Psychopathic Personality Traits and the Risk for Personal Victimization

Abstract

Previous research links psychopathic traits with involvement in criminal behavior and numerous negative life outcomes. To date, however, a relatively limited amount of research has examined whether psychopathic traits confer an increased risk of victimization. This area of research is of interest as some scholars contend that psychopathic traits may confer several advantages for life outcomes including outcomes related to criminal conduct. As a result, psychopaths may be at a decreased risk of victimization. On the contrary, research examining the victim–offender overlap indicates that as psychopaths are likely to be involved in criminal behavior they would be more likely to be victimized. This article addresses this gap in the literature by examining whether psychopathic personality traits are associated with odds of victimization in a large nationally representative sample of Americans from adolescence to adulthood. Specifically, our study employs logistic regression and Poisson regression to test associations between psychopathic personality traits and victimization in data drawn from the National Longitudinal Study of Adolescent to Adult Health (Add Health). Our findings reveal that psychopathic personality traits are positively associated with odds of victimization in adolescence and adulthood. In addition, our findings indicate that psychopathic personality traits interact with criminal involvement to predict odds of victimization. The findings of our study have implications for the literatures concerning successful psychopathy and the victim–offender overlap.

Keywords

victimization psychopathy victim–offender overlap

When it comes to criminal offenders, there is perhaps no other group of criminals that is more captivating than psychopaths. Laypersons and scholars alike are fascinated by these offenders and the threat that they pose to society (Smith & Lilienfeld, 2013). In the public setting, movies, fictional and nonfictional books, TV shows, and daily news stories prominently feature psychopaths and frequently depict them as larger-than-life persons capable of committing heinous crimes while escaping detection and apprehension by law enforcement. From a scholarly perspective, there has been a tremendous amount of research examining virtually every issue pertaining to them, ranging from the measurement of psychopathy (Lynam & Derefinko, 2006) and the prevalence of psychopaths (Hare, 1996) to the etiology of psychopathy (Lynam & Gudonis, 2005; Waldman & Rhee, 2006) and the violent acts committed by psychopaths (DeLisi, 2009; Hare, 1996). Moreover, a significant amount of research has been devoted to the life outcomes associated with psychopaths and with people who score high on psychopathic personality traits. Findings flowing from these studies have revealed, for instance, that psychopathy and psychopathic personality traits are associated with a broad array of outcomes, including involvement in crime (Beaver, Boutwell, Barnes, Vaughn, & DeLisi, 2017; Hare, 1996; Lynam, Miller, Vachon, Loeber, & Stouthamer-Loeber, 2009), social-relationship problems (Ullrich, Farrington, & Coid, 2008), success in politics and the workplace (Eisenbarth, Hart, & Sedikides, 2018; Skeem, Polaschek, Patrick, & Lilienfeld, 2011; Smith & Lilienfeld, 2013), and the onset of diseases and disorders (Beaver et al., 2014). Noticeably absent from the existing literature, however, is the potential association between psychopathic personality traits and the odds of personal victimization. This is particularly surprising, given that there is so much research showing a strong connection between offending and victimization (Lauritsen & Laub, 2007). The current study addresses this gap in the literature by examining whether variation in psychopathic personality traits is related to the odds of being victimized in adolescence and adulthood.

Psychopathy, Psychopathic Personality Traits, and Personal Victimization

Psychopathy is a clinical construct that is characterized by an array of interpersonal, affective, lifestyle, and behavioral qualities (DeLisi, 2009; Hare, 1996). Psychopaths, or persons who score high on measures of psychopathic personality traits, are frequently described as being egocentric, manipulative, lacking guilt and remorse, and violating social norms and values. But, most of the time, psychopathy is often viewed as a personality disorder that is defined by such traits as short-temperedness, an inability to empathize, and guiltlessness. A number of different measurement approaches have been advanced as a way to adequately capture variation in psychopathic personality traits that underlie the construct of psychopathy. Perhaps the most widely used and well-known measure is Hare’s Psychopathy Checklist–Revised (PCL-R; Hare, 1991). The PCL-R is usually scored as a taxonomy that is created based on scores of two factors, one of which captures the affective and interpersonal components to psychopathy and the other that captures items related to an antisocial lifestyle and behavioral patterns. Other scales measure psychopathy based solely on personality traits and view psychopathy as a continuum as opposed to a taxonomy (e.g., Derefinko & Lynam, 2007; Gudonis, Miller, Miller, & Lynam, 2008; Lynam, 2002; Miller & Lynam, 2003).

There has been a lot of debate regarding the measurement of psychopathy and the most accurate way to assess it (Lynam & Derefinko, 2006). What is important to note, however, is that the link between criminal involvement and psychopathy/psychopathic personality traits appears to be invariant to the measurement approach that is employed. To illustrate, a series of meta-analyses have reported statistically significant associations between scores on the PCL-R and antisocial behaviors, institutional rule infractions, violence, and recidivism (Edens, Campbell, & Weir, 2007; Guy, Edens, Anthony, & Douglas, 2005; Leistico, Salekin, DeCoster, & Rogers, 2008; Salekin, Rogers, & Sewell, 1996). Similarly, a meta-analysis that focused on studies that did not use the PCL-R, but rather employed measures of psychopathic personality traits, also revealed a statistically significant association with antisocial behavior and aggression (Jones, Miller, & Lynam, 2011). Collectively, the results of these meta-analyses provide strong evidence that there is a link between psychopathy/psychopathic personality traits and antisocial behaviors and that this association appears to be relatively invariant across measurement strategies.

Despite the tremendous amount of research that has examined the psychopathy–crime nexus, what has not been studied extensively in the literature is whether psychopathy and personal victimization are associated. On one hand, there is reason to believe that psychopaths and persons scoring high on psychopathic personality traits should be at increased risk for being victimized. Mounds of empirical research have revealed that persons who are engaged in a criminal lifestyle and who commit criminal acts are significantly more likely to be victimized than persons who refrain from such lifestyles and behaviors (Broidy, Daday, Crandall, Sklar, & Jost, 2006; Jennings, Higgins, Tewksbury, Gover, & Piquero, 2010; Lauritsen & Laub, 2007; Lauritsen, Sampson, & Laub, 1991; Wright & Decker, 1994). Known as victim–offender overlap, this phenomenon has been detected across time, space, and methodologies and is considered one of the most consistent findings in victimization research (Lauritsen & Laub, 2007; Wolfgang, 1958). Given the strong connection between psychopathy and criminal involvement, and given the well-established findings on victim–offender overlap, it stands to reason that psychopaths should be at great risk for personal victimization owing to their over-involvement in crime.

The available studies bearing on this possibility provide some support for this view. In one study, Daigle and Teasdale (2018) analyzed data from two different samples—the National Longitudinal Study of Adolescent to Adult Health (Add Health) and the MacArthur Violence Risk Assessment Study—to determine the association between psychopathic traits and recurring victimization. In both samples, the analyses revealed that respondents scoring higher on psychopathic traits were at greater risk for recurring victimization. Another study reported very similar findings using a sample of Saudi Arabian youth (Beaver et al., 2016). Specifically, youth who scored higher on the Levenson Self-Report Psychopathy (LSRP) scale were comparatively more likely to be victimized when compared with youth who scored lower on this scale. Other studies using clinical samples have also reported a link between measures of psychopathy and psychopathic personality traits (e.g., Silver, Piquero, Jennings, Piquero, & Leiber, 2011), but the limited findings on this topic has resulted in some scholars highlighting the lack of knowledge on the psychopathy–victimization association (Dolan, O’Malley, & McGregor, 2013).

On the other hand, there is at least some reason to believe that psychopaths may not be at a heightened risk for being victimized. The main reason for this possibility of a reduced risk for victimization among psychopaths comes from evidence that psychopaths are more adept than nonpsychopaths at picking vulnerable targets. To do so, psychopaths have been shown to assess vulnerability to victimization by focusing on visible characteristics, such as gait (Book, Costello, & Camilleri, 2013; Wheeler, Book, & Costello, 2009). If that is the case—and psychopaths are better able to identify vulnerable victims than are nonpsychopaths—then there are two noteworthy outcomes. First, by knowing some of the characteristics that might make a person more likely to be targeted for victimization, psychopaths might take extra precautions to not reveal such vulnerabilities. The outcome perhaps would be a reduced odds of being victimized. Second, if psychopaths are better able to identify vulnerable victims than other offenders, then the end result would be the successful completion of the crime/victimization incident with reduced chances of being injured/victimized during the commission of the crime. Moreover, the successful completion of the victimization incident would also reduce the chances that they would be identified and targeted for a retaliation crime by the victim.

To date, there is only a limited amount of research that has examined the link between psychopathic personality traits and the odds of personal victimization. The current study attempts to add to this body of literature by examining a longitudinal sample of youth into adulthood to examine whether variation in psychopathic personality traits is associated with the odds of being victimized throughout adolescence and adulthood. Note that while this study uses one of the same samples that Daigle and Teasdale (2018) analyzed in their study, it differs from their study in two important ways. First, we do not focus solely on recurring victimization, but rather examine victimization at each wave of data collection. Second, and perhaps more importantly, we integrate measures of criminal involvement into the models and estimate the effects of psychopathic personality traits on victimization, net of criminal involvement. We also estimate the potential interaction between psychopathic personality traits and criminal involvement on victimization risk across the waves of data.

Method

Data

This study uses data drawn from Waves 1 through 4 of the Add Health. Add Health is a multiwave nationally representative sample of Americans that began data collection in the mid-1990s. The first wave of data was collected during the 1994-1995 school year from more than 90,000 adolescents enrolled in middle school and high school. Approximately, 20,000 respondents from the first wave of the in school survey were followed up to participate in an in-home that asked questions concerning a variety of topics related to family relationships, peer relationships, daily activities, victimization, and delinquent involvement. These same respondents have been followed up for three subsequent waves of data collection. The second wave was collected 1 year later in 1996 from approximately 15,000 respondents from the original in-home sample. The third wave of the survey was collected from approximately 15,000 of the original in-home respondents in 2001 to 2002 when the majority of the respondents were between the ages of 18 and 26. As the respondents were now entering early adulthood, the questionnaire was adjusted to ask age-appropriate questions concerning economic status, occupational status, criminal involvement, and victimization status. Finally, the fourth wave of the survey was collected in 2008 from approximately 15,000 of the original in-home respondents. Upon the fourth wave of data collection, the majority of respondents were between the ages of 24 and 32. The fourth wave of the survey contained questions similar to the third wave; however, the fourth wave also contained a personality inventory that corresponds to the five-factor model (FFM) of personality. Importantly, respondents in this survey were asked questions concerning both criminal involvement and victimization at all four waves of data collection.

Measures

Outcome Measure

Victimization

At each of the four waves, the respondents were asked questions concerning personal victimization over the previous 12 months. For instance, respondents were asked to indicate how often they were shot or stabbed; jumped or beaten up; had a weapon pulled on them; or seen someone being shot or stabbed in the past year. Responses to these items included “never,” “once,” and “more than once.” These measures of victimization experiences have been summed together at each of the waves and then recoded as dichotomous measures to indicate victimization status (0 = no, 1 = yes) at each of the waves.¹ In addition, the dichotomous indicators of victimization status at each of the four waves were summed together to create an indicator of the number of waves in which respondents were victimized (range = 0-4). Descriptive statistics for victimization and all of the other variables and scales in this study are presented in Table 1.

Table 1.

Descriptive Statistics for Scales and Variables Included in the Analyses.

	M (%)	SD	Range	N
Psychopathic personality traits	−0.021	10.179	−35.819-56.722	15,584
Victimization Waves 1-4	0.901	1.012	0-4	9,904
Victimization status W1
Yes	(26.45)			5,435
No	(73.55)			15,111
Victimization status W2
Yes	(19.04)			2,790
No	(80.96)			11,867
Victimization status W3
Yes	(10.56)			1,586
No	(89.44)			13,427
Victimization status W4
Yes	(39.48)			6,180
No	(60.52)			9,472
Criminal involvement Waves 1-4	2.017	1.064	0-4	9,711
Criminal involvement W1
Yes	(78.75)			16,054
No	(21.25)			4,332
Criminal involvement W2
Yes	(69.92)			10,181
No	(30.08)			4,380
Criminal involvement W3
Yes	(26.02)			3,869
No	(73.98)			10,998
Criminal involvement W4
Yes	(26.07)			4,074
No	(73.93)			11,556
Age	16.152	1.738	12-21	20,728
Sex
Male	(49.48)			10,263
Female	(50.52)			10,480
Race
Non-White	(37.83)			7,842
White	(62.17)			12,888
Low SES
Yes	(9.69)			1,701
No	(90.31)			15,849

Note. SES = socioeconomic status.

Predictor Measures

Psychopathic personality traits

Following the example of previous research, psychopathic personality traits were measured according to 23 items included in the fourth wave of the survey, which were originally designed to measure personality traits according to the FFM (Beaver et al., 2014; Derefinko & Lynam, 2007; Miller & Lynam, 2003).² For example, respondents were asked if they sympathize with others’ feelings, feel others’ emotions, are interested in other people’s problems, do not worry about things that already happened, and avoid dealing with life problems. The 23 items utilized to measure psychopathy were summed together to create a psychopathic personality traits scale (α = .81) where higher scores correspond to higher levels of psychopathic personality traits. A body of previous research has used the FFM model to tap psychopathic personality traits (Lynam et al., 2005; Lynam & Derefinko, 2006). In general, research in this area indicates that psychopathy corresponds with low scores on agreeableness and conscientiousness and mixed neuroticism (Lynam & Derefinko, 2006).³ Psychopathic personality traits scales constructed using the FFM model have been validated in previous research (Lynam et al., 2005; Lynam & Miller, 2015; Miller & Lynam, 2003).

Criminal involvement (Waves 1-4)

Criminal involvement was measured using a battery of questions tapping frequency of delinquent and criminal involvement at each of the four waves of data collection. At each of the four waves, for instance, respondents were asked to indicate how often they broke into a house to steal something, stole something worth more than US$50, were involved in fights, deliberately damaged property, sold drugs, moved stolen property, or used a weapon to get something in the previous 12 months. In addition, respondents were asked additional questions concerning check and credit card fraud in the later waves. Responses to these questions were summed at each of the waves to create a measure of criminal involvement at each wave. Then, the measures of criminal involvement at each of the waves were dichotomized to create measures of criminal status where 0 = no and 1 = yes at each of the waves. In addition, an additional measure of criminal status was created by summing together the dichotomous indicators of criminal status at each of the four waves to create a count of the number of waves in which respondents were involved in criminal activity (range = 0-4).

Control variables

All of the analyses of this study were estimated using four control variables. First, age was measured continuously in years. Second, sex was measured dichotomously where 0 = female and 1 = male. Third, race was measured dichotomously where 0 = White and 1 = non-White. Finally, socioeconomic status (SES) was measured as a single item from the parental questionnaire where respondent’s parents were asked if they received public assistance. This item is coded so that 0 = no and 1 = yes.

Analytic Strategy

The analytic strategy for this study unfolded in two series of analyses. In the first series of analysis, we utilized logistic regression to test whether psychopathic personality traits were associated with odds of victimization at each of the four waves using three separate models. In the first model, we tested whether psychopathic personality traits were associated with likelihood of victimization using minimal controls. Then, in a second model, we included a control for criminal involvement at the respective wave to test whether the association between psychopathic personality traits and odds of victimization persisted for individuals with criminal involvement. Finally, in a third model, we included a multiplicative interaction term between psychopathic personality traits and the respective criminal involvement measure for that wave to test whether criminal involvement moderated the association between psychopathic traits and odds of victimization at each wave.

Then, after assessing the associations between psychopathic personality traits, criminal involvement, and odds of victimization at each of the individual waves, we estimated another series of analyses using Poisson regression to determine if psychopathic personality traits are associated with victimization across all four waves. To do so, we estimated three separate models. The first model tests the association between psychopathic personality traits and number of waves of victimization (0 through 4) using minimal controls. Then, the second model tests whether the association between psychopathic personality traits and victimization (Waves 1 through 4) persists when controlling for criminal involvement across all four waves. Finally, the third model incorporates a multiplicative interaction term for psychopathic personality traits and criminal involvement (Waves 1 through 4) to test whether criminal involvement moderates the association between psychopathic personality traits and victimization (Waves 1 through 4).

Results

The analysis began by examining the association between psychopathic personality traits and odds of victimization at Wave 1. As can be seen in the first model of Table 2, psychopathic personality traits are positively associated with the odds of victimization at Wave 1. This finding indicates that respondents with higher levels of psychopathic personality traits were more likely to be victimized at Wave 1. In addition, the second model reveals that psychopathic personality traits maintain a positive association with the odds of victimization when controlling for criminal involvement at Wave 1. Examination of the third model of Table 2 also reveals that psychopathic personality traits and criminal involvement appear to interact to predict the odds of victimization at Wave 1.^4,5,6

Table 2.

Logistic Regression Models for the Association Between Psychopathic Personality Traits and the Odds of Victimization at Wave 1.

	Odds of Victimization		Odds of Victimization		Odds of Victimization
	Coefficient	OR	Coefficient	OR	Coefficient	OR
Predictor variables
Psychopathic personality traits	0.020	1.020***	0.017	1.017***	0.002	1.002
Psychopathic personality traits	(0.002)		(0.002)		(0.007)
Criminal involvement W1	—		1.702	5.483***	1.711	5.534***
Criminal involvement W1			(0.081)		(0.081)
PPT × Criminal involvement W1	—		—		0.016	1.016*
PPT × Criminal involvement W1					(0.008)
Controls
Age	0.079	1.082***	0.074	1.077***	0.074	1.077***
Age	(0.012)		(0.013)		(0.013)
Sex	0.896	2.450***	0.900	2.460***	0.901	2.461***
Sex	(0.043)		(0.044)		(0.044)
Race	0.586	1.796***	0.582	1.790***	0.582	1.789***
Race	(0.043)		(0.044)		(0.044)
SES	0.524	1.688***	0.536	1.710***	0.536	1.709***
SES	(0.068)		(0.070)		(0.070)
N	13,216		13,216		13,216

Note. OR = odds ratio; PPT = psychopathic personality traits; SES = socioeconomic status.

p < .05. **p < .01. ***p < .001.

To more fully understand the nature of the interaction between psychopathic personality traits and criminal involvement for the prediction of victimization at Wave 1, we plotted the predicted odds of victimization across the range of psychopathic personality traits scores for respondents who reported criminal involvement and respondents who did not report criminal involvement. Figure 1 reveals that the odds of victimization increase as levels of psychopathic personality traits increase. In addition, the figure demonstrates that respondents who reported criminal involvement at Wave 1 were more likely to be victimized at Wave 1 compared with respondents who did not report criminal involvement across the range of psychopathic personality traits scores.

Figure 1.

The predicted odds of victimization at Wave 1 as a function of the interactive effects of psychopathic personality traits and criminal involvement at Wave 1.

Next, we examined the association between psychopathic personality traits and the odds of victimization at Wave 2. Table 3 reveals that psychopathic personality traits are positively associated with the odds of victimization at Wave 2 in the first and second models. These findings indicate that respondents with higher levels of psychopathic personality traits were more likely to be victimized at Wave 2 than respondents with lower levels of psychopathic personality traits even when controlling for criminal involvement at Wave 2. Examination of the third model of Table 3, however, reveals that the multiplicative interaction term for psychopathic personality traits and criminal involvement at Wave 2 is not significantly associated with the odds of victimization at Wave 2. This finding indicates that psychopathic personality traits and criminal involvement at Wave 2 do not appear to interact to predict Wave 2 victimization.

Table 3.

Logistic Regression Models for the Association Between Psychopathic Personality Traits and the Odds of Victimization at Wave 2.

	Odds of Victimization		Odds of Victimization		Odds of Victimization
	Coefficient	OR	Coefficient	OR	Coefficient	OR
Predictor variables
Psychopathic personality traits	0.021	1.021***	0.020	1.020***	0.009	1.009
Psychopathic personality traits	(0.003)		(0.003)		(0.008)
Criminal involvement W2	—		1.782	5.941***	1.772	5.884***
Criminal involvement W2			(0.088)		(0.088)
PPT × Criminal involvement W2	—		—		0.012	1.012
PPT × Criminal involvement W2					(0.009)
Controls
Age	0.064	1.067***	0.084	1.088***	0.085	1.088***
Age	(0.016)		(0.017)		(0.017)
Sex	0.950	2.587***	0.983	2.673***	0.985	2.677***
Sex	(0.054)		(0.056)		(0.056)
Race	0.578	1.783***	0.609	1.838***	0.609	1.839***
Race	(0.054)		(0.056)		(0.056)
SES	0.436	1.547***	0.526	1.692***	0.525	1.690***
SES	(0.082)		(0.086)		(0.086)
N	10,279		10,279		10,279

Note. OR = odds ratio; PPT = psychopathic personality traits; SES = socioeconomic status.

p < .05. **p < .01. ***p < .001.

Then, we examined the association between psychopathic personality traits and the odds of victimization at Wave 3. As can be seen in Table 4, psychopathic personality traits are positively associated with the odds of victimization in the first two models. These findings indicate that respondents with higher levels of psychopathic personality traits were more likely to be victimized at Wave 3 than respondents with lower levels of psychopathic personality traits even when controlling for criminal involvement during Wave 3. In addition, the third model of Table 4 reveals that psychopathic personality and criminal involvement at Wave 3 do not appear to interact to predict the odds of victimization at Wave 3.

Table 4.

Logistic Regression Models for the Association Between Psychopathic Personality Traits and the Odds of Victimization at Wave 3.

	Odds of Victimization		Odds of Victimization		Odds of Victimization
	Coefficient	OR	Coefficient	OR	Coefficient	OR
Predictor variables
Psychopathic personality traits	0.024	1.024***	0.021	1.021***	0.016	1.017***
Psychopathic personality traits	(0.003)		(0.003)		(0.005)
Criminal involvement W3	—		1.224	3.401***	1.211	3.556***
Criminal involvement W3			(0.068)		(0.069)
PPT × Criminal involvement W3	—		—		0.009	1.009
PPT × Criminal involvement W3					(0.006)
Controls
Age	−0.100	0.905***	−0.063	0.939***	−0.063	0.939***
Age	(0.019)		(0.019)		(0.019)
Sex	1.269	3.556***	1.011	2.748***	1.013	2.754***
Sex	(0.070)		(0.073)		(0.073)
Race	0.593	1.809***	0.612	1.844***	0.612	1.844***
Race	(0.066)		(0.068)		(0.068)
SES	0.341	1.407***	0.370	1.448***	0.371	1.449***
SES	(0.105)		(0.108)		(0.108)
N	10,936		10,936		10,936

Note. OR = odds ratio; PPT = psychopathic personality traits; SES = socioeconomic status.

p < .05. **p < .01. ***p < .001.

Finally, we examined the association between psychopathic personality traits and odds of victimization at Wave 4. Table 5 reveals that psychopathic personality traits are positively associated with the odds of victimization at Wave 4 even when controlling for Wave 4 criminal involvement. These findings indicate that respondents with higher levels of psychopathic personality traits were more likely to be victimized at Wave 4 compared with respondents with lower levels of psychopathic personality traits even when taking into account incidence of criminal involvement at the same wave. Further examination of Table 5 reveals that the multiplicative interaction term between psychopathic personality traits and criminal involvement at Wave 4 appears to be significant. This finding indicates that psychopathic personality traits and criminal involvement at Wave 4 interact to predict the odds of victimization at Wave 4.

Table 5.

Logistic Regression Models for the Association Between Psychopathic Personality Traits and the Odds of Victimization at Wave 4.

	Odds of Victimization		Odds of Victimization		Odds of Victimization
	Coefficient	OR	Coefficient	OR	Coefficient	OR
Predictor variables
Psychopathic personality traits	0.023	1.023***	0.014	1.014***	0.021	1.021***
Psychopathic personality traits	(0.002)		(0.002)		(0.002)
Criminal involvement W4	—		2.035	7.651***	2.063	7.870***
Criminal involvement W4			(0.046)		(0.046)
PPT × Criminal Involvement W4	—		—		−0.023	0.977***
PPT × Criminal Involvement W4					(0.004)
Controls
Age	−0.027	0.973**	0.002	1.002	0.002	1.002
Age	(0.010)		(0.012)		(0.012)
Sex	0.369	1.446***	0.194	1.214***	0.196	1.216***
Sex	(0.036)		(0.040)		(0.040)
Race	0.316	1.372***	0.268	1.307***	0.266	1.305***
Race	(0.038)		(0.042)		(0.042)
SES	0.271	1.311***	0.250	1.283***	0.250	1.284***
SES	(0.063)		(0.069)		(0.069)
N	13,361		13,361		13,361

Note. OR = odds ratio; PPT = psychopathic personality traits; SES = socioeconomic status.

p < .05. **p < .01. ***p < .001.

To garner a better understanding of this relationship, we plotted predicted values of odds of victimization at Wave 4 across the range of psychopathic personality traits scores for respondents who reported criminal involvement and respondents who did not report criminal involvement. This plot is displayed in Figure 2. As can be seen, odds of victimization at Wave 4 appear to increase as levels of psychopathic personality traits increase. In addition, this figure demonstrates that respondents who reported criminal involvement at Wave 4 were more likely to be victimized at Wave 4 than respondents who did not report criminal involvement at this wave. This pattern of findings is similar to the pattern of findings depicting the relationship between psychopathic personality traits, criminal involvement at Wave 1, and victimization at Wave 1.

Figure 2.

The predicted odds of victimization at Wave 4 as a function of the interactive effects of psychopathic personality traits and criminal involvement at Wave 4.

After establishing a consistent relationship between psychopathic personality traits and odds of victimization at each of the waves individually, we then examined the relationship between psychopathic personality traits and the number of waves in which respondents were victimized. As can be seen in Table 6, psychopathic personality traits are positively associated with victimization (Waves 1 through 4), indicating that respondents with higher levels of psychopathic personality traits were more likely to be victimized at more waves than individuals with lower levels of psychopathic personality traits. In addition, examination of the second model of Table 6 reveals that the relationship between psychopathic personality traits and victimization (Waves 1 through 4) persists even when controlling for the number of waves in which respondents reported criminal involvement. However, further examination of Table 6 reveals that the multiplicative interaction term between psychopathic personality traits and criminal involvement (Waves 1 through 4) is not significant, indicating that psychopathic personality traits and criminal involvement do not appear to interact to predict victimization across Waves 1 through 4.⁷

Table 6.

Poisson Regression Models for the Association Between Psychopathic Personality Traits and Victimization in Waves 1 to 4.

	Victimization		Victimization		Victimization
	Coefficient	IRR	Coefficient	IRR	Coefficient	IRR
Predictor variables
Psychopathic personality traits	0.016	1.016***	0.011	1.011***	0.015	1.015***
Psychopathic personality traits	(0.001)		(0.001)		(0.003)
Criminal involvement W1-4	—		0.356	1.428***	0.358	1.431***
Criminal involvement W1-4			(0.012)		(0.012)
PPT × Criminal involvement W1-4	—		—		−0.002	0.998
PPT × Criminal involvement W1-4					(0.001)
Controls
Age	0.020	1.020**	0.038	1.038***	0.038	1.038***
Age	(0.007)		(0.007)		(0.007)
Sex	0.531	1.701***	0.388	1.474***	0.388	1.474***
Sex	(0.023)		(0.024)		(0.024)
Race	0.355	1.426***	0.325	1.384***	0.325	1.383***
Race	(0.024)		(0.024)		(0.024)
SES	0.263	1.301***	0.252	1.287***	0.253	1.288***
SES	(0.036)		(0.036)		(0.036)
N	8,510		8,510		8,510

Note. IRR = incidence rate ratio; PPT = psychopathic personality traits; SES = socioeconomic status.

p < .05. **p < .01. ***p < .001.

To understand the relationship between psychopathic personality traits, criminal involvement, and victimization, we plotted the predicted numbers of waves of victimization across the range of psychopathic personality traits scores separately for individuals who did not report criminal involvement at any of the waves and respondents who reported criminal involvement at one, two, three, and four waves. Figure 3 reveals that the number of waves of victimization increases as psychopathic personality traits increase for all five offending or nonoffending groups. In addition, the predicted number of waves of victimization increases as the number of waves of criminal involvement increases—so that respondents who reported no criminal involvement had the lowest predicted number of victimization waves and respondents who reported criminal involvement at all four waves had the highest predicted number of victimization waves. What this finding means is that respondents who reported criminal involvement at more waves were more likely to be victimized at more waves in a stepwise fashion.

Figure 3.

The predicted number of waves of victimization status as a function of the interactive effects of psychopathic personality traits and criminal involvement (Waves 1 through 4).

Discussion

A great deal of research has examined the potential consequences associated with being a psychopath or scoring high on psychopathic personality traits (Andersen, Sestoft, Lillebæk, Mortensen, & Kramp, 1999; Boccio & Beaver, 2015; Harpur, Hare, & Hakstian, 1989; Ullrich et al., 2008). For the most part, findings from these studies have shown that psychopaths tend to fare significantly worse on life outcomes when compared with nonpsychopaths. One area where they may achieve greater success, however, is when it comes to their ability to commit criminal acts while escaping personal victimization. To date, no research has directly examined the potential interrelationships among criminal involvement, psychopathic personality traits, and personal victimization in a community-based sample. The current study analyzed data drawn from the Add Health to address this issue. Findings from the analyses revealed that persons scoring higher on psychopathic personality traits were significantly more likely to be victimized during adolescence and adulthood. Importantly, the association between psychopathic personality traits and personal victimization remained significant even after controlling for criminal involvement. Moreover, interaction terms were included to test for the possibility that psychopathic personality traits and criminal involvement would interact to predict the odds of victimization. The results from these interaction models revealed that psychopathic personality traits and criminal involvement appear to predict victimization in two of the four waves examined. Precisely why the interaction was detected for only two of the waves is not entirely clear. On one hand, interaction effects are difficult to detect and thus the differences in significance across these models could be due to methodological and measurement issues. On the other hand, it is quite possible that there is something unique about the developmental processes in early adolescence and adulthood that produce an interaction effect that is not evident during the intervening years. Future research should attempt to uncover the reasons for why there are differential effects across adolescent and young adulthood development.

Examination of the relationship between psychopathic personality traits and number of waves of victimization revealed that psychopathic personality traits and number of waves of criminal involvement do not appear to interact to predict number of waves of victimization. However, the plot of this relationship reveals that the predicted number of waves of victimization increases across psychopathic personality traits scores and is higher for respondents who reported criminal involvement in more waves.

These findings cast significant doubt on the portrait painted by some that psychopaths are criminals who are able to blend in with the rest of the population and escape at least some of the serious repercussions that other criminals face. The literature on victim–offender overlap is quite strong and has shown that offenders—regardless of their traits—are significantly more likely to be victims when compared with nonoffenders. This same pattern of findings appears to be extended to psychopaths (Silver et al., 2011), wherein psychopaths are not immune to victim–offender overlap. This is particularly interesting, given that there is research revealing that psychopaths are more adept at selecting vulnerable targets. Despite this “skill,” it does not appear to provide them with any advantage of warding off offenders from victimizing them. On the contrary, in at least some of our statistical models, given the same level of criminal involvement, persons who scored high on psychopathic personality traits were more likely to be victimized than persons scoring lower on these traits.

Moving forward, victimization research should pay closer attention to the connection between psychopathic personality traits and victimization. At the very least, victimization studies should attempt to control for psychopathy in some capacity to rule out the possibility that other significant associations are not confounded by psychopathic personality traits. This is particularly important for research that focuses on individual differences, such as self-control, as there is a great deal of overlap between levels of self-control and psychopathic personality traits (DeLisi, 2009; Vaughn, DeLisi, Beaver, Wright, & Howard, 2007). Moreover, it would be interesting and important to more fully unpack the interconnections among psychopathic personality traits and known measures of victimization, particularly those that relate to lifestyles/routine activities theory. By combining these measures together, a more complete explanation of victimization may surface.

The findings from this study should be viewed with caution due to at least three key limitations. First, all three of the key measures—psychopathic personality traits, criminal involvement, and victimization—were measured by self-reports. As a result, the potential that shared methods variance might account for at least some of the covariance among these measures remains a very real possibility. Future research would benefit by using measures that were collected from multiple sources (e.g., self-reports, official crime data, observations, etc.). Second, although the analyses used data from multiple waves, the psychopathic personality traits measure was developed from Wave 4 data. As a result, the temporal ordering of some of the models was not preserved, meaning that the predictor variable (i.e., psychopathic personality traits) was measured after the outcome measure (i.e., victimization). We were forced to use this modeling strategy because the Add Health only collected information about psychopathic personality traits at Wave 4. While the results of this study certainly need to be replicated with other samples where the measurement is more consistent with temporal ordering, it is important not to completely discount the results owing to this limitation. Research has shown that psychopathic personality traits are highly stable over long periods of time (Lynam, Caspi, Moffitt, Loeber, & Stouthamer-Loeber, 2007; Lynam, Loeber, & Stouthamer-Loeber, 2008), which would tend to suggest that persons who score high (low) on these traits at Wave 4 should also score relatively high (low) on these traits at previous waves. Moreover, all that our models were attempting to establish is whether there is any association between psychopathic personality traits and victimization—we were not trying to establish causality. It is also important to note that given that we do not know of any research or theory suggesting that victimization would lead to the development of psychopathic personality traits, the most reasonable interpretation would likely be that psychopathic personality traits provide some type of risk for victimization. Third, the items included in the victimization scale do not include information about the identity of the respondent’s victimizers. As a result, the findings of this study present a general relationship between psychopathic personality traits and victimization and are not able to distinguish whether the relationship between victimization and psychopathic personality traits is dependent on the relationship between the respondent and his or her victimizer. Until future research addresses these limitations, however, the results reported here need to be viewed with skepticism.

There is a great deal of fascination with psychopaths and with people who score high on psychopathic personality traits. In many ways, they are viewed as an outlier when it comes to certain findings regarding criminals. For instance, they have historically been viewed as more elusive, more cunning, and more intelligent than the average nonpsychopathic offender. Many of these views, however, have been shown to be nothing more than falsehoods (DeLisi, Vaughn, Beaver, & Wright, 2010; Salekin, Neumann, Leistico, & Zalot, 2004; Vitacco, Neumann, & Wodushek, 2008). The findings from the current study tend to comport with some of these previous findings by underscoring the fact that when it comes to personal victimization, psychopathic personality traits do not appear to provide any type of insulation to the well-known victim–offender overlap (Silver et al., 2011). Stated differently, if psychopathic persons engage in criminal behaviors, then they are just as at risk for being victimized when compared with nonpsychopathic offenders.

Footnotes

Acknowledgements

This research uses data from Add Health, a program project designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris and funded by grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 17 other agencies. Special acknowledgment is due to Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Persons interested in obtaining data files from Add Health should contact Add Health, Carolina Population Center, 123 W. Franklin Street, Chapel Hill, NC 27516-2524 (addhealth@unc.edu). No direct support was received from grant P01-HD31921 for this analysis.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

Author Biographies

Cashen M. Boccio is an assistant professor in the Department of Criminal Justice at The University of Texas at San Antonio. Her research interests include trait-based theories of crime, life-course criminology, and the development of antisocial traits.

Kevin M. Beaver is a professor in the College of Criminology and Criminal Justice at Florida State University. His research focuses on the biosocial development of antisocial traits.

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