J Child Fam Stud (2015) 24:427–433
DOI 10.1007/s10826-013-9853-8
ORIGINAL PAPER
Females in the Juvenile Justice System: Influences on Delinquency
and Recidivism
David E. Barrett • Song Ju • Antonis Katsiyannis
Dalun Zhang
•
Published online: 19 October 2013
Ó Springer Science+Business Media New York 2013
Abstract The role of mental health history and family
dysfunction or disruption on female juvenile delinquency
was examined. Using large sample archival data from a
state juvenile justice agency, we examined the behavioral
and demographic predictors of repeat offending for a
sample of approximately 34,000 females who had been
referred for criminal offenses. Then, after merging these
data with those from multiple state agencies, we compared
the family and mental health histories of the delinquent
females with those of females from a matched control
group of the same number, constructed from the records of
the state department of education. Drug use, family delinquency, severity of first offenses, and age of first offending
were predictors of repeat offending for the females in the
delinquent sample. Compared with non-delinquent
females, delinquent females were more likely to be eligible
for free or reduced lunch, and were more likely to have
been in foster care or child protective services. The
strongest predictor of membership in the delinquent sample
was a DSM-IV diagnosis of a mental health disorder
related to aggression or impulse control. All variables
associated with delinquency remained significant when
other predictors were statistically controlled. Implications
for prevention of female juvenile delinquency were
addressed.
D. E. Barrett (&) A. Katsiyannis
Department of Teacher Education, Clemson University,
Clemson, SC 29634, USA
e-mail: bdavid@clemson.edu
S. Ju
University of Cincinnati, Cincinnati, OH, USA
D. Zhang
Texas A&M University, College Station, TX, USA
Keywords Female delinquency Child
maltreatment Mental health and delinquency Child
protective services Juvenile justice
Introduction
In 2009, 1,906,600 juveniles were arrested in the United
States for violent crimes (85,890 murder and non-negligent
manslaughter, forcible rape, robbery, and aggravated
assault), property crimes (417,700 burglary, larceny-theft,
motor vehicle theft, and arson), and non-index crimes
(1,403,010 crimes such as other assaults, drug abuse, disorderly conduct, violation of liquor laws, status offenses).
Regarding violent crimes in 2009, 47 % involved white
youth, 51 % black youth, 1 % Asian youth, and 1 %
American Indian youth. For property crime arrests, 64 %
involved white youth, 33 % black youth, 2 % Asian youth,
and 1 % American Indian youth (Puzzanchera and Adams
2011).
In 2009, 578,500 females were arrested. These arrests
accounted for 18 % of juvenile Violent Crime Index arrests
and 38 % of juvenile Property Crime Index arrests. Of nonindex crimes, arrest rates for females were 78 % for
prostitution, 55 % runaways, 42 % embezzlement, and
34 % other assaults. From 2000 through 2009, arrests of
juvenile females decreased less than male arrests in offense
categories such as aggravated assault, vandalism, and drug
abuse violations. Female arrests increased while male
arrests decreased for crimes such as simple assault, larceny-theft, and disorderly conduct. Further, whereas in
1980, the juvenile male violent crime arrest rate was 8
times greater than the female rate, in 2009 the male rate
was just 4 times greater (Puzzanchera and Adams 2011).
Overall females are more likely than males to be detained
for status offenses (truancy, running away, underage
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drinking) while males are more likely to commit more
serious offenses (Boesky 2002; Puzzanchera and Adams
2011).
Female delinquency follows specific patterns relating to
age and context. Early puberty when paired with family
conflict and neighborhoods characterized by poverty,
unemployment, and single parent families is a unique risk
factor for females (Zahn et al. 2010). Disorganized communities tend to exacerbate the frequency of violent acts
(Burman 2003), with gang membership associated with
more violent behavior among females (Zahn et al. 2008a).
There is some evidence that aggressive behavior among
female youth is associated with girls’ ambivalence
regarding obedience to parental authority. For example,
girls are more likely to fight with family members (Franke
et al. 2002) than with non-family members. Data on arrests
for intra-family aggressive behavior must be interpreted
cautiously, however; domestic behaviors which under
certain conditions might be considered ‘‘ungovernable’’
(and result in a referral for a status offense) might, in a
domestic situation, result instead in an arrest for simple
assault (Chesney-Lind and Sheldon 2004; Gaarder et al.
2004; Zahn et al. 2008a).
Despite the increasing rates of female juvenile delinquency, particularly for crimes traditionally associated with
males, addressing the needs of females in the juvenile
system has been a persistent challenge (Boesky 2002;
Quinn et al. 2005; Teplin et al. 2002). The American Bar
Association and National Bar Association Report (2001)
have concluded that there is a critical lack of prevention,
diversion, and treatment alternatives for girls in the juvenile justice system. For example, not only do females tend
to commit less serious crimes than males, they often
receive differential treatment for similar crimes (Barrett
et al. 2010; Miller et al. 1995). Consequently, many professionals argue that the majority of female delinquency
cases should be diverted from court proceedings (Bishop
and Frazier 1992; Quinn et al. 2005). Females are also
prone to emotional, physical, or sexual abuse (Acoca and
Dedel 1998; Berlinger and Elliot 2002; Teplin et al. 2002).
In fact, physical assault by a parent or caregiver, sexual
assault or neglect by a parent or caregiver, and neighborhood disadvantage are key risk factors for delinquency
(Hawkins et al. 2009) along with family criminality, drug
use, and deviance (Zahn et al. 2010). In contrast, the presence of a caring adult, school success, and religiosity have
been shown to serve as protective factors (Hawkins et al.
2009).
In the present study we were able to obtain detailed
background information on the early experiences of over
34,000 females with records of juvenile delinquency. Using
information from a state department of juvenile justice, we
examined the role of selected family and demographic
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variables in predicting female recidivism. Using information from the state’s budget and control board, we then
constructed a control group of 34,000 female youth without
histories of delinquency and matched on birth year and
race. By linking records on delinquency with records from
other state agencies, we were able to examine the influences of important child personal and experiential variables
including mental health history, maltreatment and foster
care on juvenile delinquency. Our study addressed two
major research questions. First, among delinquent females,
what are the personal and family background variables
which are useful in predicting female recidivism? Second,
to what extent can we predict membership in the delinquent
group versus the non-delinquent control group on the basis
of females’ emotional/behavioral problems and early
adverse family experiences?
Method
Sample
Data from eight cohorts of female juvenile offenders were
drawn from the South Carolina Department of Juvenile
Justice (SCDJJ) Management Information System. The
sample included 34,414 female juvenile offenders born
between 1981 and 1988, each of whom had been referred to
SCDJJ on at least one occasion. The SCDJJ is a state
cabinet agency which covers 43 of 46 counties in South
Carolina. When a juvenile is arrested or referred by a
Circuit Solicitor or a school, a SCDJJ county office will
perform the family court intake and make a recommendation to the Solicitor’s Office with advisory recommendations (e.g., diversion or prosecution). The family court
intake involves collecting data from parents or guardians
on the child’s gender, ethnicity, and date of birth; documenting the nature of the referral offense; and performing
risk and needs assessments. Data collection may also
involve other social-demographic variables, including
family income, family history of delinquency, child substance use, educational history, and family structure (see
Barrett et al. 2010).
The sample for this analysis included 34,614 female
juveniles whose age at first referral ranged from 5 to 19
(M = 14.67, SD = 1.80). A small percentage (1.4 %) of
juveniles who were not African Americans or Caucasian
(n = 486) were excluded from this study. Demographic
information is presented in Table 1. As shown in Table 1,
the racial composition was 18,007 (51.3 %) African
American and 16,607 (47.3 %) Caucasian. With respect to
numbers of referrals, 13,300 (38.4 %) had one referral
only, 13,915 (40.2 %) had two referrals, and 7,399
(21.8 %) had three or more referrals. At the statewide level,
J Child Fam Stud (2015) 24:427–433
429
Table 1 Demographic characteristics of delinquent females
Characteristics
N
%
Black
18,007
52
White
16,607
48
Total N
34,614
Race
Family delinquency
Yes
6,001
53.4
No
5,232
46.6
Total N
11,233
Family income
\$15,000
7,751
48.9
C15,000
Total N
8,096
15,847
51.1
Yes
4,396
25.1
No
13,102
74.9
Total N
17,498
Drug use history
diagnosed as having disorder of aggression and/or impulse
control based on DSM-IV classification); see Barrett et al.
2013b for information regarding criteria for evaluating a
child as having an aggression-related disorder. Variables
created from the different agency files were merged to
create a new master dataset for the delinquent sample.
Finally, a comparison, non-delinquent sample was randomly selected from a previously created control sample of
99,602 youth using data made available from SCDE; the
non-delinquent sample was constructed to have the same
proportions of birth years, gender, and ethnicities as the
DJJ cohort. Additional information about the matching
procedure has been reported (Barrett et al. 2013b). The
same data collected from the ORS for the delinquent
sample were extracted for the control group. The delinquent and non-delinquent files were merged for the purpose
of statistical analyses.
Data Analysis
1st referral severity
Misdemeanor
8,348
86.3
Felony
1,324
13.7
Total N
9,672
SCDJJ assigns all offenses a severity rating on a scale of
1–25, with ratings less than 2 representing status offenses
(e.g., truancy, running away), 2–3 representing misdemeanor offenses (e.g., simple assault and battery, criminal
domestic violence), 5–8 representing nonviolent felonies
(e.g., grand larceny, carrying a weapon on school grounds),
and 8.5–25 representing violent felonies (e.g., assault and
battery of a high and aggravated nature, sexual assault,
armed robbery). For analysis purposes, we recoded severity
of offenses into two levels, status offense or misdemeanor
(SCDJJ ratings 1 through 3) and felony (SCDJJ ratings 5
through 25).
Next, data from the DJJ were merged with data from the
SC State Budget and Control Board’s Office of Research
and Statistics (ORS). ORS data were collected from three
different state agencies: the Department of Social Services
(SCDSS), the Department of Mental Health (SCDMH), and
the Department of Education (SCDE). Each child in the
DJJ file has been assigned a unique ID generated through a
linkage algorithm and matched ORS data were then linked
to each child (see Barrett et al. 2013b for details). In this
study, variables extracted from ORS files included: (1)
foster care placements (i.e., whether or not a child had ever
been placed in foster care); (2) maltreatment [i.e., whether
or not a child had ever been placed in the custody of child
protective services (CPS)]; (3) free lunch (i.e., whether a
child was eligible for free and/or reduced lunch); and (4)
aggressive behaviors (i.e., whether a child had been
The data analysis plan included two steps. First, ‘‘proportional hazards regression’’ (also termed Cox regression)
was used to examine the risk of recidivism for female
offenders. This analysis technique is a type of survival/
failure analysis (Singer and Willett 2003) and has been
used previously to examine the likelihood of and timing of
recidivism (Zhang et al. 2011). This type of survival analysis typically examines how the risk of an adverse outcome changes over time in relation to possible covariate
effects. In this study, we used this technique to predict
recidivism (repeated offense) by time and by associated
juvenile characteristics which have been identified from
previous literature, including race, presence/absence of
family history of delinquency, family income, presence/
absence of personal drug use history, offense severity at 1st
referral, and age at first referral (Zhang et al. 2011; Barrett
et al. 2010). Data were obtained from the original SCDJJ
files (see Barrett et al. 2010). The hazards analysis was
performed using the augmented Cox regression model of
Lunn and McNeil (1995). The variable ‘‘number of referrals’’ was dummy coded with ‘‘one referral’’ coded as ‘‘0’’
and ‘‘multiple referrals’’ coded as ‘‘1’’; covariates were
included in the model. Censoring is used to determine the
termination of the period of time. In this study, censoring
was created for age 21, as this ended time to juvenile
offense.
Second, using the merged delinquent and control files
and data obtained from the ORS files, we examined differences between the behavioral, academic, and mental
health characteristics of females in the delinquent sample
and those in the control sample. First, we used Chi square
and phi coefficient analyses to compare proportions of
each group (delinquent and non-delinquent) showing
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J Child Fam Stud (2015) 24:427–433
presence or absence of each indicator. Next multivariate
logistic regression (Hosmer and Lemeshow 2000) was
chosen to examine the variables that predicted whether a
female would be in the delinquent or the non-delinquent
sample. This analysis allowed us to examine the unique
as well as the collective contributions of each risk variable. Examined covariates included free lunch, placement
in CPS, placement in foster care and disorders of
aggression. Race was also included in the model.
Adjusted odds ratios and effect sizes are reported for the
logistic model.
Results
Influences on Recidivism
The results from the Cox regression analysis are presented in Table 2. Controlling the risk factor of race, all
covariates with the exception of family income were
significant individual predictors of recidivism. Specifically, offenders who were younger at the first referral
had a higher risk of recidivism (b = -.194, p \ .001)
with the chance of recidivism expected to decrease by
18 % for each additional year if all other variables are
held constant. Offenders who had a drug use history
were almost twice as likely to have a second offense as
those not having drug use history (b = .666, p \ .001).
Compared with offenders who committed more severe
offenses, offenders who committed less severe offenses
were 30 % more likely to have a second offense (b =
-.357, p \ .001). In addition, the effect of family
criminal history was also significant at p \ .001.
Offenders with family criminal history background were
at a high risk for recidivism. On average, it took about
1.06 (SE = .01, 95 % CI 1.05–1.09) years for female
offenders to be referred again.
Delinquents Compared with Non-delinquents
As shown in Table 3, the two groups of juveniles (delinquent versus non-delinquent) were compared on four
demographic variables, including free lunch eligibility,
CPS services, foster care and disorder of aggression.
Descriptive statistics and results from the Chi square test
are summarized in the table. The two groups differed significantly on all four demographic variables. More than
50 % of juveniles were eligible for free lunch in both
groups; however, the percentage in the delinquent group
was larger than in the non-delinquent group. Overall, there
were more females from the delinquent group placed in
foster care (7 vs. 1.1 %), receiving CPS services (16.4 vs.
3.6 %) and diagnosed as having a disorder of aggression
(12 vs. .9 %) than from the non-delinquent group.
All four variables were included in the logistic regression model holding ethnicity as a constant. No significant
interaction effects were identified. The predictor coefficients for the final model are presented in Table 4. As
illustrated, female juveniles who had received CPS services
were 3.2 times more likely to be delinquent than those who
had not been placed in CPS (b = 1.171, p \ .001). Similarly, female juveniles who were placed in foster care were
two times more likely to be involved in delinquency
(b = .814, p \ .001). Eligibility for free lunch which
partially indicated the family’s socioeconomic status also
had a significant predictor effect. Female youths eligible
for free or reduced lunch were almost 1.4 times more likely
to become delinquent than those who were not eligible.
The strongest predictor of delinquency was a mental health
diagnosis related to a disorder of aggression. Female
Table 3 Background characteristics for delinquent and non-delinquent groups
Characteristics
Delinquent
group
Non-delinquent
group
Phi
coefficient
.151**
Free lunch
eligibility
Table 2 Cox proportional hazards regression with N = 34,580
Variable
b
SE
Chi
square
Exp(B)
21,776 (62.9 %)
17,818 (51.5 %)
No
12,838 (37.1 %)
16,796 (48.5 %)
CPS services
Yes
No
5,693 (16.4 %)
1,236 (3.6 %)
28,921 (83.6 %)
33,378 (96.4 %)
.096
.057
.094
1.100
Family delinquency
.234**
.056
17.510 \.001
1.264
Yes
2,439 (7 %)
Drug use history
.666**
.056
140.835 \.001
1.947
No
32,175 (93 %)
-.357**
.064
31.081 \.001
.700
Age at 1st referral
-.194**
.015
170.801 \.001
.824
Yes
4,154 (12 %)
328 (.9 %)
Family income
-.027
.056
.973
No
30,460 (88 %)
34,286 (99.1 %)
** p \ .001
123
.233
.629
.124**
Foster care
African American
Offense severity at
1st referral
2.801
p value
Yes
389 (1.1 %)
.078**
34,225 (98.9 %)
Disorder of
aggression
** p \ .001
.064**
J Child Fam Stud (2015) 24:427–433
Table 4 Logistic
delinquency
regression
431
coefficients
for
prediction
of
Predictor
R2
Free lunch
eligibility
.02**
.358**
CPS services
.05**
1.171**
.036
4.94**
3.226**
Foster care
Disorder of
aggression
.03**
.09**
.814**
2.415**
.062
.059
6.828**
13.334**
2.257**
11.188**
b
SE
.017
AORE
1.56**
AORF
1.430**
** p \ .001. R2 refers to Nagelkerke’s R2 following this step in the
equation and including the constant. Significance level for R2 is based
on the change in the log likelihood of the outcome. Significance level
for the Wald statistic is based on the final logistic regression equation.
B refers to the logistic regression coefficient in the final equation.
AORF refers to the adjusted odds ratio in the final equation. AORE
refers to the adjusted odds ratio at the initial time of entry
juveniles identified as aggressive were 11 times more likely
to be delinquent (b = 2.415, p \ .001) than those without
such a DSM-IV diagnosis. The SPSS analysis gave two
measures of R2, which were .09 (Cox and Snell R2) and .13
(Nagelkerke’s adjusted R2). These effect sizes are reasonably similar values and represent medium effects, according to Cohen (1988) (i.e., .02 for ‘‘small’’, .13 for
‘‘medium’’ and .26 for large).
Discussion
The present study provides overwhelming evidence of the
role of early social and psychological adversity in female
delinquency. Particularly important is the impact of mental
health disorders. In our study, females who had been
diagnosed with a mental health disorder involving impulse
control or aggression were approximately 11 times more
likely to commit a criminal offense than females who had
not been so diagnosed. While it is not possible to show a
direct causal effect of mental illness on delinquency, it is
important to recognize that in over 60 % of cases of female
delinquents with a diagnosis of a disorder of aggression,
the diagnosis of aggression preceded any involvement with
the juvenile justice system. The second most powerful
predictor of delinquency was child placement in CPS. The
role of attachment problems in the development of psychopathology among females has been previously noted
(Barrett et al. 2013c); in fact there is some evidence that
early disruptions in parent–child relationships may have
even more serious repercussions for females than for males
(Benda 2002).
Particularly important is the powerful influence of
removal from the home, whether in CPS or foster care,
even when child aggressiveness has been statistically
controlled. According to current systems theories (Granic
and Patterson 2011), child maltreatment (which is usually
untreated or undertreated) is likely to precipitate coercive
behaviors on the part of the child, behaviors that put the
young female at risk for delinquent behaviors. To the
extent that the pathways to delinquency may be somewhat
different for boys and girls, the needs of girls in terms of
prevention, treatment, and aftercare may also differ.
According to Quinn et al. (2005), the juvenile justice must
be prepared to address the needs of girls in a genderspecific, culturally competent manner. The overall success, however, will be dependent on the seamless service
delivery of agencies such as child welfare, mental health,
education (including special education), and juvenile justice (p. 137). With regard to recidivism, the present
findings are largely supportive of previous research on this
topic. It is interesting that the strongest predictor of female
recidivism in our study was a history of drug use, particularly given the strong association between adolescent
drug use and lack of parental control in the family
(Lamborn et al. 1991; Steinberg 2011). Also, consistent
with previous research (Barrett et al. 2010) are the findings that lower age at first referral and lower severity of
first offense are predictors of repeat offending. While the
latter finding might seem counter-intuitive, it may be due
in part to the fact that the most common status offense is
truancy and that failure to comply with mandatory attendance orders will automatically result in a referral for
contempt of court.
Interventions that help adolescent girls learn how to
manage their risk (e.g., effectively dealing with the trauma
of childhood physical and sexual assault) would be an
important contribution to the delinquency prevention field
(Ruffolo et al. 2004; see also Quinn et al. 2005). Additionally, interventions should focus on the protective factors that mitigate risk (Luthar 2006). Unfortunately,
females not only experience higher levels of mental health
problems than male peers, they are less likely to receive
treatment, and more likely to abandon treatment (Caufmann 2008). In addition, there is a paucity of gender specific programs that have empirical support to address
prevention and treatment-related challenges (Quinn et al.
2005). According to the General Accounting Office (2009),
in 2004 the Office of Juvenile Justice and Delinquency
Prevention (OJJDP) established a girls Study Group, funded by a $2.4 million multiyear contract with a research
institute, to identify ‘‘effective or promising programs,
program elements, and implementation principles’’ (p. 3).
The Study Group reported that 44 out of 61 girls’ delinquency programs had no empirical support and that
research findings on the remaining programs failed to
establish evidence of their effectiveness in preventing or
reducing girls’ delinquency (see also, Zahn et al. 2008b).
The failure to address the problem of female delinquency
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432
continues to have serious repercussions; particularly given
the strong links between female delinquency and school
failure, teen pregnancy and child bearing and later mental
health problems (Barrett et al. 2013a).
There are several limitations to the present study which
may limit the generalizability of the findings. First, because
the sample is drawn from one state, South Carolina, the
sample may not be representative of a national sample.
State agencies follow specific procedures for determining
the need for CPS, referring children for mental health
services, and referring cases to the criminal justice system;
these procedures may differ from state to state (see for
example U.S. Department of Health and Human Services
2003). Thus, the empirical relationships identified in this
study between specific risk factors for delinquency and
later delinquent behavior might be magnified or attenuated
were the study to be conducted in other settings. A second
limitation is that information regarding the background
variables collected at intake on delinquent youth (including
drug use, income, and criminal history) were not available
for the control sample. Thus we were not able to include
these variables in the logistic regression analysis in which
we predicted membership in the delinquency group.
Inclusion of these variables might have altered the magnitude of the coefficients reported for this analysis. Third,
because of the very small number of females in the sample
whose ethnicity was other than African-American or
Caucasian, only these ethnic groups were included in the
analyses. Generalizations to individuals from other population groups must be made cautiously. Finally, we
emphasize that in the present study, in our examination of
family-related variables related to delinquency, we included only the most extreme indicators of family dysfunction
or disruption (CPS and foster care); closer examination of
the influences of parent–child interaction (trust, consistency, coercive behavior) would be useful in helping us
better understand how early social experiences contribute
to later female delinquent behavior.
With juvenile delinquency among females becoming
more pervasive in the United States, the task of identifying the school, home and psychological factors that lead to
female antisocial behavior has become more urgent. In the
present study we have elucidated the role of early family
disruption, and in particular removal from the home, in
female delinquency. Also, consistent with research on
male delinquents, the previous existence of mental health
problems appears to be the strongest predictor of female
delinquency, and, as indicated by the relationship between
drug use and repeat offending, a risk factor for later
offending. While research on effective programing for
females at risk for delinquent behavior has been limited,
the present study suggests that multi-systemic programs—
involving schools, communities, families and correctional
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J Child Fam Stud (2015) 24:427–433
settings—that address the young female’s need for consistent and nurturing relationships and that provide models
for academic and social success may be most helpful in
reducing, if not preventing, female delinquency.
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Juvenile Delinquency Recidivism: Are Black and White
Youth Vulnerable to the Same Risk Factors?
David E. Barrett and Antonis Katsiyannis
Eugene T. Moore School of Education, Clemson University
ABSTRACT: Using large-sample, archival data from the state o f South Carolina's juvenile justice
agency, we examine the question o f race differences in predictors o f repeat offending for a sample of
approximately 100,000 youth who had been referred for criminal offenses. Independent variables
relating to background, adverse parenting, mental health, school-related disabilities, and features o f
first offenses contributed to more than 25% o f the variance in recidivism for both Black and White
youth. Male gender, eligibility for free or reduced school lunch, diagnoses based on the Diagnostic
and Statistical Manual of Mental Disorders (4th ed.; text rev.; American Psychiatric Association,
2000), placement in Child Protective Services, and school identification as having a classification of
emotional or behavioral disorders or learning disabilities were all predictive o f juvenile recidivism.
In addition, early age o f first offense, status offenses, and being prosecuted for a first offense were
significant predictors. Magnitudes o f prediction were similar across racial groups, suggesting similar
vulnerability o f both Black and White youth to these early adversities. Interactions between race and
other independent variables accounted for only .001 o f the variance in recidivism. However, there
were several significant interactions. Mental health history and characteristics o f the first offense
were stronger predictors for White youth than for Black youth. Gender, poverty (free or reduced
lunch), and school identification o f having a classification o f emotional or behavioral disorders were
stronger predictors for Blacks than for Whites. Implications for prevention are addressed.
■ The overrepresentation of Black youth in
the juvenile justice system has been a persistent
concern and is evident across contact points
(National Council on Crime and Delinquency,
2007). Research studies on m inority arrests
and confinement attest to race effects, direct or
indirect, at m ultiple stages of juvenile justice
processing and across jurisdictions (Pope,
Lovell, & Hsia, 2001; see also Huizinga et
al., 2007). For example, in 2010, 1,642,600
juveniles were arrested in the United States for
violent crimes. In the same year, the racial/
ethnic composition for juveniles ages 10 to 17
was 76% White, 17% Black, 5% Asian/Pacific
Islander, and 1% American Indian. However,
the racial/ethnic composition for juvenile
arrests was 47% White, 51 % Black, 1% Asian,
and 1% American Indian. Proportions of Black
offenders were 56% for murder, 67% for
robbery, 42% for motor vehicle theft, 41%
aggravated assault, 38% simple assault, 36%
for forcible rape, 36% for burglary, and 36%
for weapons (Puzzanchera, 2013). Further
more, data on cumulative prevalence arrest
rates show that Black youth are almost 50%
more likely than W hite males to have been
arrested at least once by the age of 18 (Brame,
Bushway, Paternoster, & Turner, 2014).
184 / May 2015
In addition to race differences in arrest
rates, there is also evidence of race disparities
in adjudication (prosecution and referral to
a fam ily court), w ith Black youth more likely
than W hite youth to be prosecuted for serious
crimes (Barrett, Katsiyannis, & Zhang, 2010).
Black juveniles are more likely to be sent to
secure confinement than W hite juveniles and
are more likely to be transferred to adult
facilities (The Sentencing Project, 2014). Fi
nally, Black students are also disproportion
ately represented in disciplinary proceedings
in the schools, accounting for 27% of law
enforcement referrals and 31% of schoolrelated arrests (U.S. Department of Education,
2014; U.S. Department of Education, Office of
Civil Rights, 2014a, 2014b).
To address disproportionate m inority con
finement (DMC), the Office of Juvenile Justice
and Delinquency Prevention (OJJDP) adminis
ters the Formula Grants program under Title II,
Part B, of the Juvenile Justice and Delinquency
Prevention (JJDP) Act of 1974. Based on the
most recent amendments to the JJDP Act of
1992, the need to address this problem was
elevated to a core requirement, w ith 25% of
funds linked to state compliance. Currently,
based on the OJJDP's five-phase DMC
Behavioral Disorders, 40 (3), 184-195
Reduction Model (identification, assessment/
diagnosis, intervention, evaluation, and mon
itoring), 41 states have DMC subcommittees
under their state advisory groups (18 full-time
and 37 part-time DMC coordinators), 34 states
have invested in targeted local DMC-reduction
sites, 30 states have implemented nationally
recognized models, and 39 states have pro
vided a timeline for tracking and monitoring
trends over time (OJJDP, 2012). Meta-analyses
regarding the effectiveness of these programs
have indicated positive intervention effects for
minority youth; outcomes include a reduction
in delinquent behavior and improvement in
school participation, peer relations, academic
achievement, and psychological adjustment.
But particularly striking is the finding that the
programs have been equally effective for
minority and White juveniles. In their review
of over 300 studies, including studies prior to
1992, Wilson, Lipsey, and Soydan (2003)
compared method-adjusted effect sizes (in
tervention effect sizes controlling for method
ological differences between studies) for
minority (primarily Black) and White youth;
with method controlled, race differences in the
magnitude of intervention effects were non
significant.
The finding of similar effects of interven
tions on minority and nonminority delinquents
raises the question of vulnerability and re
silience among minority youth. The fact that
targeted interventions have similar effects for
Black and White youth suggests that youth of
different backgrounds are vulnerable to the
same risk factors and benefit from the same
reparative experiences. Thus, we might expect
that for both White and Black youth, early
experiences such as parental neglect or abuse,
mental health problems, and school failure
would be predictive of delinquency (Barrett,
Katsiyannis, Zhang, & Zhang, 2014) and that
multisystemic interventions focusing on the
family environment, the school experience,
and the child's individual social adjustment
would be ameliorative (Farmer & Farmer,
2001 ).
Another possibility, however, is that mi
nority youth, and in particular Black youth, are
more resilient in the face of early adverse
experiences. The concept of Black resilience is
compelling (APA Task Force on Resilience and
Strength in Black Children and Adolescents
[APA Task Force], 2008) and has received both
popular and empirical support. For example,
family instability appears to be a stronger
Behavioral Disorders, 40 (3), 184-195
predictor of behavioral and academic prob
lems among White children than Black chil
dren (Fomby & Cherlin, 2007). Also, there is
evidence that family structure transitions (e.g.,
parental separation) have more deleterious
consequences for White than Black children
with regard to early sexual activity among
females (Wu & Thomson, 2001). Finally, there
is evidence that Black youth tend to have
higher overall self-esteem than Whites, partic
ularly with regard to their satisfaction with
body image (Steinberg, 2014), a fact that has
been linked with the lower rates of presenta
tion of weight concerns (e.g., anorexia ner
vosa) among Black Americans than White
Americans (American Psychiatric Association
[APA], 2013). Multiple explanations have been
given to explain a lower vulnerability to
certain social-environmental stressors among
Black youth. Fomby, Mollborn, and Sennott
(2010) suggested two bases for this resilience:
inclusion in broader social networks (including
church, peer group, and neighborhood) and
importance of kin relationships (i.e., extended
family).
But the concept of resilience needs to be
examined carefully. An assumption of greater
resilience among minority populations may
lead to an underestimation of the impact of the
very factors that place a population "at risk."
This is a particular concern if the early adverse
experiences have the same or even greater
impact on vulnerable populations. The com
peting hypothesis (vs. resilience) is increased
vulnerability. Vulnerability would result when
weakened bonds between youth and main
stream institutions, such as the school, and
adverse early experiences result in poorer self
perceptions and efficacy, variables which
themselves are predictive of more persisting
behavior problems among children in multi
ple-risk families (Radke-Yarrow & Brown,
1993). A third possibility is that the magnitudes
of effects of known risk factors for later
behavior problems are very similar for White
and minority youth.
In the present study we examine the role of
multiple categories of risk factors for juvenile
delinquency recidivism for large samples of
Black and White youth who had had a least
one referral to a state juvenile justice system.
Independent variables
include
parental
maltreatment (foster care, referral to Child
Protective Services [CPS]), mental health
problems (referral to state department of
mental health), and school-related disability
May 201 5/1 8 5
(learning disability [ED], emotional/behavioral
disorder [EBD]). The outcome of interest was
juvenile recidivism: a second referral to the
state juvenile justice system. In addition to
testing for race differences in the above risk
factors, we also tested for race differences in
the role of gender and age of first offense,
variables known to be highly predictive of
juvenile offending and recidivism (Barrett
et al., 2014).
Method
Source of Data
Data for this study were obtained from two
sources, the South Carolina Department of
Juvenile Justice (DJJ) and the South Carolina
Budget and Control Board's Office of Research
and Statistics (ORS). DJJ data comprised in
formation on approximately 100,000 youth
who had been born in the period of 19811988 and who had been involved in de
linquent activity. We linked the DJJ data with
data obtained from the ORS. The ORS houses
data from all of the state agencies in South
Carolina, including, but not limited to, the
South Carolina Department of Education
(SDE), the South Carolina Department of
Social Services (DSS), the South Carolina
Department of Mental Health (DMH), and
the South Carolina DJJ. These linkages
enabled us to examine environmental influ
ences on delinquency and recidivism using
data that were not available in the original DJJ
file.
D/J Data
Data were drawn from the South Carolina
DJJ Management Information System. The DJJ
sample consists of all juveniles born between
1981 and 1988 whose cases were referred to
the South Carolina DJJ on at least one occasion
("referral"). The sample was part of a m ulti
cohort, matched control study conducted in
conjunction with the South Carolina Budget
and Control Board (Barrett et al., 2014), a study
that also included nondelinquent youth. In
South Carolina cases are first processed at the
fam ily court level by the DJJ. Intake workers
from the DJJ assess risk and needs and forward
cases to the Solicitor's Office w ith advisory
recommendations (e.g., diversion or prosecu-
1 8 6 /M a y 2015
tion). If the case is prosecuted, the juvenile
may be committed to the custody of the DJJ,
given probation, or given another penalty,
such as a school attendance order.
The 1981-1988 cohorts include 99,602
individuals, 65,502 (65%) males and 35,100
(35%) females. The racial composition is
50,496 (51%) Black, 47,537 (48%) W hite, and
1,569 (2%) other (Asian and Hispanic). The
average age of the juveniles when they were
first referred to the system was 14.47 years (SD
= 1.94), and the mean total number of referrals
per juvenile was 2.21 (SD = 2.00). O f the
99,602 juveniles, 54% had one referral only,
19% had tw o referrals, and 27% had three or
more referrals. Social demographic data were
collected selectively by the DJJ and were
available for only about half of the sample
(see Barrett et al., 2010, for details). For
purposes of the present study, only Black and
W hite youth were considered in the analyses.
Individual data on delinquency history
were aggregated for each subject in the sample.
Data available for each subject included age at
first offense, severity of first offense, and severity
of second offense, if applicable. Data on
dispositions (penalties) were also collected.
The determination of the seriousness of a crime
was based on the coding scheme employed by
South Carolina. The DJJ rates crimes on an
ordinal scale, w ith lower ratings representing
less serious offenses. For purposes of this
analysis, we categorized offenses as status
offenses (DJJ severity levels 1 and 1.5) and
nonstatus offenses (rating levels of 2 and above).
OKS Data
For all individuals in the DJJ sample and
also for the matched control group (described
below), data from other state agencies (housed
in the ORS) were made available. Files on
each child in the DJJ file were linked w ith files
of the other state agencies using a probabilistic
matching algorithm. In the ORS linkage
system, once a match is identified, an ID
number is assigned. The same ID is used for all
subsequent episodes of services, regardless of
data source or service provider. Additional
information about the key linkage system is
available on request.
For the present analyses, individual data in
the DJJ files were linked w ith data for the same
individuals from the DSS, DM H, and SDE.
Data obtained from the DSS included in
formation about foster care placements and
Behavioral Disorders, 40(3), 184-195
whether or not an individual had ever been
placed in the custody of CPS. For foster care,
information about age and duration of place
ment and number of placements was obtained.
With respect to CPS, we obtained information
about the reason for and timing of CPS. Data
obtained from the DMH included information
about age at first, second, and most recent
referrals and primary diagnosis based on the
Diagnostic and Statistical Manual o f Mental
Disorders (4th ed.; text rev.; DSM-IV-TR; APA,
2000) at each referral. Primary diagnoses were
further categorized into seven major categories
(described in Analyses section). Data from the
SDE included information about the ages at
which the student was eligible for free and/or
reduced lunch and eligibility for special
education services due to LDs or EBDs. After
separate files were constructed for each
agency (D)J, DMH, DSS, DOE), files were
merged to create a new master file for the DJJ
sample.
Analyses
We used a series of logistic regression
analyses to examine the individual and com
bined influences of selected categories of
independent variables on juvenile delinquen
cy recidivism; that is, presence of referral to
the DJJ for a second offense. Our analysis
involved three steps. First, we obtained de
scriptive statistics on all independent variables
and on the dependent variable for each of the
two comparison groups, Black and White. We
also conducted cross-tabulations and calculat
ed chi-squared statistics to examine the re
lationship between ethnic group and each of
the categorical predictor variables (we con
ducted an independent sample f test on the
one continuous variable, age of first referral).
Second, we carried out a multivariable
logistic regression analysis using the whole
sample, including interaction terms for the
interaction of racial group and each in
dependent variable. The inclusion of in
teraction terms enabled us to test for the
possibility of significant differences in the
logistic regression coefficients for indepen
dent variables for Black versus White youth.
Youth incarcerated for the first referral were
excluded from the analysis. In the logistic
regression analysis we included six blocks of
predictors. In predicting the variable recidi
vist (vs. nonrecidivist), we first examined the
role of demographic variables. Included in
Behavioral Disorders, 40 (3), 184-195
this block were the variables eligible for free
or reduced lunch (coded Yes or No), race,
and gender. The second block of predictors
included two measures of family background/dysfunction, placement in foster care
(Yes or No) and placement in CPS (Yes or
No). The third set of predictors focused on
childhood psychopathology. In constructing
these variables, all DSM-IV-TR diagnoses
conferred by the DMH were assigned to one
of seven categories. Category assignments
were made by the first author, a licensed
psychologist, in consultation with collea
gues. The categories used were aggression
and conduct problems; drug-related prob
lems; attention and learning disorders, men
tal retardation and, other problems starting in
childhood; mood and anxiety disorders;
psychotic disorders; adjustment and milder
disorders; and other serious disorders. For
the present analysis, subjects were first
scored for presence or absence (at any time
in development) of a primary diagnosis
involving aggression and/or conduct prob
lems. The DSM-IV-TR classifications that
were used to define an aggressive behavior
problem included antisocial personality dis
order (DSM-IV-TR classification 301.7), im
pulse control disorder (312.30), conduct
disorders (312.81, 312.82, 312.89), disrup
tive behavior disorder (312.9), oppositional
defiant disorder (313.81), and child or
adolescent antisocial behavior (V71.02;
APA, 2000). They were then scored for
presence or absence of a primary disorder
involving any other type of disorder recog
nized in the DSM-IV-TR. These two vari
ables constituted the third block of
predictor variables. The fourth set of vari
ables included two indicators of eligibility
for special education. Subjects were first
scored for presence or absence of a schoolbased identification as eligible for special
education services due to an LD. They were
also scored for presence or absence of
a school-based identification as eligible for
services due to an EBD. The fifth block of
predictors included the variables age at first
offense (continuous variable), severity of first
offense (status offense versus misdemeanor
or felony), and prosecuted for first violation
(Yes or No). The final block of predictors
included all two-way interaction terms (e.g.,
Race X Gender). We also ran simple logistic
regression analyses for each of the indepen
dent variables to examine the relationship
May 2015 / 187
between the independent variable and re
cidivism with other independent variables
uncontrolled.
Third, we repeated the logistic regression
analyses but this time for Black and White
youth separately. We again entered variables
in blocks. Blocks were the same as those
described in the whole sample analyses, with
two exceptions: (a) The first block did not
include the variable race, and (b) interaction
terms were not included.
TABLE 1
Descriptive Statistics for Predictor Variables
in Relation to Ethnicity
Total Sample
Black
(N =
96,613 )
(n =
W hite
49 ,7 1 0 )
(n = 46,903 )
Gender (Male)
64.58%
64.14%
65.04%
Receives free or
reduced lunchi
61.73%
75.26%
49.39%
Variables
Demographic
Parenting
Results
Foster care
CPS
Descriptive Statistics
Percentages of individuals manifesting
different background characteristics and risk
indicators are shown in Table I for the entire
sample and for Black and White youth
separately. As shown in Table 7, White offen
ders were more likely than Black offenders to
have been diagnosed with a DSM-IV-TR
disorder not associated with aggressive or
impulsive behavior. Black youth were more
likely than White youth to receive free or
reduced lunch in the school system, to have
been in foster care or CPS, to have been
diagnosed with a DSM-IV-TR disorder relating
to aggression or impulse control, and to have
been identified by a school as having a classifi
cation of an EBD. White offenders were more
likely than Black offenders to have been
prosecuted for their first offense and were more
likely to be male. Black youth had a lower mean
age of first referral and were less likely than
White youth to have been first referred for
a status offense.
5.62%
5.84%
4.66%
12.27%
13.00%
11.49%
DSM-IV-TR
diagnoses
Aggression
14.51%
16.56%
12.32%
Other
25.59%
22.12%
29.24%
16.77%
16.66%
16.89%
5.62%
6.31%
4.90%
Disabilities
LD
EBD
Delinquency
history
M = 14.45
(1.95)
M = 14.20
(2.06)
M=14.73
(1.79)
First offense
adjudicated
21.02%
19.81%
22.31%
Nonstatus
offense
79.30%
79.87%
79.10%
Age at first
referral
Note. Chi-squared analyses for differences between racial
groups were conducted for all categorical variables.
Significant differences at p < .001 were detected for all
comparisons except Gender Male and LD. Comparison for
Gender Male was significant at p < .01; LD was not
significant. For Age at first referral, f(96/ 611) = 42.92 (p <
.001), standard deviations are shown in parentheses. Sample
sizes for Nonstatus Offense are n = 46,871 (Black) and n =
49,686 (White). CPS = Child Protective Services, LD =
learning disability, EBD = emotional or behavioral disorder.
Logistic Regression
Whole Sample: No Interaction Terms
Table 2 shows results of the multivariable
logistic regression analysis with interaction
terms not included, as well as results of simple
logistic regression analyses. The multivariable
analysis showed significant effects for race and
gender with Black youth and males more
likely than White youth and females to commit
a second offense, x20 , N = 96,557) = 39.74,
p < .001, and x2 = 494.08, p < .001,
respectively. There was also an effect for free
or reduced lunch with youth qualifying for free
or reduced lunch more likely to commit
a second offense (%2 = 709.10, p < .001).
188 / May 2015
While the effect for foster care was not
significant, there was a significant effect for
CPS (x2 = 279.04, p < .001). Youth who had
been in CPS were approximately 50% more
likely than those who had not been in CPS to
commit a second crime. Mental health di
agnosis was significantly related to the likeli
hood of a second offense, with youth with
either a diagnosis relating to aggressive behav
ior or any other diagnosis more likely to be
referred for a second offense (x2 = 2873.15, p
< .001, and x2 = 1353.62, p < .001,
respectively). The values for adjusted odds
ratios (AORs) show that youth with mental
Behavioral Disorders, 40 (3), 184-195
TABLE 2
Logistic Regression Analysis for Prediction of Recidivism (N = 96 ,557a)
Block
Block 1
Variable
R 2 Block
Race (Black)
Gender (Male)
Free or reduced lunch
Block 2
Foster care
CPS
Block 3
.17**
EBD
LD
Block 5
.08**
DSM-IV-TR Aggressive
DSM-IV-TR Other
Block 4
.05**
1?**
Age first offense
Severity
Adjudicated
.26**
B
AORE
AORf
0.10
39.74**
1.33**
1.10**
0.35
494.08**
1.34**
1.42**
0.42
709.10**
2.09**
1.53**
0.07
3.41
3.03**
1.07
W ald (x2i)
0.43
279.04**
2.70**
1.54**
1.21
2873.15**
4.79**
3.34**
0.64
1353.62**
2.51**
1.91**
0.36
110.77**
3.19**
1.44**
0.10
22.72**
1.55**
1.10**
-0 .3 0
5263.94**
0.71**
0.74**
-0 .3 4
367.04
0.62**
0.71**
852.36**
1.96**
1.70**
0.53
Note. R2 block refers to Nagelkerke R2 following this step in the equation and including the constant. Significance level for R2
block is based on the change in the log-likelihood of the outcome. Significance level for the Wald statistic is based on the final
logistic regression equation. B refers to the logistic regression coefficient in the final equation. AORE refers to the adjusted odds
ratio if entered alone in the equation. AORF refers to the adjusted odds ratio in the final equation. CPS = Child Protective
Services, DSM-IV-TR = Diagnostic and Statistical Manual of Mental Disorders, 4th edition, text revision, LD = learning
disability, EBD = emotional or behavioral disorder.
“ Sample size is reduced to 96,557 for multivariable analysis due to missing observations on Severity.
**p < .001.
health diagnoses relating to aggressive behav
ior were more than 3 times more likely to
commit a second offense than other first
offenders and that youth with another mental
health diagnosis were almost twice as likely as
nondiagnosed youth to commit a second
offense. Youth identified as eligible for special
education services due to an EBD or LD were
more likely to commit a second offense than
youth without these special education classi
fications (x2 = 110.77, p < .001 and x2 =
22.72, p < .001, respectively). Finally, there
was a significant relationship between age of
first offense and recidivism (x2 = 5263.94, p <
.001). The AOR of .76 shows that for each year
of reduced age of first offense, the odds of
a second offense increase by approximately
25%. In addition, youth who had been referred
for status offenses and youth adjudicated for
their first offense were more likely to commit
a second offense (x2 = 367.04, p < . 001 and
X2 = 852.36, p < . 001, respectively). Foster
care was not significantly related to recidivism.
The total adjusted R2 was .26; model x2(12, /V
= 96,557) = 20482.90, p < .001. Also, as
shown in Table 2, simple logistic regression
analyses showed that each independent vari
able when considered alone was a significant
predictor of recidivism; AOREshows values of
Behavioral Disorders, 40 (3), 184-195
adjusted odds ratios with only one variable in
the equation.
Whole Sample: Interactions
When all two-way interaction terms were
added to the equation at step six, the total value
of R2 was only slightly increased from .256 to
.257, x2(11, N = 96,557) = 159.99, p < .001.
Six interactions were significant: Race X Gender
(X2 = 11 -83, p = .001), Race X Free Lunch (x2 =
34.30, p < .001), Race X DSM-IV-TR NonAggressive (x2 = 22.38, p < .001), Race X EBD
(X2 = 21.43, p < .001), Race X Severity (x2 =
14.99, p < .001), and Race X Adjudication
(X2 = 31.60, p < .001). These interactions are
explained in the following section.
Split Files: Analyses by Racial Group
Tables 3 and 4 show the results of the
logistic regression analyses for White and
Black youth considered separately. Values of
R2 were .25 for White youth and .26 for Black
youth. As with the analysis for the total sample,
for both groups all variables except foster care
were significant predictors of recidivism in the
multivariable analysis, and all variables were
significant in the simple logistic regression
May 2 0 1 5/1 8 9
TABLE 3
Logistic Regression Analysis for Prediction of Recidivism for White Youth (N = 46,871a)
Block
Block 1
Variable
Free or reduced lunch
Block 2
Block 4
AORe
1.16**
1.35**
0.34
240.88**
2.16**
1.40**
0.08
2.03
3.21**
1.08
.08**
0.42
122.07**
2.90**
1.52**
1.17
1208.22**
4.62**
3.23**
.16**
0.72
917.57**
2.79**
2.06**
0.20
15.31**
2.65**
1.22**
0.08
8.78*
1.55**
1.09*
.05**
EBD
LD
Block 5
AORe
164.13**
DSM-IV-TR Aggressive
DSM-IV-TR Other
B
0.30
Foster care
CPS
Block 3
R2 Block
Gender (Male)
.15**
W ald ( * 2,)
Age first offense
-0 .3 0
2234.72**
0.71**
0.75**
Severity
-0 .4 0
262.27**
0.56**
0.67**
0.63
594.21**
2.37**
1.87**
Adjudicated
.25**
Note. R2 block refers to Nagelkerke R2 following this step in the equation and including the constant. Significance level for R2
block is based on the change in the log-likelihood of the outcome. Significance level for the Wald statistic is based on the final
logistic regression equation. B refers to the logistic regression coefficient in the final equation. AOR e refers to the adjusted odds
ratio if entered alone in the equation. AORF refers to the adjusted odds ratio in the final equation. CPS = Child Protective
Services, DSM-IV-TR = Diagnostic and Statistical Manual of Mental Disorders, 4th edition, text revision, LD = learning
disability, EBD = emotional or behavioral disorder.
aSample size is reduced to 46,871 for multivariate analysis due to missing observations on Severity.
*p < .01.
**p < .001.
analyses. In addition, the relative magnitudes
of the different predictors were very similar
across groups; in fact, using independent
variable as the unit of analysis, the correlation
between adjusted odds ratios (across the two
groups) was .95 (f9 =9.13, p < .001).
Significant interactions in the total sample
analysis indicated differences in the magnitude
of six predictor variables between the two
groups. Significance tests are based on differ
ences in the magnitude of logistic regression
coefficients (Paternoster, Brame, Mazerolle, &
Piquero, 1998). Three variables were stronger
predictors for White youth than for Black.
Having a DSM-IV-TR diagnosis for a disorder
not described as relating to aggression or
impulse control was a stronger predictor for
White youth than for Black (Z = 4.72, p < .001).
Also, having been referred first for a status
offense and having been prosecuted for the
first offense were stronger predictors for
Whites than for Blacks (Z = 3.88, p < .001
and Z = 5.55, p < .001, respectively). In
contrast, being male, being eligible for free or
reduced lunch, and having been classified by
a school as eligible for special education due
to an EBD were all stronger predictors of
recidivism for Black than White youth. Results
of significance tests were Z = 3.46, p < .001
for gender; Z = 5.81, p < .001 for free or
190 / May 2015
reduced lunch; and Z = 4.65, p < .001 for
EBD.
Summary of Results
Independent variables relating to back
ground, adverse parenting, mental health,
school-related disabilities, and features of first
offenses contributed to more than 25% of the
variance in recidivism for both Black and
White youth. Male gender, eligibility for free
or reduced school lunch, DSM-IV-TR diagno
ses, placement in CPS, and identification as
qualifying for special education services due to
an EBD or LD were all predictive of juvenile
recidivism. In both groups, a mental health
diagnosis relating to aggression or impulse
control was the strongest predictor of recidi
vism. In addition, early age of first offense,
status offenses, and being prosecuted for a first
offense were significant predictors. Magni
tudes of prediction were similar across ethnic
groups. While interactions between race and
other independent variables accounted for
only .001 of the variance in recidivism, there
were several significant interactions. Mental
health history and characteristics of the first
offense were stronger predictors for White
youth than for Black youth. However, gender,
Behavioral Disorders, 40 (3), 184-195
TABLE 4
Logistic Regression Analysis for Prediction of Recidivism for Black Youth (N = 49,710)
Block
Block 1
Variable
I f Block
Free or reduced lunch
Block 2
Block 3
1.51**
.04**
0.52
483.02**
1.88**
1.69**
0.08
2.37
2.82**
1.09
.08**
0.45
163.52**
2.50’ *
1.58**
1.24
1669.04**
4.80**
3.45**
0.56
458.06**
2.44**
1.75**
0.52
117.39**
3.66**
1.68**
.16**
EBD
LD
Block 5
AORf
1.62**
DSM-IV-TR Aggressive
DSM-IV-TR Other
Block 4
AOR e
356.42**
Foster care
CPS
.17**
Age first offense
Severity
Adjudicated
B
0.41
Gender (Male)
.26**
W ald (x2i)
0.11
16.01**
1.56**
1.12**
-0.31
3022.93**
0.71**
0.74**
-0 .2 6
100.57**
0.64**
0.77**
0.42
269.48**
1.66**
1.52**
Note. R2 block refers to Nagelkerke R2 following this step in the equation and including the constant. Significance level for R2
block is based on the change in the log-likelihood of the outcome. Significance level for the W ald statistic is based on the final
logistic regression equation. B refers to the logistic regression coefficient in the final equation. AORE refers to the adjusted odds
ratio if entered alone in the equation. AORF refers to the adjusted odds ratio in the final equation. CPS = Child Protective
Services, DSM-IV-TR = Diagnostic and Statistical Manual of Mental Disorders, 4th edition, text revision, LD = learning
disability, EBD = emotional or behavioral disorder.
“Sample size is reduced to 49,710 for multivariate analysis due to missing observations on Severity.
* * p < . 001.
poverty (free or reduced lunch), and school
classification of an EBD were stronger pre
dictors for Blacks than for Whites.
Discussion
It is w ell recognized that early adverse
experiences in the fam ily and in school are
strongly linked to later child behavior prob
lems (Dodge, Greenberg, Malone, & Conduct
Problems Prevention Research Group, 2008).
Recognized also is the higher prevalence of
fam ily systems disruption and school failure
among Black children (Fomby et al., 2010;
Steinberg, 2014), factors that help explain in
part the disparities in behavioral outcomes,
including both prosocial behaviors, such as
school achievement, and antisocial outcomes,
such as juvenile delinquency, between W hite
and Black youth. The findings of race dispar
ities in achievement, mental and physical
well-being, and antisocial behavior have
motivated both scholars and clinicians to
examine further the sources of strength and
resilience among Black children and families
(APATask Force, 2008). Empirical studies have
identified a number of protective factors that
may lim it the deleterious effects of early
adversity, including institutional barriers to
Behavioral Disorders, 40 (3), 184-195
social and emotional well-being. Protective
factors include individual factors, such as
emotional regulation (Mendez, Fantuzzo, &
Cicchetti, 2002); fam ily influences, such as
closeness to parents (Bynum & Kotchik, 2006);
school variables, such as authoritative teachers
(Ladson-B i 11i ngs, 1994); and comm unity fac
tors, such as quality childcare (Gottfredson,
Gerstenblith, Soule, Womer, & Lu, 2004). The
APA Task Force (2008) provided a detailed
review of the literature.
But studies on individual differences in
resilience do not address the broader question
of whether known risk factors for later de
velopmental problems— including risk factors
relating to early adversity in the home envi
ronment, mental health, school learning prob
lems, and demographic factors such as gender
and social-economic status— have similarly
deleterious effects for W hite and m inority
youth. Are Black youth as a group protected
against social-environmental and personal risk
factors by virtue of having experienced social
and economic adversity in the past and
learning how to adapt to m ultiple stressors?
O r are Black youth even more vulnerable than
W hite youth to these early environmental risk
factors, perhaps due to the same early experi
ences and unique socio-cultural contexts that
are often seen as protective?
May 2 0 1 5 / 191
The results of our study provide a mix of
conclusions. The most striking finding in the
present study is the extraordinary concordance
in the effects (and magnitudes of effects) of
early risk indicators for later antisocial behav
ior, measured in this study by the presence of
juvenile delinquency recidivism. For large
samples of both Black and White youth who
had been referred to the DJ) on at least one
occasion, juvenile recidivism (referral for
a second offense) was significantly predicted
by the same set of variables: gender, poverty,
being referred to CPS, having a DSM-IV-TR
diagnosis for a psychological disorder, being
identified as eligible for special education
services due to an EBD or LD, having an
earlier age of first offense, committing a status
offense as a first offense, and being prosecuted
for a first offense. In addition, the amount of
variance in recidivism accounted for by these
predictor variables was nearly identical: 25%
of the variance in the White sample and 26%
of the variance in the Black sample. Finally,
AORs for the two groups were highly corre
lated; with type of predictor variable as the
unit of analysis, the correlation in the magni
tudes of AORs was .95.
But there was also evidence of differential
effects of individual risk factors for Black
versus White youth. Recognizing that interac
tions between race and individual predictor
variables accounted for only .001 of the
variance in recidivism in the total sample, it
was still the case that the increase in variance
accounted for by interaction was significant.
Comparison of logistic regression coefficients
for the Black and White samples showed six
variables where the magnitudes of effects
differed for the two groups: gender, poverty,
and EBD classifications were stronger predic
tors for Black youth; DSM-IV-TR diagnoses
(nonaggressive), status offending, and prose
cution for first offense were stronger predictors
among Whites.
Several of the interactions identified are
difficult to interpret. Earlier analyses (Barrett,
Katsiyannis, & Zhang, 2006; Barrett et al.,
2010) indicated that status offenses are more
likely than nonstatus offenses to be adjudicat
ed and to lead to a second offense. Why the
relationship between status offending and
recidivism should be stronger for one ethnic
group than another is not clear. Similarly the
findings of a stronger link between nonaggres
sive (i.e., internalizing) disorders and recidi
vism among White youth and a stronger
192 / May 2015
relationship between gender and recidivism
among Black youth merit further attention.
On the other hand, the interactions in
volving poverty (free or reduced school lunch)
and EBD are more readily interpretable. For
Black youth in particular, qualifying for free or
reduced lunch was a risk factor for recidivism.
Since poverty is also associated with lower
family and neighborhood stability, this finding
is inconsistent with a view that due to
adaptation to difficult environments, Black
youth may develop a special resilience in
dealing with poverty. Rather, poverty appears
to place a particularly heavy burden on Black
youth. Findings relating to EBDs are notewor
thy. A school's identification of a child as
eligible for special education due to an EBD is
a stronger predictor of juvenile recidivism for
Black youth than for White, and is also
a stronger predictor of recidivism than identi
fication as eligible due to an LD. It is important
to recognize that Black students are dispro
portionately represented in special education,
particularly in the disability categories of
EBDs, a finding supported by the present data,
and intellectual disabilities (ID; Kauffman,
Simpson, & Mock, 2009).
Black students receiving special education
services are also less likely to be placed in the
general education classroom, resulting in
resegregation of the population (Artiles, Kozleski, Trent, Osher, & Ortiz, 2010). Further,
Black students with (and without) disabilities
are at greater risk than White peers to be
expelled or suspended (U.S. Department of
Education, Office of Civil Rights, 2014a); in
fact, one out of six Black students in public
school has been suspended at least once
(Losen & Gillespie, 2012) and 59% of Black
males have been suspended or expelled from
school, compared to 24% of White males
(Toldson, 2011). Importantly, Black students
with disabilities account for a disproportionate
ly high percentage of disciplinary inclusions
totaling 10 days or more (IDEA Data Center,
2014).
Finally, Black students, particularly males,
are also dropping out of school at almost twice
the rate for White students (Chapman, Laird, &
KewalRamani, 2010; National Center for
Education Statistics, 2013). Regarding academ
ic performance, in 2012 the percentage of 17year-old Black students at or above the
National Assessment of Educational Progress
(NAEP) reading score level of 300 (able to find,
understand, summarize, and explain relatively
Behavioral Disorders, 40 (3), 184-195
complicated literary and informational mate
rial) was 22% versus 47% for White students.
Similarly, percentages of mathematics scores
of 300 or higher (able to perform reasoning
and problem solving involving fractions, dec
imals, percentages, elementary geometry, and
simple algebra) were 33.8% for Black students
versus 70.3% for Whites; the disparity was
even more pronounced (1.1% for Black
students vs. 9.1% for White) for scoring 350
or above (able to perform reasoning and
problem solving involving geometry, algebra,
and beginning statistics and probability; Na
tional Center for Education Statistics, 2013).
Given these disparities in academic and
behavioral outcomes, it is important that
schools examine closely the methods used to
identify students' educational needs, including
the procedures that are used to identify
children as having EBDs.
Limitations and Conclusions
A major limitation in the present study was
that while we were able to include a general
measure of poverty in our analyses, more
specific indicators of socioeconomic disadvan
tage, such as parental characteristics, family
history, and family income were not included.
As noted previously, the South Carolina
DJI does obtain more specific socio-demo
graphic data (e.g., persons living in the
family, family income) on youth referred to
the DJJ; this information is collected at intake.
However, this background information is
collected selectively; in general, background
data are more likely to be collected in cases
where the DJJ intake worker sees the situation
as more serious. Because of this potential
bias, we chose to include an SDE variable—
eligibility for free or reduced lunch—that was
available for all subjects. In addition, our
definition of recidivism—presence of a second
referral to the DJJ—was very broad. W hile it
would have been possible to use a more
stringent measure of recidivism (e.g., pres
ence or absence of a felony at some time after
the first violation), we recognized that be
cause more serious violations are less com
mon, we would probably account for a lower
percentage of the variation in recidivism if we
made this decision; for this reason (the
decision to increase variability in the out
come variable), we chose to examine only
whether there was or was not a repeat
offense.
Behavioral Disorders, 40 (3), 184-195
Despite these limitations, our findings
suggest a number of conclusions and lead to
several directions for further research. First, the
risk factors for Black and White youth for
juvenile delinquency recidivism are remark
ably similar. Many scholars have noted the
ways in which Black youth appear, in some
ways, more resilient than White youth in
dealing with the challenges of adolescence.
Black youth have higher self-esteem than
White youth; this is particularly true for girls
(Biro, Striegel-Moore, Franko, Padgett, & Bean,
2006). Further, as discussed earlier, Black
youth appear to tolerate variations in family
structure that White youth do not. But these
findings should not lead us to underestimate
the impact of early adverse experiences— in
the family, neighborhood, or school— on Black
children. Individual children, regardless of
ethnic/racial group, often show great resilience
in coping with difficult circumstances. Chil
dren with strong peer relationships, realistic
and positive views of themselves, and good
decision-making skills are more able than
others to overcome multiple stressors and
adversities (Radke-Yarrow & Brown, 1993),
and among today's Black youth it is critical
that these abilities and relationships be sup
ported (APA, 2008). But all children, both
minority and nonminority, are likely to be
negatively affected by the types of early
adverse experiences we have examined in this
study.
Second, one area where Black youth may
be particularly impacted by adversity is the
area of special education. Not only are Black
children overrepresented in special education
classifications, identification as having an EBD
for Black children is a stronger predictor of
juvenile delinquency recidivism than it is for
White children. This is true when factors such
as poverty, family disruption, and mental
health history are controlled. While the iden
tification of a child as having a disability
should set in motion a chain of events resulting
in increased attention and support for the
child, the resulting experiences for the child
may not always be positive. It is imperative
that we continue to monitor the ways in which
school-based classifications are made, inter
ventions are implemented, and children are
evaluated following the introduction of special
education services.
Finally, it is important that future research
ers recognize that while adversity in the early
years of development poses significant threats
May 2015 / 193
to the individual's later social and behavioral
health, there are evidence-based interventions
that may help to reduce the likelihood of
problem behavior for children and youth
who are otherwise at risk (Brody, Breach,
Philibert, Chan, & Murry, 2009). These
interventions generally involve a multilevel,
systemic approach to prevention, including
individual-level, family-level, and schoollevel components (Farmer & Farmer, 2001).
Future research studies should continue to
address the efficacy of such preventative
programs, beginning always with the assump
tion that all children and youth—regardless of
race or background— have the same needs for
security, support from others, and belief in their
own competence and worth.
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AUTHORS' NOTE
Address correspondence to David E. Barrett,
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MANUSCRIPT
Initial Acceptance: 12/14/2014
Final Acceptance: 4/03/2015
May 2 0 1 5 /1 9 5
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Measuring the Influence of Juvenile Arrest on the Odds of Four-Year College
Enrollment for Black Males: An NLSY Analysis
Royel Montel Johnson
Spectrum: A Journal on Black Men, Volume 4, Number 1, Autumn 2015, pp.
49-72 (Article)
Published by Indiana University Press
For additional information about this article
https://muse.jhu.edu/article/613203
Access provided by University Of South Florida Libraries (22 Dec 2016 18:13 GMT)
Measuring the Influence
of Juvenile Arrest on the
Odds of Four-Year
College Enrollment
for Black Males:
An NLSY Analysis
Royel Montel Johnson
ABSTRACT: Black youth make up just 16% of public school students
in the United States, though they constitute 31% of all juvenile arrests,
with Black males outnumbering females. Very little is known from
research about the long-term consequences of such contact on their
odds of college enrollment. Thus, the purpose of this study was to
measure the relationship between Black males’ early contact with the
criminal justice system through arrest and their probability of enrolling in a four-year college using a nationally representative sample
of approximately 1,100 Black males who participated in the National
Longitudinal Study of Youth (1997). Survey data were analyzed using
descriptive, chi-square, and hierarchical binomial logistic regression
techniques. Results expose pervasive limits on Black males’ college enrollment, reveal the statistically significant influence of early arrest on
college entry, and have far-reaching implications for research, policy,
and outreach.
In light of mounting concerns about the United States’ diminishing ability
to compete globally, state and federal policy makers have adopted several education reform strategies designed to increase the number of students graduating from
Spectrum, 4(1), 49–72. Copyright © 2016, Trustees of Indiana University and The Ohio State
University. doi:10.2979/spectrum.4.1.04
50
SPECTRUM 4.1
one of the more than 4,200 colleges and universities in the country. For instance,
in his very first State of the Union address, President Barack Obama (2009) outlined an ambitious goal to once again have the world’s largest share of college
graduates by 2020. His justification was simple: “A good education is no longer
just a pathway to opportunity—it is a prerequisite. . . every American will need
to get more than a high school diploma.” Indeed, higher education has become
the most direct pathway to ensuring economic and social mobility for the individual, and increasing the number of Americans with college degrees has significant benefits for society (Haveman & Smeeding, 2006).
To meet President Obama’s “2020 Completion Goal,” significantly more
students must enroll in higher education, particularly those from traditionally
underrepresented populations (Complete College America, n.d.). And while
national figures show increases for some groups, enrollment for Black males has
stalled for several years.1 Data from the National Center for Education Statistics
(NCES) indicate that Black males accounted for only 5.3% of all students at undergraduate institutions in 2012 (US Department of Education, 2013)—nearly
the same proportion as in 1976 (Harper, 2006; Strayhorn, 2008c). Low enrollment rates cannot be explained by the talent pool alone, since national data indicate that upward of 15% of Black men in the US are college-age and many never
attempt higher education (U.S. Census Bureau, 2012).
Scholarly research on Black male college enrollment is replete with references
to factors that may drive “achievement gaps” for Black males, such as pre-college
preparation (e.g., Palmer & Young, 2009; Polite, 1999), academic tracking (e.g.,
Palmer, Davis, & Hilton, 2009; Oakes, Gamoran, & Page, 1992), and low expectations from teachers (Ferguson, 2003; Kunjufu, 1986; Strayhorn, 2008d), to name
a few. One area that has yet to be sufficiently examined in prior research is the
experiences of Black males who come into contact with the criminal justice system prior to entering or enrolling in college, which may significantly reduce their
odds of enrolling or finding success in higher education (Strayhorn, Johnson, &
Barrett, 2013).
Recent data indicate that Black youth represent just 16% of all public school
students in the US but constitute 31% of all arrested youth, with Black males
outnumbering females (Rovner, 2014). Existing research, though limited, suggests
that early contact with the criminal justice system, such as an arrest, has negative
educational consequences for all youth generally, and Black youth specifically,
although relatively little is known about the relationship between arrest and college enrollment (e.g., Hirschfield, 2009; Kirk & Sampson, 2013). Studies that do
focus on college outcomes offer limited insight into the condition of Black males,
who are overrepresented among juvenile arrests, school suspensions, and expulsions,
Johnson / Influence of Juvenile Arrest
51
yet make up only 5% of collegians in the US (Irvine, 1990; Noguera, 1997; Palmer,
Wood, Dancy, & Strayhorn, 2014).
Though scholarly research is limited, there is theoretical support for hypothesizing a relationship between juvenile arrest and college enrollment. For instance, life course theory of cumulative disadvantage (LCTCD), which draws on
the assumptions of both social control theory (Hirschi, 1969) and labeling theory
(Becker, 1963; Lemert, 1951), suggests that an arrest could serve as a negative
turning point in one’s life course, leading to a series of detachment processes that
increase one’s likelihood of school dropout. Thus, it is reasonable to believe that
Black males’ arrest experiences as juveniles have negative consequences for their
likelihood of enrolling in college, which was the focus of this study.
PURPOSE OF STUDY
The purpose of this study was to test the relationship between Black males’
early contact with the criminal justice system through juvenile arrest and four-year
college enrollment using a nationally representative sample of approximately
1,100 Black males who participated in the National Longitudinal Study of Youth
(NLSY:97). Specifically, a battery of statistical controls were employed to isolate
and test the predictive validity of Black male arrest history on their probability of
enrolling in a four-year college in 2003. Two central research questions guided
this study:
1. Are there significant differences between Black males who report being
arrested as a youth and those who do not in terms of four-year college
enrollment?
2. Controlling for a battery of background and family factors, does Black male
youth arrest status significantly predict enrollment in a four-year college?
BRIEF REVIEW OF LITERATURE
To conduct this study, it was necessary to review literature in two areas
of inquiry: (a) what we know from research about the influence of juvenile arrest
on education outcomes and (b) what we know from research about Black males’
pathways to college. This organization served as a useful frame for reviewing existing literature.
Influence of Juvenile Arrest on Educational Outcomes
The weight of empirical evidence offers compelling arguments about the
negative influence of early criminal justice contact, such as juvenile arrest, on
52
SPECTRUM 4.1
educational outcomes—the majority of which focus on high school (e.g., De Li,
1999; Hirschfield, 2009; Lochner, 2004; Tanner, Davies, & O’Grady, 1999). For
instance, Hjalmarsson (2008) analyzed data from a nationally representative
sample (N = 7,417) of youth who participated in NLSY:97 to test the relationship between juvenile justice system interactions and high school graduation. The
author reported that arrested youth were 11 times less likely to graduate high
school than non-arrested youth—results were not disaggregated by race and
sex. Sweeten (2006) drew similar conclusions for Black and Latino youth using
NLSY:97 data to test the effect of first-time arrest and court involvement during
high school on educational attainment. He noted, “first-time arrest during high
school nearly doubles the odds of high school dropout, while a court appearance
nearly quadruples the odds of dropout” (p. 473).
Though none of the studies report results disaggregated by both race and
sex, a growing number of authors have focused attention on at-risk populations such
as those from large cities or economically disadvantaged backgrounds—factors
that significantly influence one’s likelihood of arrest and educational achievement
(e.g., McDonough, 1997; Sampson & Laub, 1997; Strayhorn, 2009a). Hirschfield
(2009) analyzed a sample of nearly 4,900 students from predominantly minority
neighborhoods in Chicago, controlling for a number of confounding factors such
as race, their parents’ educational background and expectations, delinquency, and
prior academic achievement. Results from his study indicate that students who
were arrested during the 9th or 10th grade were six to eight times more likely to
dropout of high school than those who reported no arrests. Likewise, Bernburg
and Krohn (2003) found that juvenile arrest experiences significantly reduced students’ odds of graduating from high school by more than 70%, examining data
from a sample (N= 605) of seventh- and eight-grade males from Rochester,
New York, public schools. Though their results were not disaggregated by race,
nearly 70% of participants in their sample identified as African American.
Research related to juvenile arrest and college outcomes is virtually nonexistent. Kirk and Sampson (2013) provided an initial foray into this line of
inquiry, estimating the direct effect of arrest on later high school dropout and
college enrollment for youth, suggesting that arrest has “severe consequences for
the prospects of [college] educational attainment” (p. 47). Youth with arrest records in their study had only a 0.18 probability of enrolling in a four-year college
compared to nonarrestees, who had a probability of college enrollment equal to
0.34. The authors concluded, “. . . arrest in adolescence hinders the transition
to adulthood by undermining pathways to educational attainment” (p. 19). The
following section reviews what we know from research about Black males’ pathways to higher education.
Johnson / Influence of Juvenile Arrest
53
Black Males’ Pathways to Higher Education
Black males encounter significant challenges along the education pipeline
that collectively reduce their odds of college enrollment (e.g., Cuyjet & Associates,
2006; Ferguson, 2003; Ford, 1998; Irvine, 1990; Jackson & Moore, 2008; Jenkins,
2006; Polite, 1999; Steele, 1997; Strayhorn, 2008b). One line of inquiry directs
attention to the role of teachers, who often maintain low or negative expectations
of Black males (e.g., Kunjufu, 1986; Wood, Kaplan, & McLoyd, 2007). Strayhorn
(2008d) examined the relationship between teacher expectations and academic
achievement among urban Black males who responded to the National Education
Longitudinal ...
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