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. 2019 Jun;16(3):140–148.

Tracking Psychopathy in Female Italian Inmates: The Role of the DSM-5 Alternative Model of Personality Disorders Dysfunctional Personality Domains

Antonella Somma a, Andrea Fossati a,✉, Stefano Ferracuti b, Vincenzo Caretti c, Domenico Montalbò d, Donatella La Tegola d, Felice Carabellese d
PMCID: PMC8650178  PMID: 34908949

Abstract

Objective

The present study aimed at testing the relationships between measures of psychopathy and measures of the DSM-5 Alternative Model of Personality Disorders (AMPD) domains in a sample of female offenders who were serving their sentences in Italian prisons.

Method

Thirty-eight Italian adult female inmates were underwent the Italian versions of the Psychopathy Checklist-Revised (PCL-R) and of the Personality Inventory for DSM-5-Informant Report Form (PID-5-IRF).

Results

According to MM robust regression analysis results, high scores on the PID-5-IRF Disinhibition domain scale and low scores on the PID-5-IRF Anxiousness trait scale were significant and substantial predictors of the PCL-R total score.

Conclusions

As a whole, our findings support the hypothesis that assessing the DSM-5 AMPD domains (and traits) may be important for tracking psychopathy in female inmates.

Keywords: psychopathy, DSM-5 alternative model of personality disorders, psychopathy checklist revised, personality inventory for DSM-5

Psychopathy refers to a clinical condition characterized by prominent behavioral deviancy in the presence of distinctive emotional and interpersonal features (Patrick, Fowles, & Krueger, 2009). Influenced by a number of prominent theorists including Cleckley (1941), Hare (e.g., 1991), Karpman (1941), and Lykken (1957, 1995), current conceptions of psychopathy include reference to features such as superficial charm, manipulativeness, egocentricity, callousness, a lack of remorse or empathy, impulsivity and irresponsibility, along with a marked risk for violence and criminal behavior (Cooke & Michie, 2001; Marcus, Fulton, & Edens, 2013). Criminality may represent a correlate (or a consequence) of psychopathy rather than a core feature of this maladaptive personality feature (e.g., Cleckley, 1941; Cooke & Michie, 1997; Lykken, 1957, 1995).

Research findings have consistently documented associations between psychopathy and a wide range of externalizing behaviors such as crime and aggression (e.g., Gretton, Hare, & Catchpole, 2004; Porter, Birt, & Boer, 2001), criminal recidivism (e.g., Walters, Knight, Grann, & Dahle, 2008), substance use (e.g., Gustavson et al., 2007; Kennealy, Hicks, & Patrick, 2007), and sexual offending (Caldwell, Ziemke, & Vitacco, 2008).

Taxometric evidence consistently supported the hypothesis that psychopathy should be conceived as a dimensionally distributed construct (e.g., Walters, Ermer, Knight, & Kiehl, 2015). The existing scientific literature does not support the notion that psychopathy comprises a unitary diagnostic construct, although disagreement still exists as to the number of latent dimensions underlying psychopathy characteristics (e.g., Patrick, 2018). A two-factor model of psychopathy has been originally proposed (Harpur, Hakstian, & Hare, 1988; Hare et al., 1990); however, three-factor models of psychopathy (e.g., Andershed, Kerr, Stattin, & Levander, 2002; Cooke & Michie; 2001; Patrick et al., 2009) and also four-factor models (Hare & Neumann, 2006) have received strong empirical support (e.g., Hare, Neuman, & Mokros, 2018).

Although the Diagnostic and Statistical Manual of Mental Disorders– 5th Edition (DSM-5; American Psychiatric Association, 2013) does not list psychopathy neither in the Section II nor in the Alternative Model of Personality Disorders (AMPD), it should be observed that the DSM-5 AMPD provides the opportunity to specify if the Antisocial Personality Disorder diagnosis is characterized by the presence of psychopathic features, defined by low levels of anxiousness and withdrawal and high levels of attention seeking (APA, 2013). The DSM-5 AMPD provides a system of 25 dysfunctional personality traits, whose correlations are explained by 5 dysfunctional personality domains (Negative Affectivity, Detachment, Antagonism, Disinhibition, and Psychoticism), which have been shown to represents the maladaptive variants of the well-known Five Factor Model personality traits (Wright, Pahlen, & Krueger, 2017).

To assess the DSM-5 AMPD dysfunctional personality traits and domains, Krueger and colleagues (2012) developed the Personality Inventory for DSM-5 (PID-5), a 220 items self-report measures that yields scores for both dysfunctional personality traits and dysfunctional personality domains. The PID-5 has been extensively validated across different languages (Al-Dajani, Gralnick, & Bagby, 2016), including Italian (Fossati, Krueger, Markon, Borroni, & Maffei, 2013). The PID-5 factor structure has been consistently replicated across different cultures and languages (Somma, Krueger, Markon, & Fossati, 2019a). To overcome the limitations of self-report assessment (e.g., under-reporting or over-reporting of dysfunctional features; Dhillon, Bagby, Kushner, & Burchett, 2017; McGee Ng et al., 2016), Markon and colleagues (2013) proposed the PID-5-Informant Report Form (PID-5-IRF), which represents the informant-rated version of the PID-5. The PID-5-IRF has been recently validated in Italian community-dwelling adults (Somma, Krueger, Markon, & Fossati, 2019b).

Several empirical studies have been conducted to evaluate the associations between psychopathic features and PID-5 traits (Anderson, Sellbom, Wygant, Salekin, & Krueger, 2014; Crego & Widiger, 2014; Few et al., 2015; Fossati, Krueger, Markon, Borroni, & Maffei, 2013; Miller, Lamkin, Maples-Keller, Sleep, & Lynam, 2018; Strickland, Drislane, Lucy, Krueger, & Patrick, 2013; Wygant et al., 2016). Notwithstanding some authors have questioned the utility and effectiveness of the AMPD psychopathy specifier (e.g., Few et al., 2015), the construct of psychopathy can be effectively indexed using traits of the PID-5 (e.g., Fossati et al., 2013; Strickland et al., 2013). Considering the psychopathy construct in the perspective of dysfunctional personality may help to understand both the developmental pathways leading to pathology and the possible gender differences in psychopathy phenotypic manifestations (Strickland et al., 2013). Indeed, there is a burgeoning literature trying to understand psychopathy and related conditions in women (Verona & Vitale, 2018). This literature highlights a number of similarities and differences between psychopathy manifestations in women and in men, respectively (Carabellese, et al, 2018; Carabellese, et al; 2019a; Carabellese, et al, 2019b; Verona & Vitale, 2018). For instance, features of psychopathy are captured in a valid manner across gender by current conceptualizations and measures of psychopathy (Verona & Vitale, 2018). In particular, the Psychopathy Checklist-Revised (PCL-R; Hare, 2003) showed strong similarity of measurement properties across gender (although it may not perform well in predicting violent/aggressive behavior and conduct problems in adult women (Verona & Vitale, 2018).

Psychopathy may be characterized by higher levels of borderline personality disorder features/emotional dysregulation (e.g., efforts to avoid abandonment, self-harm) among women compared to men, although low levels of anxiety and high levels of impulsivity and aggression characterize both psychopathic men and women (Carabellese, et al., 2018; Verona & Vitale, 2018). Interestingly, a dearth of studies exists as to the relationships between substance use and psychopathy in women; however, there are data indicating that illicit drug use is more represented in women than in men (Verona & Vitale, 2018).

To the best of our knowledge, only one study tried to address the issue of the relationships between PCL-R scores and measures of selected DSM-5 AMPD trait scale scores in a sample of forensic participants (Wygant et al., 2016). The authors reported that the measures of the DSM-5 AMPD traits linked to the DSM-5 AMPD antisocial personality disorder outperformed the DSM-5 Section II antisocial personality disorder criteria in predicting the PCL-R total score (Wygant et al., 2016). Their findings were limited to a sub-set of DSM-5 AMPD traits assessed in a sample of male inmates.

Because of the strong link between psychopathy and risk for criminal offences (Douglas, Vincent, & Edens, 2018; Hickey, Walters, Drislane, Palumbo, & Patrick, 2018), forensic samples may be adequate participants also for understanding the link between dysfunctional personality dimensions and psychopathy in women. Female inmates represent a minority of the forensic population also in Italy; indeed, 2580 female inmates are currently serving their sentences in Italy, representing 4.3% of the total number of Italian inmates (N = 60,125; Italian Ministry of Justice, 2019). Thus, obtaining large samples of female inmates may be more difficult than obtaining large sample of inmate men.

Starting from these considerations, we aimed at testing the relationships between measures of psychopathy and measures of the DSM-5 AMPD domains in a sample of Italian female inmates who were serving their sentences in Italian prisons. Because of the difficulty of obtaining large samples of female inmates, and the need for ensuring adequate power in statistical analyses without capitalizing on chance association, in the present study we focused our attention mainly on a single, comprehensive measure of psychopathy, and on the five DSM-5 AMPD domains as potential predictors of psychopathy. Additionally, in line with previous findings highlighting the associations between emotion dysregulation and psychopathy (e.g., Garofalo, Neumann, & Velotti, 2018; Lynam & Widiger, 2007; Patrick et al., 2009), we assessed the relationships between PID-5-IRF Separation Insecurity, Emotional Lability, and Anxiousness, and psychopathy, respectively. Although roughly one-third of the Italian inmate population is composed of inmates coming from other cultures (Italian Ministry of Justice, 2019), we focused only on Italian female inmates to avoid response bias due to linguistic and/or cultural difficulties, while using psychometric instruments that were validated in Italy and were provided with normative data for the Italian population.

Consistent with the literature on psychopathy assessment in forensic samples (Hare & Neumann, 2008), we relied on the Italian translation (Caretti, Manzi, Schimmenti, & Seragusa, 2011) of the PCL-R (Hare, 2003) and on the PCL-R total score as a comprehensive measure of psychopathy level. Since taxometric research documented that psychopathy should be conceived as a dimensional construct also in female inmates (Guay, Knight, Ruscio, & Hare, 2018), in our study the PCL-R total score was treated as a continuous variable in regression analyses. Because our data were based on a relatively small sample of adult female inmates, several of the assumption of ordinary least square regression were likely to be violated. In particular, the presence of outliers or extreme observations could represent a major threat to correct parameter and standard error estimation in regression models. In order to overcome these limitations of ordinary regression, in the present study, we relied on robust regression models (Rousseeuw & Yohai 1984).

Since PCL-R cut-off values are frequently used for forensic and clinical decision-making concerning psychopathy (Hare, 2003; Skeem, Polaschek, Patrick, & Lilienfed, 2011), we replicated our analyses using the cut-off value for the PCL-R total score. In line with previous studies (e.g., Craparo, Schimmenti, & Caretti, 2013; Rutherford, Cacciola, Alterman, & McKay, 1996), we relied on PCL-R total score higher than 25 (rather than on the more common PCL-R ≥ 30 cut-off value; e.g., Hare, 2003) in order to identify subject expressing high levels of psychopathy because women are known to score lower than man on the PCL-R (e.g., Rutherford et al., 1996; Verona & Vitale, 2018).

In our sample of Italian female inmates, the DSM-5 AMPD domains (and traits) were assessed using the PID-5-IRF. The PID-5-IRF was administered as a semistructured interview by trained researchers. We relied on the American Psychiatric Association (2013) algorithm to compute the PID-5-IRF domain scale scores: the PID-5-IRF Negative Affectivity domain scale score is computed by summing the PID-5-IRF Emotional Lability, Anxiousness, and Separation Insecurity trait scale scores, whereas the PID-5-IRF Detachment domain scale score was obtained by summing the PID-5-IRF Withdrawal, Anhedonia, and Intimacy Avoidance trait scale scores. Similarly, the PID-5-IRF Manipulativeness, Deceitfulness, and Grandiosity trait scale scores were summed to yield the PID-5-IRF Antagonism domain scale score, whereas the PID-5-IRF Irresponsibility, Impulsivity, and Distractibility trait scale scores were summed to yield the PID-5-IRF Disinhibition domain scale score. Finally, the PID-5-IRF Unusual Beliefs, Eccentricity, and Cognitive and Perceptual Dysregulation trait scale scores were summed to yield the PID-5-IRF Psychoticism domain scale score.

In the present study, we relied on the American Psychiatric Association (2013) scoring algorithm in order to study the relationships between psychopathy and selected DSM-5 AMPD domains. This method choice aimed at minimizing the risk for spurious increase of the correlations because of content overlap between the measures. Indeed, DSM-5 AMPD Antagonism includes a callous antipathy toward others, encompassing both an unawareness of others’ needs and feelings and a readiness to use others in the service of self-enhancement (e.g., APA, 2013), whereas PCL-R items includes lack of remorse or guilt, and callousness and lack of empathy.

Based on the extant literature on psychopathy in women (Verona & Vitale, 2018), we expected that psychopathy measures would show selective and positive relationships selectively with measures of DSM-5 AMPD Antagonism and Disinhibition domains. Since the dysfunctional personality domain of Negative Affectivity includes traits that could be expected to show a negative association with psychopathy in both men and women (e.g., Anxiousness), as well as traits that could be expected to show a positive association with psychopathy features in women (e.g., Separation Insecurity; Verona & Vitale, 2018), in our study we expected near zero associations between psychopathy measures and measures of the DSM-5 AMPD Negative Affectivity domain.

Method

Participants

The study group was originally composed of 49 female inmates who were serving their sentences in prisons in the South East of Italy. However, 11 inmates (22.4%) came from countries/cultures in which no validated translation and normative data of either PCL-R or PID-5-IRF were available when the present study was carried out; thus, they could not participate in the study. The final study group was composed of 38 female inmates with a mean age of 44.15 years, SD = 11.14 years. Ten inmates (26.3%) were unmarried, 19 inmates (50.0%) were married, five inmates (13.2%) were divorced, and 4 inmates (10.5%) were widow. Twelve inmates (31.6%) were sentenced for drug-related offences (e.g., drug smuggling/trafficking), 17 inmates (44.7%) for offences against property (with no victim confrontation), seven inmates (18.4%) for personal injury, two inmates (5.3%) for attempted murder, and four inmates (10.5%) for murder. The overall frequency of the individual crimes exceeded the number of inmates because some inmates were sentenced for multiple crimes. As a whole, 26 inmates (68.4%) were sentenced only for non-violent offences, whereas 12 inmates were sentenced for violent offences. Eighteen inmates (47.4%) had a criminal record; 14 inmates (36.8%) reported problem alcohol/substance use.

Measures

Psychopathy Checklist Revised (PCL-R; Hare, 2003).

The PCL-R is a rating scale that uses a semistructured interview, case history information, collateral information, and predetermined scoring criteria to rate 20 items on a 3-point scale according to the extent to which they apply to a given individual. We relied only on the PCL-R total score as a measure of psychopathy. Total PCL-R scores can vary from 0 to 40, reflecting the degree to which the individual matches the prototypical psychopath. Interviews lasted from about 60 min to almost 90 min. The Italian translation of the PCL-R has been extensively validated (Caretti et al., 2011). In the present study, we relied on a PCL-R cut-off score greater than 25 to identify inmates expressing psychopathic traits to a clinically relevant degree; the PCL-R total score >25 is used to identify subjects fully expressing the psychopathic trait in the United Kingdom (Skeem et al., 2011), as well as in applied research (Craparo et al., 2013).

Personality Inventory for DSM-5-Informant Report Form (PID-5-IRF; Markon et al., 2013).

The PID-5-IRF is a 218-item informant-report inventory developed to assess the pathological personality traits of the DSM–5 Section III personality trait model. PID-5-IRF items are measured on a 4-point Likert scale from 0 (very false or often false) to 3 (very true or often The PID-5-IRF has 25 scales that load onto 5 higher order dimensions (Watters & Bagby, 2018), and this structure is replicable (Somma, Krueger, Markon, & Fossati, 2019a). We relied on PID-5-IRF domain scales as a measure of DSM-5 maladaptive personality domains. Indeed, the need to reduce the number of predictors in order to reduce the risk to capitalize on chance associations with PCL-R ratings lead us to focus our study only the PID-5-IRF domain scales. The PID-5-IRF demonstrates adequate reliability and validity (e.g., Markon et al., 2013), also in its Italian translation (Somma et al., 2018b).

Procedures

Participants were recruited from female prisons located in the South of Italy (Trani, Lecce, Foggia, Taranto). The study was carried out in compliance with the rules and recommendations provided by the ethics committee and after obtaining the necessary authorizations by the Competent Authorities (Penitentiary Administration Department of the Ministry of Justice and of Puglia).

All inmates gave their informed consent to participate in the study after the research was fully explained to them; none of them received any financial incentive or benefit for taking part in the study. All participants were introduced to the aim of the study. The researchers explained that data would be recorded according to a strict procedure to guarantee the confidentiality of information. Participants were free to withdraw from the study at any time, and they had to sign an informed consent prior to study participation.

Data were collected between November 2016 and March 2017. Following the procedure described in Schimmenti, Di Carlo, Passanisi, and Caretti (2015) seminal study, participants were interviewed in a quiet room; for safety reasons, a police officer was available nearby. Inmates were administered the PID-5-IRF and the PCL-R by independent raters who were trained in the use of the scale; specifically, the PCL-R was administered individually by highly trained researchers who had completed the accredited course for the administration, scoring, and interpretation of the interview: before the research initiated all researchers had been trained to use the PCL-R, PID-5, and PID-5-IRF by an official licensed trainer (Prof. Vincenzo Caretti).

PID-5-IRF raters were kept blind to the PCL-R score; similarly, the PCL-R was administered blind to PID-5 scores. The order of administration of the instruments was randomized.

Data analyses

Cronbach alpha coefficient was used as internal consistency reliability measure. Spearman rank-order correlation coefficient was computed in order to evaluate bivariate relationships between continuous variables. Mann-Whitney U test was used to evaluate the presence of significant associations between dichotomous variables, and the PCL-R total score the PID-5-IRF domain scale scores. Since the use of Cohen d may be inappropriate in non-parametric analyses (Glass, 1966), we relied on rank biserial r coefficient as effect size measures for U tests.

As results were collected on a small sample, we used MM robust regression (Rousseeuw & Yohai 1984). This analysis allows strong results resistant to extreme values (Yohai 1987). Indeed, this regression approach is a form of to overcome some limitations of traditional parametric and non-parametric methods (e.g., Koller & Stahel 2011).

Significance of the overall regression model (i.e., R2 value) was tested by computing ANOVA based on robust Wald test table comparing each model with a model including only the constant term (Koller & Stahel 2011). It should be observed that this procedure yields p-values that are not directly (albeit inversely) related to R2 size, since Wald-type inference in robust regression depend both on number of valid observations (excluding outliers) and number of predictors. MM robust regression models were estimated only when at least two PID-5-IRF domain scales were identified in bivariate rank-order correlation analyses. In this study, MM regression models were estimated using ‘robust base’ R package (Rousseeuw et al. 2015).

Condition index/regression coefficient variance decomposition and variance inflation factor (VIF) were used to assess the presence of collinearity/ multicollinearity among PID-5 domain scales. VIF values greater than 10, and condition index values greater than 30 also accounting for a substantial (i.e., .90 or greater) proportion of the variance of two or more coefficients are usually considered suggestive of collinearity (Hair, Black, Babin, & Anderson, 2010).

Results

The mean PCL-R total score of the sample was 14.40, SD = 8.22, Cronbach’s α = .75; six inmates (15.8%) showed a PCL-R total score >25. The PCL-R total score showed no significant association with inmate’s criminal record, Mann-Whitney U = 130.50, exact p =.15, rank-biserial r = .27, being sentenced for violent offences, Mann-Whitney U = 130, exact p =.43, rank-biserial r = .17, and being sentenced for life-threatening violent offences (i.e., attempted murder/ murder, n = 5, 13.2%), Mann-Whitney U = 59.50, exact p =.33, rank-biserial r = .28. Rather, inmates with problem alcohol/substance use (M = 18.86, SD = 7.54) scored significantly higher on the PCL-R than the inmates with no problem alcohol/substance use (M = 11.79, SD = 7.56), Mann-Whitney U = 84.50, exact p<.01, rank-biserial r = .48.

The PID-5-IRF domain mean scores ranged from 0.44 (SD = 0.38) to 1.40 (SD = 0.58); the five PID-5-IRF domain scales were moderately inter-correlated, with a median Spearman r value of .35, SD = .17 (min. Spearman r value [Detachment and Antagonism] = .09, max. Spearman r value [Detachment and Psychoticism] = .58). Cronbach’s α values for the five PID-5-IRF domain scales ranged from .87 (Negative Affectivity) to .97 (Antagonism), median α value = .91, SD = .04.

The nonparametric bivariate correlations (Spearman r values) and MM robust regression results indexing the relationships between the PCL-R total score and the five PID-5-IRF domain scale scores in a study group of Italian female inmates are summarized in table 1. The nominal significance level (i.e., p<.05) was corrected according to the Bonferroni procedure and set at p<.01. In our MM robust regression analysis, no relevant collinearity or multi-collinearity was observed. Condition indices ranged from 1.00 to 9.82; none of them explained a substantial amount of variance (i.e., 90% or greater) for two or more coefficients (Hair et al., 2010). Similarly, VIF values ranged from 1.41 (Antagonism) to 1.66 (Psychoticism).

Table 1.

The Psychopathy Checklist Revised Total Score in the Perspective of the Personality Inventory for DSM-5-Informant Report Form Domain Scale Scores: Bivariate Associations (i.e., Spearman r Coefficients) and MM Robust Regression Analysis Results (N = 38)

Psychopathy Checklist Revised Total Score
MM Robust Regression
PID-5-IRF Domain Scales Spearman r β
Negative Affectivity .09 -.11
Detachment .14 .00
Antagonism .44 * .16
Disinhibition .63* .71*
Psychoticism .11 -.14
R2 (R2 adjusted) .54* (.46*)
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Note. PID-5-IRF: Personality Inventory for DSM-5-Informant Report Form. The nominal significance level (i.e., p <.05) was corrected according to the Bonferroni procedure and set at p<.01.

* p<.01

Considering the PID-5-IRF trait scales included in the PID-5-IRF Negative Affectivity domain scale, in our of female inmates the PCL-R total score showed significant positive relationships with Separation Insecurity, Spearman r = .44, p<.01, and Emotional Lability, Spearman r = .33, p<.01, while showing a significant and negative association with Anxiousness, Spearman r = -.57, p<.01. None of the PID-5-IRF Detachment and Psychoticism trait scale scores showed a significant association with the PCL-R total score, median Spearman r value = .08, SD = .14, all ps >.05.

When we performed a MM robust regression analysis in which the PCL-R total score was entered as dependent variable, and the PID-5-IRF Disinhibition domain scale and Separation Insecurity, Emotional Lability, and Anxiousness trait scales were entered as predictors, the corresponding R2 and adjusted R2 values were .72 and .69, respectively, p<.01. The PID-5-IRF Disinhibition domain scale, β = .41, SE = .16, p <.01, and the PID-5-IRF Anxiousness trait scale, β = -.54, SE = .13, p <.01, were the only significant predictors of the PCL-R total score in our MM robust regression model. Condition index values ranged from 1.00 to 8.81; none of them explained a substantial amount of variance (i.e., 90% or greater) for two or more coefficients (Hair et al., 2010). Similarly, VIF values ranged from 1.27 (Anxiousness) to 2.16 (Separation Insecurity).

On average, the six inmates who reached the cut-off value for clinically relevant psychopathy on the PCL-R total score scored (M = 1.18, SD = 0.63) significantly higher than the remaining 32 inmates on the PID-5-IRF Antagonism domain scale (M = 0.44, SD = 0.48), Mann-Whitney U = 26.50, exact p <.01, rank-biserial r = .72. Similar considerations held also for the PID-5-IRF Disinhibition domain scale, with the inmates who scored above the PCL-R cut-off value (M = 1.49, SD = 0.44) showing a significantly higher mean rank score than the inmates who scored below the PCL-R cut-off value (M = 0.73, SD = 0.43), Mann-Whitney U = 23.00, exact p <.01, rank-biserial r = .76. The common-language effect size estimates (expressed as percentages; Ruscio, 2008) for these comparisons were 85.8% and 87.6%, respectively. None of the remaining comparisons reached statistical significance.

When we took into account the PID-5-IRF scales measuring the DSM-5 AMPD traits belonging to the Negative Affectivity domain, only the PID-5-IRF Anxiousness (inmates above PCL-R cut-off: M = 0.65, SD = 0.34; inmates below cut-off: M = 1.62, SD = 0.73

Mann-Whitney U = 24.00, exact p<.01, rank-biserial r = .75, and Separation Insecurity (inmates above PCL-R cut-off: M = 1.89, SD = 0.33; inmates below cut-off: M = 0.96, SD = 0.82), Mann-Whitney U = 29.00, exact p <.01, rank-biserial r = .70, trait scales significantly differentiated women inmates who scored above the PCL-R cut-off score from female inmates who scored below the PCL-R cut-off score. The common-language effect size estimates (expressed as percentages) for these comparisons were 87.1% and 84.4%, respectively.

Discussion

Our findings represent the first attempt to evaluate psychopathy in the perspective of the DSM-5 AMPD dysfunctional personality domains in a study group of Italian female inmates. As a whole, our data are consistent with empirical (Wygant et al., 2016) and clinical (Carpentieri et al., 2017) findings indicating that the DSM-5 AMPD dysfunctional personality dimensions may be reliably assessed and play an important role in tracking and understanding psychopathy in forensic populations.

Consistent with the triarchic conceptualization of psychopathy (Patrick et al., 2009), our bivariate correlation results suggested that disinhibition and antagonism – a DSM-5 dysfunctional personality domain that is closely related to the construct of meanness (Patrick et al., 2009) – were non-negligibly related to psychopathy also in female inmates, at least when psychopathy features were assessed using the PCL-R. Our bivariate correlation analysis results suggested that the apparent lack of relationship between psychopathy and the DSM-5 AMPD Negative Affectivity domain in female inmates is the result of a complex system of opposite relationships with psychopathy of the individual dysfunctional personality traits that are subsumed under the DSM-5 AMPD Negative Affectivity domain. These data are in agreement with Verona & Vitale’s (2018) review, as well as with previous data on the Five-Factor Model of personality (FFM) In particular, according to our results, psychopathy in Italian female inmates seemed to be characterized by low DSM-5 AMPD Anxiousness trait levels, thus suggesting that fearlessness (Patrick et al., 2009) may represent a core feature of psychopathy also in female forensic These are also consistent with previous data on violent offenders (e.g., Garofalo, Neumann, & Velotti, 2018) suggesting that emotion regulation difficulties may represent an crucial aspect in understanding psychopathy.

In our study group of female inmates, psychopathy was also characterized by high levels of intolerance to abandonment (i.e., high scores on the PID-5-IRF Separation Insecurity trait scale) and emotional dysregulation (i.e., high scores on the PID-5-IRF Emotional Lability trait scale). Interestingly, these findings confirmed and extended previous evidence on the relationships between emotion dysregulation and psychopathy (e.g., Benning, 2013; Garofalo, Neumann, Zeigler-Hill, & Meloy, 2019; Lynam & Widiger, 2007). Additionally, consistent with Carabellese and colleagues’ (2018) and Verona & Vitale (2018) indications, our correlation analysis results suggested that psychopathy in female forensic subjects may show borderline personality disorder-like features, which may lead to overlooking the underlying psychopathy features or even to misdiagnose psychopathy as borderline personality disorder in female forensic subjects.

Our MM robust regression analysis findings extend the results of our bivariate correlation analyses. When we tested a model that included the five DSM-5 AMPD domains, at least as they are measured by the PID-5-IRF, as predictors of the general psychopathy level, as it was operationalized in the PCL-R total score, only the DSM-5 Disinhibition domain resulted significantly associated with psychopathy. This finding seemed to suggest that in female forensic subjects psychopathy may be characterized primarily by poor capacity to plan ahead, unrestrained behavior, acting without thinking about the consequences of the actions, and irresponsibility – i.e., the core features of the DSM-5 AMPD Disinhibition domain – whereas its relationships with the DSM-5 Antagonism features (e.g., manipulativeness, grandiosity, sense of entitlement, etc.) may represent derivatives of the prominent role played by the DSM-5 Disinhibition dysfunctional personality domain. This model explained roughly 46% of the total variance in the PCL-R total score (i.e., adjusted R2 = .46); however, in our study group only 75.0% of the PCL-R total score variance was systematic in nature (i.e., it was not composed by random error, as it was indicated by a Cronbach’s α value of .75 for the PCL-R total score). Thus, our MM robust regression model results seemed to indicate that the DSM-5 AMPD dysfunctional personality domains, and particularly the Disinhibition domain, as they were assessed by the PID-5-IRF, were able to explain roughly 61.3% of the reliable score variance in the PCL-R total score in a study group of Italian female inmates.

When the MM robust regression model was refined by entering both the PID-5-IRF Disinhibition domain scale and the PID-5-IRF Emotional Lability, Anxiousness, and Separation Insecurity as predictors of the PCL-R total score, the key role of the DSM-5 AMPD dysfunctional personality domains and traits in shaping psychopathy in female inmates emerged further. According to our findings, this model explained roughly 69% of the variance in the PCL-R total score (i.e., adjusted R2 = .69); in other terms, the combination of high levels of DSM-5 AMPD Disinhibition dysfunctional personality domain and low levels of DSM-5 AMPD Anxiousness dysfunctional personality traits seemed to explain 92.0% of the reliable score variance in the PCL-R total score. Thus, our finding were consistent with previous works suggesting that psychopathy may be efficiently tracked using the DSM-5 AMPD in forensic settings (Carpentieri et al., 2017; Wygant et al., 2016), at least when the informant version of the PID-5 (i.e., the PID-5-IRF) is used. Indeed, previous findings (Maniscalco et al., 2018) showed that self-reported PID-5 personality traits considered as specific indicators of psychopathy (e.g., callousness, grandiosity, manipulativeness) did not correlate significantly with the correspondent items and facets of the PCL-R in a sample of convicted male individuals (N = 27). These findings suggested the importance of relying on multiple sources of information and relying on validity scales (e.g., Somma, Borroni, Kelley, Edens, & Fossati, 2018) when self-reported measures are used in the forensic context. Moreover, our findings are also suggestive of a major role of the triarchic dimensions of Boldness (at least as it may be suggested by low levels of the DSM-5 AMPD Anxiousness trait) and Disinhibition (Patrick et al., 2009) in the development of psychopathy among female inmates.

The results of our analyses based on the PCL-R cut-off scores clarified further the forensic and clinical relevance of the DSM-5 AMPD dysfunctional personality dimensions in tracking psychopathy among Italian female inmates. In our study, all common language effect size estimates (Ruscio, 2008) expressed as percentages ranged from 84.4% to 87.6% for all significant comparisons. In other terms, when the PID-5-IRF Disinhibition, Antagonism, Separation Insecurity and Anxiousness scale scores were considered, our findings seemed to indicate that there was a more-than-80% chance that a female inmate picked at random from the group scoring above the PCL-R cut-off will have a higher score (actually, lower score in the case of the PID-5-IRF Anxiousness trait scale) than a person picked at random from the control group, i.e., the group scoring below the PCL-R cut-off (Ruscio, 2008). Interestingly, PID-5 Antagonism domain showed a large correlation with the PCL-R total score in the sub-group of inmates (n = 6) who reached the cut-off value for clinically relevant psychopathy on the PCL-R total score. The clinical relevance of this issue should not be underscored; available evidence suggests that psychopathy in adults represents a treatment-resistant clinical entity, which makes psychopathic offenders at heightened risk for criminal recidivism (e.g., Kennealy et al., 2010).

Finally, we feel that the associations (or lack of significant associations) of the PCL-R total score with crime-related variables that were observed among our female inmates may deserve some comments, although our study has not been explicitly designed to yield information on the associations between psychopathy crime-related features in convicted female offenders. Although other explanations may be possible - e.g., low base rate of violent offenders in our study group and in general in women with psychopathy (Carabellese et al, 2018) - our data on the lack of significant associations between psychopathy, as it was operationalized in the PCL-R total score, and violent crime and recidivism (i.e., positive criminal record) was highly consistent with the extant literature on crime-related features of psychopathy in female forensic samples (Verona & Vitale, 2018). This finding strongly supported recent considerations on the need for developing new measures for assessing the risk for violent crime and recidivism, particularly in female offenders (Carabellese et al, 2019b; Carpentieri et al., 2017; Verona & Vitale, 2018;).

Confirming Verona and Vitale (2018) considerations on psychopathy as a risk factor for drug misuse in female forensic subjects, our data showed a positive, significant, and non-trivial association between problem substance use and high scores on the PCL-R in our study group of Italian female inmates. Although the small size of our sample did not allow to test mediation models, we feel that our findings on the role of the DSM-5 AMPD domains in tracking psychopathy in female inmates legitimate the hypothesis that the link between psychopathy and substance misuse in female offenders that has been reported in the literature (Verona & Vitale 2018) may represent a consequence of the relevance of the DSM-5 AMPD Disinhibition domain for the developments of psychopathy in female inmates.

Limitations

Our findings would be considered at the light of several limitations. First of all, our data are based on a relatively small group of female inmates; far from being a randomly selected sample, it seemed to represent at best a convenient study group, thus limiting the generalizability of our findings. Although it may be difficult to obtain large samples of female inmates because they are only 4.3% of the total number of Italian inmates (Italian Ministry of Justice, 2019), independent replications are necessary. Moreover, the participation in the study was limited to Italian mother tongue inmates because no validated cross-cultural translations of the PCL-R/PID-5-IRF were available, and in order to avoid cultural and lexical bias; thus, the study participants were homogeneous in terms of ethnicity. In addition, the majority of the criminal charges of the participants were related to drugs and alcohol, and property charges, with relatively few violent offenses, which suggests that this sample was not severe in terms of criminal offending. Thus, future studies should include samples with greater ethnic diversity, as well as participants with a broader range of criminal offenses.

Although internal consistency estimates of the PCL-R total score and PID-5-IRF domain scores suggested adequate reliability for these measures, in the present study we were not able to further check the reliability of PCL-R and PID-5-IRF ratings (i.e., only one rater could meet each inmate for security reasons, and raters were not allowed to record the interviews). Thus, replication studies which included an evaluation of the inter-rater reliability of the instruments are necessary.

In the present study, sample size considerations led us to limit our analyses to the relationships between the general psychopathy level as operationalized in the PCL-R total score and the DSM-5 AMPD domains; however, the multidimensional nature of psychopathy and the need for relying on the DSM-5 AMPD trait profile for accurate personality pathology diagnosis would have needed more fine-grained analyses than those that could be carried out on a very limited number of participants.

Although our findings were largely consistent with the extant literature on psychopathy among female offenders (Verona & Vitale, 2018), as well as with the available studies on the relationships between the DSM-5 AMPD dysfunctional personality dimensions and psychopathy in forensic samples (Wygant et al., 2016), in our study were relied on the female inmates as the main source of information for both the PCL-R and the PID-5-IRF; in turn, this shared-method variance may have spuriously increased the associations between the PID-5-IRF scores and the PCL-R total score. Even the lack of association between the PCL-R and crime-related variables that was observed in our study was consistent with the extant literature on psychopathy in female forensic samples (Verona & Vitale, 2018). To be honest, it should be observed that the PCL-R was criticized as a measure of psychopathy among female offenders (Verona & Vitale, 2018); however, no agreed-upon alternative for assessing psychopathy in female inmates is currently available, particularly in Italy.

Conclusion

Even keeping these limitations in mind, we feel that our data prove useful in tracking psychopathy in female offenders. Indeed, the results of our study are consistent with previous findings (e.g., Strickland et al., 2013; Crego & Widiger, ) suggesting that the DSM-5 AMPD dysfunctional personality domains may be useful in assessing and understanding psychopathy in forensic populations.

References

  1. Al-Dajani, N., Gralnick, T. M., & Bagby, R. M. (2016). A psychometric review of the Personality Inventory for DSM-5 (PID-5): Current status and future directions. Journal of Personality Assessment, 98(1), 62-81. [DOI] [PubMed] [Google Scholar]
  2. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: Author. [Google Scholar]
  3. Andershed, H., Kerr, M., Stattin, H., & Levander, S. (2002). Psychopathic traits in non-referred youths: A new assessment tool. In Blau E. & Sheridan L. (Eds.), Psychopaths: Current international perspectives (pp. 131-158). Amsterdam: Elsevier. [Google Scholar]
  4. Anderson JL, Sellbom M, Wygant DB, Salekin RT, Krueger RF (2014) Examining the associations between DSM-5 section III antisocial personality disorder traits and psychopathy in community and university samples. J Pers Disord., 28(5):675–97 [DOI] [PubMed] [Google Scholar]
  5. Benning, S. D. (2013). Heterogeneity in content and psychopathies: Comment on Marcus et al. Personality Disorders: Theory, Research, and Treatment, 4, 83–84. [DOI] [PubMed] [Google Scholar]
  6. Caldwell, M. F., Ziemke, M. H., & Vitacco, M. J. (2008). An examination of the Sex Offender Registration and Notification Act as applied to juveniles: Evaluating the ability to predict sexual recidivism. Psychology, Public Policy, and Law, 14, 89-114. [Google Scholar]
  7. Carabellese F, Felthous AR, Rossetto I, La Tegola D, Franconi F, Catanesi R. (2018). Female Residents with Psychopathy in a High-Security Italian Hospital. Journal of American Academy of Psychiatry and the Law, 46(2), 171-78. [DOI] [PubMed] [Google Scholar]
  8. Carabellese F, Felthous AR, La Tegola D, Rossetto I, Franconi F, Lucchini G, Catanesi R. (2019a) Female psychopathy: A Descriptive National Study of Socially Dangerous NGRI Female Offenders, International Journal of Law and Psychiatry, in press. [DOI] [PubMed]
  9. Carabellese F, Felthous AR, La Tegola D, Rossetto I, Montalbò D, Franconi F, Catanesi R. (2019b). Psychopathy and Female Gender: Phenotypic Expression and Comorbidity: A Study Comparing a Sample of Women Hospitalized in Maximun Security Facility with Women who were Criminally Sentenced and Inprisoned. J Forensic Sci. 2019 Mar 12. doi: 10.1111/1556-4029.14039. [DOI] [PubMed]
  10. Caretti, V., Manzi, G. S., Schimmenti, A., & Seragusa, L. (2011). PCL-R. Hare Psychopathy Checklist–Revised. Firenze, Italy: OS Giunti. [Google Scholar]
  11. Carpentieri R., Schimmenti A., Ferracuti S., Nicolò G., Pompili P., Tessari G., Caretti V. (2017) Valutazione della pericolosità sociale e del rischio di recidiva criminale attraverso un sistema di assessment integrato. Psichiatria e Psicoterapia, 36(3), 138–151 [Google Scholar]
  12. Cleckley, H. (1941). The mask of sanity (1st ed.). St. Louis, MO: Mosby. [Google Scholar]
  13. Cooke, D. J., & Michie, C. (1997). An item response theory analysis of the Hare Psychopathy Checklist-Revised. Psychological Assessment, 9, 3-14. [DOI] [PubMed] [Google Scholar]
  14. Cooke, D. J., & Michie, C. (2001). Refining the construct of psychopathy: Towards a hierarchical model. Psychological Assessment, 13(2), 171-188. [PubMed] [Google Scholar]
  15. Craparo G., Schimmenti A., Caretti V. (2013) Traumatic experiences in childhood and psychopathy: a study on a sample of violent offenders from Italy. Europ. Jour. Psychotraumatology, 4: 21471. doi: 10.3402/ejpt.v4i0.21471 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Crego C, Widiger TA. (2014) Psychopathy, DSM-5, and a caution. Personal Disord., 5(4), 335–47 [DOI] [PubMed] [Google Scholar]
  17. Dhillon, S., Bagby, R. M., Kushner, S. C., & Burchett, D. (2017). The impact of underreporting and overreporting on the validity of the Personality Inventory for DSM–5 (PID-5): A simulation analog design investigation. Psychological Assessment, 29(4), 473-478. [DOI] [PubMed] [Google Scholar]
  18. Few LR, Lynam DR, Maples JL, MacKillop J, Miller JD. (2015) Comparing the utility of DSM-5 Section II and III antisocial personality disorder diagnostic approaches for capturing psychopathic traits. Personal Disord., (1), 64–74 [DOI] [PubMed]
  19. Fossati, A., Krueger, R. F., Markon, K. E., Borroni, S., & Maffei, C. (2013). Reliability and validity of the Personality Inventory for DSM-5 (PID-5) predicting DSM-IV personality disorders and psychopathy in community-dwelling Italian adults. Assessment, 20(6), 689-708. [DOI] [PubMed] [Google Scholar]
  20. Garofalo, C., Neumann, C.S., & Velotti, P. (2018). Difficulties in emotion regulation and psychopathic traits in violent offenders. Journal of Criminal Justice, 57, 116–125. [Google Scholar]
  21. Garofalo, C., Neumann, C.S., Zeigler-Hill, V., & Meloy, J.R. (2019). Spiteful and contemptuous: A new look at the emotional experiences related to psychopathy. Personality Disorders: Theory, Research, and Treatment, 10, 173–184. [DOI] [PubMed] [Google Scholar]
  22. Glass, G. V. (1966). Note on rank biserial correlation. Educational and Psychological Measurement, 26, 623-631. [Google Scholar]
  23. Gretton, H. M., Hare, R. D., & Catchpole, R. E. (2004). Psychopathy and offending from adolescence to adulthood: A ten year follow-up. Journal of Clinical and Consulting Psychology, 72, 636-645. [DOI] [PubMed] [Google Scholar]
  24. Guay, J. P., Knight, R. A., Ruscio, J., & Hare, R. (2018). A taxometric investigation of psychopathy in women. Psychiatry Research, 261(3), 565-573. [DOI] [PubMed] [Google Scholar]
  25. Gustavson, C., Stahlberg, O., Sjodin, A., Forsman, A., Nilsson, T., & Anckarsater, H. (2007). Age at onset of substance abuse: A crucial covariate of psychopathic traits and aggression in adult offenders. Psychiatry Research, 153, 195-198. [DOI] [PubMed] [Google Scholar]
  26. Hair Jr., J.F., Black, W.C., Babin, B.J. & Anderson, R.E. (2010). Multivariate Data Analysis: A Global Perspective. 7th Edition, Pearson Education, Upper Saddle River. [Google Scholar]
  27. Hare, R. D. (1991). Manual for the Revised Psychopathy Checklist (1st ed.). Toronto, Ontario, Canada: Multi-Health Systems. [Google Scholar]
  28. Hare, R. D. (2003). The Hare Psychopathy Checklist—Revised (2nd ed.). Toronto, Canada: Multi-Health. [Google Scholar]
  29. Hare, R. D., Harpur, T. J., Hakstian, A. R., Forth, A. E., Hart, S. D., & Newman, J. P. (1990). The revised psychopathy checklist: reliability and factor structure. Psychological Assessment: A Journal of consulting and clinical Psychology, 2(3), 338. [Google Scholar]
  30. Hare, R. D., & Neumann, C. S. (2008). Psychopathy as a clinical and empirical construct. Annual Review of Clinical Psychology, 4, 217–246. [DOI] [PubMed] [Google Scholar]
  31. Hare, R. D., Neumann, C. S., & Mokros, A. (2018). The PCL-R assessment of psychopathy: Development, properties, debates, and new directions. In Patrick C. J. (Ed.), Handbook of psychopathy (pp. 39-79). New York, NY, US: The Guilford Press. [Google Scholar]
  32. Harpur, T. J., Hakstian, A. R., & Hare, R. D. (1988). Factor structure of the Psychopathy Checklist. Journal of Consulting and Clinical Psychology, 56, 741-747. [DOI] [PubMed] [Google Scholar]
  33. Italian Ministry of Justice (2019, January 31). Detenuti presenti. Retrieved from: https://www.giustizia.it/giustizia/it/mg_1_14_1.page;jsessionid=FmgORvaV86Qbfr2rHjN3RZet?contentId=SST168910&previsiousPage=mg_1_14
  34. Karpman, B. (1941). On the need for separating psychopathy into two distinct clinical types: Symptomatic and idiopathic. Journal of Criminology and Psychopathology, 3, 112-137. [Google Scholar]
  35. Kennealy, P. J., Hicks, B. M., & Patrick, C. J. (2007). Validity of the factors of the Psychopathy Checklist- Revised in female prisoners. Assessment, 14, 323-340. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Kennealy, P. J., Skeem, J. L., Walters, G. D., & Camp, J. (2010). Do core interpersonal and affective traits of PCL-R psychopathy interact with antisocial behavior and disinhibition to predict violence? Psychological Assessment, 22(3), 569-580. [DOI] [PubMed] [Google Scholar]
  37. Koller, M., & Stahel, W. A. (2011). Sharpening wald-type inference in robust regression for small samples. Computational Statistics & Data Analysis, 55(8), 2504-2515. [Google Scholar]
  38. Krueger, R. F., Derringer, J., Markon, K. E., Watson, D., & Skodol, A. E. (2012). Initial construction of a maladaptive personality trait model and inventory for DSM-5. Psychological Medicine, 42(9), 1879-1890. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Lynam, D. R., & Widiger, T. A. (2007). Using a general model of personality to identify the basic elements of psychopathy. Journal of Personality Disorders, 21, 160178. [DOI] [PubMed] [Google Scholar]
  40. Lykken, D. T. (1957). A study of anxiety in the sociopathic personality. Journal of Abnormal and Clinical Psychology, 55, 6-10. [DOI] [PubMed] [Google Scholar]
  41. Lykken, D. T. (1995). The antisocial personalities. Hillsdale, NJ: Erlbaum. [Google Scholar]
  42. Maniscalco, Erika & Gozzi, Luciana & La Marca, Luana & Gervasi, Alessia & Maganuco, Noemi & Lo Cascio, Silvestro & Santoro, Gianluca & Virgilio, Cristina. (2018). Is the personality inventory for dsm-5 (pid-5) really useful for the assessment of maladaptive personality traits? A study on a group of inmates. Mediterranean Journal of Clinical Psychology MJCP 6(2 Suppl). [Google Scholar]
  43. Marcus DK, Fulton JJ, Edens JF. (2013) The two-factor model of psychopathic personality: evidence from the psychopathic personality inventory. Personal Disord., 4(1), 67-76. [DOI] [PubMed] [Google Scholar]
  44. Markon, K. E., Quilty, L. C., Bagby, R. M., & Krueger, R. F. (2013). The development and psychometric properties of an informant-report form of the Personality Inventory for DSM-5 (PID-5). Assessment, 20(3), 370-383. [DOI] [PubMed] [Google Scholar]
  45. McGee Ng, S. A., Bagby, R. M., Goodwin, B. E., Burchett, D., Sellbom, M., Ayearst, L. E., ... & Baker, S. (2016). The effect of response bias on the Personality Inventory for DSM–5 (PID–5). Journal of Personality Assessment, 98(1), 51-61. [DOI] [PubMed] [Google Scholar]
  46. Miller JD, Lamkin J, Maples-Keller JL, Sleep CE, Lynam DR. (2018) A test of the empirical profile and coherence of the DSM-5 psychopathy specifier. Psychol Assess., Jul;30(7), 870–881 [DOI] [PubMed] [Google Scholar]
  47. Patrick, C. J. (Ed.). (2018). Handbook of psychopathy (2nd ed.). New York, NY, US: The Guilford Press. [Google Scholar]
  48. Patrick, C. J., Fowles, D. C., & Krueger, R. F. (2009). Triarchic conceptualization of psychopathy: Developmental origins of disinhibition, boldness, and meanness. Development and Psychopathology, 21, 913-938. [DOI] [PubMed] [Google Scholar]
  49. Petruccelli, I., Barbaranelli, C., Costantino, V., Gherardini, A., Grilli, S., Craparo, G., & D’Urso, G. (2017). Moral disengagement and psychopathy: A study on offenders in Italian jails. Psychiatry, Psychology and Law, 24(5), 670-681. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Porter, S., Birt, A. R., & Boer, D. P. (2001). Investigation of the criminal and conditional release histories of Canadian federal offenders as a function of psychopathy and age. Law and Human Behavior, 25, 647-661. [DOI] [PubMed] [Google Scholar]
  51. Rousseeuw, P., & Yohai, V. (1984). Robust regression by means of S-estimators. In Robust and nonlinear time series analysis (pp. 256-272). Springer, New York, NY. [Google Scholar]
  52. Ruscio, J. (2008). A probability-based measure of effect size: robustness to base rates and other factors. Psychological Methods, 13(1), 19–30. [DOI] [PubMed] [Google Scholar]
  53. Rutherford MJ, Cacciola JS, Alterman AI, McKay JR (1996) Assessment of object relations and reality testing in methadone patients. Am J Psychiatry., 153(9), 1189-94. [DOI] [PubMed] [Google Scholar]
  54. Schimmenti A, Di Carlo G., Passanisi A., Caretti V. (2015) Abuse in Childhood and psychopathic traits in a sample of violent offenders. Psychol. Trauma, 7(4), 340-7. [DOI] [PubMed] [Google Scholar]
  55. Skeem, J. L., Polaschek, D. L., Patrick, C. J., & Lilienfeld, S. O. (2011). Psychopathic personality: Bridging the gap between scientific evidence and public policy. Psychological Science in the Public Interest, 12(3), 95-162. [DOI] [PubMed] [Google Scholar]
  56. Somma, A., Borroni, S., Kelley, S. E., Edens, J. F., & Fossati, A. (2018). Further evidence for the validity of a response inconsistency scale for the Personality Inventory for DSM–5 in Italian community-dwelling adolescents, community-dwelling adults, and clinical adults. Psychological Assessment, 30(7), 929-940. [DOI] [PubMed] [Google Scholar]
  57. Somma, A., Krueger, R. F., Markon, K. E., & Fossati, A. (2019). The replicability of the personality inventory for DSM–5 domain scale factor structure in US and non-US samples: A quantitative review of the published literature. Psychological Assessment. Advanced online publication. doi: 10.1037/pas0000711 [DOI] [PubMed]
  58. Somma, A., Krueger, R. F., Markon, K. E., & Fossati, A. (2019). The Personality Inventory for DSM-5-Informant-Report Form: Psychometric Properties and Self-Informant Congruence in Italian Community-Dwelling Adults. Manuscript submitted for publication.
  59. Strickland, C. M., Drislane, L. E., Lucy, M., Krueger, R. F., & Patrick, C. J. (2013). Characterizing psychopathy using DSM–5 personality traits. Assessment, 20, 327–338 [DOI] [PubMed] [Google Scholar]
  60. Verona, E., & Vitale, J. (2018). Psychopathy in women: Assessment, manifestations, and etiology. In Patrick C. J. (Ed.), Handbook of psychopathy (pp. 509-528). New York, NY, US: The Guilford Press. [Google Scholar]
  61. Walters GD, Ermer E, Knight RA, Kiehl KA (2015) Paralimbic biomarkers in taxometric analyses of psychopathy: does changing the indicators change the conclusion? Personal Disord., 6(1), 41-52. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Walters, G. D., Knight, R. A., Grann, M., & Dahle, K. (2008). Incremental validity of the Psychopathy Checklist facet scores: Predicting release outcome in six samples. Journal of Abnormal Psychology, 117, 396-405. [DOI] [PubMed] [Google Scholar]
  63. Watters, C. A., & Bagby, R. M. (2018). A meta-analysis of the five-factor internal structure of the Personality Inventory for DSM–5. Psychological Assessment, 30(9), 1255-1260. [DOI] [PubMed] [Google Scholar]
  64. Wright, Z. E., Pahlen, S., & Krueger, R. F. (2017). Genetic and environmental influences on Diagnostic and Statistical Manual of Mental Disorders (DSM–5) maladaptive personality traits and their connections with normative personality traits. Journal of Abnormal Psychology, 126(4), 416-428. [DOI] [PubMed] [Google Scholar]
  65. Wygant, D. B., Sellbom, M., Sleep, C. E., Wall, T. D., Applegate, K. C., Krueger, R. F., & Patrick, C. J. (2016). Examining the DSM–5 alternative personality disorder model operationalization of antisocial personality disorder and psychopathy in a male correctional sample. Personality Disorders: Theory, Research, and Treatment, 7(3), 229-239. [DOI] [PubMed] [Google Scholar]
  66. Yohai, V. J. (1987). High breakdown-point and high efficiency robust estimates for regression. The Annals of Statistics, 15(2), 642-656. [Google Scholar]

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