REVIEW |
Findings From the Pittsburgh Youth Study: Cognitive
Impulsivity and Intelligence as Predictors of the
Age–Crime Curve
Rolf Loeber, Ph.D., Barbara Menting, M.Sc., Donald R. Lynam, Ph.D., Terri E. Moffitt, Ph.D.,
Magda Stouthamer-Loeber, Ph.D., Rebecca Stallings, B.A., David P. Farrington, Ph.D.,
Dustin Pardini, Ph.D.
Objective: This article first summarizes key research findings from the Pittsburgh Youth Study
from 1987 to the present, and focuses on delinquency in 1,517 young men who have been
followed up from late childhood into their 20s. Second, the article addresses how indicators of
self-control prospectively predict later offending, and whether the prediction shows individual
difference in the age–crime curve, particularly the up-slope, peak, and down-slope of
that curve. Method: Longitudinal analyses were conducted on a sample of boys in the
middle sample of the Pittsburgh Youth Study (n ¼ 422), whose cognitive impulsivity and
intelligence were assessed at about age 12 years. Criminal records on the sample were until
age 28. Results: The results show that cognitive impulsivity and intelligence, measured
between ages 12 and 13 by means of psychometric tests, predicted the age–crime curve. The age–
arrest curve was substantially higher in boys with high cognitive impulsivity and in boys with
low IQ. However, there was a significant interaction between cognitive impulsivity and
intelligence. For boys with high IQ, cognitive impulsivity was associated with a greater
escalation in the prevalence of offending during early adolescence, followed by a more rapid
decline in offending as boys entered early adulthood with a slight subsequent increase in
criminal offending then occurring late 20. In contrast, there was no evidence that cognitive
impulsivity independently influenced criminal offending at any developmental period for boys
with low IQ. Conclusions: The results are discussed in terms of interventions to reduce
individuals’ delinquency from childhood through early adulthood and lower the age–crime
curve for populations. However, the association was complex because it was moderated
by both age and intelligence. J. Am. Acad. Child Adolesc. Psychiatry; 2012; 51(11):1136–1149.
Key Words: impulsivity, intelligence, delinquency, age–crime curve.
T
his article first presents a selection of findings from the Pittsburgh Youth Study (PYS)
mainly pertaining to violence and property
crime, and then presents new findings concerning
the role of cognitive impulsivity and intelligence
in predicting the age–crime curve.
THE PITTSBURGH YOUTH STUDY
Although there are many longitudinal studies on
the development of male antisocial and delinquent behavior and mental health problems, most
This article will be discussed in an editorial by Drs. James J. Hudziak
and Douglas K. Novins in an upcoming issue.
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studies have relatively small cohorts, making it
difficult to trace the antecedents and causes of
relatively serious delinquency, and have only a
small number of assessments spaced over many
years. This makes it impossible to track changes in
risk factors that are followed by changes in
deviancy, which only can be achieved by assessments of both risk factors and outcomes at regular
and frequent intervals.
These requirements were in our minds when
we started, in 1987, the Pittsburgh Youth Study,
which is a prospective longitudinal survey of the
development of juvenile offending, mental health
problems, drug use, and their risk factors in innercity boys (N ¼ 1,517). To date, the study has
produced 175 published or in-press papers and
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five books.1–5 The following is only a selection of
findings to date, and focuses on developmental
pathways of antisocial and delinquent behavior,
violence and homicide, and victims of homicide.
DESIGN AND METHODS
Participants. Boys attending the first, fourth, and
seventh grades in virtually all public school
system in inner-city Pittsburgh (called the youngest, middle, and oldest cohorts) were randomly
selected for participation in a longitudinal study
of the development of disruptive and delinquent
behaviors. Participant selection and assessment
methods have been described in detail elsewhere2,3,6 and are summarized only briefly here.
Of those families contacted (about 1,000 in each
grade), 85% of the boys and their parents agreed to
participate. An initial screening (S) assessment
followed to identify approximately 30% of the
boys with the most severe disruptive behavior
problems (approximately 250 boys in each of the
three cohorts). In addition, a random selection of
boys from the remaining 70% of each cohort was
made (approximately another 250 boys in each
cohort). This selection process resulted in 503, 508,
and 506 boys, in the youngest, middle, and oldest
cohorts, respectively, about equally divided
between African American and Caucasian boys,
reflecting the racial composition of Pittsburgh
Public Schools at the time. The average age at
screening were 7.0, 10.2, and 13.4 for the respective cohorts.
Follow-up. The youngest cohort has now been
followed up a total of 19 times (initially nine halfyearly assessments from age six onward; thereafter yearly from age 10 to 20, once more at age 25,
and at age 28). The oldest cohort has been
followed up 16 times (initially six half-yearly
assessments from age 13, thereafter yearly from
age 15 to 25, and currently at age 35). Because of
financial reasons, the follow-up of the middle
cohort was more restricted (seven half-yearly
assessments starting at age 10, and a single
assessment at age 24). Most of the results that
follow pertain to the youngest and oldest cohorts
because their data is the most extensive over long
periods of time, and is overlapping between ages
13 and 25.
Measures. Hundreds of measures have been
administered to the boys and their parents and
teachers (the latter two informants until age 16).
For reasons of space, these cannot be fully
documented here, and the reader is referred to
earlier summaries.2,7,8
SELECTED FINDINGS
Developmental Pathways. We tested the extent to
which homicide, violence, and serious property
crime are the culmination of a gradual developmental process over years from less serious to
serious behaviors. Research showed evidence for
three pathways. These are the overt, covert, and
authority conflict pathways.7,9,10 Youths typically
follow an orderly progression from less to more
serious antisocial behaviors from childhood to
adolescence.10,11 The Overt Pathway starts with
minor aggression, has physical fighting as a
second stage and more serious violence as a third
stage. The Covert Pathway starts before age 15
and begins with minor covert acts (shoplifting
and frequent lying), with property damage (i.e.,
vandalism and fire-setting) as a second stage,
moderate delinquency (i.e., fraud, pick-pocketing) as a third step, and serious delinquency (i.e.,
auto theft and burglary) as a fourth step. The
Authority Conflict Pathway, before the age of 12,
starts with stubborn behavior, has defiance/disobedience as a second stage, and authority
avoidance (i.e., truancy, running away from
home, and staying out late at night) as a third
stage. Young males can advance on all three
pathways at the same time; an early age of onset,
compared to a later age of onset, is associated with
young males’ progression deeper into the overt
and covert pathways. The pathways model has
been validated in other samples.9,12
Stability and Changes in Prevalence. The stability
of physical aggression tends to increase between
ages 6 to 7 and 9 to 10 years,13 well before
adolescence. However, the prevalence of aggression, including physical aggression, tends to
decrease between childhood and adolescence
indicating that some aggressive individuals outgrow physical fighting and verbal aggression.
However, a minority of aggressive juveniles start
to engage in more serious violent acts, such as
robbery, rape, aggravated assault, and homicide.14 Whereas serious theft and violence other
than homicide appear to be more common in the
early part of criminal careers (ages 13–16 years),
homicide tends to occur later. We also found that
the persistence of violence is higher than that of
serious theft. This implies that desistance from
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serious theft tends to occur earlier than desistance
from violence.4
Causation. The causes of violence lie in the
individual, family, peer group, school, and neighborhood.4,15 There is a dose–response relationship
between the number of risk factors to which
young males are exposed and the probability of
their becoming violent.15 Explanatory factors in
the PYS for violence, such as poor school achievement and having few friends, are similar to
explanatory factors for violence in inner-city
London.16,17
The causes of violence differ only partly from
the causes of theft. In the PYS, the strongest
predictors of violence were generally similar to
the strongest predictors of theft at younger ages
but not at older ages. This indicates that there are
many unique factors predictive of violence and
unique factors predictive of theft. For example,
gun carrying and the family living on welfare
were the best predictors of violence, whereas child
maltreatment, theft victimization, and white
race/ethnicity were the best predictors of theft.4
Race and Violence. Serious violence is more
common in African American than in white
males.18–20 African American males were more
likely than their white counterparts to be arrested
and convicted for violence. However, results
showed that African American race did not
predict reported violence (based on self-reports
and information from parents and teachers), once
other risk factors (such as bad neighborhood, old
for the grade) were taken into account. We
concluded that African American males were
more likely to be violent primarily because they
were exposed to more risk factors for violence.
However, when the analyses were repeated with
court reports of violence as the outcome, African
American race contributed to the prediction of
violence even when other factors were taken into
account.21
Homicide Offenders. By May 2009, of the 1,517
boys in the PYS, 37 had been convicted in court for
committing a homicide.2 Results show that the
great majority of the homicides committed by PYS
young males, like the homicides in other major
inner city, metropolitan areas, can be characterized as ‘‘street’’ homicides involving acquaintances and strangers (and often gangs, guns,
and drugs) rather than homicides of relatives
committed by individuals ‘‘engaging in dangerous, violent behaviour,’’ resulting in death (Kelly
and Totten,22 p. 146).
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The only psychiatric diagnosis related to homicide was disruptive behavior disorder (i.e., a
summary index of attention-deficit/hyperactivity
disorder [ADHD], conduct disorder [CD], or
oppositional defiant disorder [ODD]). Significantly more of the homicide offenders compared
to the controls qualified for a diagnosis of disruptive behavior disorder when young. However,
because delinquency overlaps with the criteria for
conduct disorder (one of the three disorders
comprising disruptive behavior disorders), this
indicates that many future homicide offenders
showed earlier behavior problems (see also
Hagelstam and Häkkänen23). We cannot conclude
that disruptive behavior disorder had any causal
effect on later homicide offending, but it may have
been an earlier step in a developmental pathway.
However, almost all homicide offenders (95%)
had committed violence before committing the
homicide.15
We investigated psychopathy among the homicide offenders. We did not find that factors
typically associated with psychopathy (lack of
guilt, cruelty to people, and callous-unemotional
behavior) were predictive of homicide offenders.
Interviews with homicide offenders using
the Psychopathy Checklist–Screening Version
(PCL-SV) showed that three-fourths of them
scored high on all PCL-SV factors. These results
were largely driven by higher scores on factors 3
(impulsiveness and irresponsible lifestyle) and 4
(juvenile delinquency and criminal versatility)
and not on the crucial personality Factor 1
(arrogance and deceitfulness) and Factor 2 (lack
of empathy and guilt). Thus, there was little
evidence suggesting that the homicide offenders
had a psychopathic personality.
What are the predictors of homicide? We
distinguished among three categories of
predictors: explanatory, behavioral (behaviors
correlated with delinquency), and criminal. Integrated analyses were based on factors that were
independent and statistically significant in each of
the three preceding regression analyses. The
results showed that factors from three domains
(individual, family, and neighborhood) independently contributed to the prediction of homicide
offenders. These factors included prior delinquent acts, including conviction for simple
assaults and weapon carrying; living in a bad
neighborhood, and having a young mother. Thus,
no single factor could explain or predict homicide
offenders. Instead, the integrated homicide risk
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score for the prediction of convicted homicide
offenders out of the whole population showed a
dose–response relationship between the number
of risk factors and the probability of becoming an
offender: the higher the number of risk factors, the
higher the probability of becoming a homicide
offender. None of the boys who had no risk factors
became a homicide offender, compared to 19% of
those who had five to seven risk factors. The area
under the receiver operating characteristic (ROC)
curve analyses show good predictability (AUC ¼
0.870) at a level that was higher than for explanatory, behavioral, and criminal risk factors alone.
Not surprisingly, the false-positive error rate was
high (87%), indicating overprediction, whereas
the false-negative error rate (38%) was moderately
high, indicating that about four of 10 homicide
offenders were not identified on the basis of the
prediction score.
Homicide Victims. Compared to controls, the
homicide victims (n ¼ 39) were more likely to
have carried a gun, to have used a weapon to
attack someone, to have engaged in gang fights, or
to have committed a robbery. They also were more
likely to have sold marijuana or hard drugs. The
majority of homicide victims had a history of law
breaking, especially engagement in illicit activities such as receiving stolen property, stealing
cars, or stealing from a car; in addition, aggravated assault was one of the predictors of homicide victimization.
In an analysis of explanatory, behavioral, and
criminal risk factors, the independent predictors
of shooting victims (who did not die, n ¼ 78) were
a broken home, self-reported drug selling, low
school motivation, truancy, and peer delinquency.
Less than 1% of the boys at least risk, according to
these five variables, became shooting victims,
compared with 12% of the boys at most risk.
However, shooting victims were predicted less
well by these risk factors than were homicide
victims.
Shared Features of Homicide Offenders and Homicide Victims. We were particularly interested in the
shared risks among homicide offenders and
homicide victims earlier in life and the shared
criminal activities24 that may bring homicide
offenders and their victims together for the fatal
act. We found that homicide offenders and homicide victims often engaged in the black economy
by dealing in stolen goods and drug dealing. It is
likely that the administration of personal justice to
deal with conflicts arising from these transactions
‘‘in the street’’ and gang membership for a
proportion of the offenders further fuels victimization and retaliation. This is aided by the fact that
the delinquents with the widest varierty of crime,
living in disadvantaged neighborhoods, when
they were victimized, tended to underreport their
victimization to the police.25 Instead, the victimization of innocent bystanders is most reported to
the police and receives the most media attention.
These are just a fragment of all findings to date.
Cognitive Impulsivity, Intelligence and the Age–
Crime Curve. Developmental psychopathology
and criminology over the past years have increasingly focused on changes in deviant behavior with
age. Two neglected areas of investigation are
which factors explain the up-slope and peak of
the age–crime curve, and what explains the downslope of the curve between adolescence and
adulthood. The search for answers has swayed
between individual and external factors. Within
the individual factors, several explanations have
been proposed of which we emphasize impulsivity and intelligence, but it is far from clear which
of the two is more important or how they might
interact.
In western populations of youth, the prevalence of delinquency increases from late childhood, peaks in middle to late adolescence, and
then decreases. This is known as the age–crime
curve.26 Most criminological theories have
focused on the explanation of why there are
individual differences in offending and why these
differences are maintained with age,27 and much
less on why offending decreases between midadolescence and adulthood. The two sides of the
age–crime curve pose challenges for explanatory
frameworks. Whereas risk factors are usually
considered for the prediction of who will become
an offender (the up-slope of the age–crime curve),
protective factors are called on to predict desistance from offending (the down-slope of the age–
crime curve). It appears much easier to predict
who will become a serious offender rather than
which serious offender will desist.4,28,29 Most
well-researched risk factors, such as poverty,
living in a disadvantaged neighborhood, or parents’ poor child-rearing practices are better in
predicting individual differences the up-slope of
the age–crime curve.
Research shows that desistance from offending
takes place throughout late childhood to adulthood,4 but is most common during the late
adolescent period.2,30 It is less well-known,
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however, that most of the desistance of different
age-at-onset categories of offenders (e.g., onset in
late childhood or early adolescence) occurs during late adolescence and early adulthood,31 thus
in the down-slope of the age–crime curve. The
important question that remains unresolved is
why desistance of different age-at-onset offenders
is concentrated between adolescence and early
adulthood. It appears plausible that the search
should focus on factors that are shared by all
youth who offend during this critical period
of life.
We are not aware of studies of factors that
explain both the up-slope and the down-slope of
the age–crime curve. In fact, most statistics used in
the study of risk factors, understandable for the
search of stable individual factors, have focused
on linear relationship between predictors and
outcomes.32 Furthermore, meta-analyses of the
association between impulsivity and later antisocial behavior and offending only tested for
linear models.33,34 Thus, what is needed are
analyses that test the relationship between risk
(or protective factors) with quadratic or cubic
models that are known to fit best the age–crime
curve.35
There has been a long tradition in psychology
and criminology to search for underlying
mechanisms that can explain individual differences over time. Examples are poor self-control,
reckless behavior, low intelligence, impulsivity,
and sensation seeking.36,37 Currently, there is no
agreement among scholars regarding the underlying factors that best explain individual differences in the age–crime curve,38 and different
categories of delinquent acts (e.g., theft versus
violence). Violence tends to be more stable than
theft; desistance in theft tends to occur earlier than
in violence; and risk factors for theft only partly
overlap with those for violence.4,39,40 Scholars
have speculated that the brain maturation that
usually takes place between adolescence and
early adulthood may help to predict why some
early offenders seem to exhibit improvements in
self-control and decreases in impulsivity over
time.41,42 Research shows that impulsivity is not
highly stable and tends to decrease in the first
decades of life,43,44 which may fit the down-slope
of the age–crime curve.
Most studies on the link between cognitive
impulsivity and deviant and delinquent behavior
have been cross-sectional,34,37,45,46 or retrospective,39,40 and therefore do not shed light on how
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well impulsivity predicts future delinquency,
including the age–crime curve. Thus, it is important to use longitudinal data to show first that
impulsivity is temporally earlier, before the upslope of the age–crime curve accelerates. Second,
it is necessary to have at least a decade of followup of the participants so that their age–crime
curve can be delineated.
It can be argued that intelligence, instead of
deficits in impulsivity, is the key factor predicting
the age–crime curve. Studies agree that low
intelligence is predictive of delinquency,14,37,45–49
and that delinquent youth score lower on verbal
intelligence compared to nondelinquent youth.37
It is also clear that intelligence and cognitive
impulsivity are correlated.50 The reason for this,
including the possible interaction between the
two, has been much debated.33 According to an
alternative conceptualization tested in the present
paper, both low intelligence and cognitive impulsivity are implicated in the prediction of offending. Second, we expect that low intelligence and
high impulsivity, compared to high intelligence
and low impulsivity, are associated with an age–
crime curve that has a higher peak.
One of the problems with the lack of independence of measurement of impulsivity in developmental research is that it is often based
on ratings by adults,51 and sometimes by selfratings,28,43 and combinations of different informants.52 We argue that with these informants it is
difficult to separate impulsivity from delinquent
acts and antisocial behaviors (e.g., many parents
know that their children have been arrested for
delinquent acts). Instead, measurement independence would be much better achieved if it were
based on more objective measures of cognitive
functioning, such as psychometric tests of impulsivity that are independent from measures of
offending. This was one of the aims of a study
undertaken by White under the guidance of
Moffitt using the middle sample of the Pittsburgh
Youth Study,50 in which a battery of psychometric
tests was administered in addition to parent,
teacher, and self-reported ratings of the participants’ impulsivity. Measures were found to load
on two different impulsivity factors: cognitive
impulsivity, composed of psychometric test
scores, and behavioral impulsivity, based on
behavioral ratings.
Because of the measurement complications
surrounding impulsivity, we will focus on measures of cognitive impulsivity based on laboratory
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tasks.50 We will examine the degree to which
measures of cognitive impulsivity and intelligence predict the age–crime curve.53
To that end, we will address the following
questions. First, how can the age–arrest curve be
best described in terms of linear, quadratic, or
cubic functions of age? Second, does cognitive
impulsivity predict the age–arrest curve even
when controls are taken into account, and is the
prediction the same for being charged with any
offence, theft, or violence? Third, does intelligence
predict the age–arrest curve for being charged
with any offence, theft, or violence? Fourth, does
cognitive impulsivity independently add to intelligence predicting the age–arrest curve? Finally,
does the effect of cognitive impulsivity on the age–
arrest curve depend on the level of intelligence?
METHOD
Participants
Participants were a subsample of the middle cohort
of the Pittsburgh Youth Study (PYS; details can be found
in the first part of this article),54 whose cognitive
impulsivity and intelligence were assessed in a laboratory setting. No such assessments took place for the
youngest and oldest cohorts. For 422 boys (83.1% of the
total sample of 508 boys), scores were available on the
three tests of cognitive impulsivity and the Wechsler
Intelligence Scale for Children–Revised (WISC-R) used
in this study. These 422 boys did not differ from the total
508 sample on race or screening risk status (all p 4 .05),
but boys without cognitive impulsivity data had lower
mean socio-economic status (SES) scores (F1,506 ¼ 6.77, p
¼ .01).
Procedures
Several cognitive tests including cognitive impulsivity and IQ tests were conducted, when the boys were
on average 12.73 years old (SD ¼ 0.87, range ¼ 10.75–
16.08). It should be noted that the assessments of
cognitive impulsivity and IQ were on average conducted at a slightly older age than the first assessment of
offending at age 11. The 90-minute laboratory session
was conducted by trained examiners who were unaware of the boy’s delinquency status. Tasks were
assessed in two blocks of 45 minutes and in the same
order, because a similar motivational set was required
at the beginning of each task for each boy.
Measures
Cognitive impulsivity (CI) was measured with the
three tests from the test battery with the highest factor
loadings in the CI construct described by White et al.50
These were the Trail Making Test, the Stroop Color and
Word Association Test, and Time Perception.
The Trail Making Test measures the ability to initiate,
switch, and stop a sequence of complex purposive
behavior, requiring attention and concentration skills.
After drawing lines between consecutively numbered
circles (Form A), the participants had to draw lines
between consecutive numbers and letters (Form B),
switching between the two sequences (i.e., A to 1 to B to
2 to C, etc.). Scores used were the time needed for Form
B minus the time needed for Form A.50
The Stroop Color and Word Association Test tests the
ability to inhibit an automatic overlearned response and
generate a competing new response instead,55,56 requiring sustained attention and mental control. In the first
trial, participants had to read color names, followed by
the inhibition trial, in which participants were asked not
to read the name of the color but instead to name the
different color of the ink in which the words were
printed (suppressing reading the color names). The
number of errors in the inhibition trial was used in this
study, because the time needed to finish the card and the
number of errors were highly correlated, and the error
score was more normally distributed.50
Time perception was measured with time estimation
and time production tasks, measuring cognitive
tempo.50 In time estimation, the stopwatch was run
for seven consecutive intervals of 2, 2, 4, 4, 12, 25, and 60
seconds in this study. Participants had to estimate after
each interval how many seconds had passed. In time
production, participants had to indicate when they
thought 2, 2, 4, 4, 12, 25, and 60 seconds had passed.
Time estimation and reflected time production scores (r
¼ .54) were summed.50
The three tests were significantly associated
(r ¼ 0.15–0.27). The scores at each of the three tests
were first standardized, and then summed to obtain a
CI score (with positive scores indicating higher CI, and
negative scores lower CI). This sum score was standardized once more within the sample of 422 boys.
Intelligence (IQ) was measured using a short form of
the Wechsler Intelligence Scale for Children–Revised
(WISC-R).57 Shortened versions of all 10 subtests were
used, administering every other item of each subtest.
Full-scale IQ scores were on average 101.03 (SD ¼
15.49); performance and verbal IQ are only used in
auxiliary analyses. Full scale IQ was negatively correlated with cognitive impulsivity (r ¼ 0.51).
Official Criminal Records from age 11 to 28 were
obtained via local, state and federal sources.58 Three
types of arrest data were used: any charge (i.e., being
charged with any offence, such as murder, rape,
robbery, fraud, theft, drug possession, traffic violation);
theft (e.g., larceny, burglary); and violence (e.g., assault,
robbery, homicide). For each type of criminal charge,
participants with at least one charge at a particular age
received the score 1 and participants without any
charge received the score 0.
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Race, test age, and SES were added as control
variables. Because participants were primarily white
or African-American (95.9%), race was dichotomized
into African-American (score ¼ 1) or other ethnic
backgrounds (score ¼ 0).59 Test age was participants’
age at the time the cognitive tests were conducted
(summer 1990). SES was the mean of the available SES
scores from seven semi-annual assessments based on
Hollingshead’s index (Hollingshead AB. Four factor
index of social status [unpublished manuscript]. New
Haven, CT: 1975).60
Statistical Analyses
Data were analyzed with logistic populationaveraged generalized estimating equation (GEE) models, using STATA version 10.60 GEE models account for
nonindependent observations on dependent variables,
such as repeated measures over time. The association
between the dependent variables over time was modeled using an autoregressive correlation structure. This
model assumes that the association between arrest
outcomes measured at different ages decreases as the
temporal separation between the assessments increases
in a systematic manner.61 Standard errors that are
robust to potential misspecification of dependent variable correlation structure were also used.62
For each criminal arrest outcome (i.e., any charge,
violence, theft), we first modeled developmental
change in the proportion of boys being arrested across
time by entering age, age squared (age2), and age cubed
(age3) as predictors into the model. This was done
because a preliminary examination of the data indicated that the prevalence of arrest increased rapidly
during early adolescence, peaked in middle adolescence, and rapidly declined into the early 20s, with this
decline decelerating across the mid to late 20s. The main
effect of CI was also entered into these models.
Subsequently, the interaction between CI and age,
age2, and age3 was entered into the model. This was
done to determine whether CI predicted the increase,
decrease and/or stabilization over time (i.e., whether
the shape of the curve differed between different levels
of CI). These models were then repeated using IQ in
place of CI to contrast findings across the two measures
of cognitive ability.
A final overarching model was then conducted by
including both CI and IQ, as well as their interactions
with age, into a single model. Finally, interactions
between CI, IQ and age (first two-way CI IQ, then
three-way CI IQ with age) were added to the model
to examine whether different combinations of CI and IQ
differently affected the age–arrest curves. Significant
interaction terms were probed to examine the direction
of the moderation by manipulating the 0 point for one of
the variables (i.e., IQ),63 and then estimating the effects
of CI (two-way), and/or CI Age, CI Age2
and CI Age3 (three-way) in the model at IQhigh
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(IQ ¼ 116.5) and IQlow (IQ ¼ 85.5). Except for the first
age-only step, all models controlled for race, test age,
and SES.
RESULTS
Modeling the Age–Crime Curve
Results of the GEE model with age (without
any covariates) predicting probability of being
arrested are reported in Table 1. Age (positive),
age squared (negative), and age cubed (positive)
were significant for any charge, theft, or violence
charges. As expected, the probability of being
arrested increased, followed by a decrease, which
then stabilized during adulthood.
Effects of Cognitive Functioning on the
Age–Crime Curve
Second, CI was added to the models with age,
age2, and age3 (Table 1), while controlling for race,
SES, and test age. Results showed that CI significantly and positively predicted the probability
of being arrested for any charge and violence, but
not for theft. The interaction between CI and the
three age variables was also significant for total
arrests, and a trend was found for theft. Figure 1a
depicts the estimated total age–arrest curve for
boys with high (þ1 SD) and low (–1 SD) CI scores,
showing that high CI boys had a higher probability not only of being arrested across the whole
age range, but also a stronger increase in adolescence, a decrease in late adolescence/early adulthood, and a small increase again around age 26–28
that was not observed in the low CI group.
Models examining the effect of IQ on criminal
charge outcomes are presented in Table 1. IQ
significantly and negatively predicted the probability of being arrested for any charge, theft, and
violent offences, implying a higher probability for
low IQ boys across time compared to high IQ boys.
However, there was a significant interaction
between IQ and all three age variables in predicting
any criminal charge. To elucidate this finding, the
estimated criminal charge probabilities were
plotted for boys with high (þ1 SD) and low (1
SD) IQ (Figure 1b). Similar to findings with CI, the
arrest probability of the boys with a lower IQ
increased more rapidly in adolescence, followed by
a sharper decrease in late adolescence/early adulthood, as compared with that in boys with a higher
IQ, and boys with a lower IQ, but not those with a
higher IQ, showed a slight increase in their late 20s.
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TABLE 1 Cognitive Impulsivity (CI) and IQ as Predictors of the Age–Arrest Curve (Total, Theft, and Violence) From Age 11 to 28 Years
Total
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Age effects (no covariates)
Age
Age2
Age3
CI
Step 2.1
Age
Age2
Age3
CI
Step 2.2
CI Age
CI Age2
CI Age3
IQ
Step 3.1
Age
Age2
Age3
IQ
Step 3.2
IQ Age
IQ Age2
IQ Age3
CI and IQ
Step 4.1
Age
Age2
Age3
CI
IQ
Step 4.2
CI Age
CI Age2
CI Age3
IQ Age
IQ Age2
IQ Age3
Note: OR ¼ odds ratio.
*p o .05; **p o .01; yp o .10.
Theft
Violence
B
SE
z
OR
B
SE
z
OR
B
SE
z
OR
3.885
0.185
0.0028
0.390
0.020
0.0003
9.96**
9.19**
8.27**
48.668
0.831
1.0028
4.202
0.211
0.0033
0.544
0.029
0.0005
7.72**
7.36**
6.77**
66.807
0.809
1.0033
2.870
0.129
0.0018
0.588
0.031
0.0005
4.88**
4.20**
3.45**
17.633
0.879
1.0018
4.139
0.198
0.0030
0.155
0.408
0.021
0.0004
0.070
10.2**
9.37**
8.45**
2.23*
62.721
0.821
1.0030
1.168
4.298
0.216
0.0034
0.098
0.558
0.029
0.0005
0.080
7.71**
7.36**
6.77**
1.23
73.575
0.805
1.0034
1.103
2.940
0.132
0.0018
0.177
0.596
0.031
0.0005
0.079
4.93**
4.25**
3.50**
2.23*
18.917
0.876
1.0019
1.194
0.817
0.043
0.0007
0.367
0.019
0.0003
2.23*
2.28*
2.32*
2.263
0.958
1.0007
0.813
0.043
0.0007
0.439
0.023
0.0004
1.85y
1.89y
1.93y
2.254
0.958
1.0007
0.217
0.010
0.0001
0.499
0.027
0.0005
0.43
0.37
0.29
4.160
0.199
0.0030
0.375
0.410
0.021
0.0004
0.081
10.1**
9.36**
8.45**
4.63**
64.101
0.820
1.0030
0.687
4.315
0.217
0.0034
0.406
0.559
0.029
0.0005
0.105
7.72**
7.37**
6.78**
3.85**
74.786
0.805
1.0034
0.666
2.941
0.132
0.0018
-0.420
0.598
0.031
0.0005
0.097
4.91**
4.23**
3.49**
4.35**
1.113
0.055
0.0009
0.422
0.022
0.0004
2.64**
2.47*
2.29*
0.329
1.056
0.9991
0.388
0.016
0.0002
0.588
0.032
0.0006
0.66
0.50
0.40
0.679
1.016
0.9998
0.538
0.022
0.0003
0.601
0.032
0.0005
0.89
0.70
0.52
4.165
0.199
0.0030
0.060
0.346
0.411
0.021
0.0004
0.065
0.088
10.1**
9.37**
8.45**
0.93
3.91**
64.406
0.820
1.0030
1.062
0.708
4.314
0.217
0.0034
0.007
0.410
0.559
0.030
0.0005
0.078
0.114
7.71**
7.36**
6.78**
0.09
3.60**
74.767
0.805
1.0034
0.993
0.664
2.946
0.133
0.0019
0.092
0.374
0.598
0.031
0.0005
0.075
0.105
4.92**
4.24**
3.49**
1.22
3.56**
0.423
0.025
0.0005
0.881
0.041
0.0006
0.408
0.021
0.0004
0.467
0.025
0.0004
1.04
1.18
1.31
1.89y
1.67y
1.46
1.526
0.976
1.0005
0.414
1.042
0.9994
0.932
0.053
0.0009
0.157
0.015
0.0003
0.537
0.028
0.0005
0.663
0.035
0.0006
1.74y
1.87y
1.99*
0.24
0.42
0.54
2.541
0.949
1.0009
1.170
0.985
1.0003
-0.533
0.023
0.0003
0.840
0.035
0.0005
0.581
0.031
0.0005
0.647
0.034
0.0006
0.92
0.75
0.59
1.30
1.04
0.79
0.805
1.010
0.9999
18.934
0.876
1.0019
0.657
0.584
1.023
0.9997
19.038
0.876
1.0019
1.096
0.688
0.587
1.024
0.9997
0.432
1.036
0.9995
FINDINGS FROM THE PITTSBURGH YOUTH STUDY
FIGURE 1 (a) Estimated age–arrest curve (probability of
being arrested between age 11 to 28) for any charge for
high cognitive impulsivity (CI) (mean þ SD) vs. low CI (mean
– SD), controlled for race, socio-economic status, and test
age. (b) Estimated age–arrest curve for any charge for high
IQ (mean þ SD) vs. low IQ (mean – SD).
a
0.70
Probability arrested (total)
0.60
0.50
0.40
0.30
0.20
0.10
0.00
11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
-0.10
Age
CIhigh (M+SD)
Probability arrested (total)
b
CIlow (M-SD)
charge, and a trend for violence (Table 2). Subsequently, the significant three-way interaction
terms were probed by estimating the effects of
CI Age, CI Age2, and CI Age3 in the IQhigh
model (E 116.5), and IQlow model (E 85.5).
For any charge, results showed that the three
CI Age interactions were significant in the IQhigh
model, but not in the IQlow model. Closer examination of the three CI Age interactions indicated
that these terms reached significance for those boys
whose IQs were above 97. Thus, there was a
stronger increase, peak, and decrease in the age–
arrest curve for boys with higher CI scores as
compared to lower CI scores, but only for those
with IQ scores higher than 97. Figure 2 depicts the
influence of CI on the age–crime curve for boys
with low IQ and high IQ boys. For violence, the
CI Age interactions did not reach significance
with both IQhigh and IQlow in the model.
0.70
DISCUSSION
0.60
Consistent with previous studies on the age–
crime curve, the proportion of boys in the overall
sample who had been charged with a crime
rapidly increased from early to middle adolescence, then precipitously decreased during the
transition from middle adolescence to early adulthood, with this decline beginning to asymptote
toward the late 20s.4 Both IQ and cognitive
impulsivity in early adolescence predicted individual variability within the overall shape of the
age–crime curve, particularly when these neuropsychological factors were examined in separate models. Specifically, both low IQ and high
cognitive impulsivity were associated with a more
rapid acceleration in criminal behavior across
middle adolescence, as well as a more rapid
decline in delinquency into late adolescence/
early adulthood. Even after controlling for cooccurring cognitive impulsivity, low IQ was
associated with an increased probability of being
charged with a crime from adolescence through
early adulthood. In contrast, cognitive impulsivity only affected the shape of the age–crime curve
for boys with relatively high levels of intelligence.
These findings could not be accounted for by the
potential confounds of race and socio-economic
status.
The association between cognitive impulsivity
and criminal behavior from adolescence into early
adulthood in the study was complex, as it was
moderated by both age and IQ. For boys with high
0.50
0.40
0.30
0.20
0.10
0.00
11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
Age
IQlow (M-SD)
IQhigh (M+SD)
When CI and IQ were jointly entered into a
single model, only IQ remained a significant
predictor of all offense types. Regarding the
three-way interactions with age, a trend was found
for CI and the age variables as predictors of theft
(note that Age3 CI significantly predicted theft in
Step 4.2 in Table 1), as well as for IQ and age and IQ
and age2 as predictors of any charge.
Interaction Effects Between CI, IQ, and Age
Fifth, interaction terms between CI and IQ were
calculated and added to the models, together with
the standardized main effects of CI and IQ, and
controls (Table 2). The interaction terms between
CI and IQ did not reach significance for any of the
outcomes (any charge, theft, or violence). However, the three-way interactions between CI, IQ
and age/age2/age3 were significant for any
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1145
TABLE 2
Interaction Effects Between Cognitive Impulsivity (CI), IQ, and Age
Any Charge
B
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CI IQ
CI
IQ
CI IQ
CI IQ Age
Age
Age2
Age3
CI
IQ
CI IQ
CI Age
CI Age2
CI Age3
IQ Age
IQ Age2
IQ Age3
CI IQ Age
CI IQ Age2
CI IQ Age3
SE
z
0.139
0.336
0.106
0.080
0.091
0.066
1.75
3.70
1.61
4.139
0.199
0.0030
6.479
5.123
5.957
1.153
0.064
0.0011
0.803
0.037
0.0005
1.022
0.054
0.0009
0.446
0.023
0.0004
3.453
2.854
2.752
0.556
0.029
0.0005
0.466
0.024
0.0004
0.449
0.023
0.0004
9.27**
8.60**
7.80**
1.88y
1.79y
2.16*
2.07*
2.21*
2.32*
1.72y
1.52
1.30
2.27*
2.31*
2.30*
Note: OR ¼ odds ratio.
*p o .05; **p o .01; yp o .10.
Theft
OR
1.149
0.715
1.112
62.73
0.820
1.003
0.002
167.9
3.168
0.938
1.001
0.448
1.038
0.999
0.003
2.778
0.947
1.001
B
SE
Violence
z
0.085
0.404
0.110
0.112
0.121
0.101
0.76
3.35
1.09
4.437
0.225
0.0036
8.394
0.588
3.765
1.475
0.080
0.0014
0.191
0.017
0.0004
0.654
0.034
0.0005
0.671
0.035
0.0006
4.542
4.114
3.497
0.739
0.039
0.0007
0.675
0.036
0.0006
0.570
0.030
0.0005
6.62**
6.37**
5.91**
1.85y
0.14
1.08
2.00*
2.07*
2.09*
0.28
0.48
0.61
1.15
1.14
1.07
OR
1.089
0.668
1.116
84.51
0.798
1.004
0.000
0.555
4.370
0.923
1.001
1.210
0.983
1.000
0.023
1.924
0.967
1.001
B
SE
z
0.144
0.373
0.062
0.093
0.107
0.076
1.54
3.50
0.81
3.062
0.141
0.0020
0.571
5.687
5.195
0.266
0.021
0.0005
0.828
0.035
0.0004
0.946
0.052
0.0009
0.635
0.033
0.0006
4.043
4.115
2.967
0.671
0.036
0.0006
0.661
0.035
0.0006
0.494
0.027
0.0005
4.82**
4.26**
3.62**
0.14
1.38
1.75
0.40
0.58
0.73
1.25
1.01
0.75
1.91y
1.97*
1.96y
OR
1.155
0.688
1.064
21.37
0.868
1.002
0.565
295.1
1.304
0.979
1.000
0.437
1.035
1.000
0.006
2.575
0.949
1.001
FINDINGS FROM THE PITTSBURGH YOUTH STUDY
FIGURE 2 (a) Estimated probability of arrest curve (any
charge) for high (mean þ SD) vs. low (mean – SD) cognitive
impulsivity (CI) in boys with a low IQ (mean – SD) as a
function of age. (b) Estimated age–arrest curve (any
charge) for high (mean þ SD) vs. low (mean – SD) CI in
boys with a high IQ (mean þ SD).
Probability arrested (total)
a
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00
11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
Age
CIhigh
Probability arrested (total)
b
CIlow
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00
11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
Age
CIhigh
CIlow
IQ, cognitive impulsivity was associated with a
greater escalation in the prevalence of offending
during early adolescence, followed by a more
rapid decline in offending as boys entered early
adulthood. Following this decline, higher cognitive impulsivity was associated with a slight
increase in criminal offending during the late
20s, but only for boys with a high IQ. In fact, there
was no evidence that cognitive impulsivity independently influenced criminal offending at any
developmental period for boys with low IQ.
However, the age–arrest curve is substantially
higher in low IQ boys, regardless of the level of CI.
Although previous studies have reported that
poor performance on cognitive impulsivity tasks
is associated with later increases in conduct
problems in youth even after controlling for
IQ,34,64,65 none of these studies tested the interaction between these two neurocognitive factors
JOURNAL OF THE AMERICAN ACADEMY OF CHILD & ADOLESCENT PSYCHIATRY
VOLUME 51 NUMBER 11 NOVEMBER 2012
in predicting future criminal behavior. Consistent
with the current findings, some cross-sectional
evidence does suggest that self-regulation abilities are associated with increased aggressive
behaviors for boys with high verbal IQ, but not
for those with low IQ.66 Although some studies
have found that having a high IQ may protect
boys with multiple risk factors from becoming
involved in crime,67 this may be strongest for boys
with low levels of cognitive impulsivity. The
findings suggest that there may be a subset of
cognitively impulsive boys who have a circumscribed and subtle neurobiological vulnerability
for engaging in criminal behavior that is not
adequately captured by traditional intelligence
tests. In contrast, the criminal offending of boys
with low IQ was not significantly influenced by
individual differences in cognitive impulsivity.
The overall findings indicate that low intellectual functioning and cognitive impulsivity have
the greatest negative impact on criminal behavior
during middle adolescence, when the overall base
rate of offending is relatively high. During the
transition from middle adolescence to early adulthood, the proportion of boys who continued to be
charged with a crime in any given year declines
dramatically, with this decline being somewhat
more pronounced in boys with low intelligence
(regardless of their cognitive impulsivity level),
and in boys with high IQ and high cognitive
impulsivity. As a result, by the late 20s there is
little difference in the prevalence of offending
between boys with high versus low intellectual
and cognitive problems in early adolescence.
Several other studies have found that poor neurocognitive test performance is associated with
the early onset and escalation of criminal behavior
over time, but does not explain why a relatively
small portion of adolescents continue offending
into adulthood.68,8,69 It is possible that neurodevelopmental maturation may contribute to the
lack of a strong link between neurocognitive test
performance and persistent criminal behavior. It
is well documented that dramatic developmental
changes in brain structure and function occur
during adolescence, including significant increases
in axon myelenation, decreases in cortical and
subcorticol gray matter, and functional changes in
the prefrontal neural networks subserving cognitive control abilities.70 It is also possible that a
significant proportion of boys who were cognitively
impulsive in early adolescence experienced significant maturational changes in brain morphology
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LOEBER et al.
and function over time, which served to improve
their cognitive control abilities and decreased their
criminal behavior.
The current findings regarding the association
between neurocognitive abilities and trajectories
of different types of offending (i.e., theft, violence)
contrasts somewhat with a previously published
longitudinal study in the area.39 Although both
studies found that measures of higher cognitive
impulsivity and lower IQ were related to higher
levels of violence over time, the findings for theft
were in opposing directions. Specifically, Barker
et al.39 found that better performance on IQ and
cognitive control tasks was associated with an
increased risk of engaging in theft from adolescence to adulthood. This discrepancy may have
resulted from several methodological differences
between the two samples. Specifically, the prior
study consisted of assessments that occurred
every 3 years over with a more limited age range
(i.e., 12–24 years), and assessed cognitive abilities
in late adolescence and early adulthood (i.e.,
18–24 years). This study also reported on the
association between neurocognitive test performance and theft after controlling for co-occurring
violent behavior.39 Finally, self-report measures
were used to assess violence and theft, rather than
official record data. It is possible that males with
lower cognitive abilities are less cunning when
stealing from others, increasing the likelihood that
their thefts will be detected by the police, even
though they are less likely to report stealing.
However, other research has found that lower IQ
is associated with increased levels of self-reported
stealing in adolescents,71 and lower IQ has not
been associated with an increased risk for being
arrested in delinquent adolescents.72
The study had several limitations. First, it
focused on an enriched sample of urban males
and it remains unclear whether the findings will
apply to all males or will translate to females. In
addition, the assessment of neuropsychological
functioning took place at a single time point in
early adolescence, preventing us from investigating the influence of cognitive factors on later
criminal behavior changes across development. It
also made it impossible to determine whether
significant changes in cognitive abilities occur
across time for some adolescents and parallel
fluctuations in criminal behavior. Similarly,
cognitive impulsivity was measured using a
limited number of executive function tasks that
are considered indirect and relatively crude
indicators of neural functioning. The subtle neurological deficits in cognitive control that underlie
persistent criminal behavior may not be adequately assessed using these behavioral methods,
and more sophisticated neuroimaging techniques
design to explicitly asses the functional integrity
of the neural networks subserving cognitive
control may demonstrate greater utility in predicting future criminal behavior. Similarly, there is
an ongoing debate about whether cognitive
impulsivity is best conceptualized as a unidimensional construct as was done in the current study,
or a set of distinct, yet related, cognitive processes
that have unique developmental outcomes.73 It is
possible that certain forms of cognitive impulsivity may be more strongly related to violent
behavior than theft. For example, youth who have
particular problems with cognitive impulsivity
when affectively aroused may be particularly
prone to engage in violent behaviors. Finally,
the current study did not examine the potential
environmental factors that may lead to poorer
neuropsychological functioning in boys, such as a
lack of adults teaching children and adolescents to
control their impulses. A further examination of
these factors could be used to develop preventative interventions for boys exhibiting early features of cognitively impulsivity.
Although the overall elevated arrest rate of
boys who had high cognitive impulsivity or low
IQ is consistent with prior literature, we did not
expect that these boys would show more rapid
desistence in our police arrest data during their
late teens and early 20s. Prior theory has implicated neurodevelopmental difficulties such as
low intelligence and poor impulse control in the
persistence of crime beyond the adolescent period.27,30 However, as young adulthood is the peak
age for incarceration, individuals who offend at a
high rate as adolescents are particularly likely to
become incarcerated, and incarceration precludes
further arrest for a period of time. As such, it has
been observed that research into the age–crime
curve is enhanced if it is possible to compare
periods of ‘‘street time’’ versus ‘‘jail time’’ in timevarying models.74 One possibility that we were
unable to rule out is that some males in the
Pittsburgh Study cohort who scored low on IQ
and high on cognitive impulsivity spent more
time behind bars during our period of arrest
observation. If so, this could have generated the
appearance that they had desisted from crime, as
indicated by arrest records. Unfortunately, we did
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FINDINGS FROM THE PITTSBURGH YOUTH STUDY
not have information on the length of incarceration for participants in the study, so we were
unable to rule out this possibility. &
Accepted August 23, 2012.
Drs. Loeber, Stouthamer-Loeber, and Pardini, and Ms. Stallings are with
the University of Pittsburgh. Ms. Menting is with Vrije University,
Amsterdam. Dr. Lynam is with Purdue University. Dr. Moffitt is with
Duke University. Dr. Farrington is with the University of Cambridge.
This article is part of a special series on recent findings and progress in
the fields of birth cohort and longitudinal studies of child and adolescent
psychopathology. This special series is intended to showcase some of
the most important new findings and promising leads in the study of
developmental psychopathology over time, and to demonstrate the
Journal’s renewed commitment to publishing the highest quality articles
on the topic. Each article is in part a review of the most important
findings to date from the study and in part original research to allow
readers to learn about a new research finding with a more complete
context of the study than is usually possible.
Research for this paper was supported by grants from the Office of
Juvenile Justice and Delinquency Prevention, the National Institute of
Justice, and the Commonwealth of Pennsylvania.
Disclosure: Drs. Loeber, Lynam, Moffitt, Southamer-Loeber, Farrington,
and Pardini, and Ms. Menting and Ms. Stallings report no biomedical
financial interests or potential conflicts of interest.
This article will be discussed in an editorial by Drs. James J. Hudziak and
Douglas K. Novins in an upcoming issue.
Correspondence to Rolf Loeber, Ph.D., 201 N. Craig Street,
Sterling Plaza, Suite 408, Pittsburgh, PA 15213; e-mail:
loeberr@upmc.edu
0890-8567/$36.00/C 2012 American Academy of Child and
Adolescent Psychiatry
http://dx.doi.org/10.1016/j.jaac.2012.08.019
REFERENCES
1. Boots DP. Mental Health and Violent Youth: a Developmental/
Lifecourse Perspective. New York, NY: LFB Scholarly Publishing;
2008.
2. Loeber R, Farrington DP. Young Homicide Offenders and Victims:
Development, Risk Factors and Prediction from Childhood. New
York, NY: Springer; 2011.
3. Loeber R, Farrington DP, Stouthamer-Loeber M, Van Kammen WB.
Antisocial Behavior and Mental Health Problems: Explanatory
Factors in Childhood and Adolescence. Mahwah, NJ: Lawrence
Erlbaum; 1998b.
4. Loeber R, Farrington DP, Stouthamer-Loeber M, White HR.
Violence and Serious Theft: Development and Prediction from
Childhood to Adulthood. New York, NY: Routledge; 2008.
5. Stouthamer MVK, WB. Data Collection and Management: a
Practical Guide.. Newbury Park, CA: Sage; 1995.
6. Loeber R, Stouthamer-Loeber M, Van Kammen W, Farrington DP.
Initiation. Crim Law Criminol. 1991;82:36-82.
7. Loeber R, DeLamatre M, Keenan K, Zhang QA. Prospective
Replication of Developmental Pathways in Disruptive and Delinquent Behavior. In: Cairns R LB, Kagan J, eds. Methods and Models
for Studying the Individual. Thousand Oaks, CA: Sage; 1998a:
185-215.
8. Loeber R, Pardini D, Stouthamer-Loeber M, Raine A. Do cognitive,
physiological, and pschosocial risk and promotive factors predict
desistance from delinquency in males? Dev Psychopathol. 2007;
19:867-887.
9. Loeber R, Wei E, Stouthamer-Loeber M, Huizanga D, Thornberry
TP. Behavioral antecedents to serious and violent offending: joint
analyses from the Denver Youth Survey, Pittsburgh Youth Study
and the Rochester Youth Developmental Study. Stud Crime Crime
Prev. 1999;8:245-263.
10. Loeber R, Wung P, Keenan K, et al. Developmental pathways in
disruptive child behavior. Dev Psychopathol. 1993;5:103-133.
11. Loeber R, Keenan K, Zhang Q. Boys’ experimentation and
persistence in developmental pathways toward serious delinquency. J Child Fam Stud. 1997;6:321-357.
12. Tolan PH, Gorman-Smith D, Loeber R. Developmental timing of
onstets of disruptive behaviors and later delinquency of inner-city
youth. J Child Fam Stud. 2000;9:203-220.
13. Loeber R, Hay D. Key issues in the development of aggression and
violence from childhood to early adulthood. Annu Rev Psychol.
1997;48:371-410.
14. Loeber R, Farrington DP. Child Delinquents: Development, Intervention, and Service Needs. Thousand Oaks, CA: Sage; 2001.
15. Loeber R, Pardini D, Homish DL, et al. The prediction of violence
and homicide in young men. J Consult Clin Psychol. 2005;
73:1074-1088.
16. Farrington DP. Predictors, causes, and correlates of male youth
violence. Crime Justice. 1998;24:421-475.
JOURNAL OF THE AMERICAN ACADEMY OF CHILD & ADOLESCENT PSYCHIATRY
VOLUME 51 NUMBER 11 NOVEMBER 2012
17. Farrington DP, Loeber R. Transatlantic replicability of risk factors
in the development of delinquency. In: Cohen P CS, Robins N, eds.
Historical and Geographical Influences on Psychopathology.
Mahwah, NJ: Lawrence Erlbaum Associates; 1999:270-299.
18. Cook P, Laub J. The unprecedented epidemic in youth violence. In:
Tonry M, ed. Crime and Justice: an Annual Review of Research.
Chicago, IL: University of Chicago Press; 1998: p. 26-64.
19. Mercy JA, Rosenberg ML, Powell KE, Broome CV, Roper WL.
Public health policy for preventing violence. Health Affairs. 1993;
12:7-29.
20. Snyder H, Sickmund M. Juvenile Offenders and Victims: a Focus
on Violence. Pittsburgh, PA: National Center for Juvenile Justice;
1995.
21. Farrington DP, Loeber R, Stouthamer-Loeber M. How can the
relationship between race and violence be explained? In: Hawkins
DF, ed. Violent Crimes: Assessing Race and Ethnic Differences.
New York, NY: Cambridge University Press; 2003: p. 213-237.
22. Kelly K, Totten M. When Children Kill: a Social Psychological
Study of Youth Homicide. Peterborough, ON, Canada: Broadview
Press; 2002.
23. Hagelstam C, Häkkänen H. Adolescent homicides in Finland:
offence and offender characteristics. Forensic Sci Int. 2006;
164:110-115.
24. Benedek E, Cornell D. Juvenile Homicide. Washington, DC:
American Psychiatric Association; 1989.
25. Berg MT, Slocum LA, Loeber R. Illegal behavior, neighborhood
context, and police reporting by victims of violence [published
online November 11, 2011]. J Res Crime Delinq. 2011; doi:10.1177/
0022427811423107.
26. Farrington DP. Age and crime. In: Tonry M, Morris N, eds. Crime
and Justice: An Annual Review of Research, Vol 7. Chicago, IL:
University of Chicago Press; 1986: p. 189-250.
27. Farrington DP. Integrated Developmental and Life-Course Theories of Offending. New Brunswick, NJ: Transaction; 2005.
28. Kazemian L, Farrington DP, Le Blanc M. Can we make accurate
long-term predictions about patterns of de-escalation in offending
behavior? J Youth Adolesc. 2009;38:384-400.
29. Stouthamer-Loeber M, Wei E, Loeber R, Masten AS. Desistance
from persistent serious delinquency in the transition to adulthood.
Dev Psychopathol. 2004;16:897-918.
30. Moffitt TE. Adolescence-limited and life-course-persistant antisocial
behavior: a developmental taxonomy. Psychol Rev. 1993;100:674-701.
31. Van Domburgh L, Loeber R, Bezemer D, Stallings R, StouthamerLoeber M. Childhood predictors of desistance and level of
persistence in offending in early onset offenders. J Abnorm Child
Psychol. 2009;37:967-980.
32. Monahan KC, Steinberg L, Cauffman E, Mulvey EP. Trajectories of
antisoical behavior and psychosocial maturity from adolescence to
young adulthood. Dev Psychol. 2009;45:1654-1668.
www.jaacap.org
1148
LOEBER et al.
33. Jolliffe D, Farrington DP. A systematic review of the relationship
between childhood impulsiveness and later violence. In:
McMurran M RH, ed. Personality, Personality Disorder, and
Violence. New York, NY: Wiley; 2009: p. 41-61.
34. Morgan AB, Lilienfeld SO. A meta-analytic review of the relation
between antisocial behavior and neuropsychological measures of
executive function. Clin Psychol Rev. 2000;20:113-136.
35. Long JD, Loeber R, Farrington DP. Marginal and random intercept
models for longitudinal binary data with examples from criminology. Multivar Behav Res. 2009;44:28-58.
36. Moffitt TE. Self-control, then and now. In: Loeber R BW, ed. The
Future of Criminology. New York, NY: Oxford University Press;
2012: p. 40-45.
37. Jolliffe D, Farrington DP. Individual differences and offending. In:
McLaughlin E TN, ed. The Sage Handbook of Criminological
Theory. London: Sage; 2010: p. 40-55.
38. Loeber R, Pardini D. Neurobiology and the development of
violence: common assumptions and controversies. Philos Trans
R Soc Lond B Biol Sci. 2008;363:2491-2503.
39. Barker ED, Seguin JR, White HR, et al. Developmental trajectories
of male physical violence and theft: relations to neurocognitive
performance. Arch Gen Psychiatry. 2007;64:592-599.
40. Barker ED, Tremblay RE, Van Lier PAC, et al. The neurocognition of
conduct disorder behaviors: specificity to physical aggression and
theft after controlling for ADHD symptoms. Aggress Behav. 2011;
37:63-72.
41. Steinberg L, Cauffman E, Woolard J, Graham S, Banich M. Are
adolescents less mature than adults? Minors’ access to abortion,
the juvenile death penalty, and the alleged APA ‘‘flip-flop’’. Am
Psychol. 2009;64:583-594.
42. Steinberg L. A dual systems model of adolescent risk-taking. Dev
Psychobiol. 2010;52:216-224.
43. Winfree TL, Taylor TJ, He N, Esbensen F-A. Self-control and
variability over time: multivariate results using a 5-year multi-site
panel of youth. Crime Delinq. 2006;52:253-286.
44. Nigg JT, Huang-Pollock CL. An early-onset model of the role
of executive functions and intelligence in conduct disorder/
delinquency. In: Lahey BB, Moffitt TE, Caspi A, eds. Causes of
Conduct Disorder and Juvenile Delinquency. New York, NY:
Guilford Press; 2003:227-253.
45. Moffitt TE, Caspi A, Silva PA, Stouthamer-Loeber M. Individual
differences in personality and intelligence are linked to crime:
cross-context evidence from nations, neighborhoods, genders,
races, and age-cohorts. In: Hagan J, ed. Current Perspectives on
Aging and the Life Cycle (Vol. 4): Delinquency and Disrepute in
the Life Course. Greenwich, CT: JAI Press; 1995:1-34.
46. Jolliffe D, Farrington DP. Individual differences and offending. In:
McLaughlin E, Newburn T, eds. The Sage Handbook of Criminological Theory. London: Sage; 2010: p. 40-55.
47. Koolhof R, Loeber R, Wei EH, Pardini D, D’Escury AC. Inhibition
deficits of serious delinquent boys of low intelligence. Crim Behav
Ment Health. 2007;17:274-292.
48. Wilson JQ, Herrnstein RJ. Crime and Human Nature. New York,
NY: Simon and Schuster; 1985.
49. Lynam D, Moffitt TE, Stouthamer-Loeber M. Explaining the
relation between IQ and delinquency: class, race, test motivation,
school failure, or self-control? J Abnorm Psychol. 1993;102:187-196.
50. White JL, Moffitt TE, Caspi A, Bartusch DJ. Needles DJ,
Stouthamer-Loeber M. Measuring impulsivity and examining
its relationship to delinquency. J Abnorm Psychol. 1994;
103:192-205.
51. Vazsonyi AT, Huang L. Where self-control comes from: on the
development of self-control and its relationship to deviance over
time. Dev Psychol. 2010;46:245-257.
52. Gardner TW, Dishion TJ, Connell AM. Adolescent self-regulation
as resilience: resistance to antisocial behavior within the deviant
peer context. J Abnorm Child Psychol. 2008;36:273-284.
53. Maslowsky J, Keating DP, Monk CS, Schulenberg J. Planned versus
unplanned risks: neurocognitive predictors of subtypes of adolescents’ risk behavior. Int J Behav Dev. 2011;35:152-160.
54. Loeber R, Farrington DP, Stouthamer-Loeber M, Van Kammen WB.
Antisocial Behavior and Mental Health Problems: Explanatory
Factors in Childhood and Adolescence. Mahwah, NJ: Lawrence
Earlbaum Associates; 1998.
55. Stroop JP. Studies of interference in serial verbal reactions. J Exp
Psychol. 1935;18:643-662.
56. Dodrill CB. A neuropsychological battery for epilepsy. Epilepsia.
1978;19:611-623.
57. Wechsler D. Manual of the Wechsler Intelligence Scale for
Children—Revised. New York, NY: Psychological Corporation;
1974.
58. Loeber R, Pardini D, Homish DL, et al. The prediction of violence
and homicide in young men. J Consult Clin Psychol. 2005;
73:1074-1088.
59. Pardini DA, Fite PJ, Burke JD. Bidirectional associations between
parenting practices and conduct problems in boys from childhood
to adolescence: the moderating effect of age and African-American
ethnicity. J Abnorm Child Psychol. 2008;36:647-662.
60. StataCorp. Intercooled stata 10.0 for Windows. College Station, TX:
Stata Corporation; 2007.
61. Shults J, Sun W, Tu X, et al. A comparison of several approaches for
choosing between working correlation structures in generalized
estimating equation analysis of longitudinal data. Stat Med. 2009;
28:2338-2355.
62. Zeger SL, Liang K-Y, Albert PS. Models for longitudinal data: a
generalized estimating equation approach. Biometrics. 1988;
44:1049-1060.
63. Holmbeck GN. Post-hoc probing of significant moderational and
mediational effects in studies of pediatric populations. J Pediatr
Psychol. 2002;27:87-96.
64. Séguin JR, Nagin D, Assaad JM, Tremblay RE. Cognitiveneuropsychological function in chronic physical aggression and
hyperactivity. J Abnorm Psychol. 2004;113:603-613.
65. Nigg JT, Quamma JP, Greenberg MT, Kusche CA. A two-year
longitudinal study of neuropsychological and cognitive performance in relation to behavioral problems and competencies in
elementary school children. J Abnorm Child Psychol. 1999;27:51-63.
66. Ayduk O, Rodriguez ML, Mischel W, Shoda Y, Wright J. Verbal
intelligence and self-regulatory competencies: joint predictors of
boys’ aggression. J Res Personal. 2007;41:374-388.
67. White JL, Moffitt TE, Silva PA. A prospective replication of the
protective effects of IQ in subjects at high risk for juvenile
delinquency. J Consult Clin Psychol. 1989;57:719-724.
68. Moffitt TE, Caspi A, Harrington H, Milne BJ. Males on the lifecourse-persistent and adolescence-limited antisocial pathways:
follow-up at age 26 years. Dev Psychopathol. 2002;14:179-207.
69. Raine A, Moffitt TE, Caspi A, Loeber R, Stouthamer-Loeber M,
Lynam D. Neurocognitive impairments in boys on the life-course
persistent antisocial path. J Abnorm Psychol. 2005;114:38-49.
70. Luna B, Garver KE, Urban TA, Lazar NA, Sweeney JA. Maturation
of cognitive processes from late childhood to adulthood. Child
Dev. 2004;75:1357-1372.
71. Austin RL. Intelligence and adolescent theft: a multiple indicator
solution. Crim Justice Behav. 1978;5:211-226.
72. Moffitt TE, Silva PA. IQ and delinquency: a direct test of the
differential detection hypothesis. J Abnorm Psychol. 1988;
97:330-333.
73. Giancola PR. Executive functioning: a conceptual framework for
alcohol-related aggression. Exp Clin Psychopharmacology. 2000;
8:576-597.
74. Piquero A. Taking stock of developmental trajectories of criminal
activity over the life course. In: Liberman A, ed. The Long View of
Crime: a Synthesis of Longitudinal Research. New York, NY:
Springer; 2008.
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