J Abnorm Child Psychol (2015) 43:1529–1541

DOI 10.1007/s10802-015-0041-8

Callous-Unemotional Traits Trajectories Interact with Earlier

Conduct Problems and Executive Control to Predict Violence
and Substance Use Among High Risk Male Adolescents
Arielle R. Baskin-Sommers 1 & Rebecca Waller 2 & Ari M. Fish 1 & Luke W. Hyde 2

Published online: 18 June 2015

# Springer Science+Business Media New York 2015

Abstract Callous-unemotional (CU) traits, conduct problems findings highlight the utility of identifying subgroups of youth
(CP), and deficits in executive control are all linked to the who differ on trajectories of CU traits for understanding the
development of more severe antisocial behavior, including development and maintenance of severe antisocial behavior.
violence and substance use. Though previous research has
examined the impact of these factors on antisocial outcomes,
little work has examined trajectories of CU traits across ado- Keywords Antisocial behavior . Callous-unemotional traits .
lescence and how these trajectories predict greater antisocial Conduct problems . Violence . Substance use . Executive
behavior in adulthood. Moreover, no study has assessed how control . Trajectories
severity of early CP and executive control may exacerbate
these pathways and increase risk for later violence and sub-
stance use. The current study (a) identified trajectories of CU Antisocial behavior (AB), including early conduct problems
traits among a large, high-risk sample of adolescent males, (b) (CP) and later violence and substance use, entails great cost to
examined the relationship between CU traits trajectories and society through its impact on perpetrators, victims, and family
future violence and substance use, and (c) examined whether members. Recent research has examined the role of callous-
early CP and executive control moderated the effects of a high unemotional (CU) traits (e.g., lack of empathy and guilt) in
CU traits trajectory membership and high CP on violence and the development of AB. In particular, the presence of high CU
substance use. Results indicated that: (a) CU traits could be traits appears to put youth at risk for severe and persistent forms
grouped into three stable trajectories across adolescence, (b) of aggression and violence (Frick et al. 2014). However, despite
the ‘high’ CU traits trajectory, particularly in the presence of being conceptualized as ‘traits’, few studies have examined CU
‘elevated’ CP, was related to higher violence and substance traits longitudinally. Further, no studies have tested whether
use, over and above a variety of environmental risk factors, knowing about trajectories of CU traits adds to our understand-
and (c) the effects the ‘high’ CU traits trajectory on both vio- ing of the development of specific types of AB, particularly
lence and substance and in the presence of ‘elevated’ CP was during the early adulthood period when AB may evolve in its
stronger among youth with high executive control. These severity (i.e., violence, substance use). Importantly, it is yet to be
established the extent to which elevated levels of early CP in-
Arielle R. Baskin-Sommers and Rebecca Waller contributed equally to teract with CU traits trajectories to predict worse AB outcomes.
this work. Finally, beyond CU traits, neuropsychological deficits in exec-
utive functioning (e.g., low executive control) are strongly re-
* Arielle R. Baskin-Sommers lated to AB (e.g., Ogilvie et al. 2011). However, no previous
arielle.baskin-sommers@yale.edu studies have examined whether deficits in executive control
exacerbate the risk posed by high levels of CU traits and early
1
Department of Psychology, Yale University, P.O. Box 208205, New CP in the prediction of later violence or substance use. The
Haven, CT 06520, USA goals of the present study were to identify trajectories of CU
2
Department of Psychology, University of Michigan-Ann Arbor, Ann traits, examine the prediction of adult AB by CU traits trajecto-
Arbor, MI, USA ries while controlling for early CP, and explore the impact of
1530 J Abnorm Child Psychol (2015) 43:1529–1541

elevated CP and executive control on the prediction of later membership are strengths of this study. However, more work is
violence and substance use by CU traits trajectory membership. needed to examine trajectories of CU traits using multiple time
points in high risk youth and across later adolescence, when
CU traits may become more stable. This is also a period when
Links Between CU Traits and Antisocial Behavior CU traits trajectories could predict more diverse and severe
forms of AB (e.g., substance use and violence). Indeed, adoles-
Youth with CU traits are characterized by a lack of empathy, cence is important because it is a developmental transition char-
lack of remorse and guilt, and reduced affective responsivity to acterized by increasing independence and social and physical
others (Frick et al. 2014). High CU traits have been shown to change, but in the context of immature regulatory functioning
predict increased risk of AB and violence among youth across (e.g., Arnett 2004). These changes are compounded by greater
different developmental stages and sample types (see Frick opportunity to be involved with deviant peers and enact more
et al. 2014 for a review). However, while several studies have severe AB. Finally, the transition from adolescence to early
linked broader measures of adolescent psychopathic traits (i.e., adulthood is when AB peaks (e.g., Arnett 2004; Shaw and
including impulsive/life-style components) to high risk for sub- Gross 2008), making this an important period in which to un-
stance use (e.g., Andershed et al. 2002), only one study has derstand CU trait trajectories, particularly as they may predict
examined links between CU traits and substance use. Among escalation into persistent and severe violence or substance use.
youth assessed in the 6th grade, CU traits predicted onset and
recurrence of substance use by the 9th grade (Wymbs et al.
2012). The lack of attention to potential links between CU traits Interaction Between CU Traits and CP
and substance use is surprising given theoretical links between in the Prediction of Severe Antisocial Behaviors
CU traits/psychopathic traits with substance use (Frick et al.
2014), and the established high comorbidity between substance The role of earlier CP also needs to be considered in relation to
use and psychopathy (Smith and Newman 1990). CU traits trajectory group membership and AB outcomes. First,
Together, previous studies underscore the importance of the extent to which high and stable levels of CU traits add to the
CU traits in developmental models of AB. However, previous prediction of AB needs to be established, taking into account
research is limited by a focus on CU traits assessed at one time the severity of early CP. That is, it is important to establish that
point, which contradicts widely cited perspectives on individ- any predictive power of CU trajectories is not due to CU tra-
ual differences in AB over the life course (Moffitt 1993; jectories tapping existing, elevated levels of CP. Second, we
Piquero 2008). Indeed, despite well-established individual dif- need to identify whether there are interactive effects between
ferences in the onset, chronicity, and stability of AB over time, CU traits trajectories and CP, such that knowing about both may
particularly during adolescence, few studies have considered be helpful in identifying youth most likely to persist in their AB.
trajectories of CU traits. However, this kind of person- For example, youth with high CU traits and CP have been
centered approach is appealing because it might identify spe- shown to exhibit higher impulsivity (Andershed et al. 2002),
cific discontinuities or groups that emerge based on patterns of more instrumental and reactive aggression (e.g., Frick et al.
data over time, rather than at one time point. Thus, identifying 2003), and increased risk for persistent delinquency into adult-
whether youth show stable or high levels of CU traits over hood (Byrd et al. 2012). Further, in the one study that has
time may be a more valid way to predict outcomes compared examined substance use in relation to CU traits, males with both
to simply examining their ‘rank’ or mean score at any one time elevated CU traits and CP were at highest risk of substance use
point (see Fontaine et al. 2011; Salihovic et al. 2014). by 9th grade, when compared to those with elevated CU traits-
For example, Fontaine and colleagues (2011) examined a only, CP-only, or low CU traits and low CP (Wymbs et al.
large sample of twins from a community sample (N=9578). 2012). Taken together, these findings suggest that identifying
Joint trajectories of CU traits (high, increasing, decreasing, high CU traits trajectories may be most powerful when also
and low) and CP (high and low) were identified using three knowing about levels of CP. To date, however, no previous
time-points from ages 7 to 12. There was asymmetry between studies have examined the interactive effect of elevated CP
CU traits and CP, such that high CU traits trajectory member- and subsequent CU traits trajectories across adolescence in
ship was strongly related to having high CP, whereas having the prediction of substance use or violence.
high CP was only moderately related to CU traits. Importantly,
the small proportion of children with a joint high/increasing CU
traits and CP trajectories were at risk of the most negative out- What is the Role of Executive Control in Predicting
comes at age 12, including emotional problems and hyperactiv- AB?
ity (Fontaine et al. 2011). Use of a large, community sample of
children followed longitudinally during a critical period of In addition, a large body of research has examined neuropsy-
childhood and the group-based approach to examine trajectory chological deficits associated with AB. Studies have focused on
J Abnorm Child Psychol (2015) 43:1529–1541 1531

executive functioning, an umbrella term referring to a range of trajectory model with five time points, and controlling
cognitive processes, including executive control, working for baseline levels. Specifically, we examined whether
memory, and selective attention (e.g., Chan et al. 2008; Morgan there were qualitatively different groups within our sam-
and Lilienfeld 2000). AB among adults and youth has been ple, based on their developmental trajectory of CU traits
linked to impairments in many of these processes, including across a key period of adolescence, and the extent to
failures to learn from punishment or to alter behavior in the face which these trajectories would be marked by change
of changing contingencies. Deficits in executive function are across time. Consistent with previous literature, we hy-
thought to explain why antisocial individuals persist in aggres- pothesized that a small subset of our high-risk adoles-
sive or sensation-seeking behavior despite the likelihood of cent sample would show a stable trajectory of high
negative consequences (De Brito and Hodgins 2009). In sup- levels of CU traits across time. Second, in line with
port of this notion, meta-analytic studies examining childhood, previous literature noting the importance of considering
adolescent, and adult populations with conduct disorder, oppo- CU traits in the context of CP, we examined the joint
sitional defiant disorder, or antisocial personality disorder, have predictive effects of earlier CP and CU traits trajectories
demonstrated strong links between executive function deficits on later violence and substance use. We hypothesized
and AB (e.g., Morgan and Lilienfeld 2000; Ogilvie et al. 2011). that elevated CP symptomatology in tandem with a high
However, studies have yet to consider how executive function- CU traits trajectory would be associated with the
ing might interact with or exacerbate the effects of CU traits highest levels of AB (Frick et al. 2014). Third, we
across adolescence in relation to the prediction of later AB, examined the role of executive control (a subtype of
including violence or substance use. executive function), in relation to links between CU
traits trajectories, early CP, and violence/substance use.
As no previous studies have examined how or whether
Other Risk Factors for Antisocial Behaviors and CU executive control moderates the prediction of AB by
Traits CU traits and early CP, this third study aim was explor-
atory. Finally, in order to elucidate specific effects of
Beyond individual-level risk factors, studies have also linked the CU traits trajectories, early CP, and executive control
development of AB to a range of contextual risk factors, includ- on violence or substance use, all models controlled for
ing parenting practices and criminality (e.g., Loeber et al. 1998), the well-established effects of putative contextual, pa-
neighborhood dangerousness (Barnes and Jacobs 2013), and rental, and child-level risk factors (Waller et al. 2013).
deviant peers (Dishion and Patterson 2006). Further, evidence
also suggests an important role of parenting in the development
of CU traits (Waller et al. 2013). Thus, any examination of the Methods
unique main effects of CU traits, as well as interactive effects of
CU traits, early CP, and executive control, needs to take into Participants
account the influence of these other key sources of risk. How-
ever, while studies have highlighted the importance of consid- The present study used data from the Pathways to Desis-
ering family and contextual risk factors as predictors for CU tance project, a multisite, longitudinal study of serious ju-
traits and violence/substance use (see Waller et al. 2013), the venile offenders (see Schubert et al. 2004, for complete
present study focused on examining the main effects of CU traits details of study methodology). Participants in the current
trajectories, and thus included family and contextual risk factors study were male youth adjudicated delinquent or found
as covariates (see Waller et al. under review for an examination guilty of a serious (overwhelmingly felony level) offense
of these and other factors as predictors of CU trait trajectories). at their current court appearance in Philadelphia, PA (N=
605) or Phoenix, AZ (N=565). We restricted analyses to
male offenders (N=1,170), as the data set had an insuffi-
Current Study cient number of females in the sample (n=184) to obtain a
stable trajectory model (Nagin 2005). Youth were eligible
The current study sought to improve our knowledge of for study participation if they were between the ages of 14
the development of violence and substance use via three and 18 and had been charged with a felony or similarly
research questions. Given that only two previous studies serious non-felony offense (e.g., misdemeanor weapons of-
have examined trajectories of CU traits and both fense, misdemeanor sexual assault) (see Table 1). Since a
sassessed community samples where levels of CU traits large proportion of offenses committed by youth were drug
may be relatively low, our first goal was to examine the offenses, the proportion of males whose enrollment offense
stability of CU traits in a large sample of high-risk, was a drug offense was capped at 15 % at each of the sites.
male youth over a 5 year period, using a group-based Of eligible youth, 67 % of those who were located and
1532 J Abnorm Child Psychol (2015) 43:1529–1541

Table 1 Sample characteristics

N Minimum Maximum M SD

Baseline variables
Age 1170 14 18 16.05 1.16
14 (N=144) (12.3 %)
15 (218) (18.6 %)
16 (346) (29.6 %)
17 (358) (30.6 %)
18 (104) (8.9 %)
Sex (Male) 1170
Race
White 225 0 1 0.19 –
Black 493 0 1 0.42 –
Latino 398 0 1 0.34 –
Other 54 0 1 0.05 –
School dropout 1169 0 1 0.16 –
Single parent 1169 0 1 0.45 –
Proportion family arrested 1162 0 1 0.31 0.40
Proportion friends arrested 1168 0 1 0.45 0.38
Neighborhood conditions 1167 1 4 2.35 0.74
# Early onset problems 966 0 5 1.51 1.19
IQ 1158 55 128 84.50 12.84
Anxiety (RCMAS) 1169 1 28 9.79 5.94
Emotion control (Walden) 1169 1 4 2.77 0.66
Executive control (Stroop) 1150 21 79 50.46 7.08
Independent variables
CP 1170 0 1 0.705 0.456
CU (YPI) Group trajectories 1170
Low 299 0 1 0.256 0.495
Moderate 673 0 1 0.575 0.500
High 198 0 1 0.169 0.358
CU Traits+CP 1170
‘Low’ CP
‘Low’ CU 120 0 1 0.103 0.315
‘Moderate’ CU 193 0 1 0.165 0.384
‘High’ CU 32 0 1 0.027 0.168
‘Elevated’ CP
‘Low’ CU 179 0 1 0.153 0.330
‘Moderate’ CU 480 0 1 0.410 0.471
‘High’ CU 166 0 1 0.142 0.319
Dependent variables
Variety of violence
Baseline 1084 0 8 0.501 1.14
5-year follow-up 995 0 7 0.316 0.863
Variety of substance use
Baseline 1165 0 9 2.07 1.92
5-year follow-up 995 0 9 0.630 1.02

invited to participate in the research agreed to enroll in the follow-up). Sample retention for the Pathways Project was
study. Participants completed six annual face-to-face inter- high at each follow-up, ranging from 84 to 94 % (M=90 %)
views over the course of the study (one baseline and five (see Mulvey et al. 2004 for details).
J Abnorm Child Psychol (2015) 43:1529–1541 1533

Measures Moderating Variables (Assessed at Baseline)

Primary Independent Variable Early Conduct Problems (CP) To compute a variable that
assessed self-reported symptoms of early CP, we used the SRO
Callous-Unemotional (CU) Traits CU traits were assessed via measured and general life history interview variables assessed
self-report using the Youth Psychopathic Traits Inventory at baseline (Mulvey 2013). In total there were 11 items included
(YPI; Andershed et al. 2002). The CU traits subscale includes in the CP measure: 3 items assessed aggression (e.g., bullying,
15 items, rated on a four-point Likert scale (0=‘does not apply school fights, and cruelty to animals) and 8 items assessed rule
at all’ to 4=‘applies very well’). Examples of CU traits items violations (e.g., running away, school problems, fire-setting,
include: ‘I usually feel calm when other people are scared,’ fraud). We computed a total score of number of CP items en-
and ‘I think that crying is a sign of weakness, even if no one dorsed (i.e., continuous measure). It is noteworthy that all par-
sees you’. Items were written so that individuals high in CU ticipants at baseline were adjudicated as serious felony level
traits would read the statements as reflecting positive or admi- offenders, reducing the potential variability of CP. However,
rable qualities. The YPI was administered annually starting the continuous CP measure was normally distributed (skew-
with ages 15–19 over a 6-year period (ages 20–24). CU traits ness=0.35, kurtosis=−0.61). We also created a binary CP score
scores showed good internal consistency (range, α=0.73– based on youth endorsing 3 or more items (‘elevated’ CP; i.e.,
0.79 over the course of the study and the cross-time correla- elevated being a relative term referring to scores being ‘high’
tion was high (average interclass r=0.85)). within our high-risk sample) or fewer than 3 items (‘low’ CP).
We also recomputed the CP measure including 8 items
assessing violence (e.g., fight, fights as a part of gang activity,
Dependent Variables assault, carjack, robbery with weapon, robbery without weap-
on, shooting at someone, carrying a gun), producing similar
Self-Reported Violent Offending A modified version of the results. We report findings based on the binary CP measure
Self-Report of Offending (SRO; Elliott 1990; Huizinga et al. without the violence items however, to reduce overlap with
1991) scale, focused on the items tapping violence, was used at our outcome assessment of violence versatility. Models exam-
the final assessment point to measure the adolescent’s account ining interactions between CP and CU traits trajectory in the
of his involvement in eight different violent crimes (fights as prediction of later violence and substance use also controlled
part of gang activity, assault, carjacking, robbery with weapon, for the main effect of earlier CP on later violence (i.e., the
robbery without weapon, shooting someone, shooting at some- heterotypic continuity/escalation from CP to later violence).
one, carrying a gun). Youth indicated whether they had done
any of these activities over the last 12 months. Each item was Executive Control Executive control, a subtype of executive
coded to reflect whether the respondent reported engaging in function, was assessed through the use of the widely-used
each act at least once. Dichotomized items were then summed Stroop Color-Word Test (Golden 1978), which indexes cog-
together. A sum of the number of types of violent offenses nitive flexibility and resistance to interference from outside
committed (a general versatility or variety score) was calculated stimuli. The Stoop Color-Word task is a gold-standard mea-
for each subject at each interview. Variety scales are often com- sure of executive control, with many previous studies estab-
pared with frequency scales that index the number of times that lishing its psychometric properties and construct validity in
a specific act occurred. For the current study, we focused on a both healthy and psychiatric populations (see Cauffman
variety scale as research indicates that variety scales are more et al. 2009; Golden 1978; Homack and Riccio 2004; Mulvey
internally consistent and more stable (Bendixen et al. 2003). 2013). This measure assesses the effects of interference on
The intra-class correlation for violence across time was 0.75. reading ability and comprises three parts: first, participants
read a word page (the names of colors printed in black ink),
Self-Reported Substance Use We examined self-reported second they read a color page (rows of X's printed in colored
substance use at both the baseline (as a control for ink) and finally the read a word-color page (the words from
autoregressive effects) and final assessment points. Adoles- the first page are printed in the colors from the second page;
cents reported on the frequency of their use of nine substances however, the word meanings and ink colors are mismatched or
(marijuana, opiates, cocaine, stimulants, ecstasy, sedatives, incongruent). The task included five columns containing 20
hallucinogens, inhalants, amyl nitrate) over the past items. During the standardized task, subjects look at each
12 months. A variety scale (i.e., number of types of substances sheet and move down columns, reading words or naming
used in the past year) was calculated and used in the study ink colors as quickly as possible within a 45-s time limit.
analyses. Analyses controlled for baseline substance use as a The present study used the standard T-score for interference
predictor of the 5-year follow-up interview report. The intra- based on normed data (see Mulvey 2013; Cauffman et al.
class correlation across time was 0.75. 2009). Higher scores reflect better performance and less
1534 J Abnorm Child Psychol (2015) 43:1529–1541

interference on reading ability, and higher executive control. point in the study. Trajectories, over 5 years, controlling for
T-scores of 40 or less are considered Blow^ and above 40 are baseline, overlapped in time with the outcome measures. We
considered in the Bnormal^ range (Golden 1978). used the Latent Gold 4.5 program (Vermunt and Magidson
2008) to estimate the probability that each individual belonged
Risk Factor Covariates (Baseline Measures) to a given group based on data. We simultaneously derived
maximum-likelihood parameter estimates associated with
Research has also linked child-level, family, and contextual membership in each of the defined trajectories. On the basis
sources of risk to AB. To examine the unique effect of CU of posterior probabilities, individuals were assigned to their
traits trajectories on AB, we included as many of these factors most likely group trajectory (Nagin 2005). CU traits were ex-
as possible as covariates. Each of the measures below was amined across six measurement points including baseline, with
evaluated via self-report at baseline. a total accelerated longitudinal age range of 14 to 24. Data were
tested for different numbers of latent classes, and the fit of
Individual Characteristics (i) School dropout was coded as different models was compared with the Bayesian information
a dichotomous variable (yes/no); (ii) Intelligence was mea- criterion (BIC; Jones et al. 2001). Mixtures of up to six latent
sured by the Wechsler Abbreviated Scale of Intelligence classes were considered. The best trajectory solution was deter-
(WASI; Wechsler 1999). The WASI produces an estimate of mined by three criteria: the lowest BIC value, posterior proba-
general intellectual ability based on two subtests, Vocabulary bilities, and a model in which each group included at least 5 %
and Matrix Reasoning (see Bowen et al. 2014; Mulvey et al. of the sample (Nagin 2005). The shape of each trajectory was
2010 for examples of this measure within youth with AB); (iii) determined by initially including linear, quadratic, and cubic
Emotion regulation was measured via self-report using an parameters, and then dropping non-significant trajectories.
adapted version of the Children’s Emotion Regulation scale The shape of each trajectory is identified by the highest order
(Walden et al. 1995). Of the 33 original items contained in this term included in the model. In the first iteration, linear, qua-
scale, 12 were included in the version for Pathways to Desis- dratic, and cubic parameters were included for each of the
tance (e.g., ‘I can change my feelings by thinking of some- three trajectories. The cubic parameters were non-significant
thing else’). Participants responded on a 4-point Likert-type for each. Thus, in iteration 2 the cubic parameters were
scale ranging from ‘not at all like me’ to ‘really like me.’ dropped from each trajectory. The quadratic parameter was
Higher scores indicate a better ability to regulate emotion non-significant for the moderate trajectory. Thus, in the final
(α=0.81); and, (iv) Anxiety assessed via the 28-item total model the quadratic parameter was dropped from the moder-
score on the Revised Children’s Manifest Anxiety scale ate trajectory. For the final model, the moderate trajectory was
(RCMAS; Reynolds and Richmond 1985) (α=0.87). linear, and the low and high trajectories were quadratic. Mul-
tiple imputation (MI) was used to address missing data be-
Family/Peer Characteristics (i) Family arrests were cause other strategies for managing missing data (e.g., listwise
assessed by computing the proportion of family members re- or pairwise deletion, mean imputation) may result in biased
siding with the participant who had been arrested; (ii) Peer analyses (Bodner 2008; Graham 2009). In the MI for the cur-
deviance was assessed by computing the proportion of each rent study, we included age at baseline, ethnicity, CP at all
participant’s four closest friends who had been arrested; and, follow-up time periods, and measures derived from official
(iii) Neighborhood conditions were measured using 21 items records (e.g., total number of court petitions prior to and in-
adapted from other large-scale studies of neighborhood func- cluding baseline and total number of arrests during the 5-year
tioning and impoverishment (Sampson and Raudenbush follow-up) to obtain more stable imputed values based on
1999). Items assessed physical and social disorder in the more information. Following recommendations by Bodner
blocks surrounding their homes (e.g., abandoned buildings, (2008), 20 data sets were imputed using STATA 12. This
gang activity) and were rated on a four-point scale ranging approach, with age-locked trajectories treats this sample as
from 1 (never) to 4 (often). A mean score was computed an accelerated longitudinal design with planned missingness
(α=0.94). (iv) Single-parent household was measured dichot- and thus leverages 5 years of study data collection to model
omously (single parent household/not). 8 years of trajectories (Raudenbush and Chan 1992).

Data Analytic Strategy Prediction of Violence and Substance Versatility Second,

negative binomial regression was used to examine prediction
Identification of CU Traits Trajectories First, we used of violent offending and substance use at the final 5-year fol-
group-based trajectory modeling, a type of mixture modeling, low-up assessment. We added child-, family-, and contextual
to identify subgroups of individuals who followed similar pat- risk factors (assessed at baseline) and CU traits trajectory
terns of CU traits over time. Trajectories were created based on membership (assessed over the 5 year study period). Tradi-
chronological age but results are nearly identical using time tional linear regression models would have been inappropriate
J Abnorm Child Psychol (2015) 43:1529–1541 1535

for analyzing count outcomes because count data do not fol- traits across the study period (‘moderate’). Group 3 (16.1 %)
low, or approximate, a normal distribution (King 1989). In the had high CU traits at baseline that remained stable and high in
current study, an initial conditional Poisson distribution model the follow-up periods (‘high’). Posterior probabilities indicat-
deviance statistic indicated over-dispersion (when the true var- ed that, on average, individuals were well matched to the
iance is bigger than the mean), thus, negative binomial regres- groups to which they were assigned (average posterior prob-
sion analyses were used to examine outcomes of violence and abilities were as follows: ‘low’ group = 69 %, moderate
substance use. Variables were entered simultaneously to as- group=73 %, high group=82 %). Next, we used multinomial
sess relative associations of the covariates with CU traits tra- regression to examine whether differences among trajectory
jectory group membership. groups existed in AB and other relevant variables. Not surpris-
ingly, the ‘high’ CU group committed the highest average num-
Interaction with CP Third, a binary CP variable (i.e., ‘ele- ber of violent crime types at 5 year follow-up (0.66) compared to
vated’ or ‘low’ CP) was entered into regression models to the ‘low’ (0.16) and ‘moderate’ (0.29) groups. Participants in the
examine interactive effects with CU traits trajectories. We fo- ‘high’ CU group also used more types of substances at follow-
cused on the interaction between ‘high’ CU traits trajectory up (0.97) compared to the ‘low’ (0.46) and the ‘moderate’ CU
membership and ‘elevated’ CP. Note that although a ‘high’ (0.60) trajectory groups. Also, as expected, a large proportion of
CU traits and ‘elevated’ CP group was our primary focus, the ‘high’ CU traits trajectory group (83.8 %) was classified as
we also examined other possible interactions between ‘high’, being in the ‘elevated’ early CP group compared youth in the
‘low’, and ‘moderate’ CU traits trajectories and ‘elevated’ ‘low’ (59.9 %) or ‘moderate’ (71.3 %) groups. Indeed, youth in
versus ‘low’ CP groups. We provide brief reference to effects the ‘low’ and ‘moderate’ CU groups were 54.3 and 23.7 % less
for these other interaction terms, but focus on the results for likely to be in the ‘elevated’ CP group respectively compared to
the ‘high’ CU traits and ‘elevated’ CP group, who represented the ‘high’ CU traits group. By contrast, only 19.3 % of youth
youth in whom we were most interested and for whom the who we classified as being in the ‘elevated’ CP group were in
most robust effects emerged. Given our relatively large sam- the ‘high’ CU trajectory group.
ple size, we were able to model interactions within this ‘high’
CU traits trajectory group, an option not usually available in
smaller datasets where cell sizes would be too small. Main Effect of CU Traits Trajectories in the Prediction
of Violence and Substance Use
Moderation by Executive Control Finally, we examined
whether the effects of CU traits trajectories and CP symptoms We examined whether CU traits trajectories predicted vio-
on later violence and substance use were further moderated by lence, controlling for individual-, family-, peer-, and
executive control, as indexed by the Stroop Color-Word inter- neighborhood-level risk factors1 and baseline CP. First, we
ference score. An interaction term between ‘high’ CU traits found that individuals in the ‘low’ and ‘moderate’ CU traits
trajectory and ‘elevated’ CP group membership and executive trajectory groups were 68.8 and 45.5 % less likely, respective-
control score was added to models. Using the PROCESS tool ly, to exhibit violence versatility at follow-up compared to
(Hayes 2012), we also ran an interactional model that exam- those in the ‘high’ CU traits trajectory group (Table 2,
ined at which specific Stoop T-scores the interaction between Model 1). Significant associations were also found between
‘high’ CU and ‘elevated’ CP (CU+CP+) was significant (e.g., CU traits trajectories and substance use at the 5-year follow-up
PROCESS provides information about the interaction and the assessment, controlling for covariates as before (Table 3,
simple effects at levels of the moderator where the interaction Model 1). Youth in the ‘low’ and ‘moderate’ CU traits trajec-
is significant). tory groups were 42.6 and 30.7 % less likely, respectively, to

1
The inclusion of these covariates is important given the num-
Results ber of individual, peer, and family factors linked to violence
and substance use. Our analyses demonstrate that CU traits
Trajectories of CU Traits trajectories predicted violence and substance use above and
beyond these other factors. However, the ‘high’ CU-violence
First, we found that a three-group solution for CU traits tra- (p<0.001) and ‘high’ CU–substance use (p=0.001) relation-
jectories fit the data best (Fig. 1). The estimate for entropy was ships were significant even when not including these covari-
0.897, indicating appropriate distinction of the three trajecto- ates. A model not including covariates indicated that the
ries. Overall, the data revealed a uniform pattern of low-to- ‘high’ CU group was 2.83 times more likely to engage in
high CU traits over time. Group 1 (26.5 %) had low CU traits violent versatility at the 5 year follow-up and 1.44 times more
at baseline that remained low and stable in the follow-up pe- likely to engage in versatile use of substances at the 5 year
riods (‘low’). Group 2 (57.4 %) had a moderate level of CU follow-up point.
1536 J Abnorm Child Psychol (2015) 43:1529–1541

Fig. 1 Trajectories of CU traits

use a greater variety of substance types at follow-up compared traits but ‘low’ CP was not significant, highlighting the robust
to participants in the ‘high’ CU traits group. and unique effect of high CU traits trajectories on future vio-
lent offending, regardless of earlier CP.
Interaction Between CU Traits Trajectories and Early CP For the prediction of substance use, analyses comparing
in the Prediction of Violence and Substance Use specific subgroups to the CU+CP+ group indicated that youth
in the ‘low’ and ‘moderate’ CU traits trajectory groups with
For the prediction of violence, the interaction between CU ‘low’ CP were 60.4 and 44.4 % less likely to exhibit substance
traits trajectories and CP symptoms indicated significant dif- versatility at follow-up, respectively (Table 3, Model 2). All
ferences between the ‘high’ CU traits group and all subgroups other group comparisons (i.e., ‘high’ CU traits with ‘low’ CP;
with the exception of the ‘high’ CU traits and ‘low’ CP group ‘low’ CU traits and ‘elevated’ CP; ‘moderate’ CU traits and
(Table 2, Model 2).2 Specifically, compared to the CU+CP+ ‘elevated’ CP) with the CU+CP+ group did not reach signif-
group, the ‘low’ CU traits trajectory group who were classi- icance. These non-significant comparisons suggest the impor-
fied as having ‘low’ CP was 82.8 % less likely to show vio- tance of considering either ‘high’ CU traits trajectory mem-
lence versatility at the 5-year follow-up assessment. Results bership or earlier elevated CP symptoms in relation specifical-
were similar when the CU+CP+ group was compared to other ly to the prediction of substance use versatility. As before, the
groups: ‘moderate’ CU traits group with ‘low’ CP (75.7 % results were robust to the effects of various putative family,
less likely), ‘low’ CU and ‘elevated’ CP group (62.0 % less child and contextual sources of risk, as well as earlier base-
likely), and the ‘moderate’ CU and ‘elevated’ CP group lines assessments of substance versatility.
(32.5 % less likely). These comparisons indicate that CU traits
trajectory group membership over time was a better predictor
of later outcomes than early levels of CP only. Further, the Further Moderation by Executive Control
comparison between CU+CP+ and the group with ‘high’ CU
The results of the moderation analyses are presented in Col-
2
We re-ran the analyses using the continuous CP measure and umn 3 of Tables 2 and 3 (Model 3). The main effect of exec-
results were consistent with the binary CP measure. Specifi- utive control at baseline was not significant in predicting later
cally, individuals with higher levels of CP were more likely to violence or substance use. However, we found a significant
display violence versatility (B=0.21, p<0.01) and substance interaction between executive control and all subgroups when
use (B=13, p<0.01) at the 5-year follow-up assessment. compared to the CU+CP+ group in the violent offending
Moreover, adolescents with ‘high’ CU traits trajectories and model. Probing of these significant interactions suggested that
elevated levels of earlier CP were more likely to exhibit vio- youth in the CU+CP+ group with high executive control (i.e.,
lence and substance use at follow-up. high Stroop difference T-scores) showed higher violence
J Abnorm Child Psychol (2015) 43:1529–1541 1537

Table 2 Odds ratio for the impact of CU trajectories, CU+CP, and executive control on violence versatility

Model 1: CU trajectories Model 2: CU+CP+ Model 3: CU+CP+ × Executive control

CP (Baseline) 2.64*** 2.64*** 2.63**

‘Low’ CU 0.312*** – –
‘Moderate’ CU 0.545*** – –
‘Low’ CU/‘Low’ CP – 0.172*** 0.956***
‘Moderate’ CU/‘Low’ CP – 0.243*** 0.974***
‘High’ CU/‘Low’ CP – 0.919 0.984**
‘Low’ CU+‘Elevated’ CP – 0.380*** 0.970***
‘Moderate’ CU+‘Elevated’ CP – 0.675* 0.990**

The reference group in Model 1 ‘High’ CU trajectory group. The reference group for Models 2–3 is ‘High’ CU+‘Elevated’ CP. *p<0.05 **p<0.01 ***
p<0.001

versatility scores (Fig. 2). Specifically, CU+CP+ youth with interactions between CU traits trajectories and earlier CP
Stroop T-scores above the 85th percentile (n=29) were more symptoms on later AB, and the moderating role of executive
likely to exhibit violence versatility at follow-up than other control in these pathways. This is the third study to have
groups. For substance use, youth in the ‘low’ and ‘moderate’ examined trajectories of CU traits among youth, and the first
CU traits trajectory groups with ‘low’ CP were significantly to do so among a large group of males at high risk of engaging
less likely to exhibit substance versatility at follow-up com- in high levels of future violence and substance use. Our find-
pared to the participants in the CU+CP+ and high executive ings extend understanding of the development of CU traits
control group. All other group comparisons with the CU+ and severe AB in three ways.
CP+ group did not reach significance. The significant com-
parisons indicate that respondents in the CU+CP+ group with
Stroop T-scores above the 61st percentile (n=75) were more Identification and Predictive Validity of CU Traits
likely to use a variety of substances at follow-up compared to Trajectories in Adolescence
the ‘low’ and ‘moderate’ CU traits groups with ‘low’ CP
(Fig. 3). First, our findings yielded three meaningful trajectories of CU
traits across adolescence that appeared stable over the assess-
ment period. Our results fit with the broader literature,
highlighting the need for studies to consider individual differ-
Discussion ences in the level of adolescent personality and antisocial fea-
tures over time (e.g., Moffitt 1993; Piquero 2008; Waller et al.
In the current study we examined the impact of CU traits under review). In particular, a small subset of youth (16.1 %)
trajectories, self-reported CP symptoms, and executive control was classified as showing high and stable levels of CU traits
on AB among a large high-risk sample of adolescent males. across the study period. This ‘high’ CU traits trajectory mem-
We explored several questions relating to identifying trajecto- bership was related to violence and substance use, even after
ries of CU traits and their association with later AB, the controlling for a variety of individual-, family-, and peer-level

Table 3 Odds ratio for the impact of CU trajectories, CU+CP, and executive control on substance use versatility

Model 1: CU trajectories Model 2: CU+CP+ Model 3: CU+CP+ × Executive control

CP (Baseline) 1.45** 1.43** 1.47**

‘Low’ CU 0.574*** – –
‘Moderate’ CU 0.693** – –
‘Low’ CU/‘Low’ CP – 0.396*** 0.983**
‘Moderate’ CU/‘Low’ CP – 0.556** 0.988*
‘High’ CU/‘Low’ CP – 1.63* 1.01
‘Low’ CU+‘Elevated’ CP – 0.805 0.996
‘Moderate’ CU+‘Elevated’ CP – 0.851 0.996

The reference group in Model 1 ‘High’ CU trajectory group. The reference group for Models 2–3 is ‘High’ CU+‘Elevated’ CP. *p<0.05 **p<0.01 ***
p<0.001
1538 J Abnorm Child Psychol (2015) 43:1529–1541

Fig. 2 Interaction among CU All Other Groups (N=1,004) CU + CP+ (N=166)

traits, CP, and executive control 55

Mean Stoop Interference Score

on violence. Note: CU+CP+=
‘High’ CU+‘Elevated’ CP group. 54 * *
Asterisks indicate significant 53
effects at p<0.05 52

(T-score)
51

50

49

48

47

46

45
No Violence 1 Violence Type 2 or More Violence Types

risk factors, as well as baseline levels of CP and earlier sub- there was evidence of asymmetry in the relationship between
stance use. These results are in line with previous studies CU traits and CP. Specifically, youth with high CU traits were
suggesting that the presence of CU traits is related to more highly likely to be classified as having ‘elevated’ CP (83.8 %)
severe AB and substance use among youth, but also extends but youth with ‘elevated’ CP were only moderately likely to be
this prior literature to assess developmental trajectories using a classified as being in our ‘high’ CU traits trajectory group
stringent control for confounding variables (e.g., Frick et al. (19.3 %), indicating that those with CU traits are very likely
2014; Wymbs et al. 2012). In particular, CU traits are theo- to have ‘elevated’ levels of CP but most individuals with ‘ele-
rized to increase risk for violent and substance use behaviors vated’ CP are not high on CU traits. Our findings fit with a
because youth may be less responsive to the emotional distress previous trajectory analysis among a population sample of chil-
of victims (Marsh and Blair 2008) and are highly focused on dren, where a similar asymmetrical relationship emerged
reward, with little care for consequences (Blair 2013). Our (Fontaine et al. 2011), indicating that CU traits may primarily
findings are also in line with the adult literature, where studies be considered as a particularly severe subgroup within adoles-
have demonstrated high overlap between psychopathy and cents exhibiting CP. Further, these findings are in line with the
substance use (Taylor and Lang 2006). adult literature, where antisocial personality disorder does not
always overlap with psychopathy, whereas most individuals
Risk Associated with CU Traits and Elevated Levels with psychopathy meet criteria for antisocial personality disor-
of Earlier Self-Reported CP Symptoms der (e.g., Forsman et al. 2010). Unlike Fontaine and colleagues,
however, we did not identify subgroups with ‘changing’ CU
Second, our results highlight the risk associated with youth traits – including increasing or decreasing CU traits. Instead, we
having high and stable levels of CU traits and existing, elevated found relatively high within- and between-person stability in
levels of self-reported CP symptoms. As predicted, the ‘high’ CU traits across time. One explanation for this difference may
CU trajectory group had significantly higher levels of self- arise from sample type. We focused on a high-risk, older sample
reported CP symptoms at baseline when compared to youth of youth who had already had contact with the law and among
with either ‘moderate’ or ‘low’ CU traits trajectories. However, whom trajectories of CU traits may have been more stable. In

Fig. 3 Interaction among CU All Other Groups (N=1,004) CU + CP+ (N=166)

traits, CP, and executive control 55
on substance use. Note: CU+
54 * *
CP+= ‘High’ CU+‘Elevated’ CP
Mean Stoop Interference Score

group. Asterisks indicate 53

significant effects at p<0.05 52

51
(T-score)

50

49

48

47

46

45
No Substance Use 1 Substance Type 2 or More Substance Types
J Abnorm Child Psychol (2015) 43:1529–1541 1539

contrast, Fontaine and colleagues examined CU traits trajecto- flexibility and resistance to interference from stimuli) in-
ries among a population-based sample of twins who were creased the likelihood that youth would exhibit violence and
assessed at much younger ages (7–12 years old), when CU substance versatility. This finding is surprising given the sep-
features may be expected to be less stable. Thus, sample type arate literatures linking each of these three variables (high CU
and developmental stage may affect relative ‘stability’ of CU traits, elevated self-reported CP symptoms, and low executive
traits (also see Waller et al. 2013). control) to worse AB. However, in our sample, this small
Moreover, we found that youth with a joint ‘high’ CU traits group of youth (n=29) may represent an important at-risk
trajectory and elevated levels of self-reported CP (CU+CP+) subtype who account for the very worst AB outcomes. It is
showed the highest likelihood of later violence, but they did noteworthy that in a previous cross-sectional study that exam-
not differ significantly from youth with ‘high’ CU traits and ined detained males adolescents, Muñoz and colleagues found
‘low’ levels of CP. Thus, although CU+CP+ youth showed that males with high CU traits and high verbal ability scores
the most violence later, the effect appeared to be driven by CU reported greater violence compared to males with low CU
traits trajectories rather than CP levels. Thus, findings suggest traits and low verbal ability (Munoz et al. 2008). Further, in
that within adjudicated youth, high and chronic levels of CU a large population sample of males followed from ages 12 to
traits may be more important in predicting outcomes over and 24, Barker and colleagues (2011) found that higher
above existing AB (also see Frick et al. 2014). For substance neurocognitive ability was related to chronic theft over time.
use however, there was a subtly different pattern of findings. Finally, Waschbusch and Willoughby (2008) also reported
As with violence, CU+CP+ youth were most likely to show that CU traits and CP were the strongest predictors of aggres-
more substance use, but only when compared to groups with sion in children with low ADHD symptoms (a disorder asso-
‘low’ or ‘moderate’ CU traits and low levels of CP. Specifi- ciated with deficits in executive control). Taken together, these
cally, a ‘high’ CU traits trajectory (regardless of the level of previous studies, along with the present findings, suggest that
CP) or a classification of elevated CP (even for ‘low’ or ‘mod- higher executive control may enable CU+CP+ youth to suc-
erate’ CU traits) were both related to later substance use. In cessfully engage in violence or substance versatility. In partic-
this regard, findings suggest that while CU traits may exacer- ular higher executive control may support the planning and
bate risk, elevated levels of CP even in the absence of CU implementation of more effective strategies to obtain their
traits, increase the likelihood of later substance use. Though desired goals, either through violence or other methods, with
speculative, it is possible that the mechanisms leading to en- less chance of spending time incarcerated.
gagement in substance use differ between these groups – for
example, elevated CP levels in the absence of CU traits have Strengths and Limitations
been linked to emotional dysregulation and impulsivity,
whereas elevated CP and CU traits appear to be related to There were a number of strengths to the current study, includ-
lower emotional responsivity and punishment insensitivity ing assessment of a large, high-risk sample of male youth,
(Frick and Morris 2004). Further, work among adults has followed for 5 years, novel examination of interactive effects
demonstrated that psychopathic traits, AB, and substance of CU traits trajectories, self-reported CP, and executive con-
use disorders may overlap at a latent level because of a shared trol, and stringent control in models for the potential effects of
heritable risk for AB (Blonigen et al. 2005). Thus, CU traits relevant covariates. However, our findings should be consid-
may act a specific risk factor for violence but a more general ered alongside a number of limitations. First, with the excep-
correlate of AB, producing overlap with CP and substance tion of the measure of executive control, we relied on self-
use. However, future studies are needed to test this question report for all measures. Though youth may be the best re-
among samples of children and adolescents. porters of some of these behaviors (i.e., substance use, vio-
lence) and use of autoregressive effects can somewhat miti-
Moderation by Executive Control gate shared-method bias, our approach may have
overestimated effects through shared method biases. Future
Finally, a novel aspect of this study is that we examined mod- studies examining prospective links between CU traits trajec-
erating effects of executive control on links between CU traits, tories and CP and violence or substance use should include
CP, and later violence and substance use. A large body of objective reports or official records to avoid potential limita-
literature suggests that deficits in executive control are related tions associated with single reporter data collection. Second,
to higher aggression and sensation-seeking behavior (De Brito because some items assessing self-reported CP symptoms
and Hodgins 2009). However, the present study found that the were only available at baseline assessments, we were unable
effects of CU+CP+ on both violence and substance versatility to examine interactive effects of CP at later assessment waves
were stronger among youth with high executive control. In with CU traits trajectories. Joint trajectory analysis of CP and
other words, for youth with high, stable CU traits and elevated CU traits in this sample would be very interesting but with our
levels of early CP, higher executive control (i.e., cognitive current measure of CP, it was not possible. Third, because of
1540 J Abnorm Child Psychol (2015) 43:1529–1541

power issues, we were unable to include females from the Conflict of Interest None declared
Pathways study, as we would not have been able to estimate
trajectory group memberships. Previous studies have estimat-
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