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Article
The Cycle of Violence
Revisited: Childhood Victimization, Resilience, and Future Violence
Journal of Interpersonal Violence
1–26
© The Author(s) 2016 Reprints
and permissions: sagepub.com/journalsPermissions.nav DOI:
10.1177/0886260516651090
jiv.sagepub.com
Kevin A. Wright,1 Jillian
J. Turanovic,2 Eryn N. O’Neal,3 Stephanie
J. Morse,1 and Evan T. Booth4
Abstract
The individual and social protective factors that help break the
cycle of violence are examined. Specifically, this study investigates (a) the individual and social protective factors
that reduce violent
offending among previously victimized children, and (b)
whether certain protective factors are more
or less important depending on the type and frequency of childhood
victimization experienced. Data on young adults from Wave III of the National
Longitudinal Study of Adolescent to Adult Health are used (N = 13,116). Negative
binomial regression models are estimated to examine the protective factors that promote
resiliency to violent offending among individuals who reported being
physically and sexually
victimized as children. Results indicate that a number
of individual and social protective factors reduce violent offending in young
adulthood. With a few exceptions, these factors are specific to the type,
frequency, and comorbidity of abuse experienced. The results suggest a number
of promising approaches to break the cycle of violence
among previously victimized children. Future
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1Arizona State University, Phoenix, USA
2Florida State University, Tallahassee, USA
3Sam
Houston State University, Huntsville, TX, USA
4University
of Denver, CO, USA
Corresponding Author:
Kevin A. Wright, School of Criminology and Criminal Justice,
Arizona State University, 411 N. Central Ave. Ste. 600, Phoenix, AZ 85004, USA.
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2 Journal of Interpersonal Violence
research should move beyond explaining the cycle of violence to
examine how the cycle may be broken.
Keywords
treatment/intervention, child abuse, physical abuse, sexual
abuse, intergenerational transmission of trauma
Introduction
Over the last 25 years, the cycle
of violence hypothesis (Widom, 1989a; 1989b) has provided a solid foundation
for studying the long-term crimino- genic effects of childhood victimization.
Research within this tradition has established two major components to the
thesis. First, victims of child abuse are at an increased risk of perpetrating
violence in adolescence and young adulthood (Currie & Tekin, 2011; Maxfield & Widom, 1996; Watts & McNulty, 2013), and second,
not all victims
of child abuse
go on to perpetrate future
violence (DuMont, Widom, & Czaja, 2007; Jaffee, Caspi, Moffitt, Polo-Tomas,
& Taylor, 2007). Comparatively
speaking, childhood victims that engage in violence have received far more
scholarly attention than those who
do not. Indeed, the majority of research in this area focuses on identify- ing
risk factors that increase the likelihood of violence among abused chil-
dren—factors such as biased and deficient social-information-processing
patterns (Dodge, Bates, & Pettit, 1990), endorsement of antisocial
attitudes and associations with deviant peers (Herrenkohl, Huang,
Tajima, & Whitney, 2003), and genetic
susceptibility to maltreatment-induced changes to neu- rotransmitter systems
(Caspi et al., 2002).
Much less
understood, however, are the protective factors
that explain why some abused children can avoid completing the cycle of
violence (McGloin & Widom,
2001). And existing
research suggests that most abused children do avoid completing this
cycle. Even in Widom’s widely cited 1989 Science
study, only 29% of her abused
and neglected sample had an adult criminal record, and only 11% of that sample had a violent criminal
record as an adult. The
relative inattention to the protective factors exhibited by these resilient
individuals is unfortunate given that they may provide valuable information
regarding how the cycle may be broken for those less resilient.
The purpose of
the current study is to identify the protective factors that increase
resilience to violent offending among young adults who were previ- ously abused as children.1 We focus specifically on a subset of young adults
from the National Longitudinal Study of Adolescent to Adult Health (Add
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Wright et al. 3
Health) who were abused prior to
the sixth grade to examine two broad research questions.
Research Question 1: What
factors are associated with reduced violence
among young adults who identify as being previously abused as children?
In answering this, we focus on
determining the individual and social protec- tive factors that are distinctive
to the “negative cases” of the cycle of vio- lence—those individuals who might
be expected to engage in adult violence given their childhood victimization yet
do not.
Research Question 2: Are
certain protective factors more or less impor- tant depending on the type and
frequency of abuse experienced?
To answer this question,
we examine whether
protective factors differ accord-
ing to the type, frequency, and comorbidity of childhood abuse. Our work comes
as part of a more general victimization literature that seeks to explain
variations in behavioral responses among victims (e.g., Boxer & Sloan-
Power, 2013; Grych, Hamby, &
Banyard, 2015; Turanovic & Pratt, 2015). Ultimately, then, our broader
objective is to refocus cycle
of violence research on determining the modifiable
protective factors that may break the cycle.
Child Abuse and the Cycle of
Violence
Research on child abuse has come a
long way since Kempe, Silverman, Steele, Droegemueller, and Silver (1962)
wrote of physical
abuse in the form
of “parental assault” as part of battered-child syndrome. This body of work has
advanced our understanding of child abuse in significant ways, and yet reviews of the literature identify a number
of important conceptual and meth- odological
issues to address for any study that examines the consequences of childhood victimization (see
especially Malvaso, Delfabbro, & Day, 2015;
Thornberry et al., 2012). Four issues in particular merit special attention for
research that seeks to identify protective factors that may break the cycle of
violence. Two of these are
conceptual and relate to how abuse and resilience are defined, and two are
methodological and concern the nature of the sam- ples most often used in
existing research.
First, existing
works often overlook the distinctions between type, fre- quency, and
comorbidity of child abuse (Malvaso et al., 2015). The original cycle of
violence research documented that physical abuse was most impor- tant for the
prediction of violence in adulthood (Widom, 1989a; see also
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4 Journal of Interpersonal Violence
Maxfield & Widom, 1996). Yet the type
of abuse experienced by children may produce different behavioral outcomes,
with sexual abuse assuming a critical importance alongside physical abuse
(e.g., Currie & Tekin, 2011; see
also Noll, 2005; Yun, Ball, &
Lim, 2011). The frequency with which
abuse occurs is also likely to impact whether a previously abused child engages
in violent behavior as an adult. For example, Heller, Larrieu, D’Imperio, and
Boris (1999) identify the number of instances of abuse as a potentially con-
founding factor that is consistently unaddressed in studies of maltreated
children (see also DuMont et al., 2007; McGloin & Widom, 2001). In addi-
tion, the comorbidity of types of
abuse is likely to affect the relationship between childhood victimization and
future adult violence. This becomes particularly important when acknowledging
resilience research that suggests personal resources (e.g., above-average
intelligence) may no longer be suf- ficient as a protective factor when the
child experiences multiple forms of stress (Jaffee et al., 2007).
Second,
existing works often examine static protective factors that cannot be addressed or altered through
intervention programs. In particular, demo- graphic characteristics are often
offered as protective factors, which lead to perplexing assertions that “being
White” or “being older” serve as a buffer against the deleterious effects
of child abuse. Instead, if modifiable protective factors such as success in
school or mentorship from a caring adult can be identified, then interventions
can be tailored toward promoting these factors within previously abused
individuals (Cicchetti, 2004; Haskett, Nears,
Ward, & McPherson, 2006).
Third, existing works often use
agency-based samples of abused chil- dren that may not be representative of the larger abused population of childhood victims.
Differences between victimized children who were referred to agencies (such as child protective services) and those
who were not may include the severity of abuse and levels of familial
or extrafamil- ial support systems (Heller et al., 1999). Furthermore, data based on agency
samples tend to underreport actual
childhood victimization instances, which could result in conservative estimates of the relationships between abuse, resilience, and future
violence (Topitzes, Mersky, Dezen, &
Reynolds, 2013). Due in part to these
concerns, Thornberry and col- leagues
(2012) recommend that cycle of violence
studies use a sample that is representative
of a general population, as selected using probability sampling
techniques, with a satisfactory
participation rate. A large sample of
this nature would also allow for separate examinations of the protective factors of different subtypes
(e.g., physical abuse only, physical and sexual
abuse; Haskett et al., 2006).
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Wright et al. 5
Finally,
existing works within the cycle of violence and resilience litera- tures often
focus on outcomes
in childhood or adolescence, and less is known
regarding the protective factors that promote resilience into early adulthood
(McGloin & Widom, 2001; Topitzes et al., 2013; Topitzes, Mersky, &
Reynolds, 2012). Studies have documented that previously abused children may
appear resilient in adolescence, but not in early adulthood (DuMont et al., 2007);
youth who appear
resilient in adolescence may not have truly bro- ken the cycle. We believe that the essence of the cycle of
violence is that adults use violence toward children who then use violence toward
others when they are adults.
The child
abuse and resilience literatures address the above issues
to vary- ing degrees—with available reviews suggesting that the bulk of this literature
falls short on conceptual and methodological rigor (Haskett et al., 2006;
Heller et al., 1999; Thornberry et al., 2012).
Nevertheless, this work suggests
that studies that examine the factors that can break the cycle of violence
should (a) differentiate between the type, frequency, and comorbidity of child
abuse, (b) focus on dynamic
protective factors that can be modified, (c) use a large
and diverse sample,
and (d) use self-reported outcomes
of violence that are measured in adulthood.
Current Focus
A key disclaimer of the original
cycle of violence hypothesis research is
that future adult violence is far from inevitable. Widom (1989a, p. 169)
suggests, “It is important to understand the potential protective factors that
intervene in the child’s development and to compare the development of those
who succumb and those who are ‘resilient’ and do not.” We take this advice and begin with a sample of young adults who
have reported being abused as children. According to the cycle of violence, we
would expect
most of these individuals to “succumb” and be at an increased
risk of engag- ing in violence. So
what sets those who perpetrate violence apart from those who do not? Based on
the existing child abuse and resilience literatures, our current study has two objectives. First, we identify the
protective factors that reduce violent
offending in early adulthood among individuals who were abused as children. Second, we examine whether certain
protective factors are more or less important depending on the type and frequency
of child abuse experienced. By addressing the above objectives, we
identify the protective factors that
contribute to resilience in young adulthood, and
we also hope to encourage future work on how victims of child abuse are able to break the cycle of violence.
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6 Journal of Interpersonal Violence
Method
Data
We use data from Add Health,
which is an ongoing, nationally representative study of adolescent health and well-being (Harris, 2009). A sample of 80 high schools and 52 feeder middle schools
and junior high schools was selected through a disproportionately stratified,
school-based, clustered sampling design. The sample was representative of U.S.
schools with respect to region of
the country, urbanicity, school type, school size, and ethnicity (Harris,
2011). At Wave I in 1994 to 1995,
in-school surveys were administered to more than 90,000 students enrolled in
grades 7 to 12, from which a random subsample of 20,745 adolescents was
selected to participate in the Wave I,
in-home component of the study. Wave III follow-up interviews with the Wave I sample were conducted 7 years later
during 2001 to 2002. The aver- age age of participants was 15 years at Wave I (ranging from 11 to 21 years) and 22 years at Wave III (ranging from 18 to 28 years). Of
the original Wave I respondents, approximately 15,000 participated in the Wave III in-home interview (N =
14,322 with valid
sample weights).2 We focus primarily on the Wave III survey as it contains information
on childhood physical and sexual abuse that was not captured in previous waves.
As is common
with large-scale survey data, information was missing on some of our key
variables due to item nonresponse (10.7% of Wave
III respondents had item-missing data). To address the potential bias produced by missing data, multiple
imputation was used (Allison, 2000).
This involved a procedure in
which 10 imputed data sets were generated by a missingness equation that
included all variables in the present study,
and which adjusted estimates according to the clustered surveying of
respondents in schools (using the mi suite
in Stata 13). The results from 10 imputed data sets were combined using pooled
parameter estimates to account for the possible underestimation of standard errors observed in single imputation procedures.3 Cases
with missing information on the dependent variable (i.e., violent offending),
and those without information on childhood victimization were excluded (n = 1,206). As a result, 91.6% of Wave III respondents were retained (N = 13,116).
Childhood Victimization
During the Wave III interview, Add
Health respondents were asked to ret- rospectively report information on
physical and sexual victimization that occurred during childhood.4 The following two questions were asked: “By
Wright et al. 7
the time you started 6th grade,
how often had your parents or other adult caregivers slapped, hit, or kicked
you?” and “By the time you started 6th grade, how often had one of your parents
or other adult caregivers touched you in a sexual way, forced you to touch him
or her in a sexual way, or forced you to have sexual relations?” Responses to
each question ranged from 0 (this has
never happened) to 5 (more than 10
times), and approxi- mately 29.9% of respondents reported experiencing at
least one instance of childhood physical or sexual victimization.5 These questions were adminis- tered using
audio computer assisted self-interview (A-CASI), which is thought to elicit
more accurate reporting of sensitive information involving victimization and
sexual encounters (Turner et al., 1998). To improve the accuracy of lifetime
event data, the Wave III interview also used an event history calendar as a
memory aid. Other incidents of physical and sexual victimization that occurred
after respondents reached the sixth grade were not captured in the Add Health
survey. Measures of childhood victimization in the Add Health were adapted from
previous surveys and have been used frequently in the literature (see, for
example, Hussey, Chang, & Kotch, 2006).
Consistent with
our research objectives, the sample is split into several groups that reflect
different forms and frequencies of childhood victimiza- tion. For physical
abuse, these include respondents who experienced no physical abuse (70.9%, n =
9,303), a low frequency of physical abuse
(meaning 1-2 times; 14.2%, n =
1,856), and a high frequency of physical
abuse (meaning 3 or more times; 14.9%, n
= 1,957). Similarly, for sexual abuse,
we categorize respondents as those who experienced no sexual abuse (95.4%, n =
12,510), a low frequency of sexual abuse (1-2
times; 2.9%, n = 379), and a high frequency of sexual abuse (3 or
more times; 1.7%, n = 227). We also
developed categories to examine individuals who experi- enced both physical and
sexual abuse. These include respondents who did
not experience both physical and
sexual abuse (96.2%, n = 12,617),
those who reported a low frequency of
both physical and sexual abuse (no more than two instances of each form of
abuse; 1.8%, n = 240), and those who reported a high frequency of both physical and sexual abuse (experiencing both
forms of abuse, at least one of which happened 3 or more times; 2.0%, n = 259). Individuals who experienced
three or more instances of either physical
or sexual abuse scored above the 90th percentile of childhood vic- timization
in the Add Health data. Consistent with prior research on child- hood exposure
to trauma (e.g., van der Wal, de
Wit, & Hirasing, 2003), this percentile was chosen as the cutoff point for
the “high frequency” categori- zations of child abuse. Sample statistics for
all groups of childhood victims are available by contacting the authors.6
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8 Journal of Interpersonal Violence
Violent Offending
The dependent variable, violent offending, is a four-item
variety score that reflects whether participants committed the following types
of violence dur- ing the year prior to the Wave III interview: “hurt someone
badly in a physi- cal fight,” “used or threatened to use a weapon to get
something from someone,” “used a weapon in a fight,” “and “pulled a knife or
gun on some- one.” All forms of violence were fairly rare in the full sample
(5.5%, 2.0%, 1.8%, and 1.3%, respectively), and approximately 7.7% of Wave III
respon- dents reported committing a violent offense at Wave III.7
Individual Protective Factors
To better understand heterogeneity in adult
violence among victims of child abuse, the effects
of several individual and social protective factors on violent offending are assessed.
Specifically, we include four individual protective factors commonly associated
with positive life outcomes: self-control, low depression, self-esteem, and
verbal intelligence.
Self-control at Wave III is
measured using nine items from the novelty- seeking dimension of Cloninger’s
(1987) Tridimensional Personality Questionnaire (e.g., “I sometimes get so excited
that I lose control of myself,”
“I like it when people can do whatever they want, without strict rules and
regulations”). These nine items are often used to measure
self-control in early adulthood (see, for example,
Turanovic, Reisig, & Pratt, 2015). Each item featured a 5-point response
set, ranging from 1 (very true) to 5 (not true). The scale exhibits a high level of internal consistency (α =
.87), and is coded so that higher scores
indicate higher levels
of self-control. Principal components analysis indicated that the self-control scale was
unidimensional (λ = 4.34; factor loadings > .66).
Low depression
is measured using nine
items from the 20-item Center for Epidemiologic
Studies Depression (CES-D) scale (Radloff, 1977)
that are available in the Add Health data.
Participants were asked
to report how often
they experienced feelings related to depression in the past 7 days (e.g., “you
were sad” [reverse-scored], “you cried a lot” [reverse-scored], “you enjoyed
life”). Closed ended responses for each item ranged from 0 (never/rarely) to 3
(most of the time/all of the time),
and were summed to create a scale where larger values reflect lower levels of depression (range 0-27; α = .81). Previous
research has shown the 20-item CES-D to cluster into four subfactors—
somatic-retarded activity, depressed affect, positive affect, and interpersonal relationships (Ensel, 1986)—and all
four components are represented in the nine items
used here. The CES-D
has been previously
validated among
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Wright et al. 9
adolescents and adults (e.g.,
Radloff, 1991),
and principal components analysis confirmed that the scale was unidimensional (λ = 3.74;
factor loadings > .44).
Self-esteem is assessed using four items from Rosenberg’s (1965)
Self- Esteem Scale: “you have many good qualities,” “you like yourself just the
way you are,” “you have a lot to be proud of,” and “you feel like you are doing everything just about right.”
Items ranged from 0 (strongly disagree) to 4 (strongly agree),
and were summed so that higher scores indicate higher levels of self-esteem
(range 0-16; α = .78). Prior research has shown the Rosenberg scale
to be highly reliable (e.g.,
if a person completes the scale on two occasions, the two scores tend to
be similar) and unidimensional (e.g., Baumeister, Campbell, Krueger, & Vohs, 2003). Principal
components analy- sis
confirmed that the items used here are associated with a single construct (λ =
2.46; factor loadings > .74).
Verbal intelligence is captured using respondents’ age-normed Add Health Picture Vocabulary Test (PVT) score. Add Health PVT scores come from a shorter,
computerized version of the Peabody Picture Vocabulary
Test (Revised) that was
administered to participants at the beginning of the Wave III interview. During this test, interviewers would read a
series of words aloud, and respondents would select pictures that best fit the
words’ mean- ings. Each word in the PVT corresponded to four simple,
black-and-white illustrations arranged in a multiple-choice format. There were 87 items in the Add Health PVT, and raw scores were standardized by age.
Social Protective Factors
In addition to the individual
factors, five forms of social protective factors are also assessed at Wave III:
marriage, job satisfaction, mentorship, religi- osity, and educational
attainment. These factors are considered protective as they can provide
victimized children with supportive coping resources to overcome adversity
(Agnew, 2006), and they can serve as important sources of restraint that
prevent child victims from engaging in crime and violence later on.
Marriage reflects whether respondents were currently married at
the time of the Wave III interview (1 = yes, 0 = no). Nearly 17.3% of young adults reported being married, and this
proportion is consistent with estimates from
the 2000 U.S. Census for young adults between the ages of 20 and 24 (Kreider & Simmons, 2003). Although
data limitations prevent assessing the quality
of these marriages (e.g., marital attachment, connectedness to spouse, and
marital satisfaction), it is important to examine marital status in light of
the body of work indicating that married persons are less likely to engage in
crime than their unmarried counterparts (Sampson & Laub,
1993). Still,
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10 Journal of Interpersonal Violence
because this measure cannot
differentiate between people who have healthy marriages and those who do not,
the effects of marriage observed here may be conservative (see, for example,
Kuhl, Warner, & Wilczak, 2012).
Job satisfaction is captured using a single-item indicator for whether
respondents had a job that they were satisfied with (1 = yes, 0 = no).
Approximately 70.0% of young adults reported being employed at Wave III, and 53.2% of all respondents
reported having a satisfying job. While job satisfaction is more commonly
measured using different multi-item indexes (e.g., Hackman & Oldham, 1975),
such scales were not available in the data.
The use of a single
global indicator of job satisfaction is consistent with prior
research using the Add Health (e.g., Siennick,
2007).
Mentorship is captured using the following survey question at Wave III: “Other than your parents or
step-parents, has an adult made an important positive difference in your life
at any time since you were 14 years old?”
(1 = yes, 0 = no). The majority of Wave III respondents indicated that an adult
had made a positive difference in their life (75.8%), and, most commonly, these
mentors came in the form of siblings and extended family members (34.7%),
teachers/guidance counselors (19.7%), and friends (17.1%).
Three
dichotomous indicators of educational
attainment were included. At Wave III,
respondents were asked to identify the number of years of schooling they had
received, as well as the educational degrees they received. Using this
information, the following indicators of highest level of educational attainment were created: high school graduate (35.4%), some
college (36.8%), and college graduate
(18.7%), where no high school degree
serves as the reference category. College attendance included both 2-year and
4-year postsecondary institutions. Approximately 12.5% of par- ticipants at Wave III had not received a high school
diploma or graduate equivalency degree.
Religiosity is a four-item summated scale composed of the following
sur- vey items at Wave III: “How important is your religious faith to you?”
“How important is your spiritual life to you?” “To what extent are you a spiritual person?” and “To what extent are you a religious
person?” Responses to each
item ranged from 0 (not at all/not
important) to 3 (very/more important than anything else), where higher
values indicate greater religiosity (α = .88). Principal components analysis confirmed
that the items used to measure reli- giosity were unidimensional (λ =
2.95; factor loadings > .83).
Additional Explanatory Variables
Demographic variables and several
important correlates of child abuse and violent offending are also included in
the multivariate analyses. Financial
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Wright et al. 11
hardship
is a three-item scale at Wave III reflecting
whether respondents or someone in their
household did not have enough money in
the past year to: “pay the full amount of rent or mortgage,” “pay the
full amount of a gas, electricity, or oil bill,” or “had
services turned off by the gas or elec-
tric company or the oil company
wouldn’t deliver because payments were
not made.” Items were dummy-coded and
summed to create an index where higher scores reflect greater financial
hardship (range 0-3). Factor analysis of tetrachoric correlations confirmed that these items are associ- ated with a single construct (λ= 2.08;
factor loadings > .76). A single-item indicator of childhood neglect available in the data is also included that reflects
how often, before the sixth grade, respondents’ parents or other adult
caregivers “had not taken care of
your basic needs, such as keeping you clean or providing food or
clothing.” Closed ended responses to the childhood
neglect item ranged from 0 (never) to 5 (more than 10 times),
and 11.15% of respondents reported at
least one instance of neglect. Finally,
variables are included for age (the respondent’s age in years at Wave III), male (1 = male, 0 = female), and race/ethnicity (including Black, Hispanic, and Other minority, where non-Hispanic White serves as the reference category).
Analytic Strategy
The analyses proceed in two
stages. First, several diagnostic tests are con- ducted to rule out the presence
of harmful levels
of collinearity. Next,
a series of multivariate
regression models are estimated to assess whether the indi- vidual and social protective factors reduce violent
offending within each sub-
sample of victims. As descriptive statistics indicate that the distributions
for violent offending are overdispersed within each subsample of victims (e.g.,
M = .18, variance = .31 among those
experiencing low frequency physical abuse), negative binomial regression is
used (Long & Freese, 2006). The negative binomial model is a generalized
linear regression model for count data that is appropriate to use when there is
overdispersion in the dependent variable (i.e., when the conditional variance
is greater than the mean; see Cameron & Trivedi, 2013; Hilbe, 2011).8
In addition, coefficient estimates and standard errors
may be biased if fea- tures of the Add Health sampling
design are not taken into account (Chen & Chantala, 2014). As a result, the
multivariate models are estimated using the Wave
III Add Health sampling weights adjusted for subpopulation analyses, and
clustered robust standard errors that account for the school-based sam- pling
design.9 All analyses are
conducted using Stata 13 (StataCorp, College Station, TX).
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12 Journal of Interpersonal Violence
Results
Before proceeding with the
multivariate analyses, a series of model diagnos-
tics were examined. Bivariate correlations between independent variables in all
models did not exceed an absolute value of .40, and variance inflation factors
were under 1.6. Furthermore, the condition index values did not exceed 28,
which puts them beneath the commonly used threshold of 30 (Tabachnick &
Fidell, 2012). According to this evidence, the relationships between
independent variables should not result in biased estimates or inef- ficient
standard errors due to multicollinearity.
Tables 1 to 3 display the
negative binomial regression models predicting violent offending among the different groups of childhood
victims—physical abuse victims, sexual abuse victims, and victims of
both physical and sexual abuse. Each model is estimated using all of the
individual and social factors to determine which have more “general” protective effects on violent
offend- ing across groups, and which tend to be more specific to
particular types of childhood victims. For purposes of comparison, each table
also includes a reference group of individuals who did not experience each type of childhood
victimization.10
Childhood Physical Abuse
Table 1 displays the effects of various protective factors on violent
offending according to the amount of physical abuse experienced. As can
be seen, sev- eral key variables are negatively related to violent offending
for victims of low and high frequency physical abuse in Models 2 and 3. In particular, self- control reduces violent offending
across both groups of victims, where inci- dence rate ratios (IRR) from Models
2 and 3 indicate that a one unit increase
in self-control decreases the rate of violent offending by 7% (IRR = .93) for
those who experienced a low frequency of physical abuse,
and by 4% (IRR =
.96) for those who experienced a high frequency of physical abuse.11 In addi- tion, low depression is negatively
related to violent offending for both low frequency (IRR = .95) and high
frequency (IRR = .93) physical abuse vic- tims. Being married (IRR = .48),
attending college (IRR = .53), and graduat-
ing college (IRR = .29) also emerged as protective factors against violence,
but only for individuals who experienced a high frequency of physical abuse (see Model 3). Thus,
while the protective effects of marriage
and educational attainment
are specific to victims of high frequency physical abuse, the effects of
self-control and depression appear to be protective for both groups of physical abuse victims
examined in Models 2 and 3. However,
upon closer
Wright et al. 13Table 1. Negative Binomial Regression Models Predicting Violent Offending
by Frequency of Physical Abuse.
Violent
Offending in Early Adulthood
No Physical Abuse in Childhood
Low
Frequency Physical Abuse
High
Frequency Physical Abuse
Model 1 Model 2 Model 3
Variables b (SE) z b (SE) Z b (SE) z
Self-control −0.06 (.01) −6.10** −0.07
(.01) −5.78** −0.04 (.02) −2.06*
Low
depression
−0.04 (.02) −2.52* −0.05 (.02) −2.12* −0.07 (.02) −2.81**
Self-esteem −0.03 (.03) −1.02 −0.03
(.05) −0.66 0.04 (.03) 1.19
Verbal intelligence
−0.04 (.03) −1.44 −0.07
(.04) −1.94 0.07 (.04) 1.83
Marriage
|
−0.52 (.23)
|
−2.29*
|
0.26 (.33)
|
0.79
|
−0.74 (.34)
|
−2.19*
|
Job satisfaction
|
0.30 (.24)
|
1.27
|
−0.52 (.21)
|
−1.24
|
0.18 (.26)
|
0.71
|
Mentorship
|
−0.02 (.16)
|
−0.16
|
0.12 (.23)
|
0.52
|
−0.11 (.26)
|
−0.44
|
Religiosity
|
−0.03 (.02)
|
−1.41
|
−0.01 (.03)
|
−0.18
|
−0.03 (.04)
|
−0.91
|
High school
|
−0.24 (.21)
|
−1.14
|
0.29 (.30)
|
0.99
|
−0.17 (.29)
|
−0.59
|
grad
|
||||||
Some
college
|
−0.87 (.25)
|
−3.44**
|
0.15 (.31)
|
0.49
|
−0.64 (.29)
|
−2.24*
|
College grad
|
−0.76 (.29)
|
−2.62**
|
0.04 (.46)
|
0.08
|
−1.24 (.46)
|
−2.68**
|
Financial hardship
|
−0.04 (.11)
|
−0.33
|
0.03 (.14)
|
0.18
|
0.21 (.18)
|
1.17
|
Childhood neglect
|
0.02 (.08)
|
0.19
|
0.06 (.07)
|
0.77
|
0.01 (.06)
|
0.11
|
Age
|
−0.11 (.04)
|
−2.57*
|
−0.10 (.06)
|
−1.52
|
−0.14 (.07)
|
−2.13*
|
Male
|
1.10 (.22)
|
4.98**
|
1.69 (.26)
|
6.45**
|
0.89 (.23)
|
3.84**
|
Black
|
0.72 (.20)
|
3.68**
|
0.73 (.25)
|
2.95**
|
0.87 (.26)
|
3.35**
|
Hispanic
|
0.32 (.21)
|
1.56
|
0.38 (.27)
|
1.37
|
0.04 (.29)
|
0.14
|
Other minority
|
0.04 (.26)
|
0.14
|
0.18 (.46)
|
0.40
|
0.09 (.34)
|
0.25
|
F test
|
15.97**
|
17.68**
|
9.62**
|
|||
N
|
9,303
|
1,856
|
1,957
|
Note. Entries
are unstandardized partial regression coefficients (b), clustered robust
standard errors in parentheses, and z tests. Coefficients and standard errors for verbal intelligence
are multiplied by 10 for ease of interpretation.
*p < .05. **p < .01 (two-tailed test).
examination, differences can be
detected.12 More specifically, the
protective effects of self-control on violence are weaker for individuals who
experi- enced a high frequency of physical abuse
(z = |3.33|, p <
.01). In contrast, the effects of low depression do not vary by frequency of
physical abuse.
|
14 Journal of Interpersonal Violence
|
Table 2. Negative Binomial Regression Models Predicting Violent Offending by Frequency of Sexual Abuse.
Violent
Offending in Early Adulthood
No Sexual Abuse in Childhood
Low
Frequency Sexual Abuse
High
Frequency Sexual Abuse
Model 1 Model 2 Model 3
Variables
|
b (SE)
|
z
|
b (SE)
|
z
|
b (SE)
|
Z
|
||
Self-control
|
−0.06
(.01)
|
−7.00**
|
−0.04 (.01)
|
−2.76**
|
−0.08 (.04)
|
−2.22**
|
||
Low depression
|
−0.05 (.02)
|
−3.42**
|
−0.02
(.03)
|
−0.85
|
−0.18
(.06)
|
−2.73**
|
||
Self-esteem
|
−0.02 (.02)
|
−0.83
|
−0.06
(.06)
|
−0.91
|
−0.01
(.03)
|
−0.39
|
||
Verbal
|
−0.01 (.02)
|
−0.64
|
−0.06
(.05)
|
−1.07
|
−0.03
(.02)
|
−1.71
|
||
intelligence
|
||||||||
Marriage
|
−0.45 (.18)
|
−2.44*
|
0.28 (.42)
|
0.65
|
−1.16 (1.35)
|
−0.86
|
||
Job satisfaction
|
−0.16 (.19)
|
−0.85
|
−0.67 (.36)
|
−1.86
|
−0.44 (.42)
|
−1.06
|
||
Mentorship
|
0.05 (.13)
|
0.37
|
−0.08 (.29)
|
−0.27
|
0.70 (.38)
|
1.87
|
||
Religiosity
|
−0.04 (.02)
|
−2.16*
|
0.05 (.04)
|
1.19
|
−0.15 (.08)
|
−1.92
|
||
High school
grad
|
−0.09 (.18)
|
−0.48
|
0.13 (.35)
|
0.37
|
−1.07 (.56)
|
−1.90
|
||
Some college
|
−0.65 (.19)
|
−3.36**
|
0.51 (.40)
|
1.27
|
−0.71 (.57)
|
−1.24
|
||
College grad
|
−0.64 (.25)
|
−2.58*
|
−0.06 (.51)
|
−0.11
|
−5.02 (.83)
|
−6.08**
|
||
Financial hardship
|
0.04 (.10)
|
0.40
|
−0.01 (.21)
|
−0.05
|
1.28 (.68)
|
1.87
|
||
Childhood neglect
|
−0.01 (.05)
|
−0.21
|
−0.01 (.12)
|
−0.03
|
0.23 (.13)
|
1.72
|
||
Age
|
−0.11 (.04)
|
−3.25**
|
−0.07 (.08)
|
−0.82
|
−0.11 (.20)
|
−0.55
|
||
Male
|
1.19 (.16)
|
7.52**
|
1.49 (.30)
|
4.71**
|
2.27 (.72)
|
3.16**
|
||
Black
|
0.79 (.15)
|
5.23**
|
0.52 (.30)
|
1.75
|
0.60 (.51)
|
1.19
|
||
Hispanic
|
0.30 (.18)
|
1.61
|
0.26 (.47)
|
0.56
|
1.69 (.73)
|
2.30*
|
||
Other minority
|
0.06 (.22)
|
0.30
|
0.95 (.77)
|
1.22
|
−0.76 (.47)
|
−1.63
|
||
F test
|
25.90**
|
4.60**
|
5.23**
|
|||||
N
|
12,510
|
379
|
227
|
|||||
Note. Entries
are unstandardized partial regression coefficients (b), clustered robust
standard errors in parentheses, and z tests. Coefficients and standard errors for verbal intelligence
are multiplied by 10 for ease of interpretation.
*p < .05. **p < .01 (two-tailed test).
Childhood Sexual Abuse
Table 2 presents findings with respect to the
frequency of sexual abuse vic- timization during childhood. As seen in Models 2
and 3, several statistically significant protective effects emerge.
Self-control reduces violent offending among individuals who experienced a low
frequency of sexual abuse (IRR =
.96) and a high frequency of sexual abuse (IRR
= .92) during childhood. Having low depression (IRR = .84) and graduating from
college (IRR = .01)
Wright et al. 15Table 3. Negative Binomial Regression Models Predicting Violent Offending
by Frequency of Physical and Sexual Abuse.
Violent Offending in Early adulthood
No
Physical and Sexual Abuse in
Childhood
|
Low Frequency
Physical and Sexual Abuse
|
High
Frequency Physical and Sexual Abuse
|
||||||
Model
1
|
Model
2
|
Model
3
|
||||||
Variables
|
b (SE) z
|
b (SE) z
|
b (SE) z
|
|||||
Self-control
|
−0.06 (.01) −6.99**
|
−0.03 (.01) −2.56*
|
−0.05 (.02) −2.44*
|
|||||
Low
depression
|
−0.05 (.01) −3.43**
|
−0.02 (.03) −0.56
|
−0.12 (.04) −3.28**
|
|||||
Self-esteem
|
−0.02 (.02) −0.81
|
−0.04 (.06) −0.68
|
0.09 (.06) 1.59
|
|||||
Verbal intelligence
|
−0.01 (.02) −0.62
|
−0.05 (.06) −0.85
|
−0.03 (.08) −0.44
|
|||||
Marriage
|
−0.45 (.18)
|
−2.45*
|
−0.58 (.39)
|
1.50
|
0.10 (.55)
|
0.18
|
||
Job satisfaction
|
−0.18 (.19)
|
0.94
|
−0.23 (.38)
|
−0.61
|
−0.95 (.42)
|
−2.24*
|
||
Mentorship
|
0.05 (.13)
|
0.37
|
0.02 (.27)
|
0.09
|
0.10 (.39)
|
0.26
|
||
Religiosity
|
−0.04 (.02)
|
−2.19*
|
0.09 (.05)
|
1.84
|
−0.04 (.05)
|
−0.87
|
||
High school
|
−0.09 (.18)
|
−0.48
|
0.12 (.36)
|
0.32
|
−0.66 (.37)
|
−1.78
|
||
grad
|
||||||||
Some
college
|
−0.66 (.19)
|
−3.40**
|
0.62 (.33)
|
1.90
|
−0.57 (.44)
|
−1.30
|
||
College grad
|
−0.65 (.25)
|
−2.60**
|
0.01 (.51)
|
0.02
|
−5.15 (.89)
|
−5.77**
|
||
Financial hardship
|
0.04 (.10)
|
0.42
|
−0.04 (.23)
|
−0.19
|
0.32 (.25)
|
1.24
|
||
Childhood neglect
|
−0.01 (.08)
|
0.30
|
0.08 (.16)
|
0.50
|
0.21 (.12)
|
1.76
|
||
Age
|
−0.10 (.05)
|
−0.28
|
−0.09 (.08)
|
−1.03
|
0.05 (.09)
|
0.58
|
||
Male
|
1.20 (.16)
|
7.58**
|
1.21 (.31)
|
3.84**
|
1.91 (.37)
|
5.17**
|
||
Black
|
0.79 (.15)
|
5.33**
|
0.24 (.33)
|
0.72
|
0.31 (.43)
|
0.73
|
||
Hispanic
|
0.29 (.18)
|
1.60
|
0.19 (.43)
|
0.45
|
0.35 (.37)
|
0.97
|
||
Other minority
|
0.07 (.22)
|
0.30
|
1.01 (.70)
|
1.44
|
−0.04 (.54)
|
−0.09
|
||
F test
|
26.06**
|
2.57**
|
4.52**
|
|||||
N
|
12,617
|
240
|
259
|
|||||
Note. Entries
are unstandardized partial regression coefficients (b), clustered robust
standard errors in parentheses, and z tests. Coefficients and standard errors for verbal intelligence
are multiplied by 10 for ease of interpretation.
*p < .05. **p < .01 (two-tailed test).
are also negatively related
to violence, but only among
individuals who expe- rienced a high frequency
of sexual abuse (see Model 3). Similar
to the pattern of findings observed with respect to physical abuse in Table 1, the effects of self-control on
violent offending vary
between high frequency
and low
 |
16 Journal of Interpersonal Violence
frequency abuse victims. Specifically, self-control has a significantly weaker effect on violent offending among
individuals who experienced a high fre- quency of childhood sexual abuse (z = |2.43|, p < .05).
Childhood Physical and Sexual Abuse
The models presented in Table 3 assess violent offending across
groups of individuals who experienced a combination of both physical and sexual
abuse during childhood. In keeping
with the pattern
of findings thus far, self- control reduces violence among individuals who experienced low (IRR =
.97) and high (IRR = .95)
frequencies of both physical and sexual abuse
(see Models 2 and 3). In addition, low depression (IRR = .89), job
satisfaction (IRR = .39), and graduating from college (IRR = .01) also emerge
as protec- tive factors against violent offending, but these effects are
specific to indi- viduals who experienced high frequencies of abuse (see Model
3). Although self-control reduced violence
among both groups
of victims assessed
in Table 3, invariance tests revealed that the effects of
self-control on violent offend- ing are weaker among high frequency abuse
victims (z = |2.60|, p < .01).
Additional Analyses
Despite the robustness of the
results to listwise deletion and selection effects
(see Notes 3 and 10), a series of supplemental analyses were conducted (not
shown in table form). In particular, models were estimated
separately for men and women to determine whether the
findings were sensitive to using a mixed-gender sample (e.g., Topitzes et al.,
2012). Indeed, men are responsi- ble for perpetrating the majority of violent offenses,
and it is possible that the
impact of protective factors vary by gender
(e.g., Belknap, 2015).
Accordingly, gender-specific analyses were estimated for subtypes of
victims where the sample sizes were large enough to accommodate all covariates
in a stable way. This excluded groups
of victims who experienced high frequencies of sexual abuse, and those who
experienced low and high frequencies of both physical and sexual abuse.13
These supplemental analyses revealed that findings were generally similar across men and women. Consistent
with the results presented previously, self-control reduced violent offending
across all groups of male and female victims assessed, and low depression was
linked to lower violence among high frequency victims of physical abuse.
Nevertheless, some differences arose with respect to females who experienced
three or more instances of physical abuse. For such women, going to college and
graduating college were no longer
related to violent
offending (compare with Table 1, Model
3).
|
Wright et al. 17
Despite these differences, the findings remained
similar across both male and female victims of child abuse. Taken altogether, there is a great deal of
het- erogeneity in violent offending among victims of physical and sexual
abuse, and several prosocial
individual and social factors can help explain
why some victims of child
abuse are more likely to engage in violence in early adult- hood than others.
Discussion
The debate over the existence of a cycle of violence
has gone on for decades. Such a reality led Thornberry and
colleagues (2012, p. 145) to lament “there are almost as many review pieces as
there are original studies.” One thing seems clear: Abused children are at an
increased risk for perpetrating future violence as adults. The strength of this relationship can be debated
for another several decades,
but doing so misses an opportunity to understand why future
violence among previously victimized children is not an absolute certainty.
Most abused children do not complete the cycle, and we know surprisingly little about why that is the case—especially when it comes to identifying mal- leable protective factors. The current study sought to
build on the limited literature by examining the protective factors that reduce
violent offending among young adults who were previously abused in childhood.
Our work here leads to three broad conclusions.
First, a number
of protective factors reduced the likelihood of violent offending among
victims of child
abuse. The most consistent and strongest of these
factors was the individual protective factor of self-control. This finding is
consistent with the broader criminological literature that documents a strong
relationship between low self-control and criminal behavior (Pratt &
Cullen, 2000), as well as that which
links victimization, low self-control, and future criminal behavior (Turanovic
& Pratt, 2013). We add to this
literature by finding that self-control may reduce offending
for victims of abuse. Stated differently, building self-control
among abused children may be a way of breaking the cycle of violence. And
although self-control was a consistent predictor in all models, we note that it
was a weaker predictor of violence in some subsamples (especially among
individuals who were abused more fre- quently). The individual protective
factor of low depression and the social protective factors of job satisfaction,
attending college, and graduating from college
emerged as protective factors in one or more of our models. The cycle
of violence is not inevitable, and these protective factors offer ways it may be
broken.
Second, certain
factors may be protective for some forms and extents of abuse but not others.
Social protective factors were more protective
among
|
18 Journal of Interpersonal Violence
those young adults who had been
frequently abused as children. Indeed, no social protective factors were
significant in any models examining individu- als who experienced a low
frequency of abuse. This pattern is somewhat at odds with research that suggests
that social factors are more critical for resil-
ience in nonabused children (and perhaps less serious cases of abused chil-
dren) whereas personality characteristics and self-esteem processes are more important for resilience in abused children
(Cicchetti & Rogosch,
1997). Our results suggest
that self-control in particular is less protective among children who
were more frequently abused, and factors such as education and job satisfaction mattered
more for these
victims (see also Jaffee et al., 2007).
And even among the social factors, the protective impact varied
depending on the type, frequency,
and comorbidity of abuse. Marriage was protective among victims who had a high
frequency of physical abuse but not among other victims. Job satisfaction was protective among victims who were both physi-
cally and sexually abused at a high frequency but not among victims experi-
encing either type of abuse separately at a high frequency. The broader
implication of this pattern of results is that remaining resilient to the negative
consequences of abuse is not a “one size fits all” endeavor.
Third, the
dynamic protective factors in our model suggest specific pro- gramming
implications for those who may be interested in intervening in the lives of abused children.
The pattern of self-control being significant in all of our models is consistent with research
documenting self-regulation as a pro- tective factor in adaptation by childhood
victims (Cicchetti & Rogosch, 1997). Based on this finding, emotion and behavior
regulation training may be beneficial toward reducing the likelihood of future
violence perpetration among victims of child abuse (Haskett et al., 2006;
Topitzes et al., 2013; Topitzes et al., 2012). The self-control criminological
literature suggests a number of promising ways to increase and strengthen
self-control (Piquero, Jennings, & Farrington, 2010). Beyond the
self-control finding, our models suggest additional protective factors that may be promoted
based on type and
extent of abuse—a conclusion that affirms that different types of victims
may respond to treatment differently (Cicchetti, 2004).
Individual resources alone may not be enough to promote
resilience among young adults who experi- enced multiple forms and higher
frequencies of abuse (Jaffee et al., 2007), and it is encouraging that our
results suggest a number of social protective factors may break the cycle of
violence for those who experienced more severe forms of abuse.
We believe our
findings have an even greater importance when consider- ing the full extent of
child abuse in America. It cannot be assumed that all abused children will come
to the attention of social service agencies and receive the
appropriate programming. Indeed,
the very reason
we used
|
Wright et al. 19
nationally representative, self-report data was to capture these hidden victims who likely would never be
identified as abused. It seems pertinent, then, that some of the policy implications from our findings are
those that can be emphasized among the general population—with the added benefit
being that they could
potentially break the cycle of violence among abused children specifically. For
example, a focus on eliminating truancy and encouraging high school completion—a necessary
step before attaining
the additional pro- tective factors of higher education—may be especially critical
for youth who were previously abused (Johnson,
Wright, & Strand, 2012; see Tanaka, Georgiades, Boyle, & MacMillan, 2015).
While we were
able to shed light on the processes that shape whether victims of abuse can
remain resilient, we also hope that our work prompts additional research that
may address some of the things we could not. For
example, although our data allow us to examine abuse that may have gone
undetected and provide a sufficient number of cases to look at specific sub-
types of abuse, a prospective longitudinal study within a community sam- ple is
typically regarded as the gold standard for this type of research (Thornberry
et al., 2012). In addition, we have the same actor report on both abuse and violence, and retrospective
recall bias could introduce addi- tional threats to the validity of our
findings (Heller et al., 1999; Widom, 1989b; see also Note 4). The simple truth
is that there are going to be strengths and weaknesses to any type of approach
taken. Both child abuse and adult violence could be underreported when using
official records, and asking children about
their possible abuse presents serious ethical and
logistical issues.
We have also used a
relatively limited measure of resilience given our focus on the cycle of
violence. It is possible that these adults are not truly resilient in other areas such as cognitive and emotional functioning (McGloin & Widom, 2001). Relatedly, our measure of childhood
victimization could include elements such as exposure to violence (Sousa et
al., 2011), our mea- sure of violence could include elements such as family
violence and intimate partner violence
(Tomsich, Jennings, Richards, Gover, & Powers,
2015), and future studies
could also include additional individual, familial, and commu- nity protective factors identified in the literature
(Caspi et al., 2002). Finally, it
is likely that the victimization, protective factors, and resilience linkages
operate differently across race and ethnicity. This is an important avenue for
future research, but that research
should not simply consider race or ethnicity as risk or protective factors.
Instead, future work should examine how modi-
fiable risk factors are conditioned by race
and ethnicity. This approach may take us closer toward understanding how to
break the cycle of violence among those children who may be most at risk.
|
20 Journal of Interpersonal Violence
No one can
dispute that the effects of child abuse (including future vio- lence) are
substantial, and work moving forward should continue to try to understand the potential cycle
of violence that abuse may set in motion. That said, rather than simply examining
whether that cycle occurs, it is important that future research also examine how that cycle may be broken. In recent
years, the field of criminology has seen a renewed interest in explaining the
negative cases that do not conform to theoretical expectations (Sullivan, 2011; Wright
& Bouffard, 2016).
We began our work by stating
that we were going to look at the negative cases in the cycle of violence—those individuals who could be expected to
perpetrate future violence given their previous abuse, yet do not. The truth is that these are not actually
the negative cases as
most abused children do not go on to future violence. These cases deserve more
scholarly attention, and they may be able to teach us about the protec- tive
factors necessary to break the cycle of violence for those less fortunate.
Acknowledgments
The authors
wish to thank Travis Pratt for his helpful comments on a previous draft.
Authors’ 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. No direct support was received from Grant P01-HD31921 for this
analysis. 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).
Declaration of Conflicting Interests
The
author(s) declared no potential conflicts of interest with respect to the
research, authorship, and/or publication of this article.
Funding
The
author(s) received no financial support for the research, authorship, and/or
publi- cation of this article.
Notes
e1c.onWceive
of resilience as “successful adaptation or development during or following
adverse conditions” (Masten & Wright, 1998, p. 10). We use the term
|
Wright et al. 21
“resilience” within the cycle of violence to
connote a successful adaptation in young adulthood (i.e., being less likely to
engage in violence given one’s expo- sure to childhood victimization) rather
than “resiliency,” which suggests a per- sonality trait (McGloin & Widom,
2001, p. 1022).
2.
For more information on the National
Longitudinal Study of Adolescent to Adult
Health (Add Health) sample, research design, survey content, and data quality,
see http://www.cpc.unc.edu/projects/addhealth/data/guides/DesignPaperWIIV. pdf
3.
To
determine the robustness of the findings,
supplemental analyses were con- ducted using listwise deletion to handle
missing data. The results closely mir- rored those observed using multiple
imputation, and findings were the same in terms of sign and significance.
4.
It is possible that
retrospective accounts of child abuse might result in over- or underreporting abuse as respondents might “forget” or redefine their experiences
in light of later life circumstances and their current situation. Based on previous
research, we suspect that abuse
is underreported in this sample
(Widom & Morris, 1997; Widom & Shepard, 1996). Nevertheless, Add Health prevalence estimates of child physical
and sexual abuse
are generally consistent with those from other
national surveys (see, for example,
Boney-McCoy & Finkelhor, 1995).
5.
We
focus here on the type and frequency of abuse
as no additional information was captured on the contexts surrounding each
instance of childhood victimiza- tion (i.e., when and where it occurred, who
the perpetrator was, and whether there were injuries) that could tap into
dimensions of severity or chronicity.
6.
We
do not explicitly examine neglect as a form of
child victimization. We agree with
other scholars who argue that child neglect is a more general problem of
inadequate parenting (Dodge, Bates, & Pettit, 1990) and that it reflects an
act of omission rather than commission
(Cicchetti & Rogosch, 1997). Indeed, we examine here the cycle of violence
with respect to childhood victimization: Violence inflicted upon children will
result in them inflicting violence on others
in adulthood. Nevertheless, given that neglect represents the most commonly
reported type of child maltreatment, we include it as an important explanatory
variable in all regression models.
7.
According to Sweeten
(2012, p. 554), variety scores are the preferred way to measure criminal
offending because they “possess high reliability and validity, and are not
compromised by high frequency non-serious items in the scale.”
8.
The negative
binomial model has the same mean structure as the Poisson
model, but it includes an extra parameter to model overdispersion (see
Hilbe, 2011). If applied to overdispersed data, Poisson regression can result
in underestimated standard errors and spuriously large z values (Long & Freese, 2006). Because there is overdispersion
in violent offending (our dependent variable), the nega- tive binomial model is
a good fit for the data.
9.
The Add Health
sampling weights are used to address potential bias originat- ing from
differential sampling probabilities and attrition, and to prevent the
underestimation of standard errors. For more detailed guidelines on analyzing
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22 Journal of Interpersonal Violence
10.
Additional models
were estimated to rule out issues with sample selection bias. As individuals
within each subsample of victims were not selected by ran- dom assignment, it is possible that the
results could be biased in several ways (Bushway, Johnson,
& Slocum, 2007).
Accordingly, models were estimated using setpoisson
in Stata 13 (Miranda, 2012). This involved the estimation of a probit model
with exclusion restrictions for selection into each subsample, which was
estimated simultaneously with a second-stage Poisson regression model predict- ing violent offending. The setpoisson model forces overdispersion
in the depen- dent variable to guard against
the underestimation of standard errors
observed in standard Poisson
regression. The findings
remained the same in terms
of sign and significance, and likelihood ratio
tests of independent equations for these
models indicated that sample
selection was not a
detectable source of bias.
11.
Exponentiating the negative binomial
regression coefficient gives us the incident
rate ratio (IRR).
12.
Comparisons between models were conducted using the test
statistic recom- mended by Brame, Paternoster, Mazerolle, and Piquero (1998)
for maximum-
Comparisons between models were conducted using the test
statistic recom- mended by Brame, Paternoster, Mazerolle, and Piquero (1998)
for maximum-
likelihood regression coefficients, where z = (q1 - q2 ) / .
13.
Specifically, there
were 65 males who experienced a high frequency of sexual abuse, 90 females who
experienced low frequencies of both physical and sexual abuse, and 96 males who
experienced a high frequency of both physical and sexual abuse during childhood.
Given that females are overrepresented in the high frequency sexual abuse
groups, additional research with a larger number of sexual abuse victims could examine whether the protective
factors are gendered for these subgroups.
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Author Biographies
Kevin A. Wright is an associate professor
in the School of Criminology and Criminal Justice
at Arizona State University. His primary research interests focus on crimino-
logical theory and correctional policy.
Jillian J.
Turanovic is an assistant professor in the College
of Criminology and Criminal Justice at Florida State University. Her research
focuses on victimization and offending over the life course, and the collateral
consequences of incarceration. She received her PhD in 2015 from Arizona State
University and is a graduate research fellow of the National Institute of
Justice.
Eryn N. O’Neal is an assistant professor in the College of Criminal Justice at
Sam Houston State University. Her primary research interests include decision
making in sexual assault cases, intimate partner sexual assault, victim
decision making, and qualitative methods.
Stephanie J.
Morse is a master’s student in the School of
Criminology and Criminal Justice at Arizona State
University. Her research
interests include offender
rehabilita- tion and reentry.
Evan T. Booth is a master’s student in the Josef Korbel School of
International Studies at the University of Denver. His research interests include the effects
of child- hood victimization.
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