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Callous-Unemotional Traits and Violent Offending

Journal: Youth Violence and Juvenile Justice

Manuscript ID: YVJJ-15-0022

Manuscript Type: Original Articles

Keywords: callous-unemotional traits, juvenile offending, violence, risk factors

Abstract:

The study examined whether callous-unemotional (CU) traits predict the onset of juvenile violent offending and the extent to which CU traits predict future violent offending. The analyses were based on 1,170 male adolescent offenders from the Pathways to Desistance project. The study found that adolescent males who were high in CU traits were more likely to commit a violent act before age 13, even when controlling for other known risk factors for violence. Participants high in CU traits were twice as likely than those with low or moderate CU traits to have committed more violent crimes at the 5-year follow-up.

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Abstract

The study examined whether callous-unemotional (CU) traits predict the onset of juvenile

violent offending and the extent to which CU traits predict future violent offending. The

analyses were based on 1,170 male adolescent offenders from the Pathways to Desistance

project. The study found that adolescent males who were high in CU traits were more likely to

commit a violent act before age 13, even when controlling for other known risk factors for

violence. Participants high in CU traits were twice as likely than those with low or moderate CU

traits to have committed more violent crimes at the 5-year follow-up.

Keywords: callous-unemotional traits, violent offending, risk factors, juvenile offenders

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Callous-Unemotional Traits and Violent Offending

A number of researchers argue that callous-unemotional (CU) traits (i.e., general

disregard for others, lacking empathy, shallow affect; Frick et al., 2003; Frick et al., 2005)

delineate a subgroup of conduct-disordered youth that is more likely to engage in violent

criminal behavior than conduct-disordered youth without such features (Flexon & Meldrum,

2013; Pardini & Frick, 2013). Evaluating this assumption, however, is challenging because (a)

children in this subgroup often have not only callous-unemotional features but also more serious

impulsivity and antisocial behavior than their conduct-disordered counterparts; and (b) callous-

unemotional features are generally less predictive of future aggression, crime, and other

antisocial behavior than are impulsivity and past antisocial behavior (Skeem, Polaschek, Patrick,

& Lilienfeld, 2013). In other words, it is often not clear whether more serious past misbehavior

(rather than callous-unemotional features per se) is driving most of the prediction of future

misbehavior.

Previous studies have typically created groups of children based on extreme scores on

scales that assess CU features and/or conduct-disorder symptoms. These studies tend to support

the utility of CU traits for predicting violent and other antisocial behavior. However, the effect of

CU is often not significant after controlling for initial differences between groups in antisocial

behavior (Frick et al., 2003). In contrast, dimensional studies (i.e., studies that utilize continuous

measures of callous-unemotional traits and conduct disorder) suggest that CU features add

modest predictive validity to impulsivity and past antisocial behavior (Pardini, Obradović, &

Loeber, 2006; Salekin, Ziegler, Larrea, Anthony, & Bennett, 2003).

However, previous research on juvenile psychopathy in general, and CU specifically,

suffer from important methodological limitations. Some of the methodological deficiencies

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comprise inadequate sample sizes (Dyck, Campbell, Schmidt, & Wershler, 2013; Edens &

Cahill, 2007; Salekin, 2008); retrospective designs or short follow-up durations (Edens & Cahill,

2007); failure to include other dimensions of psychopathic personality, such as impulsivity

(Flexon & Meldrum, 2013); and limited inclusion of additional risk factors (Vaughn, Litschge,

DeLisi, Beaver, & McMillen, 2008). Additionally, little research has been done to delineate the

correlation between early onset CU traits (i.e., those under the age of 13 who possess callous-

unemotional traits) and violent offending. Altogether, these issues raise the need for additional

research on the relationship between CU traits and violence.

The present study extends previous work examining CU traits in several ways. First, we

utilize the Youth Psychopathy Inventory (YPI; Andershed, Kerr, Stattin, & Levander, 2002; see

Mulvey, 2004) as our measure of juvenile psychopathy. The YPI has several advantages over

other child psychopathy scales (e.g., Anti Social Process Screening Device, Frick & Hare, 2001;

the Childhood Psychopathy Scale, Lynam, 1997): (a) The YPI has multiple (5) items per trait

enhancing its use in research on trait-level; (b) the YPI describes feelings and opinions as

competences, rather than deficiencies; and (c) it is designed to be free of explicit antisocial

behavior. Second, we examine CU in the context of a large, high-risk longitudinal study of the

causes, correlates, and consequences of serious antisocial behavior. Third, we include a variety

of reliably and validly measured control variables taken from multiple domains to isolate the

specific effects of CU. Fourth, we include a measure of impulse control.

CU Traits and Psychopathy

The classical conceptualization of adult psychopathy is described as a constellation of

affective, interpersonal and behavioral characteristics (Frick et al., 2003; Frick et al., 2005). The

affective characteristics are defined by callousness, a lack of empathy and remorse together with

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short-lived emotions (Frick et al., 2003; Frick et al., 2005). The interpersonal characteristics of

the psychopath include narcissism, good intelligence, verbal and manipulative abilities,

superficial charm, egocentricity and glibness (Frick et al., 2003; Frick et al., 2005). Behaviorally,

psychopaths were described as impulsive, irresponsible, prone to boredom, novelty seeking and

antisocial (Frick et al., 2003; Frick et al., 2005).

With aims toward intervention and treatment, researchers have turned attention to

children and adolescents for clues in understanding psychopathy. Empirical evidence supports

the existence of childhood psychopathy (Lynam & Gudonis, 2005), and research tends to support

the notion that childhood psychopathy looks much like adult psychopathy (Flexon & Meldrum,

2013). Of particular importance to youthful psychopathy is the presence of CU traits (Ribeiro da

Silva, Rijo, & Salekin, 2012). Growing evidence indicates that youth with elevated CU traits are

at risk for exhibiting severe and persistent antisocial behavior, even after controlling for co-

occurring disruptive behavior disorder symptoms (Frick et al., 2003; Pardini & Fite, 2010). CU

traits seem to further delineate childhood onset conduct disorder cases that are more likely to

persist in their antisocial behavior into adulthood.

Risk Factors for Violence

While the present study focuses primarily on CU traits, it also considers a range of

covariate variables that may affect the relationship between CU traits and violent offending, to

include executive function, psychosocial maturity (emotion regulation), risky family, risky

neighborhoods, peer deviance, low intelligence quotient (IQ), school dropout, and impulsivity.

Executive functioning. First, a growing body of research (e.g., Morgan & Lilienfeld,

2000; Pharo, Sim, Graham, Gross, & Hayne, 2011) demonstrates that executive function (EF),

particularly during early adolescence, might serve as a risk factor for antisocial behaviors. EFs

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are believed to be central abilities necessary for self-regulation, including the regulation of

emotion and socially appropriate adult conduct; impairments in EF often result in socially

inappropriate behavior, an inability to plan and problem solve, distractibility, aggressiveness,

impulsive behavior, poor judgment of behavioral consequences, and poor memory. The overlap

of EF impairments to features of severe antisocial behavior suggests that EF processes are

important in the etiology of violent offending.

For example, the results of two meta-analyses (Morgan & Lilienfeld, 2000; Pharo, Sim,

Graham, Gross, & Hayne, 2011) confirm that a robust and statistically significant association is

found between antisocial behavior and deficits in EF. This effect holds across varying study

methodologies, including different antisocial groups and EF measures. Overall, these findings

suggest that inhibitory control is one of the best predictors of adolescent risk-taking.

Psychosocial maturity. As youth mature, they become more future-oriented, develop

better planning and impulse control, and are therefore more able to self-regulate (Steinberg,

2008). According to Dishion and Patterson (2006), youths higher in self-regulation are less likely

to be negatively affected by dysfunctional family environments and deviant peer influence and,

for that matter, they are more likely to distance themselves from risky families, peers, and

neighborhood influences, and even desist from earlier depression and anxiety (Hammen,

Brennan, Keenan-Miller, & Herr, 2008). In other words, those who lack psychosocial maturity

are more likely to have a history of risky families, peers, and neighborhood influences, which, in

turn, could lead to CU traits and, by extension, violence.

Impulse control. While all of the previously mentioned risk factors receive considerable

support, impulsivity is considered one of the most important emotional dysregulatory risk factors

for the development of extreme antisocial behavior. It also exacerbates the effects of risky

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families on behavior, neighborhood contexts, and other personality traits related to dysregulation

(Castellanos-Ryan & Conrod, 2011).

Family deviance. Although families are expected to provide for the emotional and

physical wellbeing of their children, those that don’t meet these expectations are deemed “risky”

(Repetti, Taylor, & Seeman, 2002). Risky families are characterized by a variety of negative

attributes, including justice system involvement, substance abuse, and parental hostility.

Additionally, single parenthood is considered a risky family structure that puts kids at a

developmental disadvantage. Parental justice system involvement, whether due to drug arrests or

other offenses, contributes to family risks by negatively affecting marital status, family stability

and household income, independent of other family risk factors (Phillips, Erkanli, Keeler,

Costello, & Angold, 2006). This makes children particularly vulnerable to emotion

dysregulation, anxiety, and depression (Dallaire & Wilson, 2010), which can further contribute

toward both CU traits and violent tendencies.

Risky neighborhoods. Additionally, neighborhood characteristics have been tied to

similar negative outcomes for youth. Although lower socioeconomic status often determines

who lives in these communities, it is the overconcentration of other factors (e.g., physical and

social decay) that is most associated with adverse mental health for children (Turner, Shattuck,

Hamby, & Finkelhor, 2013).

Exposure to violence. The effects of exposure to community violence on adolescents

has been established in prior research (e.g., Baskin & Sommers, 2013). By and large, studies

reveal a wide range of adverse consequences, including mental health symptomatology

(Amstadter et al., 2011; see Baskin & Sommers, 2013), substance abuse (Fehon, Grilo, &

Lipschitz, 2001; see Baskin & Sommers, 2013), and juvenile offending (Haynie, Petts, Maimon,

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& Piquero, 2009; see Baskin & Sommers, 2013).

Peer deviance. The influence of deviant peers also contributes to antisocial behavior and,

in turn, CU traits and violence. The elimination of prosocial friendships reduces exposure to such

protective factors as psychosocial maturity (see Baskin & Sommers, 2013) and social support

(O’Donnell, Schwab-Stone, & Muyeed, 2002), thereby fortifying pathways to adverse

consequences.

Low IQ and school dropout. Although not all children who are exposed to community

and family dysfunction suffer adverse consequences, those with lower IQs and those who drop

out of school are also at increased risk toward developing deviant behaviors (Schwartz &

Gorman, 2003; see Baskin & Sommers, 2013).

Purpose of the Study

The present study seeks to answer two questions: First, to what extent do CU traits

predict the onset of juvenile violent offending? Second, to what extent do CU traits predict

future violent offending? Based on past research, we hypothesize that those who exhibit high

CU traits will be violent offenders at a relatively early age (before the age of 13) and that they

will be more versatile in their violent offending at the 5-year follow-up period.

Method

The present study is a secondary analysis of data from the Pathways to Desistance project

(Mulvey, 2004), a multisite, longitudinal study of serious juvenile offenders. Beginning in 2000,

project staff recruited 1,354 adolescents aged 14 through 18 who were adjudicated delinquent or

found guilty of a serious (overwhelmingly felony level) offense at their current court appearance

in Philadelphia, PA (N = 654) and Phoenix, AZ (N = 700). Immediately after enrollment,

researchers conducted a structured four-hour baseline interview with each adolescent. The

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interview included a thorough assessment of the youth’s social background, developmental

history, psychological functioning, psychosocial maturity, attitudes about illegal behavior,

intelligence, school achievement and engagement, work experience, mental health, current and

previous substance use and abuse, family and peer relationships, use of social services, and

antisocial behavior. At each annual follow-up interview, researchers gathered information on the

adolescents’ self-reported behavior and experiences during the prior 12 months, including any

illegal activity, drug or alcohol use, and involvement with treatment or other services.

We restricted our current analyses to male adolescent offenders (N = 1,170), as the data

set had only a marginally sufficient number of females in the sample (N = 184). The ethnicity of

the selected sample is 19.2% White, 42.1% African American, 34.0% Hispanic, and 4.6% other.

The participants’ average age was 16.0 years (standard deviation [SD] = 1.2 years) at the time of

the initial interview. Table 1 displays the complete list of variables used in the regression

analyses.

Measures

Dependent variables.

Self-reported violent offending. A modified version of the Self-Report of Offending

scale (SRO; Elliott, 1990; see Mulvey, 2004) was used at each interview to measure the

adolescent’s account of his involvement in one or more of eight violent crimes (fights as part of

gang activity, aggravated assault, carjacking, robbery with weapon, robbery without weapon,

shooting someone, shooting at someone, carrying a gun). Participants indicated whether they had

done any of these activities over the last 12 months. Each of the items was coded to reflect

whether the respondent did or did not report engaging in each act at least once. The

dichotomized items were then summed together for the analyses. A sum of the number of types

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of violent offenses committed (a “general versatility or variety” score) was calculated for each

subject at each interview. Variety scales are often compared with frequency scales that index the

number of times that a specific act occurred. For this study, a variety scale was used in light of

research indicating that variety scales are more internally consistent and stable (Bendixen,

Endresen, & Olweus, 2003).

Age of onset for violent offending. The age of onset for each of the eight violent offenses

were recorded during the baseline interviews. Utilizing the age of initiation data, a three-

category variable was constructed and included the following: (1) no violent offending, (2) early

onset (12 years old or younger), and (3) later onset (13 years old to 18 years old). Respondents

were placed in the respective categories based on having committed at least one of the eight

violent crimes in their lifetime (see Mulvey, 2004).

Primary independent variable. Callous-unemotional traits were assessed with the

Youth Psychopathic Traits Inventory (Andershed et al., 2002; see Mulvey, 2004). The CU

subscale includes 15 items, and is rated on 4-point Likert scales ranging from “does not apply at

all” to “applies very well.” Examples of items indicating CU traits include “I usually feel calm

when other people are scared” and “I think that crying is a sign of weakness, even if no one sees

you.” The items were written so that the statements could be read by those with high levels of

psychopathic traits as reflecting positive or admirable qualities. The baseline measure of CU

(continuous scale) was used in the onset of violent offending analysis. For the 5-year follow-up

regression model, a time-averaged (across 6 years) CU variable was computed; subjects were

then categorized into three groups: low (< mean), moderate (mean - 1 SD) and high CU traits (>

1 SD).

Risk factor covariates (baseline measures). Research links child dispositional traits

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(comprising early temperamental attributes and later personality traits) to an array of negative

outcomes, including antisocial behavior and violence. Given empirical support for the role of

temperament, personality, and emotion regulation in the development and maintenance of violent

offending, constructs related to these dispositional factors as well as additional individual,

family, peer, and neighborhood risk factors are included as covariates in the regression models.

Individual characteristics. First, we included school dropout, early onset of problem

behavior, executive function, impulse control, intelligence, emotion regulation (a measure of

psychosocial maturity), and anxiety as our measures for individual risk factors for violence.

First, school dropout was coded as a dichotomous variable (yes/no; see Mulvey, 2004).

Second, early onset problems before age 11 were measured by utilizing a total count of five

problems: getting into trouble for cheating, disturbing class, getting drunk/stoned, stealing, or

fighting (coded as 0–5; see Mulvey, 2004).

Third, cognitive dysfunction related to impairment to the frontal cortex of the brain (a

measure of executive function) was assessed with the Stroop Color-Word Test (Golden, 1978;

see Mulvey, 2004). The dimensions tapped by the Stroop have been associated with cognitive

flexibility, resistance to interference from outside stimuli, creativity, psychopathology, and

cognitive complexity (Golden, 1978; see Mulvey, 2004). The interference t-score for calculated

difference between color-word and predicted color-word was used in the regression analyses.

Higher scores reflect better performance and less interference on reading ability.

Fourth, impulse control was measured with an 8-item Impulse Control scale from the

Weinberger Adjustment Inventory (Weinberger & Schwartz, 1990; see Mulvey, 2004).

Participants rated the frequency with which their behavior in the past six months matched a

series of statements (e.g., “I stop and think things through before I act”) on a scale from 1

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(Almost Never) to 5 (Almost Always). Higher scores indicate greater impulse control.

Fifth, the Wechsler Abbreviated Scale of Intelligence (WASI; Wechsler, 1999; see

Mulvey, 2004) was used to produce an estimate of general intellectual ability (intelligence) based

on two subtests, (1) Vocabulary (42 total items that require the subject to orally define 4 images

and 37 words presented both orally and visually), and (2) Matrix Reasoning (35 incomplete grid

patterns that require the participant to select the correct response from five possible choices). The

WASI is a quick estimate of an individual's level of intellectual functioning, with higher scores

indicating greater intellectual ability.

Sixth, an adapted version of the Children’s Emotion Regulation scale served as a self-

report measure of the adolescents’ ability to regulate emotions (Walden, Harris, Weiss, &

Catron, 1995; see Mulvey, 2004). Of the 33 original items contained in this scale, 12 were

included in the Pathway’s version. Examples of items are “I know things to do to make myself

more happy” and “I can change my feelings by thinking of something else.” Participants

responded on a 4-point Likert scale ranging from “not at all like me” to “really like me.” Higher

scores indicate a better ability to regulate emotion.

Finally, a total anxiety score was derived from the Revised Children’s Manifest Anxiety

scale (RCMAS; Reynolds & Richmond, 1985; see Mulvey, 2004) and was based on 28 items

comprising three anxiety subscales: physiological anxiety (10 items measuring somatic

manifestations of anxiety such as sleep difficulties, nausea, and fatigue), worry/oversensitivity

(11 items measuring obsessive concerns about a variety of things as well as fears about being

hurt or emotionally isolated), and social concerns/concentration (7 items measuring distracting

thoughts and fears that have a social or interpersonal nature). Higher scores indicate greater

anxiety.

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Environmental covariates. Next, we included family arrests, peer deviance,

neighborhood conditions, and exposure to violence as risk measurements for violence. First,

family arrests were measured as the proportion of family members who resided with the subject

and who had been arrested (see Mulvey, 2004). Second, peer deviance was measured as the

proportion of four closest friends ever arrested (see Mulvey, 2004). Third, neighborhood

conditions were measured using items adapted from other large-scale studies of neighborhood

functioning (Sampson & Raudenbush, 1999; see Mulvey, 2004). Adolescents were asked about

21 examples of physical and social disorder in the blocks surrounding their homes (e.g.,

abandoned buildings, gang activity). They responded using a 4-point scale ranging from 1

(never) to 4 (often). Scores were averaged across all items to determine levels of neighborhood

disorder.

Finally, the Exposure to Violence Inventory (ETV; Selner-O’Hagan, Kindlon, Buka,

Raudenbush, & Earls, 1998; see Mulvey, 2004; Baskin & Sommers, 2013) was modified for this

study to assess the frequency of exposure to violent events. Items document the types of violence

the adolescent both experienced (i.e., Victim—six items, e.g., “Have you ever been chased where

you thought you might be seriously hurt?”) and observed (i.e., Witnessed—seven items, e.g.,

“Have you ever seen someone else being raped, an attempt made to rape someone or any other

type of sexual attack?”). Higher scores indicate greater exposure to violence. The baseline

measure of ETV was used in the onset of violent offending analysis. The 5-year follow-up

regression model used a time-averaged (i.e., mean level based on 6 years of data) ETV measure.

Analytic Strategy

To assess the influence of CU on violent offending outcomes, we used two multivariate

approaches. First, given a trichotomous dependent variable (no violent offending, early onset,

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later onset), a multinomial logistic regression model was estimated to test the association

between CU and onset of violent offending. Second, a negative binominal regression model was

used to examine Time 1 predictors (i.e., baseline) of violent offending at Time 2 (i.e., 5-year

follow-up). Traditional linear (i.e., least squares) regression models are inappropriate for

analyzing count outcomes because count data do not follow or approximate a normal distribution

and thus analyses from these models would lead to biased and inconsistent estimates (King,

1988). Initially we computed a conditional Poisson distribution model, but because the deviance

statistic indicated overdispersion (when the true variance is bigger than the mean), we ultimately

used negative binomial regression analyses for violent offending.

Results

Descriptive Statistics

Table 1 displays descriptive statistics for all variables used in the analyses.

Approximately 24% of the sample committed at least one violent crime before the age of 13 (i.e.,

early onset). The mean number of types of violent offenses committed at baseline (violent acts

committed in the previous 12 months) was 2.08; however, 75.9% did not report any violent

crimes at the baseline interview. On average, this sample of serious adolescent offenders

exhibited a low-moderate level of CU traits (mean = 33.07). Additionally, virtually all (95.3%)

of the respondents were exposed to community violence at baseline. The average number of

lifetime exposures (direct victimization and witness) was 5.49.

Differentiating Onset of Violent Offending Groups

A multinomial logistic regression (entering all variables simultaneously) was performed

to assess the relative associations of the covariates with onset of violence group membership.

Table 2 presents the odds ratios of this analysis. Three variables distinguished consistently the

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no violence and later onset violence groups from the early onset group. Respondents who

experienced greater exposure to community violence and who had more early onset problem

behaviors (i.e., before age 10) were more likely to be in the early onset violence group when

compared to their counterparts in the other two groups. Most notably, individuals with higher

CU traits were more likely to engage in early onset violence.

In addition, respondents who engaged in early onset violence (as compared to no

violence) had lower levels of impulse control and executive functioning, a higher proportion of

friends that were arrested, and lived in neighborhood with more physical and social problems.

CU Traits and the Prediction of Future Violent Offending

Table 3 contains the odds ratios from the negative binominal regression that examined

the impact of CU traits on the 5-year follow-up in violent offending. The findings support the

robust effect of CU on violent offending. Respondents high in CU traits were almost twice as

likely (odds ratio = 1.79) than those with low or moderate CU traits to have committed more

types of violent crimes at the 5-year follow-up time period. It is important to state that the

effects of CU on versatility in violent offending occurred even when controlling for a number of

risk factors, including individual differences related to personality traits and social risk factors,

as well as controlling for baseline violence. The data show that respondents with higher IQs and

greater impulse control committed fewer types of violent crimes at the follow-up period.

However, emotion control and executive functioning were not significant predictors of violent

offending at the 5-year follow-up.

Discussion

Do CU Traits Predict the Early Onset of Violence?

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The present study found that adolescent males who were high in CU traits were more

likely to commit a violent act before the age of 13, even when controlling for covariate variables

that were known risk factors for violence. When comparing the early onset violence group to the

no violence offending group and the later onset violence group, respectively, adolescent males

with high CU traits were 7% less likely to be in the no violence group and 3.2% less likely to be

in the later violence onset group. In other words, male adolescents with high CU traits are more

likely to commit violent acts before the age of 13. These findings are consistent with past

research (Fontaine, McCrory, Boivin, Moffitt, & Viding, 2011; Frick et al., 2003; Kahn, Frick,

Youngstrom, Findling, & Youngstrom, 2012; Pardini et al., 2006) that has shown statistical

significance between CU traits, antisocial behavior, and conduct problems, including violence.

The present study also assessed known risk factors for violence and found that

respondents who had a high proportion of their closest friends arrested were 44.9% less likely to

fall within the no violence offending group than those who met the criteria for the early onset

violence group. Furthermore, the study results show that neighborhood conditions and exposure

to community violence differentiated the early onset from the no violence group. This finding is

consistent with past research (Gorman-Smith & Tolan, 1998; see Baskin & Sommers, 2013) that

predicts violent outcomes when individuals have been exposed to violence in the past.

Additionally, adolescent males with early onset problems were 39.5% less likely to be in

the no violence offending group and 34.2% less likely to be in the later onset group compared to

those in the early onset violence group. This is consistent with past research regarding conduct

problems and its relationship to violent offending (Baskin & Sommers, 2013; Loeber, Burke, &

Pardini, 2009a, 2009b; Pardini et al., 2006). Finally, related to early problem behaviors is

impulse control and executive functioning, which have generally been demonstrated to be risk

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factors for violence (Moffitt et al., 2011). The study found that when comparing the early onset

violent offending group to the no onset violent offending group, adolescent males who greater

impulse and higher executive functioning were 1.67 and 1.04 times, respectively, to be in the no

violence offending group.

Do CU Traits Predict Future Violent Offending?

The study found that those adolescent males who measured high in callous-unemotional

traits were 1.79 times more likely to commit more types of violent crimes in the future, even

when controlling for individual differences. This is consistent with past research (i.e., Fontaine

et al., 2011; Frick, Stickle, Dandreaux, Farrell, & Kimonis, 2005; Kahn et al., 2012) that has

shown statistical significance between CU traits, antisocial behavior, and conduct problems,

including violence. Overall, our findings confirm our hypotheses: Those who are high in CU

traits tend to be violent offenders at a relatively early age (before the age of 12) and that they will

commit one or more of the eight listed violent crimes in the future.

Limitations

Although the present study found a robust relationship between CU traits and violent

offending, there are considerable limitations. First, the participants selected for this study did not

include female adolescents. There could be considerable differences between teenage girls who

present with CU traits than teenage boys, in terms of predicting the onset of violence and future

violent offending outcomes. Second, the study only included juveniles who had committed

serious crimes and who were involved in the criminal justice system. Taken together, the study

findings might not be generalizable to the entire population of juveniles. A third limitation is

that the study did not include all types of violent crimes. Specifically, sexual assault and

domestic violence were not among the eight types of violent crimes included in the measure of

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violent offending. Similarly, there was no distinction between instrumental versus reactive

forms of violence. Finally, the study did not include substance use as a covariate, a risk factor

that is highly correlated with violence. Notwithstanding these limitations, the findings strongly

support the hypotheses that juveniles who are high in CU traits were more likely to be violent

offenders before the age of 13 and were more likely to commit violent crimes in the future.

Implications and Directions for Future Research

The predictive utility of CU traits for violent outcomes has important practical and

theoretical implications. First, if we are to help parents, teachers, clinicians, and forensic

professionals find ways to prevent violence and provide treatment and interventions early on, it

is important to understand the nature of each of the core risk factors for violence in youth,

including CU traits. Intervening early in the developmental trajectory of childhood-onset

behavioral problems represents an important avenue for preventing later serious aggression and

antisocial behavior. Second, implementing preventative interventions with children exhibiting

early CU traits (prior to the onset of serious conduct problems) seems particularly important.

Given the large number of risk factors across multiple domains that have been associated with

violence effective preventative interventions should be capable of providing a comprehensive

array of services to families that target multiple risk factors. However, it is also important to

individualize these programs to effectively target the specific developmental mechanisms

underlying each child’s antisocial behavior. If interventions can be better tailored to the unique

characteristics of children based on the developmental mechanisms underlying their conduct

problems, more pronounced and sustained treatment effects will likely be achieved. Finally,

future research should continue to explore the pathways, trajectories, and treatments for youth

who exhibit early CU traits.

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Table 1

Descriptive Statistics (Baseline)

Baseline N Min. Max. Mean Standard

Deviation

Age 1170 14 18 16.05 1.16

Sex (male) 1170

Race:

White 225 0 1 .192

Black 493 0 1 .421

Latino 398 0 1 .340

Other 54 0 1 .046

School Dropout 1169 0 1 .159

Single Parent 1169 0 1 .446

Proportion Family Arrested 1162 0 1 .312 .399

Proportion Friends Arrested 1168 0 1 .446 .380

Neighborhood Conditions 1167 1 4 2.35 .741

Exposure to Community Violence 1166 0 13 5.49 2.99

# Early Onset Problems 1169 0 5 1.51 1.19

IQ 1158 55 128 84.5 12.84

Anxiety (RCMAST) 1169 1 28 9.79 5.94

Emotion Control (Walden) 1169 1 4 2.77 .656

Executive Function (Stroop) 1150 21 79 50.46 7.08

Impulse Control (WAI) 1166 1 5 2.96 .945

CU (YPI) 1085 15 60 33.07 6.46

Onset of Violence:

No violence 322 0 1 .275

Early onset (< 13 years old) 279 0 1 .239

Late onset (13-18 years old) 568 0 1 .486

Variety of Violence:

No violence

Baseline 888 0 0 0 0

5-year follow-up 968 0 0 0 0

Violence history

Baseline 282 1 8 2.08 1.46

5-year follow-up 202 1 8 1.83 1.25

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Table 2

Multinomial Regression: Odds Ratios for Onset of Violent Offending

Variable No Violence v. Early Onset Later Onset v. Early Onset

Age .949 .906

Race

Black 1.07 1.07

Latino 1.20 .919

Other 2.26 1.52

% Family Arrests .759 .898

Single Parent .749 .950

% Friends Arrests .551* 1.03

Neighborhood Conditions .670* .997

ETV (exposure to violence) .635*** .898**

Early Problem Behaviors .605*** .658***

School Dropout .857 1.29

Impulse Control 1.67*** 1.13

Anxiety 1.02 1.02

IQ .985 .992

Emotion Control 1.32 .957

Executive Function 1.04* 1.01

CU .930*** .968*

Log Likelihood Ratio: 1751.72

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

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Table 3

Negative Binominal Regression: Odds Ratios for 5-Year Follow-up of Violent Offending

Variable 5-year follow-up

Age .946

Race

Black .993

Latino 1.30

Other .966

Family Arrests .761

Single Parent .788

Friends Arrests 1.06

Neighborhood Conditions .827

ETV (exposure to violence) .961

Early Problem Behaviors .990

School Dropout .728

Impulse Control .837*

Anxiety 1.01

IQ .990*

Emotion Control .977

Executive Function 1.02

Baseline Violence 1.21**

CU (high) 1.79***

Log Likelihood Ratio: 63.26

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

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