criminal justice and behavior-2013-barnes-519-40.pdf

8/16/2019 Criminal Justice and Behavior-2013-Barnes-519-40.pdf

1/22

58907 CJBXXX10.1177/0093854812458907CRIMIDBEHAVIORBarnes/ ORIGINS OF LIFE COURSE–PERSISTENTOFFENDING

ANALYZING THE ORIGINS OF

LIFE-COURSE-PERSISTENT OFFENDING

A Consideration of

Environmental and Genetic Influences

J. C. BARNESUniversity of Texas at Dallas

Moffitt’s developmental taxonomy has sparked much attention among criminologists interested in explaining the etiology oflife-course-persistent (LCP) offending. The taxonomy suggests that genetic factors influence LCP offending, that genetic risk

factors will be mediated by neuropsychological deficits, and that genetic factors interact with environmental factors to influ-

ence LCP offending. Various behavior genetic methodologies were used to estimate the genetic influence on LCP offending,

to determine whether these genetic factors were mediated by the presence of neuropsychological deficits, and to control for

genetic factors while simultaneously estimating the impact of numerous environmental influences. The findings suggested

that genetic factors influence persistent offending and that these influences are partially mediated by levels of self-control.

No parental influences predicted persistent offending after controlling for genetic effects, no Gene × Environment interac-

tions were found, and few environmental influences operated as a nonshared environmental predictor of persistent offending.

Keywords: Moffitt taxonomy; genes; Gene Environment interaction; nonshared environment; biosocial criminology

Criminologists have long noted that the best predictor of future criminality is pastcriminality (Nagin & Paternoster, 2000; Robins, 1978). This finding has sparkedmyriad lines of research into the developmental origins of offending behaviors, the life

course trajectories of offending behaviors, and the correlates of long-term offending

(DeLisi & Piquero, 2011). One of the most consistent findings to emerge from this research

is that a small portion of the population is far more likely to be involved in criminal behav-

ior beginning in adolescence and continuing into adulthood (Moffitt, 2006). This group is

commonly referred to as life-course-persistent offenders (LCP; Moffitt, 1993).

LCP offenders (or LCPs) are grossly overrepresented in crime statistics (DeLisi, 2005).

Scholars argue that roughly 10% of the general population are LCP offenders (Moffitt,1993), but their criminal behavior is believed to account for more than 50% of all crimes

AUTHOR’S NOTE: This research uses data from Add Health, a program project directed by Kathleen Mullan

Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North

Carolina at Chapel Hill and funded by Grant P01-HD31921 from the Eunice Kennedy Shriver National Institute

of Child Health and Human Development, with cooperative funding from 23 other federal agencies and founda-

tions. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original

design. Information on how to obtain the Add Health data files is available on the Add Health website: http://www.

cpc.unc.edu/addhealth. No direct support was received from Grant P01-HD31921 for this analysis. Correspondence

concerning this article may be addressed to J. C. Barnes, University of Texas at Dallas, School of Economic, Political and Policy Sciences, 800 West Campbell Rd., Richardson, TX 75080; e-mail: [email protected].

CRIMINAL JUSTICE AND BEHAVIOR, Vol. 40, No. 5, May 2013, 519-540.

DOI: 10.1177/0093854812458907

© 2012 International Association for Correctional and Forensic Psychology

519

by guest on May 30, 2015cjb.sagepub.comDownloaded from

http://cjb.sagepub.com/http://cjb.sagepub.com/http://cjb.sagepub.com/http://cjb.sagepub.com/


2/22

520 CRIMINAL JUSTICE AND BEHAVIOR

(Piquero, 2011). Not only are LCP offenders more involved in crime than other members

of the population, but they also tend to commit interpersonal violent crimes at a higher rate

(Moffitt, 2006; Moffitt, Caspi, Harrington, & Milne, 2002). When one ties all of this together,

it is easy to see why criminologists have devoted much attention to these offenders (DeLisi &

Piquero, 2011).

Though researchers have explored many aspects of LCP offending, one of the more

pressing questions that has yet to be fully addressed is, “What causes persistent offending?”

A number of theories have been proffered, and the potential explanations include familial

or parental influences (Gottfredson & Hirschi, 1990), progression and state dependence

effects (Loeber, 1996; Sampson & Laub, 1993), and the presence of neuropsychological

deficits (Moffitt, 1990). Although each of the extant theories offers a unique perspective on

the etiology of persistent offending, one of the most prominent explanations comes from

Moffitt’s (1993) developmental taxonomy.

The following sections will provide an overview of Moffitt’s (1993) theory, with par-ticular emphasis on her hypotheses concerning the etiology of LCP offending. Next, an

analysis will be presented using a probability sample from the United States. The analysis

will test different hypotheses laid out in the taxonomy. Finally, the results of the analysis

are discussed in terms of their importance for theoretical advancement.

MOFFITT’S DEVELOPMENTAL TAXONOMY

In 1993, Moffitt set forth a theory of criminality that has drawn attention from scholars

around the world (Bartusch, Lynam, Moffitt, & Silva, 1997; DeLisi & Piquero, 2011). At

the most basic level, Moffitt hypothesized that aggregate crime statistics, such as the age-

crime curve, masked important variation in offending patterns. Her main thesis was that

two types of offenders are identifiable in the population: adolescence-limited (AL) offend-

ers and LCP offenders. The AL category encompasses the vast majority of offenders.

Indeed, Moffitt argued that roughly 90% of the offending population are AL offenders (or

ALs). Although this group is large in number, the group’s offending is age patterned and

consists of minor deviant and delinquent acts. Perhaps most importantly, AL offenders

cease their involvement in criminal activity promptly after entering early adulthood

(around age 20).Research generally supports the existence of an AL offender group (Moffitt, 2006), but

some studies have painted a more complicated picture suggesting that other groups exist

(Eggleston & Laub, 2002; Laub & Sampson, 2003; Skardhamar, 2009). Regardless, the

criminological literature has produced mounds of studies finding that for most individuals,

youthful delinquency peaks in adolescence and returns to preadolescent levels quickly dur-

ing early adulthood (Wright, Tibbetts, & Daigle, 2008). Also, AL offenders are less likely

to be involved in violent and aggressive crime (Piquero & Brezina, 2001) and appear to be

influenced by a combination of social and biological influences referred to as the maturity

gap (Barnes & Beaver, 2010).

Although AL offenders represent an important and large piece of the criminal careers

puzzle (DeLisi & Piquero, 2011), the vast majority of research into Moffitt’s (1993) theory

has centered on the LCP offender group (Moffitt, 2006). LCP offending is said to originate

early in the life course, within the first few years of life (Farrington, 1998). During early


http://cjb.sagepub.com/http://cjb.sagepub.com/http://cjb.sagepub.com/


3/22

Barnes / ORIGINS OF LIFE COURSE–PERSISTENT OFFENDING 521

childhood, LCP offenders will develop difficult temperaments (Moffitt, 1990), will display

aggressive and violent behavior (Tremblay et al., 1999), and will display a limited ability

to regulate their attention and behavior (Gottfredson & Hirschi, 1990; Moffitt & Caspi,

2001). In early adolescence, these individuals will begin their foray into deviant and

criminal activity. LCPs will initiate themselves into drug use, sexual activity, and interper-

sonal crime at a much earlier age than their AL counterparts (Moffitt et al., 2002; Odgers

et al., 2007). During late adolescence and early adulthood, around the time AL offenders

are desisting from crime, LCPs will maintain their frequent involvement in criminal activ-

ity and may progress to more serious criminal activity (Loeber, 1996). Although conflicting

interpretations of the theory abound (Laub & Sampson, 2003), Moffitt (1993) argued that

LCP offenders are unlikely to completely cease their involvement in antisocial behavior,

although their crime involvement may wane with old age (Moffitt, 2006). LCP offenders

have drawn much attention from criminologists and with good reason, given their height-

ened frequency of offending and their overinvolvement in serious and violent activity(Raine, Brennan, & Mednick, 1994).

ETIOLOGY OF LCP OFFENDING

The etiology of LCP offending has roots in two factors: genetic-biological influences

and early-rearing environment influences (Moffitt, 1993). Stated succinctly, LCP offenders

are born with neuropsychological deficits that increase the risk that antisocial behavior will

develop (see, generally, Hirschi & Hindelang, 1977; Ogilvie, Stewart, Chan, & Shum,

2011; Utendale, Hubert, Saint-Pierre, & Hastings, 2011). At the same time, LCPs are born

into adverse rearing environments that are unable to respond to their behavior in a prosocialmanner (Moffitt & Caspi, 2001). The confluence of these two factors (i.e., neuropsycho-

logical deficits and adverse home environment) works to increase the chances that an LCP

offending pattern will develop beginning in early childhood.

A large literature has examined Moffitt’s (1993) hypotheses concerning the origins of

LCP offending. In broad strokes, research has revealed that LCP offenders are more likely

to suffer from neuropsychological deficits (Piquero, 2001; Raine et al., 2005), to be born

to at-risk parents (Tibbetts & Piquero, 1999), and to suffer from a combination of neuro-

cognitive impairments and environmental risk factors (Raine et al., 1994; Turner, Hartman,

& Bishop, 2007). Raine et al. (1994) reported that fewer than 5% of their sample suffered

from both birth complications and early maternal rejection. Nonetheless, these individuals

accounted for roughly 20% of all violent offenses. Tibbetts and Piquero (1999) reported

that low birth weight (a proxy for neuropsychological deficits) interacted with familial

socioeconomic status (SES) and with a measure of family structure in predicting an early

onset of offending. In line with the theory, both interactions revealed that the impact of

neuropsychological deficits (i.e., low birth weight) on offending were greater in the pres-

ence of an adverse rearing environment (i.e., low SES or weak family structure).

In a similar analysis, Gibson, Piquero, and Tibbetts (2000) assessed whether children

born to mothers who smoked while pregnant (a proxy for neuropsychological deficits; but

see McGloin, Pratt, & Piquero, 2006) were more likely to display an early onset of delin-quency. Their regression models revealed that maternal smoking predicted early onset, as

did low birth weight and SES. Raine et al. (2005) analyzed a group of adolescents who

were on the LCP pathway for a range of neuropsychological deficits. These authors




4/22


reported that LCP offenders had lower IQ scores than ALs and controls, they had greater

memory impairments as compared to control participants, they had neurocognitive impair-

ments, and they were more likely to have suffered head injuries that resulted in uncon-

sciousness. In all, these studies have revealed strong support for the neuropsychological

element of LCP offending. Also, many have supported the interactional hypothesis between

neuropsychological deficits and an adverse rearing environment (e.g., Raine et al., 1994).

GENETIC INFLUENCES ON LCP OFFENDING

Although an impressive literature has analyzed the impact of neuropsychological defi-

cits on LCP offending (DeLisi & Piquero, 2011; Gibson et al., 2000; Raine et al., 2005),

few have traced these effects to genetic factors. This oversight is surprising given that

neuropsychological deficits are likely the result of environmental and genetic influences

(Beaver, Wright, & DeLisi, 2007; Raine, 2008). Brain structure and function appear to be

regulated largely via genetic factors (Devlin, Daniels, & Roeder, 1997; Thompson et al.,

2001; Toga & Thompson, 2005), although some environmental influences are believed to

impinge directly on neurocognitive functioning (Wright, Boisvert, & Vaske, 2009; Wright,

Dietrich, et al., 2008). Recall that one of the hallmarks of LCP offending is an early onset

of antisocial behavior. Researchers have investigated the genetic influences on early child-

hood antisocial behavior, and the results have been surprisingly consistent; childhood

antisocial behavior is, at least partially, the result of heritable factors (Arseneault et al.,

2003; Jaffee et al., 2005; van Beijsterveldt, Bartels, Hudziak, & Boomsma, 2003; Van

Hulle et al., 2009). Taylor, Iacono, and McGue (2000), for example, reported that genetic

factors were more prominent for youth who displayed an early onset into delinquency ascompared to those who had a late onset. These findings hint that LCP offending may be

partially driven by genetic influences.

Perhaps the clearest indication that genetic factors underlie LCP offending comes from

a recent analysis by Barnes, Beaver, and Boutwell (2011). Drawing on data from the

National Longitudinal Study of Adolescent Health (Add Health; K. Harris, 2009), Barnes

et al. first separated the sibling subsample into three groups: LCPs, ALs, and abstainers.

Next, the authors estimated the genetic influences on each offending pattern. The findings

from this portion of the analysis revealed that the LCP pattern was between 56% and 70%

heritable, with the remaining variance being attributable to nonshared environmental fac-

tors. In other words, genetic influences accounted for more than half of the variance in

being identified as an LCP offender. Nonshared environmental influences—environmental

experiences that are unique to each individual—accounted for the remaining variance.

THE CURRENT STUDY

Juxtaposing the findings from behavioral genetic research (e.g., Taylor et al., 2000) with

Moffitt’s (1993) theory raises several interesting and important questions. First, if genetic

factors underlie LCP offending, how do these effects operate? As noted above, any genetic

influence on LCP offending is almost certainly mediated by the brain and is likely tied toMoffitt’s arguments regarding neuropsychological dysfunction. Put another way, genetic

factors identified by previous studies may indirectly affect LCP offending via their impact

on neuropsychological deficits (Wright, Tibbetts, et al., 2008). See Figure 1 for a graphical




5/22


depiction of the hypothesized relationship. Notice that the figure includes three latent

terms: genetic factors, neuropsychological deficits, and LCP offending. Genetic factors are

hypothesized to affect neuropsychological deficits, which in turn influence LCP offending.

When this relationship is taken into account, the link between genetic factors and LCPoffending is substantially weakened or is reduced to zero. Thus, the arrow connecting

genetic factors directly to LCP offending is displayed as a broken line. The current study

will analyze these relationships.

The second question that emerges from contemporary research concerns the nonshared

environmental factors that influence LCP offending. The nonshared environment has been

shown to affect LCP-like offending (Barnes et al., 2011; Taylor et al., 2000) and is believed

to account for the majority of environmental influences on antisocial behavior generally

(Moffitt, 2005). Although scholars agree that the nonshared environment is of utmost

importance (J. Harris, 1998), it is not yet clear what these environments are (Plomin,

Asbury, & Dunn, 2001). Nonshared environments are defined as any factor (other than

genetics) that is different between two siblings and operates to make them less similar to

one another (Plomin & Daniels, 1987). Although defining nonshared environments was

simple, the empirical search for them has been substantially more difficult (Plomin et al.,

2001; Turkheimer & Waldron, 2000).

Moffitt (1993) hypothesized that LCP offending would arise as the result of neuropsy-

chological deficits and an adverse rearing environment. Adverse rearing environments may

tap anything from poor parenting to being raised in a disadvantaged neighborhood (e.g.,

Tibbetts & Piquero, 1999). The current study will examine whether a host of parental influ-

ences operates as nonshared environmental influences on LCP offending. Although theseconstructs may be viewed as shared environmental factors (i.e., they, theoretically at least,

should lead siblings to be more similar to one another), scholars have argued for the inves-

tigation of whether ostensibly shared environmental factors actually operate as nonshared

influences. This may occur for several reasons, not least of which is that two people who

experience the same event (e.g., parental discipline) may have nonshared interpretations of

that event, leading to a nonshared environmental effect (Turkheimer & Waldron, 2000).

Parental influences are one of the cornerstones of criminological research (e.g., Hirschi,

1969). The effect that parenting has on LCP offending, however, has yet to be analyzed

after controlling for genetic influences. This is an important oversight because as prior

scholars have noted (Beaver, 2008; DiLalla, 2002; Jaffee & Price, 2007; Kendler & Baker,

2007; Plomin & Bergeman, 1991; Walsh, 2002; Wright & Beaver, 2005), environmental

influences cannot be analyzed properly unless genetic factors are accounted for first. The

Figure 1: Graphical Depiction of the Hypothesized Relationship Between Genetic Factors, Neuro-psychological Deficits, and Life Course–Persistent Offending




6/22


current study will estimate the effect of various parental factors (all of which have been

linked with offending behaviors by prior research) on the probability of being an LCP

offender. These effects will be observed after controlling for genetic factors that influence

LCP offending.

In summary, four hypotheses drawn either directly from Moffitt’s (1993) theory or by

combining current research findings with Moffitt’s theory are analyzed in the current study.

Hypothesis 1: The effect of genetic risk factors on LCP offending will be mediated by the pres-ence of neuropsychological deficits.

Hypothesis 2: Parental influences, even after accounting for genetic risk, will predict LCPoffending.

Hypothesis 3: Genetic risk factors for LCP offending will interact with parental and neighbor-hood influences such that the presence of poor parenting or the presence of a disadvantagedneighborhood will exacerbate genetic risk.

Hypothesis 4: Measures of neuropsychological deficits and parental influences will operate as

nonshared environmental factors on LCP offending risk.

To date, no research has directly examined any of the above hypotheses.

METHOD

DATA

Data for the current study come from Add Health (K. Harris, 2009). As a longitudinal

and nationally representative sample of adolescents, the Add Health is an ideal data set forthe current focus. The Add Health study unfolded in a number of steps, beginning with a

survey of more than 90,000 students who were attending 132 different schools in 1995.

This round of data collection is referred to as the in-school survey and provided the sam-

pling frame from which the longitudinal portion of the study was drawn.

Immediately after the in-school surveys were completed, a subsample of the students

who completed the questionnaire were contacted and asked to complete a follow-up inter-

view, along with their primary caregiver, in their homes. Information from 20,745 adoles-

cents and 17,700 primary caregivers was gathered during this round of data collection. This

round of surveys is referred to as Wave 1. The Wave 1 surveys were designed to gain moredetailed information about the adolescent, his or her social experiences, and his or her rear-

ing environment. Approximately 1 year after the Wave 1 interviews were completed,

14,738 of the respondents were again interviewed in their homes. This round of surveys is

known as Wave 2. Only a short amount of time elapsed between Wave 1 and Wave 2, and

as a result, the questionnaires remained very similar. For instance, respondents were asked

about their behaviors and their ability to get along with others.

Nearly 6 years after Wave 1 interviews were conducted (and roughly 5 years after Wave 2),

a third round of interviews took place with 15,197 respondents (i.e., Wave 3 in-home inter-

views). By this time, the respondents had reached early adulthood. To account for these age

differences, the survey was redesigned to include age-appropriate questions. For example,respondents were asked about their employment histories, their marital relationships, and

their involvement in criminal behavior. Finally, a fourth round of interviews was completed

between 2007 and 2008. Roughly 12 years had passed since Wave 1 interviews were




7/22


conducted, and all of the respondents had reached adulthood. The age range at Wave 4 was

24 to 34 years. Similar to Wave 3, participants were asked questions that tapped their

employment histories, romantic relationships, and involvement in criminal behavior. Wave

4 surveys were completed by 15,701 participants.

Nested within the Add Health data is a subsample of sibling pairs who resided in the

same household at Wave 1. This subsample of sibling pairs is used in the current analysis.

During Wave 1 in-home interviews, all respondents who lived with an identical twin

(monozygotic [MZ]), a fraternal twin (dizygotic [DZ]), a half sibling, or a stepsibling were

identified, and their sibling was automatically included in the study. Additionally, full sib-

lings were included in the sample, but these pairs entered the subsample as a result of

chance. The current study was limited to MZ twins, DZ twins, and full siblings. All other

sibling pairs were removed from the sample to limit the possibility that assortative mating

effects would artificially bias heritability estimates.

MEASURES

Persistent offending. LCP offenders were identified by following two steps. First, a scale

of each respondent’s involvement in crime and delinquency at Wave 1, Wave 2, Wave 3,

and Wave 4 was generated. Participants were asked about their involvement in 17 different

delinquent activities at Wave 1 and Wave 2 (questions were identical at both waves).

During both waves, the reference period for each of the delinquency questions was “during

the past 12 months.” The Wave 1 scale was created by summing each respondent’s answers

to the 17 questions so that higher values reflected a greater involvement in delinquency

(α = .85). These same questions were asked at Wave 2, allowing for the calculation of aWave 2 delinquency scale by summing across the 17 items (α = .81). During Wave 3 inter-

views, respondents were asked about the frequency with which they had engaged in 12

criminal behaviors in the past 12 months. As with the previous scales, each respondent’s

answers to the 12 questions were summed together to create the Wave 3 criminal behavior

scale so that higher values indicated a greater involvement in crime (α = .71). Twelve ques-

tions were asked about the respondent’s involvement in criminal activities during Wave 4

interviews. When summed together, higher scores reflected more involvement in criminal

activity at Wave 4 (α = .71).

The second step toward identifying LCP offenders was carried out by generating a new

variable whereby a 1 was assigned to all respondents who scored 1 or higher on each of the

four crime and delinquency scales (Barnes et al., 2011; and see Turner et al., 2007, for a

similar coding strategy). Respondents who did not score 1 or higher across all four scales

were assigned a value of 0. As shown in Table 1, this approach led to roughly 5% of the

sample being identified as an LCP offender.

Genetic Risk Scale. Behavioral genetic scholars have developed myriad ways to measure

and control for genetic risk factors. The current study used an established strategy for cap-

turing genetic influences on a dichotomous trait (Beaver, Barnes, May, & Schwartz, 2011;

Kendler et al., 1995). Building on the knowledge that MZ twins share 100% of their DNAand DZ twins and full siblings share 50% of their distinguishing DNA (on average; Carey,

2003), a genetic risk continuum can be constructed by combining genetic relatedness infor-

mation with information about the cosibling’s status as a persistent offender.




8/22


Generating the scale involved five steps. First, the data were arranged so that each sib-

ling appeared once as the target sibling and once as the cosibling . Second, because MZ

twins share 100% of their DNA, any target MZ twin whose cotwin was identified as a

nonpersistent offender would have the lowest genetic risk for becoming a persistent

offender. Thus, all target MZ twins whose cotwin was not a persistent offender were coded

as 0. Third, target DZ twins and full siblings whose cotwin or sibling was not a persistentoffender have the next lowest genetic risk of becoming a persistent offender themselves.

These participants, therefore, were coded as 1. Fourth, target DZ twins and full siblings

whose cotwin or sibling was identified as a persistent offender have a higher genetic prob-

ability of being a persistent offender. To account for these influences, all target DZ twins

and full siblings who had a cotwin or sibling who was identified as a persistent offender

were coded as 2. Finally, target MZ twins whose cotwin was identified as a persistent

offender have the highest genetic liability toward persistent offending. Thus, these respond-

ents were coded as 3.

The Genetic Risk Scale has been used previously to control for genetic influences on a

range of outcomes, such as childhood externalizing problems (Boutwell, Franklin, Barnes,

& Beaver, 2011), depression (Kendler et al., 1995), and psychopathy (Beaver et al., 2011).

Important for the current focus, the Genetic Risk Scale will allow for the examination of

the link between genetic risk factors for LCP offending and neuropsychological deficits

(see Figure 1).

Verbal IQ. Moffitt (1993, p. 681) noted that a low verbal IQ may reflect neuropsycho-

logical dysfunction, and prior scholars have linked verbal IQ with problems with self-

control (Ratchford & Beaver, 2009), psychopathy (Johansson & Kerr, 2005), and persistent

delinquent involvement (Moffitt, Lynam, & Silva, 1994). In other words, scholars haveused verbal IQ as a proxy for neuropsychological dysfunction in prior research (Piquero,

2001). The current study included a measure of verbal IQ gleaned from the Wave 3 inter-

views. During these interviews, respondents were administered the Peabody Picture

TABLE 1: Descriptive Statistics for Add Health Siblings

Variable Frequency M SD Min Max

Persistent offender .05 .22 0 1

Persister 120Nonpersister 2,225

Genetic Risk Scale .90 .46 0 3

Neuropsychological deficits

Verbal IQ (Wave 3) 98.21 15.68 7 122

Low self-control (Wave 3) 28.59 10.91 3 67

Birth weight 6.84 1.41 4 11.63

Parental influences (Wave 1)

Maternal attachment 9.40 1.10 2 10

Maternal involvement 3.91 1.95 0 10

Parental permissiveness 5.08 1.59 0 7

Maternal disengagement 9.03 3.34 5 25

Maternal aspirations 8.68 1.81 2 10

Maternal Scale –0.001 0.74 –4.11 1.63

Neighborhood disadvantage (Wave 1) 0.00 0.94 –1.22 4.94




9/22


Vocabulary Test. Standardized scores were used so that the distribution for the full Add

Health sample was normal with a mean of 100 and a standard deviation of 15 (the mean

and standard deviation are slightly different for the sibling subsample). Higher scores

reflected a greater verbal IQ.

Low self-control. Moffitt (1993, p. 681) noted that individuals with neuropsychological

deficits may display signs of low self-control. In other words, measures of self-control may

be used as an indicator of neuropsychological dysfunction. Building on this hypothesis,

a measure of self-control was constructed using the Wave 3 survey data. Specifically,

Beaver, Ratchford, and Ferguson (2009) identified 20 items that, when combined, tap each

respondent’s level of self-control. Factor analysis indicated that all 20 items hung together

on a single construct, and the alpha coefficient indicated a strong degree of reliability (α = .83).

To generate the scale, responses to the 20 items were summed so that higher values

reflected lower levels of self-control.

Birth weight. Previous research has used birth weight as an indicator of neuropsycho-

logical deficits (Tibbetts & Piquero, 1999). Following the lead of these scholars, a measure

of the respondent’s birth weight was included in the current analysis. During Wave 1 inter-

views, primary caregivers were asked to report on their child’s (i.e., the target respond-

ent’s) birth weight. Responses were recorded in pounds and ounces. As a result, the birth

weight measure was created whereby whole numbers reflected pounds and ounces were

coded as a fraction of a pound. The substantive conclusions were unchanged when the birth

weight measure was dichotomized into an indicator of low birth weight (i.e., those born at

or below 5.5 pounds).

Parental influences. Moffitt (1993) noted that “parents of children who are difficult to

manage often lack the necessary psychological and physical resources to cope construc-

tively with a difficult child” (p. 681). Clearly, Moffitt anticipated a parental influence on

the child’s probability of becoming an LCP offender. For capturing these influences, a

series of parenting scales was constructed and included in the analysis. First, a measure of

maternal-child attachment was constructed. During Wave 1 interviews, target respondents

were asked about the level of closeness they felt toward their mothers and how much they

thought their mothers cared about them. Both items were coded so that higher values indi-cated a greater bond between the respondent and his or her mother. Responses to these two

questions were summed together to create the scale (α = .63).

The second parenting scale indexed the level of involvement that mothers had with their

children. At Wave 1, target respondents were asked whether they had taken part in 10

activities with their mother in the past 4 weeks. Activities referenced were shopping, going

to a religious service, and going to a movie, among others. Each measure was coded

dichotomously so that 0 = the respondent did not participate in the activity with his or her

mother and 1 = the respondent did participate in the activity with his or her mother. To

generate the scale, responses to the 10 items were summed so that higher values reflected

greater maternal involvement (α = .53).The third parenting scale measured the amount of autonomy granted to the child by his

or her parents at Wave 1. Target respondents were asked to reflect on whether their

parent(s) allowed them to make decisions regarding their curfew, their peer group, and the




10/22


clothes they wore. Each of the seven questions were coded dichotomously (0 = no, 1 = yes)

so that when summed, higher values reflected greater parental permissiveness (α = .63).

A fourth measure of parenting practices tapped the level of disengagement between the

mother and the target respondent. Five questions measured the level of warmth expressed

by the respondent’s mother, the level of encouragement offered by the mother, and the

level of satisfaction with the mother-child relationship. Each question was asked to the

target respondent at Wave 1. Answers to the five items were summed, and higher values

indicated more maternal disengagement (α = .82).

The fifth parental measure tapped the mother’s aspirations for the target respondent’s

future. Importantly, the questions were asked to the target respondent during Wave 1 so they

reflected the participant’s perceptions of his or her mother’s aspirations. The first question

asked how disappointed the respondent’s mother would be if he or she did not graduate from

college (ranged from 1 = low disappointment to 5 = high disappointment ). The second ques-

tion was similar, but the reference was high school. Responses to these two items weresummed so that higher values reflected higher perceptions of maternal aspirations (α = .57).

The final parental measure was a composite scale constructed from the items mentioned

above. Specifically, each of the five scales outlined above was subjected to a factor analy-

sis. The results from this analysis indicated that three of the scales (Maternal Attachment,

Maternal Involvement, and Maternal Disengagement [reverse coded]) could be combined

together into a single index of maternal influence. Because each scale was measured on a

different metric, the scales were first standardized and then an average score was generated.

The resulting Maternal Scale was coded so that higher values reflected “better” or more

positive relations between mother and child (α = .59).

Neighborhood disadvantage. A Neighborhood Disadvantage Scale was created by com-

bining information from the following five variables taken from the 1990 Census: unem-

ployment rate, proportion of residents on public assistance, proportion of residents living

below the poverty line, proportion of residents who are Black, and proportion of female-

headed households (Sampson, Raudenbush, & Earls, 1997). Each of the constituent varia-

bles, which were measured at the block-group level, was factor analyzed, and the results

suggested that the variables be combined into a single construct. Thus, one factor was

extracted using regression scoring techniques (α = .79). Higher values reflected a greater

degree of neighborhood disadvantage.

ANALYSIS

The analysis unfolded in three broad steps. The first step was to analyze the relationship

between genetic risk and the persister status variable. Two analyses were conducted for this

step. First, the behavior genetic technique known as the ACE model was estimated. The

ACE model has been described at length elsewhere (Neale & Maes, 2004; see Wright,

Beaver, DeLisi & Vaughn, 2008, for a recent example of the ACE model in criminology).

Briefly, the ACE model will decompose the variance in the persister status variable to

determine whether genetic factors (i.e., A) are operative. The ACE model will also estimatethe degree to which shared environments (i.e., C; environments that operate to make sib-

lings more similar to one another) and nonshared environments (i.e., E; environments that




11/22


operate to make siblings dissimilar) are operative. The threshold ACE model was estimated

to account for the dichotomous coding of the outcome variable (Derks, Dolan, & Boomsma,

2004). Whether the ACE model revealed genetic influences on the persister status variable

was critical to the remainder of the analysis—indeed, the saliency of the Genetic Risk Scale

rests on the assumption that genetic factors are operative on the outcome of interest (i.e.,

persister status). The findings from the ACE model, therefore, paved the way for the

remaining analyses, which used the Genetic Risk Scale.

The next set of analyses employed the persister status indicator as the dependent varia-

ble, and the Genetic Risk Scale was used as the primary predictor variable. The bivariate

relationship between these two variables was examined first. This model provided a base-

line relationship that could then be compared to subsequent models (i.e., when the other

covariates were entered). Recall that prior work has identified a strong genetic link to LCP

offending (Barnes et al., 2011) and that this relationship is most likely mediated via neu-

ropsychological deficits (Moffitt, 1993). To test this hypothesis (Hypothesis 1), the threeneuropsychological deficit measures (i.e., verbal IQ, low self-control, and birth weight)

were entered into the model in a stepwise fashion. Entering the variables into the model in

this way allowed for the examination of mediating effects. Specifically, the degree to which

the Genetic Risk Scale is mediated by the neuropsychological deficits measures will be

identified by a reduction in the genetic risk coefficient from Model 1 through the subse-

quent models.

The second step to the analysis observed whether the parental influence scales and the

Neighborhood Disadvantage Scale increased the odds of being identified as an LCP

offender after controlling for genetic risk (Hypothesis 2). These models explored the main

effects of each measure to determine the relative magnitude of the effects among the vari-

ous items. Additionally, interaction terms were analyzed to test Moffitt’s (1993) interac-

tional hypotheses (Hypothesis 3). The interaction terms were calculated by mean centering

the constituent variables and then generating a new variable that was the product of the two

items.

The third step to the analysis examined the link between each of the covariates (except

for the neighborhood disadvantage measure because it did not vary between siblings; see,

generally, Caspi, Moffitt, & Plomin, 2000) and LCP offending, but this time the covariates

were included as measures of the nonshared environment (Hypothesis 4). Prior research

has identified a substantial nonshared environmental component to LCP offending, but itis unclear what these nonshared environments might be. For capturing nonshared environ-

mental effects, each of the neuropsychological deficits measures and the parental influence

measures was entered as between-sibling difference scores (Beaver, 2008), and a logistic

DeFries-Fulker (DF) equation was estimated (Rodgers, Rowe, & Li, 1994). The logistic DF

equation can be expressed algebraically as follows:

loge( P [ K

1]/1– P [ K

1]) = b

0 + b

1( K

2) + b

2( R*[ K

2]) + b

3( ENVDIF ) + e.

The logistic DF equation has two features that deserve mentioning. First, genetic influ-

ences on the outcome variable (i.e., persistent offender) are captured by the heritabilitycoefficient (i.e., b

2). Second, the nonshared environmental influences of the covariates will

be captured by the ENVDIF coefficient estimate (i.e., b3).




12/22


RESULTS

The first step to the analysis was to estimate the ACE model. Recall that the ACE model

decomposes variance in an outcome measure into genetic (A), shared environmental

(C), and nonshared environmental (E) influences. Behavior geneticists typically estimate

the full ACE model and then estimate several supplemental models to determine whether

equivalent model fit can be reached with a more parsimonious model; the procedure is

often referred to as hierarchical model fitting in the structural equation modeling literature.

The full ACE model was estimated first, and results (not reported in a table) suggested that

a combination of genetic (A = .51) and nonshared environmental factors (E = .49) explained

variance in the persistent offending variable. Next, an AE model was estimated. In this

model, the C parameter was fixed to zero. The AE model produced the best fit to the data

and revealed that genetic (A = .51) and nonshared environmental factors (E = .49) explained

the totality of the variance in the persistent offending variable. As compared to the fullACE model, the AE model produced an equivalent chi-square value and a reduced Akaike

information criterion (AIC) value (because of the fixed C parameter) (∆χ 2 = .00, p > .05,

∆AIC = –2.00). The CE model produced a nonsignificant chi-square value, but the AIC

was larger than the AIC gleaned from the AE model (∆χ 2 = 1.87, p > .05, ∆AIC = –.14).

Finally, the E model was estimated. This model also produced a nonsignificant chi-square

statistic, but when compared to the ACE model, the AIC indicated a worsening of fit

(∆χ 2 = 4.50, p > .05, ∆AIC = .50). In all, the findings from the ACE model-fitting analysis

suggested that genetic factors influenced the persister status variable. This analysis, there-

fore, supported the utility of the Genetic Risk Scale by revealing that genetic factors

explain a portion of the variance in the persistent offending variable.

Presented in Table 2 are the findings from seven separate logistic regression models.1

The first four models analyzed the link between the Genetic Risk Scale and the various

measures of neuropsychological deficits. Model 1 revealed that the Genetic Risk Scale was

positively and significantly associated with LCP offending. The odds ratio (OR) is reported

and indicates that each one-unit increase on the Genetic Risk Scale was associated with a

148% increase in the odds of being identified as an LCP offender. This finding offers sup-

port for Hypothesis 1 and is an important first step in the examination of the remaining

three hypotheses.

To explore the link between the Genetic Risk Scale and LCP offending more closely, predicted probabilities were generated using the model estimates from Model 1 in Table 2.

These predicted probabilities are presented graphically in Figure 2, which shows that the

probability of a respondent’s being identified as an LCP offender was strongly tied to his

or her genetic risk score. Respondents with the lowest genetic risk (i.e., MZ twins whose

cotwin was not identified as LCP) had a predicted probability of .02 for being an LCP

offender. Note that this predicted probability is lower than the population incidence of LCP

offending within this sample (see Table 1). Participants with the highest genetic risk (i.e.,

MZ twins whose cotwin was identified as LCP) had a predicted probability of .25, a probability

that is more than 11 times greater than those with the lowest genetic risk.

Models 2 through 4 (Table 2) test for mediation of the genetic effect via neuropsycho-

logical deficits (Hypothesis 1).2 In broad strokes, one of the three neuropsychological

deficit measures substantially mediated the genetic effect. Indeed, the genetic effect was

unchanged from Model 1 to Model 2 (when verbal IQ was entered into the equation) and




13/22


was only marginally decreased in Model 4 when birth weight was entered. The only meas-ure of neuropsychological deficits that appeared to mediate the genetic effect was the Low

Self-Control Scale (Model 3), where roughly 13% of the genetic effect was mediated (b = .87

for Model 1; b = .76 for Model 2). A Sobel mediation test suggested that this effect was

TABLE 2: Logistic Regression of Persistent Offending on Genetic Risk Scale, Neuropsychological

Deficits, Parental Influences, and Neighborhood Disadvantage

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Variable OR SE OR SE OR SE OR SE OR SE OR SE OR SE

Genetic Risk Scale 2.48* .70 2.48* .72 2.14* .64 2.32* .89 2.18* .62 2.20* .64 2.76* .82

Neuropsychological deficits

Verbal IQ 1.00 .01

Low self-control 1.06* .01

Birth weight 1.15 .10

Parental influences

Maternal attachment 1.11 .19

Maternal involvement 0.94 .06

Parental permissiveness 1.04 .07

Maternal disengagement 1.06 .04

Maternal aspirations 0.97 .05Maternal Scale 0.84 .11

Neighborhood disadvantage 1.05 .13

Gene-environment interactions

Genetic Risk × Maternal Scale 1.02 .20

Genetic Risk × Neighborhood

Disadvantage

0.86 .20

Note . Standard errors are corrected for clustering of siblings within homes. OR = odds ratio.*p


14/22


statistically significant ( z = 3.59, p .05).

The final model presented in Table 2 (Model 7) explored the link between genetic risk,

neighborhood disadvantage, and an interaction between the two on the probability of per-

sistent offending. As shown in the table, the main effect for neighborhood disadvantage did

not attain significance ( p = .80 when only the main effect was included), nor did the effect

of the Gene × Environment interaction.3

Thus far, the results have revealed that the genetic risk toward LCP offending is partially

mediated by levels of self-control. This finding offers some support for Hypothesis 1.

Hypothesis 2 was examined by entering the parental influence variables as covariates. As

was shown, none of the parenting scales predicted LCP offending. These findings do not

offer support for Hypothesis 2 (but see the possible limitations in the Discussion section).

Finally, the results did not provide support for Hypothesis 3, which expected an interaction

between genetic risk and the environmental variables (i.e., Maternal Scale and neighborhood

Figure 3: Predicted Probability of Being a Persistent Offender as a Function of Low Self-Control




15/22


disadvantage). Hypothesis 4 has yet to be examined. Recall that Hypothesis 4 stated that

each of the covariates would operate as nonshared environmental influences on LCP

offending. These analyses are considered in Table 3.

The logistic DF equation described earlier was estimated, and the results can be found in

Table 3.4 Before discussing the findings, it is important to reiterate that the DF model esti-

mated environmental influences as sibling differences after controlling for genetic factors.In this way, any environmental effect that is identified in Table 3 is free of genetic confound-

ing. As shown in Model 1 (Table 3), genetic factors accounted for a statistically significant

portion of the variance in LCP offending. This finding is consistent with the regression

models presented in Table 2 and the ACE model results discussed above. Model 2 entered

the nonshared environmental influence of verbal IQ, and the effect did not reach statistical

significance. Model 3 entered the Low Self-Control Scale as a nonshared environmental

factor. The effect of low self-control was positive and statistically significant, indicating that

the sibling with lower self-control was more likely to be identified as a persistent offender.

Model 4 indicated that birth weight operated as a source of nonshared variance. The coef-

ficient was positive, however (opposite to predictions), indicating that the sibling who was

born of higher birth weight was more likely to be identified as a persistent offender.

Model 5 entered the parental influence variables as nonshared environments. As can be

seen, only the Maternal Involvement scale emerged as a statistically significant predictor.

The effect was negative, indicating that the sibling who received more maternal involve-

ment was less likely to be identified as an LCP offender. Only the Maternal Involvement

scale attained statistical significance when the parenting scales were analyzed individually.

Model 6 analyzed the Maternal Scale and statistical significance was not reached.

DISCUSSION

Moffitt’s (1993) developmental taxonomy has been extremely influential to criminol-

ogy, and it has sparked a line of research that has vastly expanded knowledge about long-term

TABLE 3: Logistic DeFries-Fulker (DF) Model Results

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Variable OR SE OR SE OR SE OR SE OR SE OR SE

Genetic factors 5.62* 4.71 5.79* 4.84 4.78 4.38 7.74* 7.29 4.45 4.13 4.10 3.81Nonshared environments

Verbal IQ 1.00 0.01

Low self-control 1.02* 0.01

Birth weight 1.33* 0.15

Maternal attachment 1.06 0.12

Maternal involvement 0.89* 0.05

Parental permissiveness 1.00 0.06

Maternal disengagement 0.97 0.03

Maternal aspirations 0.98 0.04

Maternal Scale 0.98 0.12

Note . Standard errors are corrected for clustering of siblings within homes. Nonshared environment measures arebetween-sibling difference scores. OR = odds ratio.*p


16/22


criminal careers (DeLisi & Piquero, 2011). Although a number of studies have tested por-

tions of the theory (Moffitt, 2006), no study has simultaneously examined the interconnec-

tions between genetic risk, neuropsychological deficits, adverse rearing environments, and

LCP offending. The current effort sought to fill this gap in the literature by testing four

hypotheses drawn or developed directly from Moffitt’s (1993) theoretical statements.

Hypothesis 1 suggested that genetic risk factors for LCP offending would operate indi-

rectly through neuropsychological deficits. Three statistical models were analyzed and one

revealed evidence to support the hypothesis. Specifically, when a measure of the respond-

ent’s level of self-control was entered into the equation, the effect of genetic risk for LCP

offending was weakened. To the extent that self-control reflects neuropsychological defi-

cit, this finding offers support for Hypothesis 1. Neuroscience has revealed that self-control

and other executive functions are housed in the prefrontal cortex of the brain (Beaver et al.,

2007), suggesting that deficiencies in self-control may be indicative of neuropsychological

deficits. Also, Moffitt (1993) anticipated that low self-control reflected neuropsychologicaldeficit:

Longitudinal studies suggest that neuropsychological dysfunctions that manifest themselvesas poor scores on tests of language and self-control—and as the inattentive, overactive, andimpulsive symptoms of ADHD—are linked with the early childhood emergence of aggressiveantisocial behavior and with its subsequent persistence. (p. 681).

In all, the finding that genetic risk factors for LCP offending are mediated by levels of self-

control provides support for Hypothesis 1 and is consistent with Moffitt’s theory.

Finding a link between genetic risk for LCP offending and self-control suggests twoavenues for future researchers to consider. First, scholars should prioritize research that

seeks to identify the other neurological pathways that mediate genetic risk for LCP offend-

ing. Indeed, research has shown that persistent or psychopathic offenders have functional

brain differences as compared to controls (Raine et al., 2003; Weber, Habel, Amunts, &

Schneider, 2008; Yang et al., 2005). The second avenue for future research involves the

identification and examination of different proxies for neuropsychological deficits (Moffitt,

1990; Piquero, 2001). The current study employed three measures that have been used

previously to index the presence of neuropsychological deficits. Certainly, these are not the

only measures that can serve as proxies, and therefore, Hypothesis 1 must be considered in

future work.Hypothesis 2 examined the effect of parental influences on LCP offending. Early crimi-

nological theorizing (e.g., Hirschi, 1969) exalted parenting as a key component to antiso-

cial behavior. More recent analyses suggest that parenting may not be as consequential for

offending (or to correlates of offending) as was once believed (J. Harris, 1998; Pinker,

2002; Wright & Beaver, 2005). To add to this literature, the current study examined the

influence of parenting on LCP offending in two ways. First, the effects of six parenting

scales were analyzed after controlling for genetic risk factors toward LCP offending. The

results from these tests revealed that none of the parenting measures predicted LCP offend-

ing (Table 2).The second way that parenting was analyzed was as a nonshared environmental factor.

Although parenting is typically conceptualized as a shared environmental influence (i.e., it

is typically argued that parents treat their children the same, thus falling under shared envi-

ronmental influences; J. Harris, 1998), some have argued that parenting may operate as a




17/22


nonshared environmental factor to the extent that siblings interpret parenting differently

(Turkheimer & Waldron, 2000). To explore this possibility, each of the six parenting scales

was entered as a nonshared environmental influence. The results from these models

provided little evidence to suggest that parenting operated as a nonshared environmental

influence. In fact, only one of the scales reached statistical significance (Maternal

Involvement).

Collectively, the results provided very weak support for Hypothesis 2 (i.e., parental

influences affect LCP offending after controlling for genetic factors). Indeed, only 1 out of

12 tests revealed a significant influence. Note also that the correlation between the parent-

ing scales and persister status could reflect child-driven effects (i.e., parenting strategies are

a reaction to the child’s behavior; see Burt, McGue, Krueger, & Iacono, 2005; Larsson,

Viding, Rijsdijk, & Plomin, 2008). If so, then the limited relationships found here are even

more suspect because they may reflect child-driven effects or, at a minimum, bidirectional

effects. It is also worth noting that the parenting variables used in the current study weredrawn from Wave 1 of the Add Health and, therefore, captured parenting as it occurred

during adolescence. Some evidence suggests that parenting effects are identifiable in the

first years of life but fade over time (Ferguson, 2010). The pattern of results presented

herein is certainly compatible with this reasoning; shared environmental factors may matter

in childhood and, to a lesser extent, in adolescence and adulthood (see, generally, J. Harris,

1998; Sampson & Laub, 1993). In summary, the current findings suggest that criminolo-

gists should begin to look elsewhere when trying to identify the environmental (especially,

nonshared environmental) influences for LCP offending. To offer one suggestion, it may

turn out that parenting styles and strategies (as analyzed here) are inconsequential for LCP

offending as measured during adolescence or adulthood, but this says nothing about other

parental risk factors, such as intergenerational transmission effects (Boutwell & Beaver,

2010; Rowe & Farrington, 1997). Criminology will do well to expand the environmental

focus to factors beyond parental strategies (Wright & Beaver, 2005).

The third hypothesis examined by the current analysis is sometimes referred to as

Moffitt’s (1993) interactional hypothesis (Tibbetts & Piquero, 1999). Specifically,

Hypothesis 3 argued that the effect of genetic risk on LCP offending would be strengthened

in the presence of environmental risk factors, such as poor parenting or a disadvantaged

neighborhood. This hypothesis was examined in two models, and neither produced results

consistent with the hypothesis. Neither the Maternal Scale nor the NeighborhoodDisadvantage Scale interacted with the Genetic Risk Scale to predict LCP offending. This

finding stands in contrast to a sizable body of research (Caspi et al., 2002; Moffitt, 2006).

For this reason, it is only appropriate to speculate as to why the findings turned out the way

they did. Two explanations are possible. First, it may be the case that the Maternal Scale

and Neighborhood Disadvantage Scale do not adequately tap the “adverse rearing environ-

ment” described by Moffitt (1993). Although this is certainly a possibility, it should be

noted that both scales were developed out of prior research that has shown robust environ-

mental effects on offending (Wright & Beaver, 2005; Sampson et al., 1997).

The second explanation for the lack of supportive evidence for Hypothesis 3 has roots

in the differences between shared and nonshared environmental influences. Recall that the

nonshared environment has consistently been found to explain the largest portion of envi-

ronmental variance on offending (Ferguson, 2010; Moffitt, 2005). The measures employed

by the current study may, however, tap shared environmental influences. If this is the case,




18/22


and the Maternal Scale and the Neighborhood Disadvantage Scale are actually shared envi-

ronments, then the current results are less surprising (J. Harris, 1998). Nonetheless, the fact

that the current findings are inconsistent with a large body of research should compel future

studies to reanalyze these relationships using different data sets and different operationali-

zations of the environmental measures.

The fourth and final hypothesis examined was generated as a by-product of Moffitt’s

(1993) theory being blended with recent behavioral genetic research (Ferguson, 2010;

Moffitt, 2005). Specifically, Hypothesis 4 stated that the neuropsychological deficits meas-

ures and the parental influence measures would operate as nonshared environmental influ-

ences on LCP offending. Some limited support was found for Hypothesis 4. Indeed, two

variables emerged as a statistically significant (and in the predicted direction) predictor of

LCP offending: self-control and maternal involvement. These results indicated that siblings

with lower levels of self-control and siblings with lower levels of maternal involvement

were more likely to be identified as persistent offenders. The limited support received forHypothesis 4 should be interpreted with caution because the current analysis was explora-

tory in the sense that it is unclear whether (and to what degree) each of the environmental

variables reflects nonshared environmental influences. Take, for instance, the Maternal

Scale. Relying on criminological theorizing (e.g., Sampson & Laub, 1993) would suggest

that parenting variables should act as shared environmental factors. This may be the case,

but scholars have also argued that many ostensible shared environmental influences will

have nonshared environmental effects to the extent that two individuals have different per-

ceptions or interpretations of the event (Turkheimer & Waldron, 2000). Thus, for this

reason, the current analysis sought to uncover whether any of the included variables oper-

ated as nonshared environmental influences. Overall, the results from Hypothesis 4 are

encouraging for criminological research because they reveal that much work is left to be

done in terms of uncovering the nonshared environmental factors that affect persistent

offending.

Limitations of the current study must be acknowledged. First and foremost, the measure

of LCP offending may include measurement error. Although others have taken a similar

approach to identifying LCP offenders (Barnes et al., 2011; Turner et al., 2007), scholars

have struggled with properly identifying the different offending patterns (Laub & Sampson,

2003; Moffitt, 2006) and a consensus has yet to be reached. As such, the current results

must be viewed as tentative until they are replicated with alternative measurement strate-gies. The second primary limitation also hinges on measurement. Specifically, the identifi-

cation and measurement of neuropsychological deficits poses a difficult task for social

science researchers. At its core, the term neuropsychological deficit refers to an abnormal-

ity (however small) in brain formation or function (Wright, Tibbetts, et al., 2008). This

means that a proper measurement of neuropsychological deficits requires access to brain

scans or analyses of brain functioning. These technologies have only recently begun to

make their way onto the criminological landscape (Moffitt, Ross, & Raine, 2011) and will

require time before they are readily available to a majority of researchers. Nonetheless,

priority should be given to studies that replicate the findings here by using more direct

measures of neuropsychological deficit.

In summary, the current study offers one of the first glimpses into the interconnections

between genetic risk, neuropsychological deficit, parental influences, neighborhood influ-

ences, and LCP offending. No study has sought to examine the link between various envi-

ronmental influences and LCP offending after accounting for genetic factors. In this way,




19/22


the present research moves the body of developmental and life course research forward to a

clearer understanding of the mechanisms that play a role into the etiology of LCP offending.

NOTES

1. Standard errors were adjusted to account for the clustering of siblings within families by using the Cluster command

in Stata 12.1.

2. To account for missing data, each model presented here was compared against a baseline model that was restricted to

cases with usable data on the mediation variable of interest. For instance, in Model 2, the baseline Genetic Risk Scale coef-

ficient was gleaned from a model restricted to cases with nonmissing verbal IQ scores and compared to the effect of the

Genetic Risk Scale in the mediation model.

3. Interactions were explored, individually, for each of the six parenting scales and the Neighborhood Disadvantage Scale.

None of the interaction terms attained statistical significance. The Maternal Scale and Neighborhood Disadvantage Scale are

discussed in the text because they summarize the overall pattern of results from these tests.

4. Standard errors were adjusted to account for the clustering of siblings within families by using the Cluster command

in Stata 12.1.

REFERENCES

Arseneault, L., Moffitt, T. E., Caspi, A., Taylor, A., Rijsdijk, F. V., Jaffee, S. R., . . . Measelle, J. R. (2003). Strong genetic

effects on cross-situational antisocial behaviour among 5-year-old children according to mothers, teachers, examiner-

observers, and twins’ self-reports. Journal of Child Psychology and Psychiatry, 44, 832–848.

Barnes, J. C., & Beaver, K. M. (2010). An empirical examination of adolescence-limited offending: A direct test of Moffitt’s

maturity gap thesis. Journal of Criminal Justice, 38, 1176–1185.

Barnes, J. C., Beaver, K. M., & Boutwell, B. B. (2011). Examining the genetic underpinnings to Moffitt’s developmental

taxonomy: A behavioral genetic analysis. Criminology, 49, 923–954.

Bartusch, D. R. J., Lynam, D. R., Moffitt, T. E., & Silva, P. A. (1997). Is age important? Testing a general versus develop-

mental theory of antisocial behavior. Criminology, 35, 13–48.

Beaver, K. M. (2008). Nonshared environmental influences on adolescent delinquent involvement and adult criminal behav-

ior. Criminology, 46 , 341–370.

Beaver, K. M., Barnes, J. C., May, J. S., & Schwartz, J. A. (2011). Psychopathic personality traits, genetic risk, and gene-

environment correlations. Criminal Justice and Behavior, 38, 896–913.

Beaver, K. M., Ratchford M., & Ferguson, C. J. (2009). Evidence of genetic and environmental effects on the development

of low self-control. Criminal Justice and Behavior, 36 , 1158–1173.

Beaver, K. M., Wright, J. P., & DeLisi, M. (2007). Self-control as an executive function: Reformulating Gottfredson and

Hirschi’s parental socialization thesis. Criminal Justice and Behavior, 34, 1345–1361.

Boutwell, B. B., & Beaver, K. M. (2010). The intergenerational transmission of low self-control. Journal of Research in

Crime and Delinquency, 47 , 174–209.Boutwell, B. B., Franklin, C. A., Barnes, J. C., & Beaver, K. M. (2011). Physical punishment and childhood aggression: The

role of gender and gene-environment interplay. Aggressive Behavior, 37 , 559–568.

Burt, S. A., McGue, M., Krueger, R. F., & Iacono, W. G. (2005). How are parent-child conflict and childhood externalizing

symptoms related over time? Results from a genetically informative cross-lagged study. Development and Psychopathology,

17 , 145–165.

Carey, G. (2003). Human genetics for the social sciences. Thousand Oaks, CA: Sage.

Caspi, A., McClay, J., Moffitt, T. E., Mill, J., Martin, J., Craig, I. W., . . . Poulton, R. (2002). Role of genotype in the cycle

of violence in maltreated children. Science, 297 , 851–854.

Caspi, A., Moffitt, T. E., & Plomin, R. (2000). Neighborhood deprivation affects children’s mental health: Environmental

risks identified in a genetic design. Psychological Science, 11, 338–342.

DeLisi, M. (2005). Career criminals in society. Thousand Oaks, CA: Sage.

DeLisi, M., & Piquero, A. R. (2011). New frontiers in criminal careers research, 2000–2011: A state-of-the-art review. Journal of Criminal Justice, 39, 289–301.

Derks, E. M., Dolan, C. V., & Boomsma, D. I. (2004). Effects of censoring on parameter estimates and power in genetic

modeling. Twin Research, 7 , 659–669.

Devlin, B., Daniels, M., & Roeder, K. (1997). The heritability of IQ. Nature, 388, 468–471.

DiLalla, L. F. (2002). Behavior genetics of aggression in children: Review and future directions. Developmental Review, 22,

593–622.




20/22


Eggleston, E. P., & Laub, J. H. (2002). The onset of adult offending: A neglected dimension of the criminal career. Journal

of Criminal Justice, 30, 603–622.

Farrington, D. P. (1998). Predictors, causes, and correlates of male youth violence. Crime and Justice: A Review of Research,

24, 421–476.

Ferguson, C. J. (2010). Genetic contributions to antisocial personality and behavior: A meta-analytic review from an evolu-

tionary perspective. Journal of Social Psychology, 150, 160–180.Gibson, C. L., Piquero, A. R., & Tibbetts, S. G. (2000). Assessing the relationship between maternal cigarette smoking dur-

ing pregnancy and age at first police contact. Justice Quarterly, 17 , 519–542.

Gottfredson, M. R., & Hirschi, T. (1990). A general theory of crime. Stanford, CA: Stanford University Press.

Harris, J. R. (1998). The nurture assumption: Why children turn out the way they do. New York, NY: Free Press.

Harris, K. M. (2009). The National Longitudinal Study of Adolescent Health (Add Health), Waves I and II, 1994–1996; Wave

III, 2001–2002; Wave IV, 2007–2009 [Machine-readable data file and documentation]. Chapel Hill: University of North

Carolina at Chapel Hill, Carolina Population Center.

Hirschi, T. (1969). Causes of delinquency. Berkeley: University of California Press.

Hirschi, T., & Hindelang, M. J. (1977). Intelligence and delinquency: A revisionist review. American Sociological Review,

42, 571–587.

Jaffee, S. R., Caspi, A., Moffitt, T. E., Dodge, K. A., Rutter, M., Taylor, A., & Tully, L. A. (2005). Nature × Nurture: Genetic

vulnerabilities interact with physical maltreatment to promote conduct problems. Development and Psychopathology, 17 ,67–84.

Jaffee, S. R., & Price, T. S. (2007). Gene-environment correlations: A review of the evidence and implications for prevention

of mental illness. Molecular Psychiatry, 12, 432–442.

Johansson, P., & Kerr, M. (2005). Psychopathy and intelligence: A second look. Journal of Personality Disorders, 19,

357–369.

Kendler, K. S., & Baker, J. H. (2007). Genetic influences on measures of the environment: A systematic review. Psychological

Medicine, 37 , 615–626.

Kendler, K. S., Kessler, R. C., Walters, E. E., MacLean, C., Neale, M. C., Heath, A. C., & Eaves, L. J. (1995). Stressful life

events, genetic liability, and onset of an episode of major depression in women. American Journal of Psychiatry, 152,

833–842.

Larsson, H., Viding, E., Rijsdijk, F. V., & Plomin, R. (2008). Relationships between parental negativity and childhood anti-

social behavior over time: A bidirectional effects model in a longitudinal genetically informative design. Journal of

Abnormal Child Psychology, 36 , 633–645.

Laub, J. H., & Sampson, R. J. (2003). Shared beginnings, divergent lives: Delinquent boys to age 70 . Cambridge, MA:

Harvard University Press.

Loeber, R. (1996). Developmental continuity, change, and pathways in male juvenile problem behaviors and delinquency. In

J. D. Hawkins (Ed.), Delinquency and crime: Current theories (pp. 1–27). New York, NY: Cambridge University Press.

McGloin, J. M., Pratt, T. C., & Piquero, A. R. (2006). A life-course analysis of the criminogenic effects of maternal cigarette

smoking during pregnancy: A research note on the mediating impact of neuropsychological deficit. Journal of Research

in Crime and Delinquency, 43, 412–426.

Moffitt, T. E. (1990). The neuropsychology of juvenile delinquency: A critical review. Crime and Justice: An Annual Review

of Research, 12, 99–169.

Moffitt, T. E. (1993). Adolescence-limited and life-course persistent antisocial behavior: A developmental taxonomy.

Psychological Review, 100, 674–701.

Moffitt, T. E. (2005). The new look of behavioral genetics in developmental psychopathology: Gene-environment interplayin antisocial behaviors. Psychological Bulletin, 131, 533–554.

Moffitt, T. E. (2006). A review of research on the taxonomy of life-course persistent versus adolescence-limited antisocial

behavior. In F. T. Cullen, J. P. Wright, & K. R. Blevins (Eds.), Taking stock: The status of criminological theory

(pp. 277–312). New Brunswick, NJ: Transaction Publishers.

Moffitt, T. E., & Caspi, A. (2001). Childhood predictors differentiate life-course persistent and adolescence-limited antisocial

pathways among males and females. Development and Psychopathology, 13, 355–375.

Moffitt, T. E., Caspi, A., Harrington, H., & Milne, B. J. (2002). Males on the life-course persistent and adolescence-limited

antisocial pathways: Follow-up at age 26 years. Development and Psychopathology, 14, 179–207.

Moffitt, T. E., Lynam, D. R., & Silva, P. A. (1994). Neuropsychological tests predicting persistent male delinquency.

Criminology, 32, 277–300.

Moffitt, T. E., Ross, S., & Raine, A. (2011). Crime and biology. In J. Q. Wilson & J. Petersilia (Eds.), Crime and public

policy (pp. 53–87). New York, NY: Oxford University Press. Nagin, D. S., & Paternoster, R. (2000). Population heterogeneity and state dependence: State of the evidence and directions

for future research. Journal of Quantitative Criminology, 16 , 117–144.

Neale, M. C., & Maes, H. H. M. (2004). Methodology for genetic studies of twins and families. Dordrecht, Netherlands:

Kluwer Academic.




21/22


Odgers, C. L., Caspi, A., Broadbent, J. M., Dickson, N., Hancox, R. J., Harrington, H., . . . Moffitt, T. E. (2007). Prediction

of differential adult health burden by conduct problem subtypes in males. Archives of General Psychiatry, 64, 476–484.

Ogilvie, J. M., Stewart, A. L.., Chan, R. C. K., & Shum, D. H. K. (2011). Neuropsychological measures of executive function

and antisocial behavior: A meta-analysis. Criminology, 49, 1063–1107.

Pinker, S. (2002). The blank slate: The modern denial of human nature. New York, NY: Penguin.

Piquero, A. R. (2001). Testing Moffitt’s neuropsychological variation hypothesis for the prediction of life-course persistentoffending. Psychology, Crime and Law, 7 , 193–215.

Piquero, A. R. (2011). James Joyce, Alice in Wonderland, the Rolling Stones, and criminal careers. Journal of Youth and

Adolescence, 40, 761–775.

Piquero, A. R., & Brezina, T. (2001). Testing Moffitt’s account of adolescence-limited delinquency. Criminology, 39, 353–370.

Plomin, R., Asbury, K., & Dunn, J. (2001). Why are children in the same family so different? Nonshared environment a

decade later. Canadian Journal of Psychiatry, 46 , 225–233.

Plomin, R., & Bergeman, C. S. (1991). The nature of nurture: Genetic influences on “environmental” measures. Behavioral

and Brain Sciences, 14, 373–427.

Plomin, R., & Daniels, D. (1987). Why are children in the same family so different from each other? Behavioral and Brain

Sciences, 10, 1–16.

Raine, A. (2008). From genes to brain to antisocial behavior. Current Directions in Psychological Science, 17 , 323–328.

Raine, A., Brennan, P., & Mednick, S. A. (1994). Birth complications combined with early maternal rejection at age 1 year predispose to violent crime at age 18 years. Archives of General Psychiatry, 51, 984–988.

Raine, A., Lencz, T., Taylor, K., Hellige, J. B., Bihrle, S., Lacasse, L. . . . Colletti, P. (2003). Corpus callosum abnormalities

in psychopathic antisocial individuals. Archives of General Psychiatry, 60, 1134–1142.

Raine, A., Moffitt, T. E., Caspi, A., Loeber, R., Stouthamer-Loeber, M., & Lynam, D. (2005). Neurocognitive impairments

in boys on the life-course persistent antisocial path. Journal of Abnormal Psychology, 114, 38–49.

Ratchford, M., & Beaver, K. M. (2009). Neuropsychological deficits, low self-control, and delinquent involvement: Toward

a biosocial explanation of delinquency. Criminal Justice and Behavior, 36 , 147–162.

Robins, L. N. (1978). Sturdy childhood predictors of adult antisocial behavior: Replications from longitudinal studies.

Psychological Medicine, 8, 611–622.

Rodgers, J. L., Rowe, D. C., & Li, C. (1994). Beyond nature versus nurture: DF analysis of nonshared influences on problem

behaviors. Developmental Psychology, 30, 374–384.

Rowe, D. C., & Farrington, D. P. (1997). The familial transmission of criminal convictions. Criminology, 35, 177–201.

Sampson, R. J., & Laub, J. H. (1993). Crime in the making: Pathways and turning points through life. Cambridge, MA:

Harvard University Press.

Sampson, R. J., Raudenbush, S. W., & Earls, F. (1997). Neighborhoods and violent crime: A multilevel study of collective

efficacy. Science, 277 , 918−924.

Skardhamar, T. (2009). Reconsidering the theory on adolescent-limited and life-course persistent anti-social behavior. British

Journal of Criminology, 49, 863–878.

Taylor, J., Iacono, W. G., & McGue, M. (2000). Evidence for a genetic etiology of early-onset delinquency. Journal of

Abnormal Psychology, 109, 634–643.

Thompson, P., Cannon, T. D., Narr, K. L., van Erp, T., Poutanen, V. P., Huttunen, M., . . . Toga, A. W. (2001). Genetic

influences on brain structure. Nature Neuroscience, 4, 1–6.

Tibbetts, S. G., & Piquero, A. R. (1999). The influence of gender, low birth weight, and disadvantaged environment in pre-

dicting early onset of offending: A test of Moffitt’s interactional hypothesis. Criminology, 37 , 843–877.

Toga, A. W., & Thompson, P. M. (2005). Genetics of brain structure and intelligence. Annual Review of Neuroscience, 28,1–23.

Tremblay, R. E., Japel, C., Perusse, D., Mcduff, P., Boivin, M., Zoccolillo, M., & Montplaisir, J. (1999). The search for the

age of “onset” of physical aggression: Rousseau and Bandura revisited. Criminal Behaviour and Mental Health, 9, 8–23.

Turkheimer, E., & Waldron, M. (2000). Nonshared environment: A theoretical, methodological, and quantitative review.

Psychological Bulletin, 126 , 78–108.

Turner, M. G., Hartman, J. L., & Bishop, D. M. (2007). The effects of prenatal problems, family functioning, and neighborhood

disadvantage in predicting life-course-persistent offending. Criminal Justice and Behavior, 34, 1241–1261.

Utendale, W. T., Hubert, M., Saint-Pierre, A. B., & Hastings, P. D. (2011). Neurocognitive development and externalizing

problems: The role of inhibitory control deficits from 4 to 6 years. Aggressive Behavior, 37 , 476–488.

van Beijsterveldt, C. E. M., Bartels, M., Hudziak, J. J., & Boomsma, D. I. (2003). Causes of stability of aggression from early

childhood to adolescence: A longitudinal genetic analysis in Dutch twins. Behavior Genetics, 33, 591–605.

Van Hulle, C. A., Waldman, I. D., D’Onofrio, B. M., Rodgers, J. L., Rathouz, P. J., & Lahey, B. B. (2009). Developmentalstructure of genetic influences on antisocial behavior across childhood and adolescence. Journal of Abnormal Psychology,

118, 711–721.

Walsh, A. (2002). Biosocial criminology: Introduction and integration. Cincinnati, OH: Anderson.

Weber, S., Habel, U., Amunts, K., & Schneider, F. (2008). Structural brain abnormalities in psychopaths: A review.

Behavioral Sciences and the Law, 26 , 7–28.




22/22


Wright, J. P., & Beaver, K. M. (2005). Do parents matter in creating self-control in their children? A genetically informed

test of Gottfredson and Hirschi’s theory of low self-control. Criminology, 43, 1169–1202.

Wright, J. P., Beaver, K. M., DeLisi, M., & Vaughn, M. G. (2008). Evidence of negligible parenting influences on self-

control, delinquent peers, and delinquency in a sample of twins. Justice Quarterly, 25, 543–569.

Wright, J. P., Boisvert, D., & Vaske, J. (2009). Blood lead levels in early childhood predict adulthood psychopathy. Youth

Violence and Juvenile Justice, 7 , 208–222.Wright, J. P., Dietrich, K., Tis, M. D., Hornung, R. W., Wessel, S. D., Lanphear, B. P., . . . Rae, M. N. (2008). Association

of prenatal and childhood lead concentrations with criminal arrests in early adulthood. PLoS Medicine, 5, 732–740.

Wright, J. P., Tibbetts, S. G., & Daigle, L. E. (2008). Criminals in the making: Criminality across the life course. Thousand

Oaks, CA: Sage.

Yang, Y., Raine, A., Lencz, T., Bihrle, S., LaCasse, L., & Colletti, P. (2005). Volume reduction in prefrontal gray matter in

unsuccessful criminal psychopaths. Biological Psychiatry, 57 , 1103–1108.

J. C. Barnes is an assistant professor in the Department of Criminology at the University of Texas at Dallas. His research

seeks to understand how genetic and environmental factors combine to affect criminological phenomena. Recent works have

attempted to reconcile behavioral genetic findings with theoretical developments in criminology. His work can be found in

journals such as Aggressive Behavior, Criminology, Intelligence, Journal of Marriage and Family, Justice Quarterly, and Physiology and Behavior .

criminal justice and behavior-2013-barnes-519-40.pdf

Documents