McCord & McCord, 1964). In examining the current data, this finding was strongly confirmed with the large majority (64.0%) of the high psychopathy group evidencing violent offenses for the current charge (see Table 8). In stark contrast, only 25% of the moderate psychopathy group were currently charged with a violent crime. From a longitudinal perspective, the differences are not as dramatic when concerning the
45 most serious charge. The lack of marked differences is likely due to the inclusion procedures
(i.e. all detainees must have past felony convictions).
Table 8
Types of Current and Most Serious Charges Across Psychopathy Groups
Current Charge Most Serious Charge Moderate High Moderate High
(n = 28) (n = 50) (n = 28) (n = 50) n % n % n % n % Violent Crimes Murder/Attempted Murder 1 3.6 5 10.0 4 14.3 9 18.0 Kidnapping 0 0.0 1 2.0 0 0.0 2 4.0 Assault/Sexual Assault 5 17.9 14 28.0 12 42.9 21 42.0 Robbery 1 3.5 12 24.0 5 17.8 13 26.0 Totals 7 25.0 32 64.0 21 75.0 45 90.0 Non-Violent Crimes Burglary/Theft 5 17.9 5 10.0 4 14.3 4 8.0 Drug Related 4 14.3 2 4.0 2 7.1 1 2.0 DWI 2 7.1 0 0.0 0 0.0 0 0.0 Othera 10 35.7 11 22.0 1 3.6 0 0.0 Totals 21 75.0 18 36.0 7 25.0 5 10.0 Note. A chi-square test examined the difference between psychopathy groups and violent vs. non-violent crimes. A significant difference was found for the current charge, χ2 = 10.92, p = .001, but not for the most serious charge, which manifested a non-significant trend, χ2= 3.10, p = .08. aThe “other” category is a combination of several types of charges, including parole violation, firearm possession, unauthorized use of motor vehicle, failure to stop and render aid, and violation of protective order by phone call.
Reliability
Prior to analyses of the research questions, the reliability for the study’s measures were
examined for internal consistency and interrater reliability. Internal consistency was investigated
for each self-report measure and yielded a range from acceptable to excellent results. Both the
SRP-4 (.92) and LSRP (.90) had excellent total score alphas. The SRP-4 scale alphas ranged
from acceptable to good (.70 to .85, see Table 9), whereas the LSRP alphas were consistently
good for both the Primary (α = .88) and Secondary (α = .80) Psychopathy Scales.
46 Comparisons between this study and the literature found there were no major
discrepancies between alphas for the SRP-4 (Neal & Selbom, 2012; Paulhus, Neumann, & Hare, in press; Williams et al., 2007). Alphas for the LSRP Total Score and Primary Psychopathy scale were comparable to the literature. However, for the LSRP, Lynam et al. (1999) and
Levenson, Kiehl, and Fitzpatrick (1995) reported only acceptable reliabilities (i.e., .68 and .63) for the LSRP Secondary Psychopathy scale.
The PPI-R had more variability in internal consistencies for its content scales; five
content scales (see Table 9) demonstrated strong alphas with inter-item correlations being in the
recommended range (i.e., .15 to .50; Clark & Watson, 1995, p. 316). Only Stress Immunity had
a marginal alpha of .68 with its items showing lower than expected inter-item correlations. As
compared to the PPI-R test manual, this study found equal or better alphas for all the scales
except Fearlessness and Stress Immunity, and there were no major discrepancies between the
two studies. Reliability estimates for response style scales are expected to be lower, given that
they are measuring response styles rather than a singular, cohesive construct such as a
personality characteristic (Lilienfeld & Widows, 2005) . As noted in Table 9, the inter-item
correlations indicate very little relationship among their individual items.
Interrater reliability on the PCL-R was investigated on 12 cases, via independent ratings
by two researchers. Results indicated excellent agreement for PCL-R Factor 1 (Intra-class
Correlation Coefficient; ICC = .91) and Factor 2 scores (ICC = .90), as well as total scores (ICC
= .89). Agreement varied more within the facets, ranging from moderate to high. More
specifically, Facet 2 (ICC = .73) and Facet 3 (ICC = .77) yielded moderate reliability, while
Facet 1 (ICC = .85) and Facet 4 (ICC = .92) were moderately high to high.
47 Table 9
Reliability Estimates for Self-Report Measures
Measure Items (n) α M inter-item r SRP-4 Total Score 64 .92 .15 Interpersonal Manipulation (IPM) 16 .85 .25 Callous Affect (CA) 16 .70 .13 Erratic Lifestyle (ELS) 16 .80 .20 Criminal Tendencies (CT) 16 .74 .14 PPI-R Total Score 131 .91 .08 Machiavellian Egocentricity (ME) 20 .87 .26 Rebellious Nonconformity (RN) 16 .77 .18 Blame Externalization (BE) 15 .80 .21 Carefree Nonplanfulness (CN) 19 .83 .21 Social Influence (SOI) 18 .85 .24 Fearlessness (F) 14 .79 .21 Stress Immunity (STI) 13 .68 .14 Coldheartedness (C) 16 .81 .21 Virtuous Responding (VR) 13 .65 .12 Deviant Responding (DR) 10 .54 .12 LSRP Total Score 26 .90 .27 Primary Psychopathy (PP) 16 .88 .33 Secondary Psychopathy (SP) 10 .80 .29 PDS Total Score 40 .84 .12 Self-Deceptive Enhancement (SDE) 20 .67 .09 Impression Management (IM) 20 .83 .19 Note. α = reliability; M inter-item r = average inter-item correlation; SRP-4 = Self-Report Psychopathy Scale-4th edition; PPI-R = Psychopathic Personality Inventory-Revised; LSRP = Levenson Self-Report Psychopathy Scale; PDS = Paulhus Deception Scales.
Convergent and Discriminant Validity
A correlational matrix was utilized to establish convergent and discriminant validity
between the self-report measures and the PCL-R. These results are briefly mentioned here and more thoroughly evaluated in the Discussion chapter.
48 The SRP-4 ELS scale was moderately correlated with the PCL-R Facet 3 (.36; Table 10)
and the CT scale to Facet 4 (.44). This showed promising convergent results; however, the IPM
and CA scales only had small correlations with Facets 1 and 2. No other studies have evaluated
the SRP-4 against the PCL-R and therefore no correlations are available for comparison with the
current study’s findings.
Table 10
Correlations between PCL-R and SRP-4 Total Scores and Underlying Factors
PCL-R Total Factor 1 Factor 2 Facet 1 Facet 2 Facet 3 Facet 4 SRP-4 Total .30** .06 .46*** .02 .09 .39*** .41*** IPM .29* .14 .34** .13 .11 .26* .32** CA .30** .20 .39*** .06 .29** .36*** .32** ELS .22 -.03 .36*** -.05 .01 .36*** .28* CT .22 -.08 .46*** -.07 -.07 .34** .44*** Note. PCL-R Total = Psychopathy Checklist-Revised Total Score; Factor 1 = Personality Characteristics; Factor 2 = Behavioral Items; Facet 1 = Interpersonal Facet; Facet 2 = Affective Facet; Facet 3 = Lifestyle Facet; Facet 4 = Antisocial Facet; SRP-4 Total = Self-Report Psychopathy Scale Total Score; IPM = Interpersonal Manipulation Scale; CA = Callous Affect Scale; ELS = Erratic Lifestyle Scale; CT = Criminal Tendencies Scale. *p ≤ .05, **p ≤ .01, ***p ≤ .001.
The PPI-R FD was slightly correlated with the PCL-R Factor 1 (.29; see Table 11), and the SI factor was moderately correlated with Factor 2 (.40), while Factor C was not significantly correlated with the PCL-R at all. Correlations from Copestake, Gray, and Snowden (2011) were included in Table 11 and differed greatly from the current study with much higher correlations across most of the scales, factors, and facets.
49 Table 11
Correlations between PCL-R and PPI-R Total Scores and Underlying Factors
PCL-R Total Factor 1 Factor 2 Facet 1 Facet 2 Facet 3 Facet 4 Copestake et al., 2011a PPI-R Total .54** .50** .49** .40** .42** .51** .50** SI .51** .48** .44** .40** .48** .46** .39** FD .19 .19 .21 .05 -.04 .25 .35* C .42** .43** .46** .46** .48** .42** .31** Current Studyb PPI-R Total .31** .14 .38*** .14 .08 .38*** .29** SI .25* -.03 .40*** -.02 -.03 .46*** .26* FD .25* .29** .14 .33** .16 -.01 .22* C .17 .17 .15 .08 .21 .21 .07 Note. PCL-R Total = Psychopathy Checklist-Revised Total Score; Factor 1 = Personality Characteristics; Factor 2 = Behavioral Items; Facet 1 = Interpersonal Facet; Facet 2 = Affective Facet; Facet 3 = Lifestyle Facet; Facet 4 = Antisocial Facet; PPI-R Total = Psychopathic Personality Inventory-Revised Total Score; SI = Self-Centered Impulsivity; FD = Fearless Dominance; C = Coldheartedness. an = 52. bn = 78. *p < .05, **p < .01, ***p ≤ .001.
The LSRP SP scale was moderately correlated with the PCL-R Factor 2 (.36; Table 12); however, the PP scale was not significantly correlated with the PCL-R. These results indicate good convergent and discriminant validity for the SP scale but lack convergent validity for PP.
Three studies’ results were included in Table 12 for comparison.
50 Table 12
Correlations Between the PCL-R and LSRP Total Scores and Underlying Factors
PCL-R Total Factor 1 Factor 2 Book et al. 2007 (n = 59) LSRP Total .30** PP .20 SP .40*** Brinkley et al. 2001 (n = 549) LSRP Total .35*** .27*** .38*** PP .34*** .30*** .31*** SP .27*** .13** .36*** Poythress et al. 2009 (n = 1,472) LSRP Total .30*** PP .23* .29* SP .06* .29* Current Study (n = 78) LSRP Total .30** .09 .40*** PP .31** .18 .36*** SP .20 -.06 .36*** Note. PCL-R Total = Psychopathy Checklist-Revised Total Score; Factor 1 = Personality Characteristics; Factor 2 = Behavioral Items; LSRP Total = Levenson Self-Report Psychopathy Scale Total Score; PP = Primary Psychopathy; SP = Secondary Psychopathy. *p < .05, **p < .01, ***p < .001.
PIM on Self-report Measures of Psychopathy
Research Question 1. Are self-report psychopathy measures susceptible to PIM?
Hypothesis 1. It was hypothesized that inmates would endorse fewer psychopathic characteristics in the PIM condition, on the SRP-4, PPI-R, and LSRP total scores, than in the genuine condition.
A central issue is whether inmates can successfully engage in PIM when it would be advantageous for them to do so, for instance during parole reviews or sentencing investigations.
A 2 X 3 repeated-measures factorial ANOVA evaluated if offenders successfully engaged in
PIM on the self-report measures of psychopathy.
51 An important aspect of this research design was a direct comparison across measures
using, a repeated-measures factorial ANOVA. For this analysis, the dependent variables must be
on the same scale. To achieve this goal, the total psychopathy scores (for the three self-report
measures) were converted into proportion scores (i.e., actual total score ÷ maximum possible score for the measure X 100). This calculation gave an indication as to the proportion of psychopathic symptoms inmates endorsed on each measure for both conditions. In Hypotheses 1 and 2, the independent variables were condition (genuine and PIM) and measure (SRP-4, PPI-R, and LSRP) while the dependent variables were the proportion scores. This approach allowed for the analysis of two main effects (condition and measure) as well as the condition X measure interaction.
As predicted in Hypothesis 1, the experimental condition produced a very large effect
2 size (condition ηp = .60; see Table 13). Inmates were able to greatly reduce their psychopathy
scores on all three self-report measures (see Table 13) when instructed to do so.
Table 13
Effects of Condition and Measure on Psychopathy Scores
Effect MS df F p partial η2 Condition 20208.34 1,77 113.96 < .001 .60 Measurea 1445.36 1.78, 136.74 37.82 < .001 .33 Condition X Measure 647.41 2, 154 25.60 < .001 .25 Note. n = 78. This analysis used a 2 X 3 Repeated-Measures ANOVA with IVs condition (genuine, Positive Impression Management) and measure (Self-Report Psychopathy Scale, Psychopathic Personality Inventory-Revised, Levenson Self-Report Psychopathy Scale). The proportion of the total psychopathy score out of the maximum allowable score for each self- report psychopathy measure was used as the dependent variable. aThe Huynh-Feldt correction was used for measure because sphericity was violated.
Hypothesis 2. It was hypothesized that there would be no significant differences between
measures, SRP-4, PPI-R, and LSRP, when evaluating the greatest magnitude of change across
52 the genuine and PIM conditions (i.e., the proportion of psychopathic symptoms inmates endorsed
on each measure across both conditions).
Initially, it was hypothesized that all three self-report measures of psychopathy would be equally susceptible to PIM when evaluating the greatest magnitude of change across measures and conditions. In testing Hypothesis 2, a significant difference was found unexpectedly across
2 measures (measure ηp = .33; see Table 13). The SRP-4 responses showed a significantly larger decrease in total psychopathy scores than the PPI-R and LSRP (see Table 14).
Table 14
Differences for Inmates Between Genuine and PIM Conditions on Self-Report Psychopathy Measures Genuine PIM M SD M SD d SRP-4 190.08 29.57 136.13 30.36 1.80 PPI-R 309.50 37.35 263.46 35.15 1.27 LSRP 58.15 13.07 43.82 10.91 1.19 Note. n = 78 for both genuine and PIM conditions. PIM = Positive Impression Management; SRP-4 = Self-Report Psychopathy Scale; PPI-R = Psychopathic Personality Inventory-Revised; LSRP = Levenson Self-Report Psychopathy Scale.
Under genuine instructions, the average total score on the PPI-R (309.50) was equal to a
T score of 59 for an offender sample (Lilienfeld & Widows, 2005) and fell into the 82nd
percentile. This is almost one standard deviation above the offender mean, with a high percentile
ranking, indicating the current study’s sample is generally considered psychopathic by the PPI-
R’s standards. More importantly, in the PIM condition, inmates were able to lower their scores
more than one standard deviation (M = 263.46, 43T) to a 27th percentile ranking, presenting
themselves as non-psychopathic.
Unlike the PPI-R, the SRP-4 and LSRP do not have manuals that provide T score
conversions and descriptions of what scores indicate. Table 15 summarizes several studies that
53 utilized the SRP-4 and reported means and standard deviations of their samples. According to
Paulhus et al. (in press) and Watt and Brooks (2011), the average male found in a community or
undergraduate sample should score around 165 on the SRP-4. The current study’s sample
attained an average score of 190 in the genuine condition, nearly one standard deviation higher
than the community and undergraduate males. Under PIM instructions, the average score was
136 (see Table 14), much lower than scores reported in the literature (see Table 15). These results indicate that inmates were able to significantly lower their SRP-4 scores and appear non- psychopathic.
Table 15
Reported Means and Standard Deviations for the SRP-4 Total and Scale Scores
Study Sample n Total IPM CA ELS CT Paulhus et al. (in press) undergraduatesa 194 165.20 47.2 44.5 46.5 26.9 (27.40) (9.5) (7.2) (9.0) (8.9) Watt & Brooks (2011) community males 100 164.34 43.97 42.41 48.18 29.77 (29.93) (10.43) (7.98) (9.61) (9.56) Note. Standard deviations are in parentheses below the means. Total = Total Psychopathy Score; IPM = Interpersonal Manipulation Scale; CA = Callous Affect Scale; ELS = Erratic Lifestyle Scale; CT = Criminal Tendencies Scale. aThe sample contained both males and females; however, we reported the means and standard deviations only for the male subsample.
The average genuine LSRP total score (58.15; see Table 14) found in the current study is
similar to those reported in incarcerated samples (49.8 & 55.84; see Table 16). Importantly,
under PIM instructions inmates were able to lower their LSRP total scores (43.82) below what
was reported in an undergraduate sample (Levenson et al., 1995; see Table 16). Considering the
range of possible LSRP total scores is between 16 and 64, it can be concluded that inmates high
in psychopathy are able to significantly lower their scores to appear non-psychopathic.
54 Table 16
Reported Means and Standard Deviations for the LSRP Total and Scale Scores
Study Sample n Total PP SP Brinkley et al. (2001) state inmates 549 54.66 (11.58) 32.99 (8.19) 21.68 (5.05) Poythress et al. (2010) inmatesa 1603 55.84 (11.69) 32.83 (8.13) 23.00 (5.29) Walters et al. (2008a) federal inmates 1972 49.80 (13.24) 28.70 (7.60) 21.10 (5.64) Levenson et al. (1995) undergraduates 487 48.45 (10.92) 29.13 (6.86) 19.32 (4.06) Note. Standard deviations are reported in parentheses to the right of means. Total = Total LSRP Psychopathy Score; PP = Primary Psychopathy Scale Score; SP = Secondary Psychopathy Scale Score. aPoythress et al. (2010) did not specify if inmates were recruited from state or federal facilities.
Psychopathy Level and PIM Ability
Hypothesis 3. Participants in the high psychopathy group were hypothesized to have
larger effect sizes when suppressing their total psychopathy scores on the SRP-4, PPI-R, and
LSRP compared to those in the moderate psychopathy group. This analysis utilized a mixed factorial ANOVA with the moderate and high psychopathy groups as the between-subjects factor. Similar to Hypothesis 1 and 2, this hypothesis also used the total psychopathy proportion scores as the dependent variable. To avoid the floor effect, five inmates with low PCL-R scores were removed from this analysis (i.e., PCL-R scores ≤ 16).
Deceitful and manipulative are key characteristics of psychopathy across the literature; therefore, it was surprising that the analysis for Hypotheses 3 found that psychopathy level
2 produced a very small effect size (classification ηp = .01; see Table 17). Condition
2 2 demonstrated the largest effect size (condition ηp = .57) and measure (measure ηp = .29) also
accounted for a significant change in psychopathy scores. At first glance, these results would
indicate that psychopathy level was not a significant factor in inmates’ abilities to engage in
PIM.
55 Table 17
Effects of Condition, Measure, and PCL-R Classification on Self-report Measures
Effect MS df F p partial η2 Condition 16649.31 1,71 92.66 < .001 .57 Measurea 1077.16 1.82, 128.99 28.87 < .001 .29 Classification 149.57 1, 71 0.73 .40 .01 Condition X Measure 545.53 2, 142 20.37 < .001 .22 Condition X Classification 155.50 1, 71 0.87 .36 .01 Measure X Classification 4.97 2, 142 0.15 .86 .00 Condition X Measure X Classification 0.73 2, 142 0.03 .97 .00 Note. 2 X 3 X 2 Repeated-Measures ANOVA with IVs condition (genuine, Positive Impression Management), measure (Self-Report Psychopathy Scale, Psychopathic Personality Inventory- Revised, Levenson Self-Report Psychopathy Scale), and Classification (moderate or high PCL- R score). The proportion of the total psychopathy score out of the maximum allowable score for each self-report psychopathy measure was used as the dependent variable. aThe Huynh-Feldt correction was used for measure because sphericity was violated.
Interestingly, psychopathy classification did not generally produce the predicted differences in effect size (Hypothesis 3) because both, the high and moderate psychopathy groups, achieved large effect sizes between genuine and PIM conditions (see Table 18).
Additionally, the self-report measures were essentially unable to discriminate between inmates classified as moderate or high psychopathy level. In looking at specific measures, the SRP-4 proved the easiest to engage in PIM, yielding the largest Cohen’s ds. In contrast, the PPI-R with its greater complexity (e.g., eight clinical scales and three validity scales) did produce the predicted results. The high psychopathy group were more successful than their moderate counterparts at decreasing their PPI-R psychopathy scores (1.51 vs. 1.07). Differences in
Cohen’s ds ≥ .30 were considered significant because this represents a difference of at least a moderate effect size. Finally, comparable effect sizes were found for the LSRP.
56 Table 18
Differences for High and Moderate PCL-R Classification between Genuine and PIM Groups
Genuine PIM M SD M SD d Moderate PCL-R Classification SRP-4 187.83 24.05 138.04 27.99 1.91 PPI-R 303.74 35.37 264.70 37.31 1.07 LSRP 57.17 12.63 43.70 9.71 1.20 High PCL-R Classification SRP-4 195.20 29.90 136.36 30.92 1.93 PPI-R 316.96 35.39 264.12 33.84 1.53 LSRP 60.08 12.38 44.28 11.20 1.34 Note. High PCL-R Classification n = 50, Moderate PCL-R Classification n = 23. PIM = Positive Impression Management; SRP-4 = Self-Report Psychopathy Scale; PPI-R = Psychopathic Personality Inventory-Revised; LSRP = Levenson Self-Report Psychopathy Scale. The PPI-R was the only measure to achieve a large difference (i.e., Cohen’s d’s that are ≥ .30 larger) between the two psychopathy groups, indicating an appreciable difference.
A refined analysis evaluated scale level differences on each self-report measure. Table
19 highlights the large increases in effect sizes between the moderate and high psychopathy groups on the SRP-4. Interestingly, two scales exhibited an appreciable difference between the two groups (i.e., ≥ .30 between the two effect sizes), IPM and CT. On the IPM scale, the high psychopathy group achieved almost a 50% increase in effect size over the moderate group. This corresponds with the literature, indicating manipulation is a core characteristic of psychopathy and the higher the level of psychopathy the more attuned to masking interpersonal deceit.
Conversely, the moderate psychopathy group had an appreciably larger effect size than the high psychopathy group on the CT scale, which looks at specific criminal behaviors. This would indicate the moderate psychopathy group was more focused on denying antisocial acts when engaging in PIM than their high psychopathic counterparts.
57 Table 19
Differences Between Moderate and High Psychopathy Group in SRP-4 Scale Scores Between Genuine and PIM Conditions Genuine PIM M SD M SD F p d Moderate Psychopathy Group Interpersonal Manipulation 42.17 8.02 34.00 8.38 12.66 .002 1.00 Callous Affect 44.70 5.76 35.35 6.25 22.48 < .001 1.56 Erratic Lifestyle 53.39 8.17 36.83 8.86 59.55 < .001 1.94 Criminal Tendencies 47.57 9.12 31.87 8.01 63.19 < .001 1.83
High Psychopathy Group Interpersonal Manipulation 46.24 10.23 33.26 7.26 60.30 < .001 1.46 Callous Affect 45.42 6.86 32.54 7.57 109.22 < .001 1.78 Erratic Lifestyle 54.14 9.50 36.34 9.81 94.90 < .001 1.84 Criminal Tendencies 49.40 9.13 34.22 10.82 73.02 < .001 1.52 Note. Moderate Psychopathy Group n = 23, High Psychopathy Group n = 50. PIM = Positive Impression Management.
Three PPI-R scales demonstrated appreciable differences (i.e., ≥ .30), between the high and moderate psychopathy groups. First, Machiavellian Egocentricity depicts individuals that are willing to manipulate others for their own gain, see themselves as superior, and can easily lie and take advantage of others (Lilienfeld & Widows, 2005). Keeping consistent with the SRP-4 results, the high psychopathy group (Cohen’s d = 1.22) dramatically lowered this scale score compared to the moderate psychopathy group (Cohen’s d = 0.84; see Table 20) across conditions. This finding combined with the SRP-4 results indicate the high psychopathy group better understood and minimized items related to manipulation and deceit.
Blame Externalization is the tendency to perceive the world as hostile, blame others for one’s own problems, and see one’s self as an innocent victim of circumstances (Lilienfeld &
Widows, 2005). The moderate psychopathy group had only a small effect size (d = 0.29), while the high psychopathy group produced an effect size more than twice the moderate group (d =
0.79). This result suggests that individuals high in psychopathy understood an internal locus of
58 control is more socially desirable than externalized. While engaging in PIM, they tended to blame others less for their problems, despite their genuine beliefs that others were to blame for their problems.
Interestingly, for the Social Influence scale the moderate group had a very small effect size (d = 0.06), barely lowering their scores a single-point across conditions while the high group had a moderate effect size (d = 0.57, see Table 20). The moderate group apparently did not view these items as socially undesirable and did not attempt to change their responses across conditions. The Social Influence scale describes individuals who are charming, skilled at influencing others, and largely free of social anxiety (Lilienfeld & Widows, 2005). Overall, these results are consistent with the scales (both the SRP-4 and PPI-R scales) that measure manipulation and indicate again, an increased ability to of the high group identify items related to influencing and manipulating others.
Table 20
Differences Between Moderate and High Psychopathy Groups in PPI-R Scale Scores Between Genuine and PIM Conditions Genuine PIM M SD M SD F p d Moderate Psychopathy Group Machiavellian Egocentricity 41.91 10.48 33.43 9.62 9.73 .005 0.84 Rebellious Nonconformity 36.00 7.60 28.39 6.76 19.54 < .001 1.06 Blame Externalization 37.52 8.03 35.13 8.58 1.33 .26 0.29 Carefree Nonplanfulness 36.96 7.28 29.17 6.58 34.13 < .001 1.12 Social Influence 47.00 9.95 46.35 10.64 0.07 .80 0.06 Fearlessness 38.09 8.12 29.83 8.59 18.58 < .001 0.99 Stress Immunity 34.57 5.70 36.39 6.26 1.94 .18 -0.30 Coldheartedness 31.70 6.53 26.00 4.84 11.89 .002 0.99 Virtuous Responding 29.57 5.20 37.87 5.54 44.23 < .001 -1.54 Deviant Responding 14.43 4.22 13.70 3.67 0.89 .36 0.18 Inconsistent Responding 29.45 8.64 28.91 10.66 0.04 .84 0.06
High Psychopathy Group Machiavellian Egocentricity 44.72 10.44 32.74 9.08 38.57 < .001 1.22 Rebellious Nonconformity 36.42 7.48 29.98 7.71 21.27 < .001 0.85 (table continues) 59 (table continued) Blame Externalization 41.54 7.14 35.18 8.86 25.43 < .001 0.79 Carefree Nonplanfulness 37.42 9.17 27.80 6.76 40.06 < .001 1.19 Social Influence 52.02 9.40 47.38 6.71 8.65 .005 0.57 Fearlessness 36.02 8.48 29.78 6.77 22.74 < .001 0.81 Stress Immunity 34.54 5.89 36.12 5.46 2.72 .11 -0.28 Coldheartedness 34.28 8.25 25.14 6.54 35.96 < .001 1.23 Virtuous Responding 27.58 5.48 38.56 6.74 83.23 < .001 -1.79 Deviant Responding 14.34 2.99 13.60 3.65 1.69 .20 0.22 Inconsistent Responding 31.89 7.27 31.93 9.31 0.00 .98 0.00 Note. Moderate Psychopathy Group n = 23, High Psychopathy Group n = 50. PIM = Positive Impression Management. The 40-item Inconsistent Responding scale was used.
Lastly, an important advantage of the PPI-R over the other self-report measures, is its
inclusion of validity scales. Table 20 illustrates how the VR scale is the most reliable regarding detection of PIM response styles. Both groups produced significant and very large effect sizes across conditions (moderate = -1.54, high = -1.79); in fact, these were the largest effect sizes on the PPI-R. These results indicate the VR scale functioned as expected; scores dramatically
increased in the PIM condition instead of decreasing like the other scales. As expected, the
Deviant and Inconsistent Responding scales did not yield significant changes across conditions
for either group. These results confirm that only the VR validity scale should potentially be
effective at identifying those respondents engaging in PIM. Hypothesis 5 allows for further
examination of the VR scale and potential cut-scores to identify “fakers.”
Unlike the SRP-4 and PPI-R, the LSRP did not show appreciable differences between
effect sizes (see Table 21). The Secondary Psychopathy scale produced the largest difference
between the moderate (Cohen’s d = 0.88) and high (Cohen’s d = 1.13) psychopathy groups;
however it was not an appreciable difference.
60 Table 21
Differences Between Moderate and High Psychopathy Group in LSRP Scale Scores Between Genuine and PIM Conditions Genuine PIM M SD M SD F p d Moderate Psychopathy Group Primary Psychopathy 34.13 8.64 25.30 6.53 16.57 .001 1.15 Secondary Psychopathy 23.04 5.87 18.39 4.59 16.57 .001 0.88
High Psychopathy Group Primary Psychopathy 35.70 8.46 25.98 7.34 36.26 < .001 1.23 Secondary Psychopathy 24.38 5.53 18.30 5.20 39.52 < .001 1.13 Note. Moderate Psychopathy Group n = 23, High Psychopathy Group n = 50. PIM = Positive Impression Management.
Impression Management Score and PIM Ability
Hypothesis 4. Hypothesis 4 theorized that the PDS Impression Management scale would
account for larger effect sizes on the SRP-4, PPI-R, and LSRP from genuine to PIM conditions compared to what was found in previous hypotheses with psychopathy level. The same total psychopathy proportion scores from previous hypotheses were utilized in a mixed factorial
ANOVA. The between-subjects factor was classification on the PDS Impression Management scale (i.e., a valid Impression Management score ≤ 8, maybe invalid Impression Management score ≥ 9). Consistent with Hypothesis 3, inmates with low PCL-R scores in the genuine condition were removed from this analysis (i.e., PCL-R scores ≤ 16), excluding a total of five inmates.
Conversely to what was found with psychopathy level, classification did produce significant results (see Table 22). This indicates that inmates with valid PDS Impression
Management scores were able to lower their scores more than those with invalid scores.
2 Additionally, the interaction effect of condition and classification was also significant (ηp = .16).
61 These results indicate the two classification groups both significantly decreased their scores on the self-report psychopathy measures across the two conditions.
Table 22
Effects of Condition, Measure, and PDS IM Scale Classification on Self-report Scores
partial Effect MS df F p η2 Condition 5797.70 1,71 37.94 < .001 .35 Measurea 887.84 1.82, 129.10 24.09 < .001 .25 Classification 841.08 1, 71 4.30 .04 .06 Condition X Measure 229.43 2, 142 8.77 < .001 .11 Condition X Classification 2062.24 1, 71 13.49 < .001 .16 Measure X Classification 32.00 2, 142 0.96 .39 .01 Condition X Measure X Classification 43.71 2, 142 1.67 .19 .02 Note. 2 X 3 X 2 Repeated-Measures Mixed ANOVA with IVs condition (genuine, Positive Impression Management), measure (Self-Report Psychopathy Scale, Psychopathic Personality Inventory-Revised, Levenson Self-Report Psychopathy Scale), and Classification (valid, maybe invalid). The proportion of the total psychopathy score out of the maximum allowable score for each self-report psychopathy measure was used as the dependent variable. aThe Huynh-Feldt correction was used for measure because sphericity was violated.
The effect sizes were calculated for valid versus maybe invalid groups to establish which group produced larger differences between conditions (see Table 23). These findings underscored the dramatic differences between classification groups; identifying that those individuals classified as maybe invalid had appreciably smaller effect sizes across all three measures. These dramatic differences contribute to the proposition that those individuals who can successfully engage in PIM are better at lowering their self-report psychopathy scores than those that are “caught.”
62 Table 23
Differences for Valid and Maybe Invalid Impression Management Scale Scores between Genuine and PIM Conditions Genuine PIM M SD M SD d Valid IM Score SRP-4 198.21 26.70 135.51 24.98 2.43 PPI-R 318.52 32.60 262.82 29.97 1.78 LSRP 61.21 11.86 43.75 10.42 1.56 Maybe Invalid IM Score SRP-4 165.75 19.15 143.92 48.61 0.59 PPI-R 283.67 37.77 271.83 53.98 0.25 LSRP 48.75 10.30 45.83 12.32 0.08 Note. Valid IM Score Classification n = 61, Maybe Invalid IM Score Classification n = 12. PIM = Positive Impression Management; IM = Impression Management; SRP-4 = Self- Report Psychopathy Scale; PPI-R = Psychopathic Personality Inventory-Revised; LSRP = Levenson Self-Report Psychopathy Scale.
VR Scale and Detection of PIM
Research Question 2: Does the PPI-R VR validity scale successfully detect PIM?
Hypothesis 5. Overall, the PPI-R VR validity scale would successfully detect those engaging in PIM. Lilienfeld and Widows (2005, p. 18) explained that high scores on the VR
scale suggest respondents “view themselves positively and relatively free from personality
flaws” and “in some cases, extremely high scores on this scale may indicate deliberate attempts
at positive impression management.” Lilienfeld and Widows intended VR to enhance the
conceptualization of the respondent, adding valuable information related to how individuals
viewed themselves. They did not want the scale used as the sole criterion to classify those
engaging in PIM; however, they recommended that it could be used in conjunction with other
well-established measures of PIM.
Edens et al. (2001, p. 247) identified that “a cut-score of 35 or greater on the Unlikely
Virtues scale (from the original PPI) optimized overall predictive accuracy” when identifying
63 individuals engaging in PIM. This research is the only independent study that has evaluated PIM
on the PPI or the PPI-R and utilized a within-subjects research design. The lack of PPI-R
research prompted the current study to assess the VR scale on the PPI-R and evaluate it for the
best possible cut score.
Recently, the literature has begun to examine a controversial topic in the evaluation of
response styles, specifically the accuracy of single-point cut scores. Single-point cut scores utilize one score to identify a cut off and establish that all scores above or below that cut score should be considered PIM, for example. Rogers and Bender (2012) discussed the misassumption that a single-point cut score offers laser-accuracy identification of simulators; they indicated high error rates for scores that should be considered “too close to call.” Rogers and Bender (2012) also offer an alternative to this problem with misclassifications, utilizing well-defined groups
(i.e., classifications that have excellent accuracy in identifying individuals who are highly likely feigning) and indeterminate scores (i.e., a narrow group of “too close to call” cases).
Hypothesis 5 evaluated whether well-defined groups with indeterminate scores removed were more effective than single-point cut scores that include all cases. For the indeterminate scores, the criterion of ± .5 Standard Error of Measurement (SEM) was utilized (i.e., the equivalent of two raw score points) as the criterion for “too close to call” cases. Therefore, any
VR scores falling within this range were excluded. The use of a full SEM was considered but it excluded too many cases from classification as genuine or PIM. The VR scores falling outside this indeterminate range represent the well-defined group (see Table 24, Table 25, and Table 26).
The following is a brief review of utility estimates (Streiner, 2003). In evaluating the effectiveness of a measure, sensitivity is defined by the proportion of individuals who have the attribute being measured and are correctly classified by the test, while specificity of a test refers
64 to the proportion of individuals without the attribute (i.e., in this case PIM) who are correctly classified by the test. In applying cut scores in individual cases, Positive Predictive Power (PPP) assesses the accuracy of the cut score in identifying cases of PIM. Conversely, Negative
Predictive Power (NPP) identifies those individual cases that respond genuinely. Lastly, Overall
Correct Classification (OCC) is simply the proportion of correct classifications.
The base rates of PIM for denied psychopathy is not known. In the context of forensic evaluations and clinical settings, Rogers, Gillard, Wooley, and Kelsey (2013) posited base rates of 15% and 25% for feigned mental disorders. Without any better estimates of denied psychopathy, these base rates were applied to the current study.
The VR cut score can be utilized to rule-out (i.e., a high likelihood the PPI-R is genuine) and rule-in (i.e., a high likelihood of simulating on the PPI-R) classification. As a rule-out, well- defined VR scores < 25 produced a sensitivity rate of 1.00 and NPP of 1.00 (see Table 24).
Although not particularly efficient (i.e., PPP’s of .18 and .30), a minority of offenders can be designated as likely genuine responders on the basis of this cut score. A less stringent, well- defined cut score (e.g., VR < 31) strengthens PPP substantially with only small costs to NPP
(i.e., .98 and .96; see Table 24).
65 Table 24
Effectiveness of PPI-R VR at Single-Point and Well-Defined Cut Scores for Likely Genuine
PPP and NPP at different base rates BR = 15% BR = 25% Raw Cut Score Group % Sens Spec OCC PPP NPP PPP NPP VR < 25 1 pt - .97 .22 .60 .18 .98 .29 .96 WD 87.8 1.00 .21 .64 .18 1.00 .30 1.00 VR < 27 1 pt - .95 .36 .65 .21 .98 .33 .96 WD 82.7 .97 .31 .69 .20 .99 .32 .97 VR < 29 1 pt - .92 .51 .72 .25 .97 .39 .95 WD 82.7 .95 .50 .75 .25 .98 .39 .97 VR < 31 1 pt - .89 .64 .76 .30 .97 .45 .94 WD 83.3 .92 .69 .82 .34 .98 .50 .96 VR < 33 1 pt - .85 .77 .81 .39 .97 .55 .94 WD 84.0 .87 .81 .84 .44 .97 .60 .95 Note. Group were established by either 1 pt = single-point discrimination or WD = well-defined cut score (i.e., the indeterminate category of + .5 SEM or + 2 raw points). % = the percentage retained for the classification when indeterminate range is removed; BR = base rate; Sens = sensitivity; Spec = specificity; OCC = overall correct classification; PPP = positive predictive power; NPP = negative predictive power; VR = Virtuous Responding scale.
A critically important issue in forensic assessment is the identification of PIM on self- report measures. To do this we evaluate markedly elevated VR scores. With the well-defined cut score, VR > 40 resulted in perfect specificity 1.00 and perfect PPP at 1.00 (see Table 25).
This means that scores greater than a raw score of 42 (i.e., 40 + 2) have a nearly certain probability of PIM and offers excellent accuracy in identifying PIM responders. These results are promising with 86% of the sample retained; it can be assumed this well-defined cut score will be useful in forensic evaluations offering excellent specificity.
66 Table 25
Effectiveness of PPI-R VR at Single-Point and Well-Defined Cut Scores for Likely PIM
PPP and NPP at different base rates BR = 15% BR = 25% Raw Cut Score Group % Sens Spec OCC PPP NPP PPP NPP VR > 35 1 pt - .72 .87 .80 .50 .91 .65 .90 WD 80.1 .78 .96 .87 .75 .96 .85 .93 VR > 36 1 pt - .62 .95 .78 .68 .93 .80 .88 WD 79.5 .68 .97 .84 .80 .95 .89 .90 VR > 37 1 pt - .58 .96 .77 .73 .93 .83 .87 WD 80.1 .61 .99 .82 .88 .93 .93 .88 VR > 38 1 pt - .49 .97 .73 .77 .92 .86 .85 WD 84.6 .47 .99 .77 .86 .91 .92 .85 VR > 39 1 pt - .44 .99 .71 .86 .91 .92 .84 WD 85.9 .43 .99 .75 .85 .91 .92 .84 VR > 40 1 pt - .35 .99 .67 .83 .90 .90 .82 WD 85.9 .31 1.00 .74 1.00 .89 1.00 .81 Note. Group were established by either 1 pt = single-point discrimination or WD = well-defined cut score (i.e., the indeterminate category of + .5 SEM or + 2 raw points). % = the percentage retained for the classification when indeterminate range is removed; BR = base rate; Sens = sensitivity; Spec = specificity; OCC = overall correct classification; PPP = positive predictive power; NPP = negative predictive power; VR = Virtuous Responding scale.
67 Table 26
Errors in the Indeterminate Category for VR Cut Scores: False Positives and False Negatives
VR Cut Scores % of Errors Raw Score Cut Off Indeterminatea False positives False negatives Unweighted Average Likely Genuine < 25 23 to 26 84.6 66.7 75.7 < 27 25 to 28 85.7 84.6 85.2 < 29 27 to 30 76.9 85.7 81.3 < 31 29 to 32 76.9 76.9 76.9 < 33 31 to 34 50.0 76.9 63.5 Likely PIM > 35 34 to 37 38.9 30.8 34.9 > 36 35 to 38 16.7 40.0 28.4 > 37 36 to 39 15.4 38.9 27.2 > 38 37 to 40 8.3 16.7 12.5 > 39 38 to 41 0 15.4 7.7 > 40 39 to 42 10.0 8.3 9.2 Note. VR = Virtuous Responding scale; % = percent. aRange of scores that are removed because they fall into the well-defined range or indeterminate range.
Hypothesis 6. Hypothesis 6 theorized that inmates who successfully evaded detection on
the VR scale in the PIM condition would have higher PCL-R scores than those that were unsuccessful. For this analysis, the well-defined cut score of 40 was utilized. To evaluate this hypothesis further several additional factors were added to the analyses; the PCL-R factors and facets, genuine total PPI-R score, age, education, arrests, and months incarcerated during their lifetime.
Contrary to what was expected, the unsuccessful group was higher on almost all of the psychopathy scores. The only significant difference between successful and unsuccessful
“fakers” was found on the PCL-R Facet 2 scores, with the unsuccessful group scoring higher on average accounting for a large effect size (d = -1.01; see Table 27). Additionally, Factor 1 produced a large effect size (d = -0.61) and the PCL-R Total Score had a moderate effect size (d
68 = -0.37), both with the unsuccessful group scoring higher than the successful group.
Interestingly, on average the unsuccessful group were younger, spent less time incarcerated, but
were arrested more often than the successful group.
Table 27
Differences between Inmates with Successful and Unsuccessful Scores on the PPI-R VR Scale Utilizing a Well-defined Cut Score of 40 Successful (n = 40) Unsuccessful (n = 18) M SD M SD F p d PCL-R Total Score 25.58 6.90 27.67 5.30 1.30 .26 -0.38 PCL-R Factor 1 9.48 3.88 11.44 3.18 3.56 .07 -0.62 PCL-R Factor 2 13.58 3.45 13.72 3.80 .02 .89 -0.05 PCL-R Facet 1 4.97 2.56 5.33 2.06 .27 .60 -0.17 PCL-R Facet 2 4.50 2.00 6.11 1.41 9.51 .003 -1.02 PCL-R Facet 3 6.73 1.45 6.61 2.00 .06 .81 0.09 PCL-R Facet 4 6.85 2.43 7.11 2.08 .16 .70 -0.13 Total PPI-R Score 310.50 36.19 308.50 45.60 .03 .86 0.06 Age 35.42 11.30 32.50 10.35 .87 .35 0.31 Education 10.88 1.79 10.61 2.17 .24 .63 0.16 Arrests 14.53 16.19 19.39 22.56 .87 .36 -0.31 Months Incarcerated 112.45 102.77 73.17 63.17 2.24 .14 0.49 Note. Successful = scores on the Virtuous Responding Scale in the PIM condition were < 39; Unsuccessful > 42 (i.e., the well-defined range was between 39 – 42 and these cases were excluded from analysis). PCL-R Total = Psychopathy Checklist-Revised Total Score; Factor 1 = Personality Characteristics; Factor 2 = Behavioral Items; Facet 1 = Interpersonal Facet; Facet 2 = Affective Facet; Facet 3 = Lifestyle Facet; Facet 4 = Antisocial Facet; PPI-R = Psychopathic Personality Inventory-Revised.
To determine if these results were influenced by group sizes, specifically, the
unsuccessful group only having 18 inmates, the single-point cut score of 40 was also evaluated.
This allowed for the inclusion of the 20 inmates who were removed from the previous analyses and bolstered the group sizes to 51 and 27 (successful and unsuccessful, respectively). The findings were virtually the same, the main difference was Factor 1 became statistically significant (p = .02; see Appendix F).
69 The PPI-R VR scale was evaluated at item level to further investigate differences
between the successful and unsuccessful groups. Both groups positively endorsed items on the
VR scale; however, the unsuccessful group almost always answered at least one point higher
than the successful group. The unsuccessful group also utilized extreme response options more
frequently (i.e., true and false) while the successful group utilized the mid-range response
options (i.e., mostly true and mostly false; see Appendix G). Only one item, 139, did not
demonstrate a significant difference. These results indicate the unsuccessful group were unable
to nuance their responses in a manner that was not overkill. These results are discussed in depth
in the discussion.
Supplementary Question. Which PCL-R facets would best predict the largest effect sizes
in total psychopathy scores on the SRP-4, LSRP, and PPI-R between conditions? To further
investigate the use of psychopathy level (i.e., PCL-R facet scores) in predicting PIM ability
(Hypothesis 3), the Supplementary Question utilized three multiple regression analyses to predict the difference scores for each self-report psychopathy measure. The difference score between the total scores from the genuine to PIM condition on the self-report measures were the dependent variables for each regression analysis. The PCL-R facet scores from the genuine condition were concurrently entered into each multiple regression equation. No significant models emerged (see Appendix H), indicating that psychopathy level, specifically PCL-R facet scores, were not useful in the prediction of inmates’ ability to engage in PIM on self-report psychopathy measures.
70 CHAPTER 4
DISCUSSION
Psychological research on risk assessment has continued to increase dramatically over the past 15 years. In 2009, a PsychINFO search of “risk assessment” would have produced 6,095 records, more than three times the number recorded up to 1999 (i.e., 1,965; Singh & Fazel,
2010). A recent search conducted for this study (i.e., on November 19, 2013), yielded 8,280 articles for “psychopathy” between 2010 and 2013, while “risk assessment and psychopathy” returned 3,359 articles for the same time period. Despite this virtual explosion in the number of studies and reviews, forensic psychologists continue facing significant challenges providing risk assessments within correctional and forensic populations.
Risk assessments often constitute an essential component of legal decisions regarding sentencing and parole evaluation outcomes that have far-reaching consequences for both the public in general and the examinees in particular. The overall goal of violence risk assessment is to render accurate decisions and offer pertinent information that will help ensure public safety by reducing the probability of future victimization (Hart, 1998). Forensic psychologists must bear in mind the negative implications of risk assessment, specifically, the harmful effects of any inaccuracies of their evaluations.
This chapter is organized into six sections, beginning with the far-reaching implications of risk assessments with potentially life-altering consequences. The next section specifically addresses psychopathy and risk assessments, including the effectiveness of the PCL-R and self- report psychopathy measures. Subsequent sections discuss the effects of PIM on self-report psychopathy measures and the efficacy of potential PPI-R VR cut scores. Additionally, the assumption that inmates with high levels of psychopathy are more adept at PIM is discussed.
71 For the final three sections, professional implications, limitations of the study, and future
directions are addressed.
Implications of Risk Assessments
As noted, the accuracy of risk assessments is imperative to minimize the significant,
negative individual and societal consequences. These separate, yet interwoven concerns have
two levels that inaccurate risk assessments can manifest considerable impact on persons’ lives
and life experiences. First, if risk assessments underestimate the likelihood of recidivism, then
offenders are likely to continue committing crimes because they were released too soon. In other
cases, moderate to high risk offenders may have been placed on an inadequate supervision level
within the community that leads to opportunities for recidivism (Austin, 2004). A direct and
significantly negative effect of this repetitive, continued criminal behavior is the impact on the
victims, both physical and psychological. An indirect and more general effect is experienced at
the community level, with increased expenditures for tax payers and noteworthy public safety
concerns.
Second, if risk assessments over-estimate offenders’ risk level, then offenders are likely
to spend more time incarcerated than necessary or receive a more restrictive level of community
supervision than is required (Lanterman, Boyle, & Ragusa-Salerno, 2014). When the latter
situation occurs, two more consequences are created. First, when limited correctional resources
are inaccurately assigned to these low risk offenders, they may be directed away from the truly
high risk offenders. Andrews and Bonta (2003) found that when low risk offenders are
inaccurately given supervision that is more restrictive, these errors contribute to higher recidivism rates. The unsuitable restrictions impede offenders’ abilities to function within society at a level that they can abide by highly restrictive parole requirements and maintain jobs
72 and families. Highly restrictive parole requirements often force offenders to choose between work and going to treatment, or fulfilling other parole stipulations; such untenable choices may cause the offender to violate their parole or reoffend. The second consequence of over- estimating level of risk subjects misclassified offenders to undeserved constraints on their freedoms. Although unintentional, the deprivation of opportunities are likely to be experienced by these offenders as negative and unfair punishments. An unexplored issue is whether undue punishment by corrections unwittingly contributes to higher recidivism.
Psychopathy and Risk Assessment
Over the past several decades, numerous scholars repeatedly have described the strong link between psychopaths’ characterological traits and their criminal tendencies (Cleckley, 1941;
Hare, 1991; Harpur et al., 1988; McCord & McCord, 1964; Schneider, 1950/1958). Within the past 30 years, psychopathy continues to be identified as a strong predictor of recidivism (Dolan
& Doyle, 2000), and therefore has become a focus in risk assessments. Due to the PCL-R’s seemingly unparalleled ability to evaluate for psychopathy and predict violence and recidivism, it has become the most widely used risk assessment measure (Hare & Neumann, 2009; Leistico,
Salekin, DeCoster, & Rogers, 2008). Despite questions about the PCL-R’s lengthiness
(Lilienfeld & Folwer, 2006; Tonnaer, Cima, Sijtsma, Uzieblo, & Lilienfeld, 2013), it remains a core component of many risk assessments.
Countless studies have found that higher PCL-R scores indicate increased recidivism risk.
As a relatively unexamined issue, Aharoni and Kiehl (2013) found that offenders with moderate and high psychopathy scores reported committing more successful crimes (i.e., crimes for which they were never apprehended) than those with low PCL-R scores. In other words, official records may seriously underestimate the actual risks posed by psychopaths. Aharoni and Kiehl
73 (2013) also demonstrated that for every point increase on the PCL-R total score, there was a
significant increase in the likelihood of committing violent crimes. Other researchers, (Porter,
Woodworth, Earle, Drugge, & Douglas, 2003) found that pedophiles with high PCL-R scores
were at a significantly higher risk for sexual reoffending than those with lower PCL-R scores.
Furthermore, several studies have demonstrated that the PCL-R can be utilized to predict
recidivism in mentally ill offenders (Harris, Rice, & Quinsey, 1993; Heilbrun, Hart, Hare,
Gustafson, Nunez, & White, 1998; Rice & Harris, 1995).
Despite these promising results indicating the PCL-R can be utilized as a measure that will predict recidivism, other researchers found opposing results. Walters, Knight, Grann, and
Dahle (2008b) evaluated the ability of the PCL-R to predict future recidivism in six diverse samples ranging from sex offenders, and violent offenders to general, non-violent offenders.
They found consistent support for the incremental validity of the PCL-R Facet 4 as a robust predictor of recidivism; however, they indicated the other three facets added little to the prediction of recidivism beyond what is available from Facet 4. This finding indicates that past criminal behavior is a better predictor of recidivism than the personality construct of psychopathy as measured by the PCL-R.
Additionally, Edens, Campbell, and Weir (2007) utilized a meta-analysis to investigate the predictive abilities of the Psychopathy Checklist measures in youth. They found psychopathy to be a weaker predictor for violent recidivism in more ethnically heterogeneous samples and that it did not outperform specific risk assessment measures (i.e., Youth Level of
Service/Case Management Inventory; Hoge, Andrews, & Leschied, 2002). On this same issue,
Rogers and Rogstad (2010) found the PCL-R was no more effective at predicting violent and
74 aggressive behavior in psychiatric inpatients than simply using the diagnosis of APD. They also found that both constructs had limited predictive power in a non-forensic setting.
Self-Report Measures of Psychopathy Compared to the PCL-R
Given the time-demanding nature of PCL-R administrations, it is not surprising that researchers have started to utilize self-report measures of psychopathy as alternatives to the PCL-
R. Surprisingly, however, few studies have directly compared these self-report measures of psychopathy with the interview-based PCL-R as a formal test of convergent validity. Instead, as subsequently described, many studies utilized confirmatory factor analyses to establish parallel factors and scales to the PCL-R. However, the accuracy between self-reports and the PCL-R remain largely unexplored because without using utility estimates, we cannot establish it is accurate even if the correlations are very high. The current study did not examine utility estimates to establish the self-report psychopathy measures’ validity; instead, it evaluated correlations and frequencies as preliminary analyses. The following three subsections utilize the
PCL-R as a quasi-gold standard in order to examine how the SRP-4, PPI-R, and LSRP compare to the PCL-R in terms of total scores and underlying factors.
SRP-4. At least within the published literature, the SRP-4 has not been directly compared to the PCL-R and therefore no information on correlations between the two measures exists. However, the literature does suggest that the SRP-4 should map onto the PCL-R because it also has a four-factor structure (Williams et al., 2007). For instance, results from Seibert et al.
(2010) produced evidence of the same four-factor structure for the SRP-III. They also produced good convergent validity with LSRP, demonstrating large correlations between SRP-4 IPM and
CA scales and the LSRP PP scale (.65 and .64). The SRP-4 ELS scale was also largely correlated with the LSRP SP scale (.53), while CT demonstrated a moderate correlation with PP
75 (.38). They also found the SRP-4 had good convergent validity with the PPI-R but were less
uniform than with the LSRP. The SRP-III ELS scale and PPI-R SOI, F, and SI scales demonstrated moderate to large correlations (.39, .69, and .42, respectively). While the IPM and
ME scales (.75) were also largely correlated, as well as the CA and C scales (.66; see Seibert et al, 2010).
The current study’s correlations between the PCL-R facets and SRP-4 scales were small to moderate, with a modest correlation between the PCL-R total score and SRP-4 total score (r =
.30; see Table 10). The SRP-4 scales ELS and CT, which were created to map onto the PCL-R
Factor 2 (and subsequently Facet 3 and 4) performed well, with moderate correlations indicating
good convergent validity. Specifically, the ELS scale was significantly correlated with Factor 2
and Facet 3 (i.e., .36 for both). The CT scale had the largest correlations with Factor 2 (.46) and
Facet 4 (.44). Conversely, ELS and CT had small, negative correlations with Factor 1, Facet 1,
and Facet2, demonstrating good discriminant validity.
Unexpectedly, the SRP-4 IPM and CA scales did not correspond with the PCL-R facets.
The IPM and CA scales should correlate with PCL-R Factor 1 and Facets 3 and 4. IPM had a
small, non-significant correlation with Factor 1 (.14) and Facet 1 (.13). CA was slightly better
with a significant small correlation with Facet 2 (.29) but demonstrated a small, non-significant
correlation with Factor 1 (.20). Surprisingly, both had moderate correlations with Factor 2 and
Facets 3 and 4. These results may demonstrate poor convergent and discriminant validity for the
IPM and CA scales or they may indicate that the SRP-4, as a whole, measures PCL-R Factor 2
traits and behaviors and does not adequately tap into the Factor 1 dimensions.
To further investigate the accuracy of these correlations, PCL-R scores were compared to
the genuine SRP-4 scores to look at classification accuracy. Specifically, the PCL-R total scores
76 were split into psychopathic (i.e., total PCL-R scores ≥ 30) and non-psychopathic groups (i.e.,
PCL-R total scores ≤ 29) and compared to frequencies of each self-report measure in an attempt to establish meaningful cut scores that correspond with the PCL-R. SRP-4 total scores ranged from 161 to 247 for the psychopathic group and 126 to 249 for the non-psychopathic group.
To match the PCL-R criterion (≥ 30 or 40 or ≥ 75%), the same criterion was applied to the SRP-4 total scores in hopes of identifying patterns. However, only four inmates scored above 240 on the genuine SRP-4 total score, which classifies them into the top 75% of possible scores (i.e., scores between 240 to 320 are the top 75% of scores for the SRP-4 total scores).
Conversely, there were 24 inmates classified in the top 75% of the PCL-R. Overall, this consistent lack of association between the PCL-R and SRP-4 total scores further demonstrates the accuracy of the correlations and indicates the two measures are only slightly to moderately correlated at best.
PPI-R. Only one study directly compared the PPI-R and the PCL-R and subsequently reported correlations between the two measures in order to demonstrate concurrent validity.
Copestake et al. (2011) reported the PPI-R total score produced a large correlation with the PCL-
R total score (r = .54, see Table 11), while the current study found the two total scores to be only moderately correlated (.31). Surprisingly, the current study consistently found much smaller correlations between the two measures than reported by Copestake et al. (2011); however, the current study’s correlations demonstrated better convergent and discriminant validity for the PPI-
R and PCL-R.
The current study found good convergent validity for the PPI-R SI factor, with moderate correlations to PCL-R Factor 2 (.40) and Facet 3 (.46), as well as slight correlations with Facet 4
(.26). SI was not significantly correlated with Factor 1 (-.03) or Facets 1 and 2 (-.02 and -.03,
77 respectively), demonstrating good discriminant validity. Conversely, Copestake et al. (2011) found SI was moderately correlated with both PCL-R factors and all four facets (r’s = between
.39 and .48). In addition, Copestake et al. (2011) reported FD was not significantly correlated with any of the PCL-R factors and facets except for Facet 4 (.35). The current study found FD was modestly correlated with Factor 1 (.29), Facet 1 (.33), and Facet 4 (.22), and was not significantly correlated with the other factors and facets. These correlations, particularly the correlation with Facet 4, demonstrate less discriminant validity than found with SI. However, as previously noted, it provides more convergent and discriminant validity than offered by
Copestake and colleagues.
Interestingly, neither study reported good convergent and discriminant validity for PPI-R
Factor C. Copestake and colleagues reported C to be moderately correlated with every PCL-R factor and facet (r’s = between .31 and .48) consistent with their SI results. Completely opposite of these results, the current study found that C was not significantly correlated with any of the
PCL-R factors and facets (r’s = between .07 and .21).
The samples utilized by these two studies were both incarcerated male offender populations; however, the current study evaluated county jail detainees with histories of felony convictions. In contrast, Copestake and colleagues studied prison inmates in the United
Kingdom with 85% serving life sentences with almost half housed in a high security prison.
Additionally, the current sample (i.e., 44.9% African American, 39.7% European American, and
15.4% Hispanic American) was much more diverse in terms of ethnicity than the British study
(i.e., 77% European American). Such marked differences in sample characteristics may have contributed to the differences in correlations.
78 Other past studies have evaluated different versions of the PPI and PCL measures and
found generally promising results. Hughes, Stout, and Dolan (2013) found that the PPI-R total
score and Psychopathy Checklist: Screening Version (PCL: SV; Hart, Cox, & Hare, 1995) total
score were significantly correlated (r = .55). Additionally, multiple studies have compared the
original, 184-item version of the PPI to the PCL-R and reported significant correlations (e.g.,
Gonsalves, McLawsen, Huss, & Scalora, 2013; Malterer, Lilienfeld, Neumann, & Newman,
2010; Poythress et al., 2010; Poythress, Edens, & Lilienfeld, 1998). In general, the revised PPI-
R produces higher correlations with the PCL-R compared to the original PPI version.
Consistent with procedures performed with the SRP-4, PCL-R scores were compared to the genuine PPI-R scores to look at classification accuracy and further evaluate the accuracy of the correlations. Total PCL-R scores were split into psychopathic and non-psychopathic groups and compared to the PPI-R in an attempt to establish meaningful, corresponding cut scores. PPI-
R total scores ranged from 266 to 361 for the psychopathic group and 233 to 390 (i.e., with one outlier at 424, utilizing the next highest score at 390) for the non-psychopathic group.
Unfortunately, like the SRP-4, no promising cut scores were identified.
Furthermore, the top 75% of scores for the PCL-R and PPI-R were evaluated for identifying patterns. However, only one inmate scored above 393 on the genuine PPI-R total score, which classified him into the top 75% of possible scores and he was considerably higher than everyone else and could be considered an outlier (i.e., scores between 393 to 524 are the top
75% of scores for the PPI-R total scores). When compared to the PCL-R (n = 24), it is obvious the use of this criterion is ineffective.
LSRP. Unlike the SRP-4 and PPI-R, a few studies have specifically compared the LSRP and PCL-R. Brinkley et al. (2001) found the LSRP total and scale scores were all significantly
79 correlated with the PCL-R total and factor scores (see Table 12). However, with all the scales
and factors significantly correlated there was no evidence of discriminant validity. Consistent
with Brinkley and colleagues, Poythress et al. (2010) also found that the LSRP and PCL-R scores were all significantly correlated and lacked discriminant validity; however, they reported smaller correlations. Finally, Book, Quinsey, and Langford (2007) was the only study that did not find the LSRP PP scale to be significantly correlated with Factor 1. Unfortunately, they did not report discriminant correlations in their article.
In contrast to the past three studies, the current study found modest convergent validity and excellent discriminant validity between the LSRP and PCL-R. Specifically, both the LSRP
PP and SP scales were moderately correlated with the PCL-R Factor 2 (i.e., both correlations =
.36; see Table 12). Neither of the scales were significantly correlated with Factor 1 (PP = .18 and SP = -.06). All three of the past studies utilized offender populations; however, the two studies by Poythress et al. and Brinkley and colleagues had very large sample sizes of 1,472 and
549, respectively. Contrastingly, results from Book and colleagues (n = 59) were much more consistent with the current study in terms of finding SP and Factor 2 correlated as well as PP not correlating with Factor 1.
PCL-R scores were compared to the genuine LSRP scores to examine classification accuracy. Consistent with the SRP-4 and PPI-R, total PCL-R scores were split into psychopathic and non-psychopathic groups and compared to the LSRP in an attempt to establish meaningful, corresponding cut scores. Unfortunately, LSRP total scores ranged from 39 to 91 for the psychopathic group and 29 to 87 for the non-psychopathic group. Furthermore, the top 75% of scores for the PCL-R and PPI-R were evaluated for identifying patterns. However, only four
80 inmates scored above 78 on the genuine LSRP total score, while 24 inmates scored in the top
75% of the PCL-R.
Overall, the current study’s results indicate the SRP-4, PPI-R, and LSRP are slightly to moderately correlated with the PCL-R and show promise of acceptable convergent and discriminant validity. Interestingly all three measures demonstrated much clearer convergent and discriminant validity with Facet 3 and 4, as well as higher correlations with Factor 2, Facet
3, and Facet 4. This indicates the self-report measures better align with the PCL-R on the behavioral and criminal tendencies facets. Overall, the PPI-R appears to demonstrate the best convergent and divergent validity, achieving significant correlations with both Factor 1 and
Factor 2 with the appropriate PPI-R factors. For a complete listing of all the measures’ correlations, see Appendix I.
Psychopathy and PIM
Risk assessments are utilized at different stages during almost every felony criminal case
(Conroy & Murrie, 2007). Risk assessments may be completed during sentencing evaluations in order to determine the conditions of sentencing such as length and need for treatment. Treatment group outcomes often influence later probation or parole evaluations and are therefore another instance in which inmates engage in PIM in order to be perceived more positively and closer to reform. Risk assessments are often utilized as a part of probation and parole evaluations in an effort to reduce reoffending. Each of these scenarios are situations in which offenders find it imperative to “put their best foot forward.” Any evaluation that assesses future risk of violence and recidivism should consider PIM (Otto, 2008).
As described in the introduction, psychopathy has long been conceptualized as
encompassing highly deceitful and manipulative characteristics. This study found evidence of
81 these historical and current assertions as well as contrasting evidence that higher levels of
psychopathy do not necessarily result in more successful PIM. For example, both high and
moderate psychopathy groups were able to engage in PIM on the self-report measures and
significantly lower their scores, but the high psychopathy group was able to lower their PPI-R
scores substantially more than the moderate group. Additionally, the high psychopathy group
lowered their scores on scales measuring interpersonal manipulation substantially more than the
moderate group. However, inmates in the high psychopathy group were not more successful at
masking their PIM and successfully avoiding detection on the PPI-R VR scale compared to the moderate group. Detailed findings are discussed in subsequent sections.
Susceptibility of Self-Report Measures to PIM at Different Levels of Psychopathy
Edens et al. (2001) established that undergraduates higher in psychopathy were more
effective at masking their psychopathy than their lower level counterparts. Edens and colleagues
specifically utilized three different scenarios for PIM, including applying for an airline pilot
position, police job, or undergoing a pre-sentence evaluation and compared high and low
psychopathy groups on the PPI. The results were staggering; they found the most substantial
difference in the high psychopathy group’s ability to lower their scores on the PPI when
engaging in the pre-sentence evaluation scenario (Cohen’s ds = 1.82 for high group vs. 0.16 for
the low group). The high psychopathy groups were also substantially more successful at
lowering their scores in the airline pilot scenario (Cohen’s d = 0.97) and police recruit (0.86)
scenarios compared to their lower psychopathy counterparts (Cohen’s ds = 0.16 for airline pilot
and 0.19 for police recruit).
Conversely to Edens and colleagues (2001), the current study utilized an incarcerated sample with much higher levels of psychopathy. Specifically, the current study compared
82 groups of moderate and high psychopathy levels established by the PCL-R instead of low and
high psychopathy groups established by the PPI. These sample differences likely explain why
the current study found that both psychopathy groups (i.e., moderate and high) were able to
engage in PIM and significantly lower their total PPI-R scores. However, the high psychopathy
group was able to substantially lower their PPI-R total psychopathy scores (Cohen’s ds = 1.53)
compare to the moderate group (1.07). This indicates that although both groups could
significantly lower their PPI-R scores, the high psychopathy group was able to lower their scores
more than the moderate group.
At the PPI-R scale level, the current study found the fundamental characterological traits
of psychopathy (i.e., deception and manipulation) to be influential in the successful engagement
of PIM. Specifically, the high psychopathy group was able to lower their PPI-R ME scale
substantially more (1.22) than their moderate psychopathy counterparts (0.84). The ME scale
specifically measures a willingness to manipulate and exploit others. The high psychopathy
group also substantially lowered the BE scale (0.79) compared to the moderate group (0.29); as
well as the SOI scale (Cohen’s d for the high group = 0.57, moderate = 0.06). The BE scale measures a hostile perception of the world and tendency to externalize blame onto others, increasing the need to protect oneself from the exploitation of others. Conversely, SOI measures a propensity to be skilled at charming, engaging, and influencing others. These three scales are consistent with characteristics that contribute to the use of deception and interpersonal manipulation.
MacNeil and Holden (2006) also found that the PPI Machiavellian Egocentricity and
Blame Externalization was related to ability to engage in PIM. Specifically, those classified as successfully engaging in PIM by scores on the HPSI had significantly higher scores on the ME
83 and BE scale scores (Cohen’s d = 0.39 and 0.56, respectively) compared to their unsuccessful
counterparts. These results support earlier assertions by Paulhus and Williams (2002) that
Machiavellianism is a defining characteristic of deceit and psychopathy.
Book et al. (2006) utilized a between-subjects design to evaluate undergraduate students’ abilities to engage in PIM on the LSRP. They found that those individuals caught engaging in
PIM (i.e., detected by their total HPSI scores) had lower total LSRP psychopathy scores compared to those who were not caught (Cohen’s d = 0.50). They also found this pattern was true across the LSRP scales, PP and SP. Scores on the LSRP PP were higher for individuals who
successfully evaded detection (Cohen’s d = 0.46) as well as for the SP scale (0.36). These results
indicate that undergraduates with higher scores on the LSRP were able to effectively avoid
detection on the HPSI when engaging in PIM.
Book and colleagues’ results were not corroborated with the current study’s findings; the
current study found the LSRP was equally susceptible to undetected PIM by inmates with high
and moderate levels of psychopathy. Specifically, the current study found the high psychopathy
group (Cohen’s d = 1.34) did not substantially lower their LSRP total scores more than their
moderate psychopathy counterparts (1.20). This finding was also consistent across the two
LSRP scales. Both psychopathy groups were able to significantly lower their PP scores across
conditions (high group = 1.23, moderate = 1.15) as well as their SP scores (high = 1.13,
moderate = 0.88).
An interesting difference between the two studies is that Book and colleagues utilized an
individual measure of response styles (i.e., the HPSI), while the current study used the PDS in
the genuine condition and the PPI-R VR scale in the PIM condition. Also contrary to Book and
colleagues, the current study determined that those inmates caught engaging in PIM by the PPI-R
84 VR scale had higher PCL-R scores than those not caught. Book et al. (2006) only utilized the
LSRP to measure genuine psychopathy levels as well as evaluate ability to lower psychopathy
levels (i.e., engage in PIM).
Rogers and colleagues (2002) evaluated antisocial youths with moderate and low levels
of psychopathy and their ability to engage in PIM on the SRP-II. They found that individuals with moderate levels of psychopathy (i.e., > 14 on the PCL: YV) were able to substantially decrease their SRP-II total scores (Cohen’s d = 0.97) compared to those with low levels of psychopathy (0.34). Interestingly, the SRP-II Factor 1 did not have significant results and
produced small effect sizes (moderate group = 0.16, low group = 0.07), while Factor 2 was
substantially influenced by PIM (moderate group = 1.10, low group = 0.36). Williams and
Paulhus (2004) described the SRP-II Factor 2 as low anxiety and self-confidence and interpreted
it as emotional stability, while Factor 1 measured more antisocial behaviors and interpersonal
manipulation.
Conversely, the current study found that both psychopathy groups (i.e., high and
moderate) significantly lowered their SRP-4 total scores across genuine and PIM conditions.
Specifically, the high psychopathy group produced an effect size of 1.91 while the moderate
group was almost identical to the high group 1.93. This result effectively mirrors the results
found with the LSRP in this study; both groups were able to equally lower their total scores
across conditions.
Additionally, unlike Rogers et al. (2002), the current study found the high psychopathy
group was able to substantially lower their scores on the SRP-4 IPM scale (Cohen’s d = 1.46)
compared to their moderate psychopathy counterparts (1.00). The IPM scale measures
interpersonal manipulation, which Rogers and colleagues (2002) found was unaffected by level
85 of psychopathy. Interestingly, the current study indicated inmates with moderate levels of psychopathy lowered their SRP-4 CT scale scores (Cohen’s d = 1.83) substantially more than the high psychopathy group (1.52). No other studies have found that individuals with lower levels of psychopathy have been able to produce substantially larger effect sizes on any measures or scales compared to those with higher levels of psychopathy.
Interestingly, in the genuine condition, the current study’s moderate and high psychopathy groups had higher self-reported psychopathy scores than what previous studies reported (Brinkley et al., 2001; Levenson et al., 1995; Paulhus et al., in press; Poythress et al.,
2010; Walters et al., 2008a; Watt & Brooks, 2011). It appears that the comparisons between moderate and high psychopathy groups (vs. low and high as found in the previous literature) and the overall higher levels of psychopathy found in this sample, contributed to the limited differences between psychopathy levels and the effects of PIM on the psychopathy self-report scores.
Classification of PIM using the PPI-R VR Cut Score
The link between psychopathy and dissimulation, particularly the use of deception and exploitation of others, has considerable intuitive and research appeal (Rogers & Cruise, 2000).
Vitacco (2008) described psychopathy as a constellation of personality and behavioral traits that are linked to a large number of deceptive behaviors and is one of the major syndromes associated with dissimulation. Research examining dissimulation on various psychological measures (e.g.,
MMPI-2) suggests that indentifying PIM may be more difficult than identifying malingering
(Edens et al., 2001). Therefore, the importance and urgency of accurately detecting PIM during forensic and risk related evaluations cannot be stressed enough.
86 Edens and colleagues (2001) was the first research study to critically examine the PPI
validity scale produced to measure PIM (i.e., UV scale) utilizing a within-subjects research
design. Specifically, they performed Receiver Operating Characteristic (ROC) analyses to
evaluate if the UV scale would accurately classify individuals based on their scores. Results
indicated a cut score of ≥ 35 provided an over-all hit rate of 68%, with sensitivity of 61%, and
specificity of 75%. Unfortunately, although the UV scale detected a large number of
respondents engaging in PIM, there were still a considerable number that avoided detection (i.e.,
false negatives).
Lilienfeld and Widows (2005) developed the PPI-R VR scale from the original PPI UV scale in an attempt to strengthen the item-total correlations and its efficacy. They created items that measured minor faults that are present in virtually all people (e.g., “I have no bad habits”) and included the 13 items with the highest item-total correlations into the VR scale. The normative samples included community, college, and correctional populations.
Methodologically, Lilienfeld and Widows (2005) utilized a between-subjects research design as well as evaluated the correlations of the VR scale to the PAI PIM scale to establish the validity of the VR scale. However, they only offer general descriptions of what high (i.e., T ≥ 65) scores suggest of respondents and do not offer cut scores for establishing genuine and PIM protocols.
Across multiscale inventories, the detection of PIM has been extensively researched and has evolved from single-point cut scores to the establishment of well-defined cut scores. When evaluating response styles, most psychological instruments (e.g., PPI-R, PAI, MMPI-2/RF) utilize single-point cut scores and offer only an interpretation of responses exceeding or falling short of that score. As discussed in the Results chapter, single-point cut scores have been misunderstood as demonstrating “laser accuracy” when this is in fact, not the case. Rogers and
87 Gillard (2011) discussed indeterminate classifications and how these “too close to call” cases
make classificatory errors more likely. With single-point cut scores, evaluators are often faced
with a higher probability of misclassifying individuals (Rogers & Bender, 2012).
Rogers and Bender (2012) also offered an alternative to the problem of misclassification,
well-defined cut scores. They recognized that by establishing well-defined groups (i.e., classifications that have excellent accuracy in (a) identifying individuals who are highly likely feigning) and (b) removing indeterminate scores (i.e., a narrow group of “too close to call” cases). With their approach, error rates can be dramatically decreased while increasing classification accuracy. The current study evaluated the effectiveness of the VR scale with both the typical approach (single-point cut score) and the new approach utilizing well-defined groups that remove indeterminate cases.
Past psychopathy research (Edens et al. 2001; Lilienfeld & Widows, 2005) demonstrated the vulnerability of self-report measures to PIM and attempted to identify respondents likely engaging in PIM. However, they failed to identify a cut score to establish when individuals are likely to be genuine responders. To rule-out, you are determining that an individual is highly likely genuine and not engaging in PIM. The current study found a well-defined cut score of <
25 yielded perfect sensitivity and NPP (both 1.00). A single-point cut score of < 25 had great results as well (i.e., .97 sensitivity and .98 NPP), but does not match the ideal numbers achieved by the well-defined cut score. This finding strengthens the utility of the VR scale by providing not only a decision that someone is not engaging in PIM but also the assurance that the respondent is in fact responding genuinely.
The most important decision with the greatest risk is to identify and rule-in respondents engaging in PIM. Past research indicates that individuals with higher levels of psychopathy are
88 less likely to be detected by a validity scale, indicating that detecting dissimulation is more
difficult with psychopathic individuals, who are more likely to engage in PIM (Book et al.,
2006). While Edens and colleagues (2001) established an overall cut score, taking a general
approach with undergraduates, the current study identified effective cut scores for forensically
relevant issues in an inmate sample. In other words, the current study had a different goal.
Specifically, the current study aimed to establish cut scores that provide a high-level certainty for
when the PPI-R is utilized within correctional populations, specifically during forensic
evaluations for which the accuracy has far-reaching consequences.
To offer that high-level of certainty for a forensic cut score, a well-defined cut score of >
40 was evaluated and yielded perfect specificity and PPP (i.e., 1.00). This result indicates that
individuals who score above 42 (i.e., 40 + 2) on the VR scale have an almost certain probability
that they are engaging in PIM. Conversely, that same cut score (> 40) utilized as a single-point
cut score provides great specificity but only acceptable PPP (.99 and .83, respectively), and
therefore, compares poorly to the well-defined cut score. Utilizing a single-point VR cut score
of > 35, the current study established better specificity rates compared to Edens et al. (2001) but
was still considered only good at .87 (see Appendix J). Conversely, a well-defined VR cut score
of > 35 dramatically increased specificity into a great range of .96. Therefore, even if a more
general, lower cut score is required or valued (e.g., screening research participants or other
screening requirements), a well-defined cut score significantly out performs single cut scores in
terms of accuracy.
As discussed previously, these two studies (i.e., Edens et al, 2001 and the current study)
differ greatly regarding sample and methods. Edens and colleagues utilized undergraduates and
established low and high psychopathy groups with genuine scores on the PPI. The current study
89 evaluated incarcerated felons and established psychopathy level with the PCL-R resulting in
moderate and high psychopathy groups. Additionally, Edens and colleagues evaluated the PPI
and the UV scale which are different from the revised version. These sample and measure
differences likely contribute to the differences found in the cut scores’ utility estimates.
High Psychopathy Does Not Equal Successful PIM
Ray and colleagues (2012) conducted a meta-analysis to evaluate if self-reported
psychopathic traits were associated with an increased ability to engage in PIM or NIM.
Specifically, they evaluated the relationships between PIM and the total and factor/scale scores
for the PPI, PPI-R, and LSRP. Contrary to past literature (i.e., Book et al., 2006; Edens et al.,
2001), Ray et al. (2012) found that the total scores, PPI/R Factor 2, and LSRP SP scores were
significantly negatively correlated with ability to engage in PIM. Interestingly, PPI/R Factor 1
and LSRP PP scores were not significantly correlated with PIM. This result adds to the controversial findings that psychopaths may not be better at successfully engaging in PIM.
Ray et al. (2012) evaluated a multitude of studies that included several different scales
(e.g., MCSDS; Balanced Inventory of Desirable Responding, Paulhus 1984) utilized to detect
PIM. In contrast, the current study evaluated only the effectiveness of the PPI-R VR scale.
From a more detailed perspective, the VR scale is composed of 13 items that measure denial and
endorsement of minor faults and shortcomings (e.g., “I sometimes put off unpleasant tasks”).
Five of the items are keyed in the true direction and the other eight are keyed in the false
direction. Its detection strategy is based on the level of denial of these common shortcomings
that all people should endorse to some degree. Ray and colleagues (2012) utilized studies with
scales that evaluate PIM with various detection strategies including the denial of common
90 shortcomings and the detection of responses that include socially acceptable but uncommon
behaviors (i.e., MCSDS).
The current study found that inmates with high PCL-R scores are not more successful at remaining undetected by the PPI-R VR scale compared to those with lower psychopathy levels.
In fact, on average, inmates with higher scores on the PCL-R Factor 1 and Facet 2 were classified as unsuccessfully engaging in PIM by the cut score of > 40 on the VR scale.
Successful classification happened when utilizing a well-defined cut score or single-point cut score and produced large effect sizes (Factor 1 d = -0.61, Facet 2 d = -1.01). Interestingly, inmates in the successful group (i.e., they scored below the well-defined cut score of > 40) tended to be older and incarcerated longer, achieving moderate effect sizes (ds of 0.30 and 0.49, respectively).
Theoretically, this lack of success for higher levels of psychopathy is contrary to both intuition and conceptual ideas of psychopathy. These findings indicate that those inmates high in psychopathy are not better at engaging in PIM than their moderate counterparts. Specifically, it
goes against the prevailing conceptualization that psychopaths are interpersonally manipulative
and deceitful to a greater degree than non-psychopaths. As an important reminder, this study
does not offer information on the proclivity of psychopaths to engage in PIM more often than
their less psychopathic counterparts. Additionally, this study is limited to self-report engagement
of PIM and did not directly assess ability to verbally deceive and manipulate.
Practically, inmates with high levels of psychopathy being “caught” are great news for
clinicians. In particular, these findings indicate that inmates high in psychopathy can be detected
by the VR scale as engaging in PIM. With further research, these findings provide initial
validation for self-report psychopathy scales with embedded response style scales. The main
91 argument against the use of self-report measures of psychopathy has been that psychopaths are
notorious for conning and lying. Thus, it has been asserted that psychopaths can easily engage in
PIM and appear non-psychopathic on a paper-and-pencil measure. Ray et al. (2012) and the current study offer findings that indicate high levels of psychopathy do not always equal better ability to engage in PIM.
Additionally, we evaluated the VR scale at the item level and found why inmates with high levels of psychopathy were unable to avoid detection. The most important factor in determining successful versus unsuccessful responders on the VR scale was the number of extreme responses (i.e., true and false) utilized. On average, the unsuccessful group answered seven of the 13 items with the extreme responses of either “true” or “false,” rather than utilizing
“mostly false” or “mostly true.” The distinguishing factor is that on average, the successful group never chose these extreme response options. For each of these seven items, the effect
sizes between the two groups were very large, with Cohen’s d values ranging from 1.10 to 1.60
(see Appendix G). These results indicate that those “caught” by the PPI-R VR scale were unable
to nuance their responses in a way that was not overkill.
It is also apparent in Appendix J that the unsuccessful group scored higher on every
single VR scale item. They not only over-utilized the extreme response options, they also chose
the response direction that was engaging in a more overtly PIM response style for each
individual item. In other words, they significantly overplayed and exaggerated how “good” they
were. This finding further indicates that those unsuccessful individuals could not be subtle in a
believable manner in order to remain undetected.
The current study’s results do not neatly fit into the literature; past researchers have
reported fairly different findings regarding psychopathy and successful PIM. For instance,
92 Lilienfeld and Widows (2005) found that undergraduates engaging in PIM achieved high VR
scores (M = 41.82), but they did not compare group differences (i.e., they did not evaluate
psychopathy levels) and had a very small sample size to evaluate PIM (n = 9). In contrast,
MacNeil and Holden (2006) found conflicting results to the current study. They reported that
undergraduates who successfully avoided detection on the HPSI and PDS were significantly
higher on the PPI-R ME and BE scales; however, they did not evaluate the VR scale.
Additionally, Edens and colleagues (2001) found that undergraduates higher in psychopathy
better evaded detection on the PPI UV scale; however, they did not evidence great efficacy for
the PPI UV scale. In general, the current study found that inmates, regardless of psychopathy level, can successfully engage in PIM on self-report psychopathy measures; however, the use of validity scales show promising results (i.e., the PPI-R VR scale).
Professional Implications
Vulnerability of self-report psychopathy measures to PIM. Psychopaths may not automatically engage in PIM during research studies (Lilienfeld & Fowler, 2006; Ray et al.,
2012); however, they may have little incentive and motivation to do so. Focusing on professional applications, the frequency of psychopaths engaging in PIM during forensic evaluations is unknown. Rogers and Cruise (2011, p. 277) reported that the literature provides an incomplete picture of the “deception-psychopathy relationship” and lying is a “common feature of both delinquent and nondelinquent populations.” They acknowledged deception is considered a typical characteristic of psychopathy; however, it is likely not a distinguishing trait.
In the current study, very few inmates engaged in PIM in the genuine condition, which provided no incentive for this response style. In contrast, offenders with moderate to high psychopathic
93 traits did have the ability to successfully engage in PIM across the self-report measures of psychopathy, when provided with personal motivation (i.e., beating the tests).
Encouragingly, the initial results indicate the PPI-R VR scale can potentially be used to identify individuals engaging in PIM (i.e., well-defined cut score of > 40) and to classify those who are genuine and not engaging in PIM (i.e., well-defined cut score of < 25). With perfect specificity (1.00) and PPP (1.00; Streiner, 2003), the forensic well-defined cut score of > 40 offers assurance that inmates scoring above this cut score are in fact engaging in PIM (i.e., subject to replication). Additionally, the well-defined cut score offers a range of scores (i.e., 39 to 42) that are considered “too close to call” and this indeterminate group should be further investigated (Rogers & Bender, 2012).
No other research study (e.g., Edens et al. 2001; Lilienfeld & Widows, 2005) evaluated the PPI-R VR scale’s ability to classify responders as genuine and not engaging in PIM. The current study identified that a well-defined cut score of < 25 yielded perfect sensitivity and NPP
(both = 1.00). With additional studies, it may be established that inmates scoring below this cut score are in fact responding genuine and can therefore be ruled-out as not engaging in PIM.
Having both rule-in and rule-out cut scores adds to the utility of the VR scale. As noted, scores falling within the indeterminate ranges and between the cut scores should be further evaluated.
The current study also offers information about those inmates that are likely engaging in
PIM but are able to do so in a believable manner. In other words, if you have an individual VR score between the two cut scores (i.e., between 25 and 40, rule-in and rule-out) you may be able to look at the item responses and determine if you should engage the individual in further testing for psychopathy or PIM. Specifically, if the responses to the VR scale are all either “mostly true” or “mostly false” and they are not utilizing any extreme response options, this may tell you
94 the individual may be engaging in PIM and is able to remain undetected. However, this study is
the initial evaluation of the forensic cut score and identification of possible PIM at the item level
and cannot be conclusive, without further research.
Substituting Self-Reports for the PCL-R. All three psychopathy self-report measures do significantly correlate with the PCL-R; however, the correlations are typically small to moderate at best. These correlations are not promising when establishing convergent validity between measures and suggest that the PCL-R and self-reports, “Assess only slightly overlapping aspects of the same construct” (Lilienfeld & Fowler, 2006, p. 112). In general, they account for much less than 50% of the shared variance. This percentage compares starkly to large correlations of
.70 and above, which are more acceptable when asserting self-reports are measuring the construct of psychopathy as defined by the PCL-R (Clark, Livesley, & Morey, 1997; Rogers,
2001).
Additionally, the current study attempted to further evaluate the accuracy of these correlations by comparing the PCL-R scores to the self-report total scores to establish possible equivalent cut scores. This approach was accomplished by splitting the sample into psychopathic (i.e., PCL-R total scores ≥ 30) and non-psychopathic groups (i.e., PCL-R total scores ≤ 29) and using the frequencies of each self-report measure, in an attempt to establish meaningful cut scores that correspond with the PCL-R. However, the range of self-report scores were essentially identical for the psychopathic and non-psychopathic groups. This nearly complete overlap prevented the establishment of relevant cut scores and further affirmed that the correlations between the self-report psychopathy measures and the PCL-R were in fact likely only small to moderate.
95 Considering the small to moderate correlations and lack of effective cut scores for
psychopathy, it can be assumed that none of the self-report measures should be used in clinical or forensic practice as a proxy for the PCL-R. Ray and colleagues (2012, p. 13) reach the same conclusion indicating that self-report psychopathy measures cannot “safely replace more
extensive clinical assessments, such as the PCL-R.” Also similar to the current findings, Ray et al. (2012) established that psychopathic individuals do not necessarily engage in PIM when there is no motivating factor present. However, they acknowledged that when incentive is present, such as in forensic evaluations, PIM is much more likely to occur than in research settings. All
the findings point to the same general conclusion; a self-report psychopathy measure will not
provide equivalent information to replace what the PCL-R offers.
Potential PIM is an important issue that the PCL-R incorporates into its assessment method, but only the PPI-R self-report has reliably considered. The PCL-R considers that the individual may engage in PIM. It not only asks redundant questions throughout the interview,
but also provides a records review to help assess for honesty and credibility of examinees.
However, contrary to this perspective, Gillard (2013) found very different results for a similar format, the Historical-Clinical-Risk Management-20 (HCR-20; Webster, Douglas, Eaves, &
Hart, 1997). He concluded that the HCR-20, a semi-structured interview, was more susceptible to PIM than two self-report risk assessment measures, the Psychological Inventory of Criminal
Thinking Styles (PICTS; Walters, 2001) and the Self-Appraisal Questionnaire (SAQ; Loza,
2005). Rogers and colleagues (2002) also found that the interview-based measure of youth psychopathy (i.e., PCL: YV) was substantially affected by PIM. While research has typically asserted that standardized interviews are less susceptible to PIM, these studies demonstrate that it is not always true (Gillard, 2013; Fiduccia, 2011).
96 Out of the three self-report psychopathy measures evaluated in this research study, only
the PPI-R currently accounts for the possibility of response styles and has several promising
validity scales to determine when individuals are engaging in a certain response style. The
current study found potential validity cut scores that are useful when the PPI-R is utilized in forensic evaluations. The PPI-R, while vulnerable to the influence of deceitful response styles,
has validity indicators, and therefore, has potential usefulness in forensic cases.
Limitations
The current study was an initial investigation of the effects of PIM on self-report measures of psychopathy. Despite the current study’s strengths, it was not without a few limitations.
The brevity of the record review conducted for each inmate to assist with the completion of the PCL-R scorings was a general limitation. In the current study, records from the jail verified inmates were convicted felons; however, due to institutional regulations, these were the
only records available directly from the jail. To supplement this verification of felony
convictions, additional online background checks were utilized to corroborate number, type, and
severity of past charges and convictions. As discussed in Chapter 2, in the Procedures Section,
this background information assisted in the scoring of the last two PCL-R items. Unfortunately,
access to official records is uncommon in correctional settings.
A second methodological limitation was an imbalance in group sizes for Hypothesis 4,
with a small group of 12 compared to 61. Hypothesis 4 found that inmates with invalid PDS IM
scale scores (n = 12) were unable to lower their self-report psychopathy scores as much as those
in the valid group (n = 61). This is an interesting preliminary finding; however, with the small
group size for the invalid individuals, this analysis should be replicated with a larger group size
97 to ensure the same results would be found. On a positive note, all other analyses had adequate
group sizes.
As a third limitation, the current study did not include females detainees. Past
psychopathy research with female offenders has demonstrated they present very differently than male offenders (Hare 2003, Lilienfeld & Widows, 2005). For example, Rogstad and Rogers
(2008) identify that women are more likely to act flirtatiously as a form of interpersonal manipulation compared to the more traditional methods of conning used by men. They also indicate women are more likely to engage in non-violent impulsive behaviors (e.g., running away, self-harm, and property crimes). With these distinct differences in how psychopathy presents across gender, a comparable female sample would be needed in order to establish how psychopathy influences female inmates’ abilities to engage in PIM.
Future Directions
The current study underscored several areas in the research of PIM on self-report
psychopathy measures that could be further developed in future studies. To start, females should
be evaluated to establish how they engage in PIM on self-report measures of psychopathy. In
addition, the current results should also be replicated not only in diverse incarcerated populations
(e.g., inmates serving long-term sentences), but also in forensic populations such as inpatient
psychiatric hospitals or treatment settings.
The concept of psychopathy has evolved over the past several decades and so has our
ability to accurately assess this construct. In the past 35 years, psychopathy has been parsed out
from other criminal syndromes such as sociopathy and APD. The assessment of psychopathy
also significantly changed with the creation of the PCL-R and subsequent research focusing on
various factor models of the psychopathy concept (i.e., two-factor, three-factor, and four-facet
98 PCL-R models). Currently, the PCL-R is the quasi-gold standard but researchers and clinicians have desired new, additional assessment methods for a while, particularly instruments with less intensive time requirements. Unfortunately, the self-report measures of psychopathy currently only produce small to moderate correlations with the PCL-R and do not have any equivalent or translatable total score interpretations. However, when compared to each other, the self-report measures of psychopathy produce very large and significant correlations (e.g., total score correlations of the SRP-4 and PPI-R is .73; see Appendix I).
Further validation of the self-report psychopathy measures against the PCL-R would greatly improve their credibility and reliability. The use of the multitrait-multimethod matrix
(MTMM; Campbell & Fiske, 1959) would not only improve the validation of the self-report measures, it would also improve the validation of the PCL-R, and help further develop the construct of psychopathy. The MTMM model would be optimal for evaluating psychopathy because in order to establish discriminant validity more than one trait must be evaluated by more than one method (Campbell & Fiske, 1959). No matter the conceptualization, psychopathy has many traits that are encompassed in the syndrome (e.g., Hare, 2003; four facets, Interpersonal,
Affective, Lifestyle, and Antisocial). Currently, the PCL-R is essentially considered the definition of psychopathy. Research utilizing MTMM would further the concept of psychopathy into more than just the PCL-R. The construct might then encompass better convergent and discriminant validity achieved by assessment through multiple methods including self-reports.
Unfortunately, the SRP-4 and LSRP are currently highly vulnerable to response styles, particularly PIM. The current study found inmates could easily appear less psychopathic and endorse more socially desirable characteristics when they were asked to engage in PIM. The addition of response style indicators would likely increase both measures’ reliability and validity.
99 Regarding item development, items with balanced desirability, specifically those that are not face
valid and not easily identified as either positively or negatively loaded items (e.g., items used on
the PPI-R; Lilienfeld & Widows, 2005), might reduce the need for validity indicators.
Validity indicators for self-report psychopathy measures could utilize empirical criterion keying in the development of their validity items. A known groups design could be used in order to include items, irrespective of content, based on their ability to distinguish between genuine and simulated responders. By identifying items that individuals endorse and deny depending on
group assignment (i.e., genuine or PIM), a list of items could be established that discriminates
between the two presentations.
The PPI-R already has validity scales that show promise for the detection of general
response styles (i.e., PIM, NIM, and inconsistency); however, these cut scores need further
development and validation. The current study only evaluated the affects of PIM on the VR
scale and it was successful in identifying potential cut scores for forensic settings. This study
approached the establishment of cut scores from a specialized nature, and developed cut scores
with high accuracy. Edens et al. (2001) established cut scores for the PPI validity scales that
could be utilized in more generalized settings. These are the only studies to independently
evaluate the PPI/R validity scales and attempt to establish cut scores.
Future research should further validate the VR forensic cut scores. Specifically, it would
be ideal for forensic research to evaluate the VR scale under different PIM scenarios. The
current study asked inmates to present themselves as a peaceful, calm person that was not at risk
for recidivism and to present their very best side. It would be interesting to evaluate different
scenarios, such as inmates appearing to be generally well adjusted and another scenario
specifically instructing them to appear non-psychopathic. These methodological modifications
100 would aid in the further development of the PPI-R VR forensic cut score and testing its efficacy
against various presentations of PIM.
Beyond PIM, research investigating the PPI-R DR scale’s efficacy in detecting NIM is
necessary for the continued development of the PPI-R validity scales. It is important to note that
this study only evaluated PIM on the VR scale and did not assess the DR scale. The DR scale is
utilized to detect malingering or NIM response sets (Lilienfeld & Widows, 2005). As discussed
in the introduction, Edens et al. (2000) evaluated the original PPI and found the DR scale could
successfully classify individuals engaging in NIM. They also found psychopathy level did not
significantly improve participants’ ability to successfully engage in NIM. More research is
necessary to evaluate potential cut scores for the DR scale that would prove useful in forensic
evaluations.
Conclusion
The current study is an initial and important step towards a better understanding of
inmate’s abilities to engage in PIM on self-report measures of psychopathy as well as the establishment of effective forensic cut scores on the PPI-R VR scale. Results indicated that
inmates can effectively lower their self-report psychopathy scores and that Factor 1 traits
associated with deceptive and manipulative characteristics (i.e., measured by PPI-R scales
Machiavellian Egocentricity, Blame Externalization, and Social Influence) aided in producing successful (i.e., undetected) PIM presentations. Self-report measures of psychopathy without validity indicators (i.e., SRP-4 and LSRP) were highly vulnerable to response styles and would greatly improve with the addition of validity scales. The PPI-R VR scale showed promise in its use with forensic samples with well-defined cut scores. This investigation was the first study to utilize a within-subjects design to evaluate inmate’s ability to engage in PIM on self-report
101 measures of psychopathy. It is hoped that future research can continue to address this important issue and further the development of self-report measures of psychopathy with validity indicators in forensic populations.
102 APPENDIX A
STANDARD INSTRUCTIONS
103 Genuine Instructions.
Please respond to all of the following questions openly and honestly. Remember, this information will not have your name on it and will not be seen by correctional officers. It is only used for this research study. It is important that you present yourself as you really are.
104 APPENDIX B
PIM INSTRUCTIONS
105 Positive Impression Management Instructions.
Scenario
Imagine that you hurt someone badly in a fight. You have already been found guilty of
aggravated assault. Now the court will decide your sentence. A presentence investigation report
will be written to help decide how long your sentence will be. If the report says you are a
dangerous person who may be violent again, you will receive a prison sentence of 5-10 years.
You want to appear to be a safe, caring person who is sorry. That way you can get a short sentence or even probation.
Your Task
Please pretend the rest of the questions in this study are for your presentence investigation report.
Think about what you should say about yourself. How can you make yourself seem like a peaceful, calm person? Can you only show your very best side? You want to make others think you are not a risk for future crime.
Caution
Are you smart enough to convince the psychologist that you deserve a short sentence, even though you are guilty of a violent crime? Can you beat the tests? Keep in mind that if you seem
“too good to be true” you will look like you are lying. Please try to be believable when answering the questions, even though you will have to bend the truth.
106 APPENDIX C
INFORMED CONSENT
107 University of North Texas Institutional Review Board Informed Consent Form
Before agreeing to participate in this research study, it is important that you read and understand the following explanation of the purpose, benefits and risks of the study and how it will be conducted.
Title of Study: The Ability of Offenders to Lower Their Level of Risk
Investigator: Dr. Richard Rogers, University of North Texas (UNT) Department of Psychology.
Key Personnel: Nathan Gillard, Katherine Kelsey, and Emily Robinson, UNT Department of Psychology
Purpose of the Study: You are being asked to take part in a research study that looks at certain personality traits and your ability to deceive when answering questions.
Study Procedures: You will be asked a series of questions and fill out short surveys about personality traits that cause trouble for some people. You will complete some surveys twice. People in court or jail are often asked these common questions. The study will take about 2 ½ to 3 hours of your time. It may take longer depending on your answers.
Foreseeable Risks: There are no known risks to completing these questions. You will be asked about past violence and some mental health problems you may have. These questions may be stressful to think about. If you feel distress or wish to stop this study for any reason, you can stop at any time.
Benefits to the Subjects or Others: This study will not really benefit you, but it may benefit the field of psychology.
Compensation for Participants: Following Tarrant County Jail rules, this study is 100% voluntary and your decision to help us will not affect on your case, charges, or stay in the Jail.
Procedures for Maintaining Confidentiality of Research Records: Information about your identity will not be recorded by researchers. A random number will identify your surveys so we know they all belong to the same person. The random number will not be connected to your name. During the interview portion of this study, we will try to protect your privacy by recording general, not specific, details. This consent form will be kept separate from all research materials. All data will be stored in a securely locked research room at the University of North Texas.
Questions about the Study: If you have any questions about the study, you may contact Dr. Richard Rogers at (940) 565-2671.
108 Review for the Protection of Participants: This research study has been reviewed and approved by the UNT Institutional Review Board (IRB). The UNT IRB can be contacted at (940) 565- 3940 with any questions regarding the rights of research subjects.
Research Participants’ Rights:
Your signature below indicates that you have read or have had read to you all of the above and that you confirm all of the following:
• A researcher has explained the study to you and answered all of your questions. You have been told the possible benefits and potential discomforts of the study. • You understand that you do not have to take part in this study. If you refuse to participate or prematurely stop you will not be punished. The researchers may choose to end your participation at any time. • You understand the purpose of the study and how it will be performed. • You understand your rights as a research participant and voluntarily consent to participate in this study. • You have been told you will receive a copy of this form.
______Printed Name of Participant
______Signature of Participant Date
For the Investigator or Designee: I certify that I have reviewed the contents of this form with the subject signing above. I have explained the possible benefits and the potential risks and/or discomforts of the study. It is my opinion that the participant understood the explanation.
______Signature of Investigator or Designee Date
109 APPENDIX D
DEMOGRAPHIC INFORMATION
110 ID #______Current date and time:
Date and time of most recent arrest/ (days in jail):
Gender:
DOB (Age):
Ethnicity:
1st Language:
Highest grade completed:
GED: yes no
Marital status: single married divorced widower
Occupation (legal):
Last year’s gross income:
Current charges:
Aggravated: yes no
Most serious charge:
Aggravated: yes no
# of total arrests:
# of psychiatric hospitalizations:
Total time incarcerated during life (years/months):
Longest sentence (years/months):
111 APPENDIX E
MANIPULATION CHECK
112 Research number: ______
1. During the first half of the study, what were your instructions? ___ correct, ___ incorrect. If questionable, record verbatim:
2. During the first half of the study, did you present yourself the way you really are? ___ yes, ___ no, ___ uncertain.
3. During the second half of the study, what were your instructions? ___ correct, ___ incorrect. If questionable, record verbatim:
4. What were your incentives (what were you trying to do)? Receive a less severe sentence for the crime: ___ yes, ___ no Smart enough to lower risk: ___ yes, ___ no Beat the tests: ___ yes, ___ no
5. Compliance: Did you follow your instructions? ___ yes, ___ no
6. If yes – How would you describe your effort at following the instructions at a scale from 1 to 10, with 1 meaning you did not try at all and 10 meaning you tried your very hardest?
1 2 3 4 5 6 7 8 9 10
7. Do you think you were successful at appearing like a safe, calm inmate who deserved an easier sentence? ___ yes, ___ no, ___ uncertain.
8. What kind of information did you attempt to hide in order to do this?
9. Have you ever been given this type of evaluation in real life
113 APPENDIX F
DIFFERENCES BETWEEN INMATES WITH SUCCESSFUL AND UNSUCCESSFUL
SCORES ON THE PPI-R VR SCALE UTILIZING A SINGLE-POINT CUT SCORE OF 40
114 Successful (n = 51) Unsuccessful (n = 27) M SD M SD F p d PCL-R Total Score 25.31 6.91 28.15 4.97 3.56 .06 -0.45 PCL-R Factor 1 9.24 3.82 11.26 3.25 5.46 .02 -0.56 PCL-R Factor 2 13.59 3.43 14.26 3.53 .66 .42 -0.19 PCL-R Facet 1 4.86 2.47 5.33 2.04 .72 .40 -0.20 PCL-R Facet 2 4.37 2.06 5.93 1.69 11.33 .001 -0.80 PCL-R Facet 3 6.71 1.58 7.00 1.96 .52 .47 -0.17 PCL-R Facet 4 6.88 2.36 7.26 2.09 .49 .49 -0.17 Total PPI-R Score 308.57 36.23 311.26 40.04 .09 .76 -0.07 Age 34.75 10.71 32.78 11.23 .58 .45 0.18 Education 11.04 1.72 10.48 2.19 1.53 .22 0.30 Arrests 15.59 16.25 19.07 19.55 .71 .40 -0.20 Lifetime Incarceration 108.73 94.26 75.85 68.35 2.56 .11 0.38 Note. Successful = scores on the Virtuous Responding Scale in the PIM condition were < 41, Unsuccessful > 40. PCL-R Total = Psychopathy Checklist-Revised Total Score; Factor 1 = Personality Characteristics; Factor 2 = Behavioral Items; Facet 1 = Interpersonal Facet; Facet 2 = Affective Facet; Facet 3 = Lifestyle Facet; Facet 4 = Antisocial Facet; PPI-R = Psychopathic Personality Inventory-Revised.
115 APPENDIX G
ITEM LEVEL DIFFERENCES BETWEEN INMATES WITH SUCCESSFUL AND
UNSUCCESSFUL SCORES ON THE PPI-R VR SCALE UTILIZING
A WELL-DEFINED CUT SCORE OF 40
116 Successful (n = 40) Unsuccessful (n = 18) M SD M SD F p d Item 20 1.83 .96 2.83 1.30 11.00 .002 -1.06 Item 37 2.88 1.14 3.89 0.32 13.70 < .001 -1.21 Item 42 2.35 1.12 3.39 0.85 12.22 .001 -1.15 Item 59 2.75 .98 3.61 0.70 11.26 .001 -1.10 Item 64 2.38 1.10 3.39 1.04 11.23 .001 -1.07 Item 81 3.08 0.73 3.94 0.24 24.20 < .001 -1.60 Item 86 2.28 0.96 3.56 0.86 23.54 < .001 -1.58 Item 95 2.17 1.06 3.28 0.96 14.24 < .001 -1.24 Item 106 2.78 0.95 3.78 0.55 17.44 < .001 -1.36 Item 117 2.73 1.01 3.89 0.32 22.55 < .001 -1.57 Item 128 2.90 1.01 3.83 0.38 14.38 < .001 -1.24 Item 139 3.20 0.91 3.50 0.96 1.28 .26 -0.37 Item 150 2.30 1.14 3.44 0.86 14.49 < .001 -1.24 Note. Successful = scores on the Virtuous Responding Scale in the PIM condition were < 39; Unsuccessful > 42 (i.e., the well-defined range was between 39 – 42 and these cases were excluded from analysis).
117 APPENDIX H
PCL-R FACET SCORES AS PREDICTORS OF SELF-REPORT CHANGE SCORES
118 Predictors β r R2 ΔR2 p SRP-4 Facet 1 -.07 Facet 2 .01 Facet 3 -.00 Facet 4 .31 .30 .09 .04 .14 PPI-R Facet 1 .13 Facet 2 .11 Facet 3 .00 Facet 4 .08 .24 .06 .00 .37 LSRP Facet 1 -.13 Facet 2 .18 Facet 3 .07 Facet 4 .13 .26 .07 .02 .28 Note. PCL-R = Psychopathy Checklist-Revised; SRP-4 = Self-Report Psychopathy Scale; Facet 1 = Interpersonal Facet; Facet 2 = Affective Facet; Facet 3 = Lifestyle Facet; Facet 4 = Antisocial Facet; PPI- R = Psychopathic Personality Inventory-Revised; LSRP = Levenson Self-Report Psychopathy Scale.
119 APPENDIX I
CORRELATIONS BETWEEN THE PCL-R, SRP-4, PPI-R, AND LSRP TOTAL AND SCALE SCORES
120 Measure 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 1. PCL-R Total .80 .76 .68 .68 .62 .69 .30 .29 .30 .22 .22 .31 .25 .25 .17 .30 .31 .20 2. Factor 1 - .28 .87 .83 .19 .29 .06 .14 .20 -.03 -.08 .14 -.03 .29 .17 .09 .18 -.06 3. Factor 2 - .20 .28 .83 .90 .46 .34 .39 .36 .46 .38 .40 .14 .15 .40 .36 .36 4. Facet 1 - .45 .07 .26 .02 .13 .06 -.05 -.07 .14 -.02 .33 .08 -.01 .06 -.12 5. Facet 2 - .26 .23 .09 .11 .29 .01 -.07 .08 -.03 .16 .21 .18 .26 .02 6. Facet 3 - .50 .39 .26 .36 .36 .34 .38 .46 -.01 .21 .40 .36 .35 7. Facet 4 - .41 .32 .32 .28 .44 .29 .26 .22 .07 .31 .27 .28 8. SRP-4 Total - .91 .74 .81 .84 .73 .74 .33 .46 .76 .74 .57 9. SRP-4 IPM - .66 .65 .70 .74 .68 .40 .51 .71 .74 .46 10. SRP-4 CA - .45 .46 .54 .47 .28 .40 .67 .71 .43 11. SRP-4 ELS - .57 .64 .71 .26 .29 .60 .55 .52 12. SRP-4 CT - .51 .57 .17 .33 .55 .48 .50 13. PPI-R Total - .82 .59 .59 .69 .70 .49 14. PPI-R SI - .11 .34 .78 .67 .73 15. PPI-R FD - .29 .09 .22 -.12 16. PPI-R C - .52 .64 .20 17. LSRP Total - .93 .83 18. LSRP PP - .58 19. LSRP SP - Note. Correlations between .23 to .29 are p ≤ .05, Correlations between .30 and .35 are p ≤ .01, Correlations ≥ .36 are p ≤ .001. n = 78. PCL-R = Psychopathy Checklist-Revised; SRP-4 = Self-Report Psychopathy Scale; IPM = Interpersonal Manipulation; CA = Callous Affect; ELS = Erratic Lifestyle; CT = Criminal Tendencies; PPI-R = Psychopathic Personality Inventory-Revised; SI = Self-Centered Impulsivity; FD = Fearless Dominance; C = Coldheartedness; LSRP = Levenson Self-Report Psychopathy Scale; PP = Primary Psychopathy; SP = Secondary Psychopathy.
121 APPENDIX J
POTENTIAL CUT SCORES FOR THE PPI-R VR SCALE
122 Group Used % Sensitivity Specificity OCC PPP NPP Current Study Rule-out VR < 25 1 pt - .97 .22 .60 .18 .98 WD 87.8 1.00 .21 .64 .18 1.00 Edens et al. 2001 Rule-in UV > 35 1 pt - .61 .75 .68
Current Study Rule-in VR > 35 1 pt - .72 .87 .80 .50 .91 WD 80.1 .78 .96 .87 .75 .96 Rule-in VR > 40 1 pt - .35 .99 .67 .83 .90 WD 85.9 .31 1.00 .74 1.00 .89 Note. Group were established by either 1 pt = single-point discrimination or WD = well-defined cut score (i.e., the indeterminate category of + .5 SEM or + 1.6, which is + 2 raw points). % = the percentage retained for the classification when indeterminate range is removed; OCC = overall correct classification; PPP = positive predictive power (using a base rate of 15%); NPP = negative predictive power (using a base rate of 15%); VR = Virtuous Responding scale; UV = Unlikely Virtues scale (PPI validity scale equivalent to the PPI-R VR scale).
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