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Question 1: (1) How are cases of domestic violence with sexual assault different from other sexual assault cases? (at least 300 words) (2) Answer the following question now that we’ve covered two weeks of sexual assault investigation research: How is a sexual assault investigation different than other investigations? (Your answer needs to be more than just the sensitive nature of sexual assault). (at least 300 words)
Question 2: How would you approach an interview with a sexual assault suspect who is unknown to a victim and a domestic violence sexual assault? Provide specific examples on the different approaches and why it would apply to one but not the other. (at least 350 words)
Question 3: (1) Does your social media usage ever put you at risk for being a victim? How? (at least 300 words) (2) Will you change you social media behavior after this study? Why or why not? (at least 300 words)
Question 4: Discuss offender characteristics and how they can be applied to a criminal investigation. (at least 350 words)
Question 5: A freshman student at the University (Jane Smith) goes out to a local bar off of campus after mid term exams. While there she meets a guy name Jack Johnson. Johnson is studying at the local community college. He is looking to transfer to the university where Jane Smith attends after he finishes at the community college. He is at the community college to start because he cannot afford the University tuition at this time.
While at the bar Jane becomes intoxicated and wanders away from her friends. Jack follows her and offers to take her home. She is initially hesitant but agrees. Hours later, Jane wakes up in a strange motel and is not wearing any pants. She goes to call 911 believing she has been raped but the sim card has been removed from her phone. She uses the phone in the room to call 911.
Police respond to the scene and Jane is transported to the hospital for a SANE /SAFE exam. Police process the crime scene in the hotel room and gather evidence including the bedding and a used condom.
The case eventually leads to the arrest of Johnson for sexual assault. He is indicted and convicted for the sexual assault. Smith files a civil suit against Johnson for the sexual assault and that suit is pending.
A year after the incident, Smith is walking through the courtyard of the school and she comes face to face with Johnson who has transferred to the University from the community college, like he originally planned. Smith immediately notifies the school administration that her rapist is on campus.
The university launches an investigation and learns that Johnson just started this past semester as a transfer student. He has been awarded pell grants and financial aide. The school is dragging its feet on the case afraid of denying Johnson an education. The school claims it is being very in depth in the investigation while it is taking its time. During this time Jane notices that she is getting looked at differently by people on campus and pointed at. There are also different postings showing up on social media with indirect references to her.
While the university is conducting its investigation a sexual assault occurs but the suspect is unknown. The suspect matches the description of Johnson only slightly. In this case no evidence was recovered. The victim did report that the sim card from her phone was missing and she could not use it.
Answer and Explain the following:
Identify risk factors associated with this scenario.
Identify a victimology of Jane Smith.
Identify a suspect profile and characterization of Jack Johnson.
Identify additional steps to take in the investigation.
Identify any violations that have been committed regarding Title IX, if any.
Identify the responsibility the school has at this point, if any.
There is a hidden clue in here that you should try to investigate and identify. It is right in front of you but we did not really discuss it. I know everyone of you can figure it out.
Feel free to contact me while working on this to brainstorm any questions or ideas and direction you want to go with this.
This assignment incorporates material from every section of this course.
Requirements:
APA formatting and citation methods – papers must contain three (3) references from the course material and three (3) additional references for outside sources. A paper that does not adhere to the APA citation methods and required references will not be eligible to receive a letter grade of an A.
Each paper should at a minimum have a well developed introduction, body and conclusion. Papers should also contain a well-developed thesis statement.
Papers should be written in third person.
Papers should be a minimum of 4 pages in length, double-spaced. Use 12 point font and Times New Roman font.
When questions are posed for a paper, failure to address the questions will be a 5-point penalty for each question that is not addressed.
Question 6: Read the two assigned journal articles and answer the two sets of journal article questions provided below. (at least 2 pages)
First Journal Article and Assigned Questions: “Examining Public Opinion about Crime and Justice”
What are some methodological issues that scholars face when measuring fear of crime, and how do the authors plan on addressing those problems?
Identify the four research questions as stated by the authors.
How is Fear of Crime operationalized?
What were the key findings and policy implications of this study?
Second Journal Article and Assigned Questions: “Contextualizing the Criminal Justice Policy-Making Process”
What is the author’s main objective?
Identify the research questions as stated by the author.
Identify the theoretical framework that was used to analyze the policy-making process.
What were the key findings and policy implications of this study?
Question 7: Read the two assigned journal articles and answer the two sets of journal article questions provided below. (at least 2 pages)
First Journal Article and Assigned Questions: “Self Protection in Rural America”
Identify the dependent and independent variables in the article.
Identify the measurement models that were used in the analysis.
Name one way in which the collective security or self-help hypothesis can be tested.
What does the collective security/self-help hypothesis predict?
What were the key findings and policy implications of this study?
Second Journal Article and Assigned Questions: “Analyzing the Origins of Life Course Persistent Offending”
Identify the four hypotheses that were formed from Moffitt’s theories.
Identify the risk factors that lead to life-course persistent offending.
How did the author measure persistent offending and low self-control?
What were the key findings and policy implications of this study?
Unformatted Attachment Preview
458907
CJBXXX10.1177/0093854812458907CRIMI
NAL JUSTICE AND BEHAVIORBarnes / ORIGINS OF LIFE COURSE–PERSISTENT OFFENDING
2012
Analyzing the Origins of
Life-Course-Persistent Offending
A Consideration of
Environmental and Genetic Influences
J. C. Barnes
University of Texas at Dallas
Moffitt’s developmental taxonomy has sparked much attention among criminologists interested in explaining the etiology of
life-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 influence 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 interactions 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
C
riminologists have long noted that the best predictor of future criminality is past
criminality (Nagin & Paternoster, 2000; Robins, 1978). This finding has sparked
myriad 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 behavior 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 foundations. 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
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 particular 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 agecrime curve, masked important variation in offending patterns. Her main thesis was that
two types of offenders are identifiable in the population: adolescence-limited (AL) offenders 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 during 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
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 interpersonal 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 activity 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 heightened 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 prosocial
manner (Moffitt & Caspi, 2001). The confluence of these two factors (i.e., neuropsychological 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 neurocognitive 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 presence 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 delinquency. 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
522 Criminal Justice and Behavior
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 impairments, and they were more likely to have suffered head injuries that resulted in unconsciousness. 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 deficits 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 childhood 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 as
compared 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 factors. 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 to
Moffitt’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
Barnes / ORIGINS OF LIFE COURSE–PERSISTENT OFFENDING 523
Figure 1: Graphical Depiction of the Hypothesized Relationship Between Genetic Factors, Neuropsychological Deficits, and Life Course–Persistent Offending
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 LCP
offending 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 neuropsychological 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 influences operates as nonshared environmental influences on LCP offending. Although these
constructs 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 investigation 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
524 Criminal Justice and Behavior
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 presence of neuropsychological deficits.
Hypothesis 2: Parental influences, even after accounting for genetic risk, will predict LCP
offending.
Hypothesis 3: Genetic risk factors for LCP offending will interact with parental and neighborhood influences such that the presence of poor parenting or the presence of a disadvantaged
neighborhood 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 for
the 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 sampling 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 interview, along with their primary caregiver, in their homes. Information from 20,745 adolescents 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 more
detailed information about the adolescent, his or her social experiences, and his or her rearing 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 interviews). 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
Barnes / ORIGINS OF LIFE COURSE–PERSISTENT OFFENDING 525
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 siblings 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 a
Wave 2 delinquency scale by summing across the 17 items (α = .81). During Wave 3 interviews, 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 questions 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 capturing 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 DNA
and 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 information with information about the cosibling’s status as a persistent offender.
526 Criminal Justice and Behavior
Table 1: Descriptive Statistics for Add Health Siblings
Variable
Persistent offender
Persister
Nonpersister
Genetic Risk Scale
Neuropsychological deficits
Verbal IQ (Wave 3)
Low self-control (Wave 3)
Birth weight
Parental influences (Wave 1)
Maternal attachment
Maternal involvement
Parental permissiveness
Maternal disengagement
Maternal aspirations
Maternal Scale
Neighborhood disadvantage (Wave 1)
Frequency
M
SD
Min
Max
.05
.22
0
1
.90
.46
0
3
98.21
28.59
6.84
15.68
10.91
1.41
7
3
4
122
67
11.63
9.40
3.91
5.08
9.03
8.68
–0.001
0.00
1.10
1.95
1.59
3.34
1.81
0.74
0.94
2
0
0
5
2
–4.11
–1.22
10
10
7
25
10
1.63
4.94
120
2,225
Generating the scale involved five steps. First, the data were arranged so that each sibling 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 persistent
offender 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 probability 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 respondents 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 neuropsychological dysfunction, and prior scholars have linked verbal IQ with problems with selfcontrol (Ratchford & Beaver, 2009), psychopathy (Johansson & Kerr, 2005), and persistent
delinquent involvement (Moffitt, Lynam, & Silva, 1994). In other words, scholars have
used 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 interviews. During these interviews, respondents were administered the Peabody Picture
Barnes / ORIGINS OF LIFE COURSE–PERSISTENT OFFENDING 527
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 neuropsychological 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 interviews, primary caregivers were asked to report on their child’s (i.e., the target respondent’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 constructively 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 indicated 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
528 Criminal Justice and Behavior
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 question was similar, but the reference was high school. Responses to these two items were
summed 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 analysis. The results from this analysis indicated
