The Two-Factor Model of Psychopathic Personality: Evidence From the Psychopathic Personality Inventory

Personality Disorders: Theory, Research, and Treatment 2011 American Psychological Association 2012, Vol. 3, No. 2, 140 154 1949-2715/11/$12.00 DOI: 10.1037/a0025282 The Two-Factor Model of Psychopathic Personality: Evidence From the Psychopathic Personality Inventory David K. Marcus Washington State University Jessica J. Fulton University of Southern Mississippi John F. Edens Texas A&M University Psychopathy or psychopathic personality disorder represents a constellation of traits characterized by superficial charm, egocentricity, irresponsibility, fearlessness, persistent violation of social norms, and a lack of empathy, guilt, and remorse. Factor analyses of the Psychopathic Personality Inventory (PPI) typically yield two factors: Fearless Dominance (FD) and Self-Centered Impulsivity (SCI). Additionally, the Coldheartedness (CH) subscale typically does not load on either factor. The current paper includes a meta-analysis of studies that have examined theoretically important correlates of the two PPI factors and CH. Results suggest that (a) FD and SCI are orthogonal or weakly correlated, (b) each factor predicts distinct (and sometimes opposite) correlates, and (c) the FD factor is not highly correlated with most other measures of psychopathy. This pattern of results raises important questions about the relation between FD and SCI and the role of FD in conceptualizations of psychopathy. Our findings also indicate the need for future studies using the two-factor model of the PPI to conduct moderational analyses to examine potential interactions between FD and SCI in the prediction of important criterion measures. Keywords: psychopathy, psychopathic personality inventory, meta-analysis, two-factor model of psychopathy This article was published Online First October 10, 2011. David K. Marcus, Department of Psychology, Washington State University; Jessica J. Fulton, Department of Psychology, University of Southern Mississippi; John F. Edens, Department of Psychology, Texas A&M University. Correspondence concerning this article should be addressed to David K. Marcus, Department of Psychology, Washington State University, Pullman, WA 99164-4820. E-mail: davidmarcus1705@gmail.com Psychopathy or psychopathic personality disorder is a constellation of traits typically thought to include superficial charm, egocentricity, irresponsibility, fearlessness, shallow emotions, pathological lying, manipulation, persistent violation of social norms, and a lack of empathy, guilt, and remorse (Cleckley, 1941/ 1988; Hare, 1996; Lykken, 1995). It is often associated with criminal behavior and an antisocial lifestyle, but there is also evidence of individuals high in psychopathy who are not criminals (i.e., successful psychopaths, for a review see Hall & Benning, 2006). More generally, psychopathy is the subject of numerous practical and theoretical controversies (Edens, 2006; Edens, Magyar, & Cox, in press; Edens, Skeem, & Kennealy, 2009; Edens & Vincent, 2008). Some of these controversies may stem in part from the fact that psychopathy it is not directly represented in the Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; DSM IV-TR; American Psychiatric Association, 2000) 1 despite its being one of the most researched personality-based forms of psychopathology. This omission from the DSM has both advantages and disadvantages for psychopathy research. Among the advantages, researchers have had greater freedom to pursue alternative definitions and conceptualizations of psychopa- 1 The text revision of the DSM does, however, claim that the APD diagnosis reflects a disorder that has historically been referred to as...psychopathy, sociopathy, or dissocial personality disorder (APA, 2000, p. 702). 140

TWO-FACTOR MODEL OF PSYCHOPATHY 141 thy than have researchers who study DSMdefined personality disorders. Additionally, the absence of a DSM diagnosis has prevented premature closure in determining the essential diagnostic features of psychopathy. Conversely, although the absence of a definition from an authoritative source may spur innovation, it may also result in miscommunications among researchers who believe that they are studying the same construct. At the extreme, contentious disagreements about the nature of psychopathy and the utility of specific assessment instruments have resulted in the threat of legal action against some researchers (Poythress & Petrila, 2010). How the construct of psychopathy is understood and defined has often been a consequence of how it is measured, and the construct of psychopathy may be confused with the instrument used to measure it (see Skeem & Cooke, 2010b). Thus, if psychopathy is assessed using the Psychopathy Checklist-Revised (PCL-R; Hare, 2003), which includes many items addressing criminal and antisocial behavior, then antisociality appears to be an essential component of psychopathy (Hare & Neumann, 2010). In contrast, if psychopathy is assessed using an instrument such as the Psychopathic Personality Inventory Revised (PPI-R; Lilienfeld & Widows, 2005), which excludes items that explicitly refer to criminal or antisocial behavior, then antisociality might appear to be an associated, but not a core, feature of psychopathy (Skeem & Cooke, 2010a). The current paper examines the nature of psychopathy through the lens of the PPI family of instruments. The decision to focus on the PPI presupposes a particular conceptualization of what might constitute the core components of psychopathy (see Lilienfeld & Andrews, 1996, p. 493, for a complete listing of the initial 24 focal constructs investigated in the development of the PPI and pp. 495 496 for a description of the eight traits that ultimately were retained). This personality-based approach is congruent with the conceptualization of psychopathy as a personality disorder. Furthermore, because the PPI assesses such traits as fearlessness and stress immunity, it may be closer to classic descriptions of primary psychopathy (e.g., Cleckley, 1941/1988; Karpman, 1941; Lykken, 1995) than other instruments (e.g., the PCL-R) that place proportionally less emphasis on these traits. In addition, by not construing criminal or antisocial behavior to be essential components of psychopathy, the PPI provides a broader consideration of the construct, extending beyond forensic settings and disentangling the assessment of psychopathy from criminal history. In fact, there is considerably greater overlap in the distribution of PPI and PPI-R scores across offender and nonoffender samples (Lilienfeld & Widows, 2005) than there is for instruments such as the PCL-R, where community samples tend to obtain exceedingly low mean scores relative to criminal samples (Hare, 2003). The original PPI (Lilienfeld & Andrews, 1996) and its revision, the PPI-R (Lilienfeld & Widows, 2005), include eight primary subscales. However, research beginning with Benning, Patrick, Hicks, Blonigen, and Krueger (2003) has generally supported a two-factor structure for the PPI (but see Neumann, Malterer, & Newman, 2008, for a study that failed to replicate the two-factor solution and instead yielded a three-factor model). These two PPI factors have been labeled Fearless Dominance (FD) and Self-Centered Impulsivity (SCI; or Impulsive Antisociality). FD includes the PPI subscales Social Potency, Stress Immunity, and Fearlessness; and SCI includes Carefree Nonplanfulness, Impulsive Nonconformity, Machiavellian Egocentricity, and Blame Externalization. The eighth PPI scale, Coldheartedness (CH), generally does not load on either factor, although some have included it as part of FD (Mullins-Nelson, Salekin, & Leistico, 2006). Because CH does not load on either factor, fewer studies have included this scale in their analyses. However, callous and unemotional traits and a lack of empathy are often considered central to psychopathy (Barry et al., 2000; Benning et al., 2003), so the CH scale also merits examination. Benning et al. s (2003) initial factor analysis and subsequent studies that examined this twofactor model of the PPI have provided evidence for the construct validity of FD and SCI by examining the associations between (a) FD and SCI, (b) these two PPI factors and various external correlates, and (c) these two PPI factors and the factor scores from other psychopathy measures. Patrick (2007) proposed that whereas FD results primarily from a deficit in emotional reactivity, SCI results from a deficit in executive functioning. Therefore, because separate

142 MARCUS, FULTON, AND EDENS etiological processes are presumed to be responsible for FD and SCI, these two factors should be orthogonal or at best weakly correlated. Both the initial factor analytic study and a number of other studies have reported that FD and SCI are orthogonal (e.g., Benning et al., 2003; Patrick, Edens, Poythress, Lilienfeld, & Benning, 2006). Furthermore, when studies have reported a significant correlation between FD and SCI, these correlations have generally been in the small to moderate range (e.g., Pryor, Miller, & Gaughan, 2009; Sellbom & Verona, 2007). Whether the two PPI factors are orthogonal or positively related may have implications for understanding the nature and etiology of psychopathy. Although FD and SCI are expected to be orthogonal or weakly positively correlated, these constructs are also expected to have distinct (and often opposite) patterns of association with a variety of other personality traits and external correlates (Benning et al., 2003). Specifically, high levels of FD should be associated with low trait anxiety, whereas SCI should be positively associated with anxiety. Additionally, FD traits may be adaptive as high levels of FD have been associated with academic and occupational success and with positive affect and extraversion (e.g., Benning et al., 2003; Patrick et al., 2006). In contrast, SCI appears to be negatively correlated with various socioeconomic measures and positively correlated with negative affect and neuroticism (e.g., Benning et al., 2003; Derefinko & Lynam, 2006). However, whereas evidence of this expected pattern of correlations might provide empirical support for the construct validity of FD and SCI, it may also raise questions about the construct validity of the PPI model of psychopathy: Specifically, is it possible to have a meaningful construct that is composed of two unrelated factors, each of which predicts distinct (and sometimes opposing) outcomes? Like the PPI, a number of other psychopathy inventories have also yielded two-factor structures (e.g., Harpur, Hare, & Hakstian, 1989; Levenson, Kiehl, & Fitzpatrick, 1995). However, it is an empirical question whether FD and SCI correspond to other such two-factor models. If FD and SCI do correspond to the other two-factor models of psychopathy, this pattern would suggest that all of these measures are assessing a similar latent construct. Alternatively, a lack of correspondence between FD and SCI and the other two-factor models would raise the possibility that each psychopathy measure is assessing a different construct or set of constructs. In particular, individual reports (e.g., Edens, Poythress, Lilienfeld & Patrick, 2008; Gaughan et al., 2009) to date have raised questions about the correspondence between FD and other Factor 1-type indicators of psychopathy and suggest that it may tap something unique or at least relatively distinct from most other psychopathy scales. It is also possible that CH is more closely related to these other Factor 1-type indicators than is FD. Despite such findings, FD has been shown to predict theoretically important criterion measures, such as five-factor model psychopathy prototype scores (Ross, Benning, Patrick, Thompson, & Thurston, 2009). Since the initial factor analysis of the PPI (Benning et al., 2003), numerous studies have used this two-factor model to explore the construct of psychopathy. To synthesize these findings, we conducted a meta-analysis of studies that examined the two-factor model of the PPI. Specifically, we assessed (a) the correlation between FD and SCI; (b) the correlations between the two factors of the PPI and other personality variables; and (c) the correlations between FD and SCI and the two factors of three commonly used measures of psychopathy. Additionally, because CH taps a construct often thought of as integral to psychopathy, we also meta-analyzed the findings from studies that reported results from this scale. Because fewer studies analyzed the CH scale than FD or SCI, the meta-analyses involving CH scale may be considered exploratory and we did not attempt any moderator analyses with this scale. Because the studies examining the associations between personality and the PPI have used a myriad of instruments to measure a diverse set of personality traits and constructs, for the purpose of a meta-analysis these various measures were condensed into a manageable number of higher-order dimensions. For the present study, we used the dimensions Positive Emotionality (PEM), Negative Emotionality (NEM) and Constraint (CON) derived from Tellegen s (1985) three-factor personality model. According to Tellegen and Waller (2008), individuals high in PEM generally feel positive emotions, are extroverted, influential and socially effective, and are high in achievement. In contrast,

TWO-FACTOR MODEL OF PSYCHOPATHY 143 high levels of NEM are associated with experiencing negative emotions, including depression, anxiety, neuroticism, and irritability. CON involves response inhibition, and individuals high in CON are cautious and conventional. There is strong empirical evidence supporting this threefactor model, including cross-cultural replications of the model, evidence for the temporal stability of these factors, and evidence demonstrating the heritability and biological basis of these dimensions (for a review see Tellegen & Waller, 2008). Therefore, when studies reported the associations between the PPI factors and various personality traits or characteristics, these traits were classified as most closely reflecting PEM, NEM, or CON. For example, measures of PEM included the Positive Affect scale from the Positive and Negative Affect Schedule (PANAS; Watson, Clark, & Tellegen, 1988), the Extraversion scale from the NEO-FFI (Costa & McCrae, 1992), the Dominance scale from the Personality Assessment Inventory (Morey, 1991), and the Dominance scale from the Revised Interpersonal Adjective Scales: Big Five Version (IASR-B5; Trapnell & Wiggins, 1990). Examples of NEM measures included the Negative Affect scale from the PANAS, the Neuroticism scale from the NEO-FFI, the Neuroticism scale from the IASR-B5, and the Welsh Anxiety Scale (Welsh, 1956). Examples of CON included the Conscientiousness scale from the NEO-FFI, the Impulsivity scale of the Eysenck Impulsivity/Venturesomeness scale (Eysenck & Eysenck, 1978), the Control scale of the Multidimensional Personality Questionnaire (MPQ; Tellegen, 1995), and the Attention Control Scale (Derryberry & Reed, 2002). 2 Tellegen s model has been linked empirically to psychopathy. Lynam and Derefinko s (2006) meta-analysis found a small positive association between psychopathy and PEM, a moderate positive association between psychopathy and NEM, and a relatively large negative correlation between psychopathy and CON. However, this meta-analysis examined psychopathy as a unitary construct, whereas findings from studies that have used the two-factor model of psychopathic personality suggest that FD and SCI are likely to be differentially related to Tellegen s three factors. In fact, the two-factor model of the PPI has been linked to Tellegen s threefactor model through the MPQ, which is the primary measure of Tellegen s three-factor model. Benning, Patrick, Blonigen, Hicks, and Iacono (2005) demonstrated that the MPQ scales could be used to estimate FD and SCI. We hypothesized that (a) FD would be positively associated with PEM and negatively associated with NEM and CON, and (b) SCI would be positively associated with NEM and negatively associated with CON. Finally, because a number of studies examined the associations between the PPI factors and sensation seeking and because sensation seeking appears to be a blend of high PEM and low CON (e.g., Livesley, Jang, & Vernon, 2003), we analyzed the correlations between the PPI factor scores and sensation seeking separately. Method To be included in the meta-analysis, studies had to (a) be written in English, (b) have used one of the PPI instruments (i.e., PPI, PPI-R, or PPI-Short Form [PPI-SF; Wilson, Frick, & Clements, 1999]), (c) report results in which the PPI measure was scored using the two-factor model, and (d) provide codable statistics. Only published studies were included (i.e., unpublished datasets and conference presentations were not included). We conducted searches of PsycINFO using the terms Psychopathic Personality Inventory, Fearless Dominance, Impulsive Antisociality, and Self-Centered Impulsivity, and also conducted a search for all papers that cited Lilienfeld and Andrews (1996; i.e., the paper in which the PPI was introduced). Additionally, we reviewed the reference sections of all papers that were included in the meta-analysis. Finally, data from the PPI-R manual (Lilienfeld & Widows, 2005) was also included. This search yielded 45 studies, involving a total of 14,517 participants. The 45 studies were presented in 36 papers (i.e., some papers reported results from multiple studies or analyzed data from independent samples separately). The studies were coded for sample size, sample type (forensic/correctional vs. not forensic/ correctional), and the version of the PPI used 2 A complete list of the scales that were classified as exemplifying PEM, NEM, and CON is available from the first author.

144 MARCUS, FULTON, AND EDENS (PPI, PPI-R, or PPI-Short Form). We examined the following effects: (a) the correlations among FD, SCI, and CH, (b) the correlations between FD, SCI, and CH with measures of PEM, NEM, CON, and sensation seeking, and (c) the correlations between FD, SCI, and CH with each of the two-factor scores from the PCL-R, the Self- Report Psychopathy-II Scale (SRP-II; Hare, 1991), and the Levenson Self-Report Psychopathy Scales (LSRP; Levenson et al., 1995). If the study included multiple measures of a personality factor (e.g., the study included two NEM measures, such as anxiety and depression), the results were pooled by converting the Pearson rs to Fisher s Z r s and averaging these Z r s. Similarly, when the effect sizes were pooled across studies for the actual metaanalyses, the Pearson rs were corrected for bias using a Fisher s Z r transformation (Hedges & Olkin, 1985) and were then converted back to rs for reporting. The effect sizes were weighted using the sample size minus 3 as the inverse variance weight (Lipsey & Wilson, 2001, p. 64). We conducted the analyses using a maximum-likelihood random effects model. The rationale for using a random effects model instead of a fixed effects model is that a random effects model assumes that studies included in the meta-analysis are only a sample of all possible studies that could have been conducted. Thus, the results from these analyses can be generalized beyond the particular set of studies that were analyzed (Field, 2003). For each metaanalysis, a Q test of homogeneity was used to examine whether the studies in the metaanalysis sampled different populations. A significant Q value suggests the possibility that moderators may explain the heterogeneity among the studies. The percentage of variance attributable to heterogeneity was described using I 2, with 0 indicating complete homogeneity and 100 indicating complete heterogeneity (Higgins, Thompson, Deeks, & Altman, 2003). For some of the variables (including all of the analyses involving CH), there were not enough studies to conduct a meaningful examination of potential moderators. When there was significant heterogeneity and a sufficient number of studies, the primary moderator examined was population type. These moderation analyses were conducted using Hedges (1982) metaanalytic analogue to the analysis of variance (ANOVA). All of the analyses were conducted using Lipsey and Wilson s (2001) macros for the SPSS statistical program. Results Are FD and SCI Orthogonal? The average r across the 37 studies that reported the correlation between FD and SCI was.12, which was small but statistically significant (95% CI.07 -.17; Z 4.43, p.001). There was considerable heterogeneity among these studies (Q [36] 273.16, p.001, I 2 80.9). A random effects meta-analytic analogue to the ANOVA indicated that whether the study used a forensic sample or a nonforensic sample accounted for much of the variability across studies, Q B (1) 7.98, p.005; Q W (35) 34.49, p.49. There was no association between FD and SCI in the forensic samples (r.03, p.36, k 13). In contrast, there was a significant, small correlation between FD and SCI in those studies that did not use a forensic sample (r.16, p.001, k 24). There was a small but significant correlation between FD and CH, r.15, p.001, k 12. There was significant heterogeneity among these results (Q [11] 27.51, p.004, I 2 60.0). There was a similar association between SCI and CH, r.15, p.001, k 11. There was also significant heterogeneity among these studies (Q [10] 87.80, p.001, I 2 88.6). How Are the PPI Factors Related to Other Personality Traits? FD and SCI were correlated with measures of PEM in 16 studies. There was a relatively large positive correlation between FD and PEM, with an average correlation of.39 (95% CI.34 to.43; Z 14.41, p.001). However, there was also considerable heterogeneity across studies, Q (15) 57.35, p.001, I 2 73.8. Type of sample did not moderate the association between FD and PEM. In contrast, across the 16 studies there was no association between SCI and PEM, r.02 (95% CI.09 to.05; Z.67, p.51). Once again there was significant heterogeneity, Q (15) 95.58, p.001, I 2 84.3. However, this heterogeneity could be explained by whether the study used a forensic or nonforensic sample, Q B (1) 7.20,

TWO-FACTOR MODEL OF PSYCHOPATHY 145 p.007; Q W (14) 16.28, p.30. There was a modest but significant negative association between SCI and PEM in the nonforensic samples, r.09, p.03, k 10. In contrast, there was a trend toward a modest positive association between SCI and PEM in the forensic samples, r.10, p.08, k 6. There were 25 studies that reported the correlation between the PPI factor scores and measures of NEM. FD was negatively associated with NEM, r.35 (95% CI.41 to.30; Z 11.38, p.001). Although the association between FD and NEM was negative in all 25 studies, there was still considerable heterogeneity across the studies, Q (24) 181.33, p.001, I 2 86.8. Type of sample could not explain this heterogeneity, but the version of the PPI used did, Q B (2) 8.81, p.012; Q W (22) 27.08, p.21. Studies that used the original PPI yielded the largest correlations between FD and NEM (r.41, p.001, k 14), followed by studies that used the PPI-SF (r.31, p.001, k 6) and those that used the PPI-R (r.24, p.001, k 5). The difference between the PPI and the PPI-R was significant, Q B (1) 8.19, p.004; Q W (17) 19.21, p.32. There was a significant positive correlation between SCI and NEM, r.30 (95% CI.23 to.38; Z 7.28, p.001). Again, there was significant heterogeneity among the studies, Q (24) 333.00, p.001, I 2 92.8, but this heterogeneity could not be explained by the sample type or version of the PPI. Most of the studies that examined the association between the PPI factor scores and constraint used self-report measures (k 16), but two used laboratory tasks. Sellbom and Verona (2007) used neuropsychological measures of response inhibition and Carlson, Thái, and McLarnon (2009) measured P3 amplitude that reflects executive inhibitory processes. There was no association between FD and constraint, r.04 (95% CI.18 to.09; Z.61, p.54). There was considerable heterogeneity among these studies, Q (15) 364.70, p.001, I 2 95.9, which could be explained by whether the sample was a forensic sample or a nonforensic sample, Q B (1) 7.20, p.007; Q W (14) 15.98, p.31. In the 12 studies that used nonforensic samples, there was a negative association between FD and CON (r.14, p.05). In contrast, the association between FD and measures of constraint was positive (r.23, p.05) in the four studies that used forensic samples. As would be expected, there was a robust negative association between SCI and CON, r.44 (95% CI.54 to.33; Z 7.37, p.001). There was also significant heterogeneity across studies, Q (15) 284.93, p.001, I 2 94.7. Sample type did not moderate the association between SCI and CON. Five studies examined associations between the PPI factors and sensation seeking. Sensation seeking was positively correlated with both FD (r.51; 95% CI.44 to.57; Z 12.13, p.001) and SCI (r.50; 95% CI.44 to.55; Z 14.65, p.001). Therefore, sensation seeking was the only personality trait that yielded robust correlations that shared the same valence with both PPI factors. There was some evidence of heterogeneity among the correlations between FD and sensation seeking, Q (4) 10.90, p.03, I 2 63.3. There were not enough studies to investigate possible moderators that could explain this heterogeneity, but it appeared to be because of the especially high correlation (r.68) reported by Ray, Poythress, Weir, and Rickelm (2009). The four other studies reported correlations that ranged from.44 to.56. The correlations between SCI and sensation seeking were homogeneous, Q (4) 7.17, p.13, I 2 44.2. Seven studies reported the association between CH and PEM and nine studies reported the association between CH and NEM. Overall, CH was negatively associated with both PEM, r.22 (95% CI.28 to.15; Z 6.10, p.001), and with NEM, r.19 (95% CI.29 to.09; Z 3.72, p.001). Although both sets of associations were significantly heterogeneous (Q [6] 17.58, p.007, I 2 65.9 and Q [8] 55.44, p.001, I 2 85.6, for PEM and NEM, respectively), there were not enough studies to test for moderators. There was also a negative association between CH and CON r.15 (k 7, 95% CI.28 to.01; Z 2.07, p.04). Again, there was significant heterogeneity among the studies, Q (6) 54.27, p.001, I 2 88.9. Only two studies reported correlations between CH and sensation seeking (see Table 1).

146 MARCUS, FULTON, AND EDENS Table 1 Meta-Analysis of the Correlations With the PPI Variable k n FD (range) SCI (range) CH (k; range) PPI SCI 37 12,536.12 (.20,.36) CH 12 4,809.15 (.02,.28) CH 11 4,463.15 (.14,.41) Personality PEM 16 5,715.39 (.10,.61).02 (.38,.29).22 (k 7;.35,.10) NEM 25 8.571.35 (.57,.08).30 (.24,.67).19 (k 9;.42,.02) CON 16 5,280.04 (.56,.48).44 (.68,.02).15 (k 7;.36,.15) SS 5 1,441.51 (.44,.68).50 (.45,.59).15 (k 2;.07,.36) Psychopathy PCL-R F1 9 5,432.21 (.10,.38).20 (.08,.48).17 (k 4;.09,.36) PCL-R F2 9 5,432.15 (.02,.32).41 (.25,.58).15 (k 4;.12,.25) SRP1 5 1,274.53 (.31,.60).05 (.22,.18).40 (k 2;.32,.45) SRP2 5 1,274.40 (.10,.48).67 (.58,.71).17 (k 2;.04,.29) LSRP1 10 3.374.17 (.09,.46).50 (.02,.62).44 (k 5;.33,.55) LSRP2 10 3,374.06 (.25,.66).65 (.14,.71).15 (k 4;.07,.28) Note. k number of effect sizes in meta-analysis; n total number of participants in each analysis; FD Fearless Dominance; SCI Self-Centered Impulsivity; CH Coldheartedness; PEM Positive Emotionality; NEM Negative Emotionality; CON Constraint; SS Sensation Seeking; PCL-R F1 Psychopathy Checklist-Revised, Factor 1; PCL-R F2 Psychopathy Checklist-Revised, Factor 2; SRP1 Self-Report Psychopathy-II Scale, Factor 1; SRP2 Self-Report Psychopathy-II Scale, Factor 2; LSRP1 Self-Report Psychopathy Scale, Primary Psychopathy; LSRP2 Self-Report Psychopathy Scale, Secondary Psychopathy. p.05. p.001. Are the PPI Factors Associated With Other Measures of Psychopathy? Compared with the number of PPI studies that reported correlations with other personality measures, there were fewer studies that examined the associations between the PPI factor scores and other measures of psychopathy. Therefore, even though 13 of the 18 metaanalyses reported in this section were heterogeneous, there was not sufficient statistical power to examine possible moderators. 3 Unlike the self-report PPI measures, the PCL-R is a 20-item measure that is scored based on an interview and file review. There has been some controversy regarding the factor structure of PCL-R, with researchers advocating for both three-factor (Cooke & Michie, 2001) and fourfacet (Hare, 2003) models. However, the traditional approach has involved two broad factors, an interpersonal-affective (F1) and an antisocial lifestyle (F2) factor. Because PCL-R F1 is associated with dominant and narcissistic personality characteristics, it would be expected to correspond to FD (e.g., Blonigen, Hicks, Krueger, Patrick, & Iacono, 2006). Additionally, although the PPI does not explicitly assess antisocial behavior, correspondence between SCI and PCL-R F2 was expected because both are associated with impulsivity and symptoms of antisocial personality disorder (e.g., Benning, Patrick, Salekin, & Leistico, 2005). Nine studies correlated FD and SCI with F1 and F2 of the PCL-R. Across studies, there were small- to medium-sized correlations between FD and both PCL-R F1 (r.21, p.001) and PCL-R F2 (r.15, p.001). Thus, although FD and PCL-R F1 were related, they only shared a small amount of variance (4%). The correlation between SCI and PCL-R F1 was in this same range (r.20, p.001), but there was a large correlation between SCI and PCL-R F2 (r.41, p.001). Four studies reported the associations between CH and the two PCL-R factor scores. Like FD, CH was modestly positively associated with both PCL-R F1 (r.17, p.001) and PCL-R F2 (r.15, p.001). The SRP-II was designed as a self-report alternative to the PCL-R (Hare, 1991). Al- 3 This unexplained heterogeneity is an additional justification for using a random effects model to compute the average effect sizes across studies (Lipsey & Wilson, 2001).

TWO-FACTOR MODEL OF PSYCHOPATHY 147 though the authors of the SRP-II rationally assigned a subset of items from the scale to parallel PCL-R F1 and PCL-F2, subsequent factor analyses (e.g., Williams & Paulhus, 2004) have not supported these item assignments. Additionally, SRP Factor 1 (SRP1) does not appear to have acceptable internal consistency (e.g., Derefinko & Lynam, 2006; Lilienfeld & Hess, 2001). Five studies reported the associations among FD, SCI, SRP1, and SRP Factor 2 (SRP2). FD strongly correlated with SRP1 (r.53, p.001), but there was also a large correlation between FD and SRP2 (r.40, p.001). In contrast, SCI did not correlate with SRP1 (r.05, p.45) but was highly correlated with SRP2 (r.67, p.001). Only two studies reported correlations between CH and the SRP scales. CH was highly correlated with SRP1 (r.40, p.001) but was not significantly associated with SRP2 (r.17, p.17). Thus, it appears that (a) both SRP scales involve FD traits, (b) SCI is uniquely associated with SRP2, and (c) SRP1 is also associated with CH. Shared method variance may explain the generally larger correlations between the PPI scales and the SRP-II scales compared with those between the PPI and the PCL-R. The two scales of the LSRP were designed to assess primary and secondary psychopathy. The primary psychopathy scale purports to measure interpersonal attitudes such as lack of remorse, callousness, and manipulativeness. The secondary psychopathy scale was designed to correspond to the antisocial lifestyle factor of the PCL-R and includes items assessing low frustration tolerance, impulsivity, and the absence of long-term goals. Given the design of the LSRP, it might be expected that primary psychopathy would correspond to FD and secondary psychopathy would correspond to SCI. However, the two LSRP scales are positively correlated with one another (r.40; Levenson et al., 1995). Ten studies reported correlations between the PPI and LSRP scales, but in fact, both LSRP scales were far more highly correlated with SCI than FD. The correlation between FD and LSRP Primary was.17 ( p.001), and FD was not significantly correlated with LSRP Secondary (r.06, p.57). In contrast, SCI was highly correlated with both LSRP Primary (r.50, p.001) and LSRP Secondary (r.65, p.001). Fewer studies reported the correlations between CH and LSRP Primary (k 5) and LSRP Secondary (k 4). CH was highly correlated with LSRP Primary (r.44, p.001) and also correlated with LSRP Secondary (r.15, p.002). Based on these correlations, it does not appear that the LSRP scales assess fearless dominant traits (at least as measured by the PPI). Instead, both LSRP scales appear to assess SCI traits and perhaps to a lesser degree CH. Discussion In the current study, we conducted a metaanalytic review of published studies that examined the two-factor model of the PPI. The pattern of results raises a number of fundamental questions about the nature of psychopathy (at least as assessed using this particular measure). The most basic question is whether psychopathy is a coherent construct. Studies using Meehl s taxometric method have already provided strong and relatively consistent evidence that psychopaths, whether assessed via selfreport measures such as the PPI or the PCL rating scales, are not qualitatively distinct from other individuals (e.g., Edens, Marcus, Lilienfeld, & Poythress, 2006; Guay, Ruscio, Knight, & Hare, 2007; Marcus, John, & Edens, 2004). In other words, psychopathy does not describe a taxon or natural class. However, the current meta-analysis raises questions about whether PPI-defined psychopathy itself is a unitary construct. Whereas the two factors of the PCL-R are generally moderately positively correlated (e.g., Blonigen et al., 2010; Hare, 2003), as are the two factors of the LSRP (e.g., Falkenbach, Poythress, Falki, & Manchak, 2007), FD and SCI were weakly correlated and not at all correlated among those studies that used offender samples. Furthermore, CH was not strongly associated with either PPI factor. The hallmark of a syndrome is that the multiple symptoms of the syndrome co-occur at a greater rate than would be expected by chance (Kazdin, 1983; Lilienfeld, Waldman, & Israel, 1994). Thus, for example, knowing that a patient has a fever increases the likelihood that the patient also has an elevated white blood cell count. Similarly, an individual who describes feeling empty is also more likely than the typical individual to engage in self-harm behavior. In contrast, it ap-

148 MARCUS, FULTON, AND EDENS pears that a prison inmate who is bold and dominant is no more or less likely to also be impulsive than any other inmate. That the multiple facets of a syndrome covary suggests the possibility of a common etiology: The fever and elevated white blood cell count co-occur because they are both produced by an underlying infection, the subjective sense of emptiness and self-harm co-occur because of some presumed underlying personality structure. If FD and SCI are unrelated (or at best weakly related), then the co-occurrence of high levels of FD and SCI in any individual is simply coincidental. Along these lines, Lilienfeld and Widows (2005) have previously suggested that PPI-psychopathy may represent something of a compound trait with relatively independent lower-order facets rather than a multifaceted trait that subsumes relatively highly covarying lower-order traits. Conversely, one might argue that the SCI factor is the true measure of psychopathy whereas FD, although conceptually interesting and rooted in Cleckley s original conceptualization, is not really central to the disorder. SCI appeared to be much more highly correlated with the factor scores from both the PCL-R (F2) and LSRP, and FD was at best modestly correlated with PCL-R F1 and LSRP primary psychopathy. Furthermore, LSRP primary psychopathy appears to be more strongly associated with CH than with FD. (FD was highly correlated with SRP1, but the SRP-II is the least researched and psychometrically sound of the psychopathy measures that we examined.) Additionally, SCI is associated with measures of antisocial personality disorder, but FD is not (e.g., Benning et al., 2003; Patrick et al., 2006). In contrast, FD was highly associated with positive traits and emotions, negatively correlated with negative characteristics such as neuroticism, and unrelated to measures of constraint. Overall, FD does not seem to be especially maladaptive, and individuals with high levels of FD are unlikely to appear overtly pathological. In contrast, high levels of SCI, even in the absence of FD, are likely to be problematic. Recently, Patrick and colleagues (Patrick, Fowles, & Krueger, 2009; see also Patrick & Bernat, 2009) proposed a new model that seeks to integrate some of the seemingly disparate findings in the psychopathy literature and also provides a theoretical framework for conceptualizing the constructs operationalized by FD, SCI, and CH. A core component of this triarchic model is disinhibition, which is construed as a global phenotypic propensity toward impulse control problems and insufficient behavioral restraint. Both SCI (and various other self-report measures of psychopathic traits such as the LSRP) and PCL-R Factor 2 are thought to be reasonably strong markers of this construct, which bears clear conceptual links with the externalizing dimension of psychopathology identified in the child (Achenbach & Edelbrock, 1978) and adult literature (Krueger et al., 2002). The results of our meta-analysis are generally consistent with such a characterization of SCI, in that it was positively associated with various constructs associated with the externalizing spectrum. Novel to Patrick and colleagues (2009) triarchic model of psychopathy and directly relevant to the relation between FD and various positive or adaptive personality traits is their differentiation between the constructs of boldness and meanness. Meanness in this model reflects deficient empathy, lack of close attachments, excitement seeking, and exploitativeness, which is more conceptually aligned with psychopathy criteria espoused by theorists such as McCord and McCord (1964) that characterized psychopathic criminals as cold and predatory. CH is the PPI scale most closely related to meanness. Consistent with this model, it is noteworthy that CH was negatively associated with both PEM and NEM, suggesting that it involves an overall lack of emotionality. Boldness in this model, which is best captured by FD on the PPI, represents a nexus of social dominance, emotional resiliency, and self-confidence, which harkens back to several of Cleckley s (1941/1988) less overtly pathological criteria for diagnosing psychopathy, such as good intelligence and social charm, the absence of delusions or other irrational thinking, and lack of anxiousness. Positive associations between FD and indicators of psychological health and adjustment (at least at the bivariate level), such as positive effect sizes between FD and PEM, are thought to reflect psychopaths positive (or perhaps inflated) self-image and capacity for resilience even in the face of danger or threats. From this theoretical framework, the modest associations between FD and PCL-R Factor 1 evident in our findings are not particularly troubling, in that PCL-R Factor 1 is primarily construed as an

TWO-FACTOR MODEL OF PSYCHOPATHY 149 indicator of meanness because of the heavy focus on callousness, interpersonal manipulation, and Machiavellian features. Given the model proposed by Patrick et al. (2009), a critical question that has for the most part been ignored in prior PPI research relates to the potential interactive effects between SCI and FD. That is, do high levels of SCI amplify the effects of FD on psychopathy-relevant constructs (or do high levels of FD amplify the impact of SCI)? Despite the low correlations between FD and SCI, which make them ideal for moderator research (Baron & Kenny, 1986), there has been surprisingly little research examining the interaction between FD and SCI in predicting psychopathy-relevant outcome variables. One important recommendation that follows from our review is that future studies using the two-factor model of the PPI should consider conducting moderational analyses. For example, it may be that FD predicts important negative (rather than positive) outcomes among offender populations, but only in the presence of elevated SCI. That is, immunity to stress and social dominance may be relatively adaptive when not also paired with a strong tendency toward externalizing behavior (or disinhibition in the triarchic model) but may lead to increased disruptiveness, predatory violence, or treatment failure when the two co-occur. In fact, such an interactive effect was recently reported by Smith, Edens, and McDermott (2011), who found that institutionalized forensic psychiatric patients with elevated scores on both FD and SCI were at highest risk for predatory aggression. The few other studies examining interactions between FD and SCI have reported somewhat mixed results, however. For example, Benning et al. (2005) identified significant interactions between FD and SCI in predicting certain theoretically important criterion measures (e.g., egocentricism) but not others (e.g., sadistic traits). This hypothesized interaction could be compared with an additive model in which total scores on the PPI are used to predict problematic outcomes. Another issue raised by the current metaanalysis is whether high levels of FD and SCI mean the same thing in forensic and nonforensic populations. First, there is the consistent pattern of FD and SCI being virtually orthogonal in forensic samples, but being weakly correlated in nonforensic samples. Second, the association between SCI and PEM was moderated by whether the sample was forensic or not. Finally, as expected, FD was negatively correlated with CON in the nonforensic samples but was unexpectedly positively correlated with CON in the forensic samples. Overall, these constellations of traits may mean different things and have very different implications across different populations and settings. Being bold, dominant, fearless, and unflappable in a military, business, or even academic setting is likely to have very different consequences than having these traits in a forensic setting. Again, it seems likely that the interaction between FD and SCI may be more important than FD as a main effect. In other words, high FD may only be problematic when combined with high SCI. As with any meta-analysis, the current study is limited by the available studies. For example, not enough studies have reported the associations with CH to permit moderation analyses. Similarly, only five studies examined the associations between the PPI factors and sensation seeking. Additionally, because of the variety of external correlates that were examined across the studies that were metaanalyzed, it was necessary to combine them into higher-order dimensions (i.e., PEM, NEM, and CON in the current study) to avoid reporting dozens of additional effect sizes, each representing only a few studies. Although the strategy of combining measures into higher-order dimensions was conceptually justified based on previous work linking Tellegen s model to PPIpsychopathy and had the advantage of providing sufficient power for moderation analyses, it also likely contributed to the heterogeneity of each of these meta-analyses and did not allow for a more fine-grained examination of the lower-order factors (e.g., the associations between SCI and anxiety vs. depression). Finally, in addition to whether the samples were drawn from forensic or nonforensic populations, there are other demographic variables (e.g., gender, age, ethnicity) that may moderate the associations between FD and SCI and those between these PPI factors and other external correlates. Although these demographic variables could have been examined as potential moderators, the extant research confounds these variables with whether the sample was forensic or not. Whereas only half (49%) of the participants in the nonforensic samples were male, the forensic samples were largely

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