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Answer the following questions using the material from this module. Your post should be 500 words (5 points) and free of grammatical and spelling mistakes (5 points).

1. Why do you think criminology has focused on males? What effect does this have on our knowledge of crime and delinquency? (10 points)

2. How does disproportionate minority contact affect the community? What can be reasonably done to remedy this? (10 points)

3. What did the Johnson article find in regards to long-term effects of the justice system? (10 points)

4. Ask a question to your classmates. This can be a question meant to garner better understanding of the material or just a question about the material. (5 points)

5. Respond to another student by answering the question they posed in part 4 above with a minimum of 150 words. (5 points) \ for this question, I will send the question from one of the classmates for you after you complete the first four questions because it will be visible after I post the answers for the first four questions.

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 123 428 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 123 J Child Fam Stud (2015) 24:427–433 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 123 430 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 123 432 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 123 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. References Acoca, L., & Dedel, K. (1998). No place to hide: Understanding and meeting the needs of girls in the California Juvenile Justice System. San Francisco: National Council on Crime and Delinquency. American Bar Association and the National Bar Association. (2001). Justice by gender: The lack of appropriate prevention, diversion and treatment alternatives for girls in the justice system. Washington, DC: Author. Barrett, D. E., Katsiyannis, A., & Zhang, D. (2010). Predictors of offense severity, adjudication, incarceration and repeat referrals for juvenile offenders: A multi-cohort replication study. Remedial and Special Education, 31, 261–275. Barrett, D. E., Katsiyannis, A., & Zhang, D. (2013a). Predictors of teen childbearing among delinquent and non-delinquent females. Manuscript submitted for publication. Barrett, D. E., Katsiyannis, A., Zhang, D., & Zhang, D. (2013b). Delinquency and recidivism: A multi-cohort, matched -control study of the role of early adverse experiences, mental health problems and disabilities. Journal of Emotional and Behavioral Disorders,. doi:10.1177/1063426612470515. Barrett, D. E., Zhang, D., Wang, Q., Katsiyannis, A., & Zhang, D. (2013c). A structural equation modeling analysis of influences on juvenile delinquency. Manuscript submitted for publication. Benda, B. B. (2002). A test of three competing theoretical models of delinquency using structural equation modeling. 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Retrieved from https://www.ncjrs. gov/pdffiles1/ojjdp/223434.pdf. Zhang, D., Barrett, D. E., Katsiyannis, A., & Yoon, M. (2011). Juvenile offenders with and without disabilities: Risks and patterns of recidivism. Learning and Individual Differences, 21, 12–18. 123 Copyright of Journal of Child & Family Studies is the property of Springer Science & Business Media B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
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. REFERENCES American Psychiatric Association. (2000). Diagnos­ tic and statistical manual o f mental disorders. (4th ed., text rev.) Washington, DC: Author. American Psychiatric Association. (2013). Diagnos­ tic and statistical manual o f mental disorders. (5th ed.) Washington, DC: Author. APA Task Force on Resilience, Strength in Black Children, & Adolescents (2008). Resilience in African American children and adolescents: A vision for optimal development. Washington, DC: Author. Retrieved from http://www.apa.org/ pi/cyf/resil ience.html Artiles, A. J., Kozleski, E. B., Trent, S. C., Osher, D., & Ortiz, A. (2010). 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Breaking barriers 2: Plotting the path away from juvenile detention and toward academic success for school-age african american males. Washington, D.C.: Congressio­ nal Black Caucus Foundation, Inc. Retrieved from http://www.cbcfinc.org/oUploadedFiles/ BreakingBarriers2.pdf U. S. Department of Education. (2014). Guiding principles: A resource guide for improving school climate and discipline. Retrieved from http://www2.ed.gov/poIicy/gen/guid/schooldisci pi ine/guidi ng-principles.pdf U.S. Department of Education, Office of Civil Rights. (2014a). Civil rights data collection data snap­ shot: School discipline. Retrieved from http:// www2.ed.gov/about/offices/list/ocr/docs/crdcdiscipline-snapshot.pdf U.S. Department of Education, Office of Civil Rights. (2014b). Dear colleague letter: Nondiscriminatory administration o f school discipline. Re­ trieved from https://www2.ed.gov/about/offices/ list/ocr/letters/colleague-201401-title-vi.pdf Wilson, S. J., Lipsey, M. W., & Soydan, H. (2003). Are mainstream programs for juvenile delin­ quency less effective with minority youth than majority youth? A meta-analysis of outcomes research. Research on Social Work Practice. 13, 3-26. doi: 10.1177/1049731502238754. Wu, L., & Thomson, E. (2001). Race differences in family experiences and early sexual initiation: Dynamic models of family structure and family change, journal o f Marriage and the Family, 63, 682-696. doi: 10.1111/j.1 741-3737.2001. 00682.x. AUTHORS' NOTE Address correspondence to David E. Barrett, Eugene T. Moore School of Education, Clemson University, 101 Tillman Hall, Clemson, SC 29634; E-mail: bdavid@clemson.edu. MANUSCRIPT Initial Acceptance: 12/14/2014 Final Acceptance: 4/03/2015 May 2 0 1 5 /1 9 5 Copyright of Behavioral Disorders is the property of Council for Children with Behavioral Disorders and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. 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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 Study (1998/2000). Results from his study suggest that teachers, on average, have lower expectations for Black male students than White male and Black female students. Moreover, approximately 16% of Black males in his sample reported that their teacher recommended work instead of school; and 20% reported feeling “put down” by their teacher, compared to 4% of White males and 4.8% of Black females. Teachers who impose low or negative expectations on Black males tend to interact least with them, limiting their beliefs in students’ ability to learn (e.g., Kunjufu, 1986). As a result, some Black males may internalize such negative selfbeliefs, which, in turn, threaten their educational success (Steele, 1997). A second line of literature focuses on the disproportionate placement of Black males in special education (e.g., Harry & Anderson, 1994; Noguera, 2003). Data suggest that Black males constitute more than 20% of all students in special education, though they represent only 9% of the total school population in the United States (National Education Association, 2011). Concerns about this inequity have prompted a wide range of research (e.g., Dunn, 1968; Dykes, 2008), some of which has argued that Black male placement in special education is, in part, a function of their relationship and experiences with teachers. In other words, Black male students are considerably less likely than White students to have positive relationships with their teachers and are, thus, more likely to be referred to special education for disciplinary reasons (Decker, Dona, & Christenson, 2007; Monroe, 2005). Analyzing data from 10 school districts, Herrera (1998) found a statistically significant relationship between the number of Black students placed in special education and the number of White teachers in the school system. On average, cities with the highest percentage of White teachers also had the highest percentage of Black students identified as “special.” Generally, students in special education are less likely to be exposed to rigorous classroom instruction and therefore are not college- and career-ready upon graduation (Ford, 1998). A third and final line of inquiry highlights the disproportionate punishment of Black male students in school (Ferguson, 2001; Irvine, 1990; Noguera, 2003). Several scholars have argued that Black males’ overrepresentation in exclusionary 54 SPECTRUM 4.1 discipline (e.g., detention, suspension, expulsion, and school replacements) is, in part, a function of school personnel’s negative perceptions of them (Darensbourg, Perez, & Blake, 2010; Ferguson, 2001; Kunjufu, 1986). Lewis and colleagues (2010) provided insight on exclusionary practices, drawing on data from an urban school district in the Midwest to examine differences in discipline responses to Black and White male students and uncovered several key findings. First, though Black males made up only 11% of the total district population, they constituted nearly 37% of all disciplinary sanctions. A great majority of the behavioral infractions were for disobedience (47%) and defiance (17%) and not fighting, threats, or thefts (15% combined). Second, 33% of behavioral sanctions were detentions, 38% were in-school suspensions, and 38 were out-of-school suspensions. Increasingly, more students are also being referred to the police or courts, criminalizing misbehavior in school, which has been referred to as the “schoolto-prison pipeline” (Cass, Curry, & Liss, 2007; Krezmien, Leone, Zablocki, & Wells, 2010). Krezmien et al. (2010) studied school referrals directly to juvenile courts in five states and found they increased between 1995 and 2004. The authors attributed this trend to increased reliance on zero-tolerance policies for school misbehavior, as well as an increase in the use of police officers to manage school misbehavior. Utilizing correctional services for typical disciplinary problems severely impacts Black males, increasing their odds of arrest and incarceration. Youth with arrest records who graduate from high school, for instance, may have poor grades and inconsistent attendance records, a potential consequence of issues faced in the criminal justice process, such as time in court, with parole officers, or court-required community service. Poor academic records may limit students’ competitiveness in college admission and securing financial aid. Furthermore, gatekeepers like guidance counselors might have little motivation to support youth with criminal records in their college search (Kirk & Sampson, 2013). Taken together, these factors may, among others, significantly lower the probability of college enrollment for such Black males. METHODS This study represents a secondary data analysis of the National Longitudinal Study of Youth (NLSY:97), which was sponsored by the U.S. Bureau of Labor Statistics. Secondary data analysis refers to the “re-analysis of data for the purpose of. . . answering new questions with old data” (Glass, 1976, p. 3). Secondary analysis of existing data permits researchers access to data from large, national samples that would otherwise be difficult for a single researcher to collect (Kiecolt & Nathan, 1985). Provided in this section is an overview of the methodology that was employed in this study. Johnson / Influence of Juvenile Arrest 55 Data Source The dataset for this study was constructed from the NLSY:97, which was designed to represent the civilian, non-institutional population of the US between the ages of 12 and 16 as of December 31, 1996 (Moore, Pedlow, Krishnamurty, & Wolter, 2000). This ongoing cohort has been surveyed 15 times, now biennially, and was most recently interviewed in 2011–12 (Hering & McClain, 2003). The NLSY:97 collects extensive information about the youth’s labor market behavior, educational experiences, as well as their family and community backgrounds. Sample The unweighted analytic sample for this study was restricted to respondents who identified as “Black” and “male” within the first wave of the survey (N = 1169). A great majority (95%) of the sample reported they were U.S. citizens (i.e., born in the US). Ages of the participants varied. For instance, 18% of the sample was 12 years old at the time of the initial survey and 20% were 15 years old. Most (77%) of the participants reported residing in an urban area at the time of the initial survey. For more information about the analytic sample, see Table 1. Measures The primary independent variable for this study measured Black males’ arrest status in 1997. Participants were asked, “Have you ever been arrested by the police or taken into custody for an illegal or delinquent offense?” Responses were coded dichotomously: 0 (no) to 1 (yes). Coding of this variable is consistent with previous research (Bernburg & Krohn, 2003; Brame, Bushway, Paternoster, & Turner, 2014; Sweeten, 2006). The dependent variable measures Black male college enrollment status in September 2003. This categorical variable was initially on a four-point scale: 1 (not enrolled in college), 2 (enrolled in a two-year college), 3 (enrolled in a four-year college), and 4 (enrolled in a graduate program). For the purposes of this study, it was recoded to exclude individuals enrolled in two-year colleges and graduate programs. Thus, responses were coded dichotomously: 0 (no, not enrolled) and 1 (yes, enrolled), as has been done in prior research (Perna, 2000). NLSY:97 permits the use of a robust set of statistical controls to isolate the net effect of the predictor variable on the dependent variable. Prior research uncovered several factors that may confound the relationship between juvenile arrest and college enrollment based on prior literature: parents’ level of education (e.g., Horn & Bobbitt, 2000), parents’ income (e.g., McDonough, 1997), parents’ expectations (e.g., Lareau, 1987), prior academic achievement (e.g., Davis, 2003), delinquency (e.g., Sampson & Laub, 1997), and urbancity (e.g., Strayhorn, 2009a). 56 SPECTRUM 4.1 Table 1. Description of Analytic Sample Characteristic/Variable % Age 11 19% 12 21% 13 20% 14 20% 15 20% College Enrollment Status (2003) Not Enrolled 75% Enrolled at four-year College 25% Ever Arrested? Yes 92% No 8% Urbanicity Urban 24% Rural 76% U.S. Citzenship Yes 95% No 5% Their parents’ income was measured on a five-point scale ranging from 1 ($5,000) to 7 (more than $250,000). For parents’ expectations of their child’s educational achievement, a composite variable (α = 0.71) was created using three items, such as “What is the percent chance that [he/she] will have received a high school diploma by the time [he/she] turns 20?” Each item was originally on a scale of 0 to 100. The composite variable was created by summing these three items; the range of the composite is from 0 to 300. Prior academic achievement (α = 0.64) was also measured using a composite variable, including their eighth grade and high school grades. Both items were originally scored on a seven-point scale: 1 (mostly below Ds), 2 (mostly Ds), 3 (about half Cs and half Ds), 4 (mostly Cs), 5 (about half Bs and half Cs), 6 (mostly Bs), and 7 (about half As and Bs). The composite variable was created by summing these two items; the range is from 2 to 14. Delinquency was measured Johnson / Influence of Juvenile Arrest 57 using an existing composite variable, including 10 self-reported items, each representing a delinquent act. The Delinquency Index is on a scale from 0 (no delinquent acts) to 10 (many delinquent acts). Finally, urbanicity was coded dichotomously: 0 (rural) to 1 (urban). Validity and Reliability Validity and reliability are both addressed in this study. Validity refers to an evaluation of whether or not a particular mode of assessment accurately measures what it intends to measure (Suskie, 1996). Moreover, “validation combines scientific inquiry with rational argument to justify score interpretation and use” (Messick, 1995, p. 742). This study addressed validity in several ways. First, NLSY:97 is a widely used and circulated instrument. Government agencies and academic institutions regularly draw on data and findings from NLSY:97 in their recommendations to—and testimony before—Congress. Second, NLSY was designed and executed by the National Opinion Research Center (NORC), one of the largest independent social research organizations in the country, established in 1941 at the University of Chicago. Third, NLSY is well respected in the academic community. To date, nearly 10,000 journal articles, book chapters, and other studies have been published using information from the NLSY. Finally, validity of this study’s variables was assessed using theoretical justification and factor analysis. Validity is important, but it is not sufficient by itself. A second important consideration is instrument reliability. Reliability is defined as the “consistence with which an instrument measures whatever it measures” (Schmidt, Viswesvaran, & Ones, 2000, p. 905). Said differently, reliability refers to the stability and internal consistency of the measures of interest. The present study addressed reliability in the following ways. First, NLSY is a nationally represented longitudinal study with repeated measures, demonstrating stability and consistency of items over time. In terms of the independent variable of interest, internal consistency reliability is not calculable, but internal consistency was calculated for multi-item scales in this study. Data Analysis Several steps were taken to prepare data for final analysis. First, data were retrieved in aggregate from the NLS website. Given the purpose of this study, data were subsequently restricted to permit analysis of the primary research questions, excluding data beyond the scope of this study. Second, all variables were screened for missing cases. Scholarly research suggests secondary analysis of national databases is often complicated by the amount of missing cases or data 58 SPECTRUM 4.1 (Graham & Hoffer, 2000; Little & Rubin, 1989; Strayhorn, 2009b). Thus, missing data were handled through case-by-case analysis. For instance, listwise deletion was used for variables with less than 5% of missing data (Cohen & Cohen, 1983)— these variables included arrest status, college enrollment status, and delinquency. One important caveat is that missing data constituted nearly 10% of all cases for college enrollment status. Since it was the dependent variable, listwise deletion was deemed appropriate, dropping all missing cases. For the remaining variables, mean substitution was used to replace missing information—this is referred to as the zero-order correction procedure (Strayhorn 2009b). Table 2 provides a summary of these results. Sampling weights were also applied to the data before analysis, given the complex sampling techniques employed in NLSY:97. The panel weight was appropriate for approximating the population of youth in 1997 with arrest records in the longitudinal study. To minimize the influence of large sample sizes (N = 140,145, 249) on standard errors while also correcting for oversampling of some groups (e.g., Blacks), cases were weighted by the NLSY panel weight divided by the average (M = 130,036.55) weight of the sample (Thomas & Heck, 2001). This procedure reduced the sample size to 1,078. Once data were prepped, analysis proceeded in three stages. First, descriptive statistics were used to calculate measures of central tendency for all independent, dependent, and control variables in this study. Second, to answer the first research question and test for significant differences between Black male arrest and college enrollment status, a Pearson chi-square test was used. This procedure is used to test for independence when both variables are categorical. Table 2. Means and Standard Deviations for all Variables Unadjusted Variables M Adjusted SD SD M Arrest Status 0.13 0.34 - - College Enrollment Status 0.14 0.35 - - Delinquency 1.74 2.02 - - Parent Expectations Parent Income Urbanicity Prior Academic Achievement 242.01 67.43 251.24 39.31 20692.15 16634.40 20966.23 11955.47 0.81 0.39 0.81 0.38 13.77 3.70 15.73 0.60 Johnson / Influence of Juvenile Arrest 59 Finally, the second research question was answered using a hierarchical binomial logistic regression given the nature of the dependent variable and the study’s goal of controlling for a battery of controls. Hierarchical regression analysis is “a method of regression analysis in which independent variables are entered into the regression equation in a sequence specified by the researcher in advance” (Vogt, 1993 p. 129). This approach yields more conservative estimates of statistical relationships, thereby reducing the chances of making type 1 errors. Also, using logistic regression was deemed the most appropriate method for examining binary outcomes (Aldrich & Nelson, 1984). Several indices were interpreted to assess the “fit” of the model, including the likelihood ratio test, omnibus test of model coefficients, and several pseudo R2 values that measure the overall strength of association between independent and dependent variables (Pampel, 2000). The Hosmer-Lemeshow goodness-of-fit test was interpreted, which assesses the degree to which the observed frequencies match the expected frequencies using a chi-square goodness-of-fit test. To evaluate the overall strength of statistical relationships, several other statistics were calculated and interpreted—including predicted probabilities, predicted odds, and adjusted odds ratios where necessary (Keith, 2006; Pampel, 2000). Probabilities refer to the probability of enrolling in a four-year college relative to arrest status, controlling for confounding variables. Predicted odds measures the odds of enrolling in a four-year college relative to the influence of an independent variable, controlling for all others. Odds ratios are “a ratio of the odds for each group” (Meyers, Gamst, & Guarino, 2006, p. 230). Limitations Before presenting the results of the present study, several limitations should be noted because they are important to consider when interpreting the results. First, some variables in this study were limited by the magnitude of the missing data. Variables with the largest share of missing data included prior academic achievement, parents’ income, and parents’ expectations. In these cases, listwise deletion would have reduced the analytic sample significantly, possibly resulting in a non-representative sample. To avoid substantial reduction in sample size, the author took several steps to address missing cases. Specifically, mean substitution was used to replace missing information, which is referred to as the zero-order correction procedure (Strayhorn, 2009b). To the extent that these adjustments alter statistical relationships, parameter estimates may be biased. Second, despite its widespread use in education and social science research, secondary data analyses are limited by the factors that can be defined, operationalized, and measured in the studies (Thomas & Heck, 2001). As such, the author 60 SPECTRUM 4.1 was limited to only those factors that could be measured by variables available in the NLSY:97. For example, the dependent variable that asked participants “What was your college enrollment status during September in 2003?” did not account for Black males who enrolled in college between the years of 1998 and 2002. We know from higher education research that Black males’ pathway to and through college is checkered with various transitions such as stop-outs, dropouts, and delayed enrollment (e.g., Cuyjet & Associates, 2006; Strayhorn, 2010). Future studies should account for such nuances, computing a new composite variable to measure and track college enrollment between those years. It is also possible that the survey did not measure all confounding variables mediating the relationship between juvenile arrest and college enrollment. Still, using this database greatly increased my ability to test the relationship between juvenile arrest and four-year college enrollment, controlling for a relevant set of confounding variables. Third, this study examined self-reported arrests of Black male youth who responded to NLSY:97. Self-reported data might differ from more objective or standardized reports of arrest histories as individuals may be inclined to underestimate their number of arrests, yet prior research suggests that self-reported data are generally reliable when (a) the information requested is known by the respondents, (b) when the questions are phrased clearly and unambiguously, and (c) when the respondents think the questions merit a serious and thoughtful response (Pace, 1985). The present study was based on these assumptions. Finally, the design of this investigation (i.e., secondary analysis) presented another limitation. The NLSY:97 study did not employ a simple random sampling strategy. Instead, a complex sampling design was used to collect data from a nationally representative sample. This sampling strategy presents researchers with a number of technical and statistical issues (Thomas & Heck, 2001). Appropriate weights were applied to the database to account for the stratified, complex sampling design used and to “weight up” sample estimates to the population parameters. While useful to discuss, these issues do not limit the importance of this analysis. The next section presents the results of this study followed by a discussion of their relevance to existing research. RESULTS Recall the purpose of this study was to test the relationship between Black male youth’s early contact with the criminal justice system through arrest and four-year college enrollment using a nationally representative sample of approximately 1,100 Black males who participated in the NLSY:97. Specifically, I employed a battery of statistical controls to isolate and test the predictive validity Johnson / Influence of Juvenile Arrest 61 of Black males’ arrest history on their probability of enrolling in a four-year college in 2003. Research Question One: Chi-Square Test A Pearson chi-square test of independence was performed to examine the relationship between juvenile arrest and four-year college enrollment status for Black males, given the binary nature of each variable. Results suggest statistically significant differences in the expected and observed frequencies of enrollment in four-year college for Black males in 2003 on the basis of their 1997 arrest status: X2 (N = 1079) = 23.52, p < 0.01. In other words, Black males who reported being arrested at some point in their life by 1997 were less likely to be enrolled in a four-year college in 2003 than their same-race male peers who were never arrested. Approximately 2% of Black males who were arrested by 1997 were enrolled in a four-year college in 2003. Table 3 presents a summary of these results. Research Question Two: Hierarchical Binomial Logistic Regression Hierarchical binomial logistic regression techniques were used to examine the relationship between 1997 arrest status and probability of four-year college enrollment in 2003 for Black males in the NLSY:97 national sample. The final model (including the predictor and all control variables) was not considered to be a good fit. Several model-fitting indices support this conclusion. Results from the Hosmer-Lemeshow (2000) test (X2 [8] = 16.37, p < 0.05) suggest statistically significant differences between the predicted and observed frequencies, rendering the model a bad fit. A small observed change in scaled deviance (Δ−2 log likelihood = 23.66) also suggests that the model was not a good fit. Generally, the smaller the statistic, the better the model (Sweet & Grace-Martin, 1999). Other indicators were also used to evaluate the ability of the final model to predict four-year college enrollment, including Cox and Snell and Nagelkerke Table 3. Descriptive Statistics for College Enrollment by Arrest Status College Enrollment Arrest Status No Yes No 758 (84%) 172 (98%) Yes 145 (16%) 4 (2%) Note. χ2 = 23.52, df = 1, p < 0.01. Numbers in parentheses indicate column percentages. 62 SPECTRUM 4.1 pseudo-R squared. Cox and Snell pseudo-R2 was 0.09, and Nagelkerke pseudoR2 was 0.14 in the final model. In other words, only a small portion of the variance or change in probability of four-year college enrollment was accounted for by the factors in the final statistical model. Approximately 84% of cases could be correctly classified using the final regression model. Several independent variables were significant predictors of Black males’ four-year college enrollment in the last and final model: delinquency, parents’ expectations, parents’ education, parents’ income, and arrest status in 1997. Black males who reported higher levels of delinquency (b = -0.13) had a lower probability of enrolling in a four-year college in 2003 than their less delinquent, samerace male peers. Parents’ education (b = 0.08), parents’ expectations (b = 0.02), and parents’ income (b = 0.00) were all statistically significant positive predictors of four-year college enrollment. In other words, Black males whose parents reported higher levels of education, higher educational expectations for their children, and higher incomes had a greater probability of enrolling in a four-year college in 2003 than those who did not. Reported arrest status (b = -1.58) in 1997 was also a significant negative predictor of four-year college enrollment for Black males in 2003. Black males who reported ever being arrested by 1997 were significantly less likely to enroll in a four-year college in 2003 than their samerace male peers who reported never being arrested (see Table 4). To evaluate the overall strength of statistical relationships, predicted probabilities, predicted odds, and adjusted odds ratio were also computed. Consistent with the literature, Black males in the NLSY:97 sample were unlikely to enroll in college by Table 4. Logistic Regression Results Factor Model 1 (β) Arrest Status – Model 2 (β) -1.58* Delinquency -0.21** -0.13* Parents’ Education 0.09** 0.08* Parent’s Expectations 0.02** 0.02** Parent’s Income 0.00* 0.00* Prior Academic Achievement 0.16 0.16 Urbanicity -0.05 -0.01 Constant -12.31 -11.69 Note. *p < 0.05**p < 0.01. Johnson / Influence of Juvenile Arrest 63 2003. Those who were arrested by 1997 were even less likely and had a predicted probability of 0. DISCUSSION Results from this study suggest a negative relationship between early criminal justice contact through arrest and four-year college enrollment for Black male youth. Statistical differences were observed in Black males’ four-year college enrollment by arrest status. Specifically, Black males who reported being arrested as a juvenile were less likely than their same-race male peers who were never arrested to enroll in a four-year college by 2003. Likewise, juvenile arrest was a significant predictor of the probability of four-year college enrollment by 2003 for Black males in the sample. Although scholarly literature examining the nexus between juvenile arrest and college enrollment is sorely underdeveloped, results from the present study generally affirm conclusions drawn in previous research. Kirk and Sampson (2013) analyzed data from 9,000 Chicago residents and found that juvenile arrest is related to odds of college enrollment. In their study, only 16% of individuals with juvenile arrest records enrolled in a four-year college. Similarly, results from the current study suggest clear differences in college enrollment based on Black males’ juvenile arrest status—those with juvenile arrest records were significantly less likely to enroll in a four-year college by 2003. Additionally, data from this study demonstrate that Sampson and Kirk’s findings, which were based on a diverse sample of Chicago residents, hold for Black males in the NLSY sample: juvenile arrest significantly predicts the probability of enrolling in a four-year college, controlling for more traditional academic and background predictors. Even though two Black males may have similar personal and academic records, the one with a juvenile arrest record is significantly less likely than the one without to enroll in college, all other things being equal. Results from the present study also relate to prior research on Black males’ experiences with the criminal justice system. Data has shown that among Black men ages 18 and older, the national incarceration rate is 1 in 15 (Pew Charitable Trusts, 2008). Results from this study not only affirm the fact that some Black males report early contact with the correctional system through juvenile arrest, but extends what is known by demonstrating that juvenile arrest can have a deleterious impact on one’s educational opportunities. Black men in the study’s analytic sample were significantly less likely to enroll in college if they were arrested as a juvenile—only 2% who were arrested as juveniles went on to enroll in a four-year college. This adds important information to the growing literature on 64 SPECTRUM 4.1 the Black male crisis in higher education (Cuyjet, 1997; Cuyjet & Associates, 2006), mass incarceration (Alexander, 2012), and the juvenile justice system (Kirk & Sampson, 2013; Rovner, 2014). Recall, results from this study suggest that Black males were unlikely to enroll in college by 2003; and those who were arrested by 1997 were even less likely, with a predicted probability of zero. Decades of research on the “Black male crisis” in higher education converge with these results (Cuyjet, 1997; Cuyjet & Associates, 2006). Black males’ low college enrollment rates have been attributed to many factors such as pre-college preparation (e.g., Strayhorn, 2011), overrepresentation in remedial and special education (e.g., Noguera, 2003), and disproportionate punishment in school (Ferguson, 2001). Findings from this study contribute to this line of inquiry, identifying juvenile arrest as yet another factor diminishing Black males’ odds of college enrollment. There were also other significant predictors of four-year college enrollment for Black males, like delinquency. This relationship seems rather intuitive, as delinquency often leads to criminal justice contact through an arrest (Sampson & Laub, 1997). Said differently, individuals who engage in delinquent activities are more likely to be arrested, though I recognize that certain groups, like Black males, experience criminal justice contact at disproportionate rates regardless of delinquency (Alexander, 2012). Results from this study relate to other research conclusions as well. For example, dozens of studies have shown that juvenile delinquency is associated with lower levels of educational attainment for all students (e.g., De Li 1999; Lochner, 2004; Tanner et al., 1999). Yet the weight of empirical evidence to date focuses exclusively on secondary educational outcomes, such as high school dropout. In one such study, Ward and Williams (2014) found that delinquency by the age of 16 reduces males’ probability of graduating from high school or a four-year college. Results from the present study go a step further and show that juvenile arrest distinguishes Black men who enroll in college from their same-race male peers who do not. The study also provides evidence that juvenile arrest reduces the probability that Black males will enroll in a four-year college, taking Ward and Williams’ conclusions to the postsecondary level and focusing specifically on Black males’ chances of enrolling in college. No doubt strategies are needed to prevent juvenile delinquency/arrest as well as ways to overcome the long-term impacts of juvenile arrest. Conceivably, a Black male who was arrested at the age of 12 should be given the opportunity, upon release, to successfully reintegrate into society as a law-abiding citizen without reproach. In fact, the juvenile justice system was designed, at least in part, with that goal in mind. Findings from this study suggest Johnson / Influence of Juvenile Arrest 65 that early criminal justice contact through arrest for Back males may have negative and stigmatizing long-term effects, significantly reducing their odds of four-year college enrollment—the most direct pathway to ensuring economic and social mobility. Said differently, a juvenile arrest may operate as a new scarlet letter, so to speak, denying Black men critical educational opportunities important for their success and livelihood. This quite frankly is unacceptable. In her seminal book, Alexander (2012) argued that “[Black males] are part of a growing undercaste, permanently locked up and locked out of mainstream society” (p. 8). Findings from study converge with Alexander’s assertion. Implications for Future Practice, Research, and Policy Results suggest a number of important conclusions that have implications for future practice, research, and policy. In terms of practice, college outreach and academic support programs (COASPs) that specifically target individuals who have been involved in the juvenile justice system may be an appropriate strategy for bolstering college access. Indeed, COASPs have become increasingly popular vehicles for broadening participation, enhancing academic skills, and promoting engagement among students (Strayhorn, Kitchen, Johnson, & Tillman-Kelly, 2015). Such programs, designed with juvenile offenders in mind, might help mitigate labeling affects and the attenuating prosocial bonds with school that many Black males face as a result of an arrest status. COASP directors might target juveniles with criminal records to reconnect them with prosocial peers and groups and dispel myths about who “qualifies” for college. Perhaps most importantly, COASPs might serve as the mechanism through which accurate and clear information is shared with students and their families about their legal rights in terms of disclosure of their criminal records in their college applications. Many youth do not pursue a four-year college education because of anxiety about disclosing their juvenile records. However, few know that there are laws and polices in place that are designed to protect them from discrimination on this basis. For instance, a Black male youth under the age of 18 who has been arrested, or even adjudicated under the court of law (even if found guilty), may select “no” on a college or job application when asked if they have ever been convicted of a crime. Such information is critical in expanding access to four-year colleges for all juvenile offenders generally and Black males specifically. Professional development and training is also necessary for educators who teach, advise, and work with students in schools to enhance their capacity to help and support Black male youth with juvenile records. To do so, educators must acknowledge, challenge, and ultimately suspend biases and stereotypes that may get in the way of meaningfully supporting Black males, especially those with juvenile 66 SPECTRUM 4.1 records. Project Implicit at Harvard University provides training on implicit bias, diversity and inclusion, and biases in decision-making. School leaders should consider organizations like Project Implicit when making plans for professional development training. This study represents a significant contribution to scholarly literature. A careful review of existing research returned very few studies examining the relationship between juvenile arrest and four-year college enrollment and none on Black males specifically. This is surprising, as we have known from research that Black males are disproportionately overrepresented in the criminal justice system (Alexander, 2012) and underrepresented in college (Cuyjet, 1997; Cuyjet & Associates, 2006). More research on the negative and unintended outcomes associated with early criminal justice contact is necessary. Researchers might examine specific types of arrests and their impact on college enrollment, such as violent crimes and robbery. It could be the case that certain types of arrests have a more significant impact on one’s odds of four-year college enrollment. One might also consider examining differences across race and sex. Results from this study hold promise for various policy makers as well. Federal policy makers, for example, should call for the reauthorization of the Juvenile Justice and Delinquency Prevention Act (JJDPA), which requires states to track, examine, and address the disproportionate representation of minority youth across multiple points of contact (e.g., arrest, referral to court, secure detention, etc.). The current JJDPA delineates four “core protections” that states must comply with as a condition for receiving federal juvenile justice funding, one of which requires states to track disproportionate minority contact (DMC) at critical junctures in the juvenile justice system, as well as develop plans to address such disparities. Findings from this study underscore the seriousness of juvenile arrest for Black males, a subpopulation remarkably impacted by DMC. Federal policy makers should implement more strict requirements for states whose DMC ratios are high, requiring them to develop and implement plans to address disparities using evidencebased policies and practices. Policy makers might also establish policies under JJDPA that allocate funds to states and agencies for reducing DMC and juvenile delinquency. ACKNOWLEDGEMENTS This article is based on research supported by grants from the Criminal Justice Research Center, as well as the Student Personnel Assistantship Program at The Ohio State University. A special thank you to the co-editor Dr. Terrell Strayhorn, who provided substantive feedback on earlier drafts of this manuscript. Johnson / Influence of Juvenile Arrest 67 NOTES 1. 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J Abnorm Child Psychol (2013) 41:641–652 DOI 10.1007/s10802-012-9695-7 Sex and Age Differences in the Risk Threshold for Delinquency Thessa M. L. Wong & Rolf Loeber & Anne-Marie Slotboom & Catrien C. J. H. Bijleveld & Alison E. Hipwell & Stephanie D. Stepp & Hans M. Koot Published online: 25 November 2012 # Springer Science+Business Media New York 2012 Abstract This study examines sex differences in the risk threshold for adolescent delinquency. Analyses were based on longitudinal data from the Pittsburgh Youth Study (n0503) and the Pittsburgh Girls Study (n0856). The study identified risk factors, promotive factors, and accumulated levels of risks as predictors of delinquency and nondelinquency, respectively. The risk thresholds for boys and girls were established at two developmental stages (late childhood: ages 10–12 years, and adolescence: ages 13–16 years) and compared between boys and girls. Sex similarities as well as differences existed in T. M. L. Wong (*) : A.-M. Slotboom : C. C. J. H. Bijleveld Faculty of Law, Department of Criminal Law and Criminology, VU University Amsterdam, De Boelelaan 1105, 1081 HV, Amsterdam, The Netherlands e-mail: thessawong@gmail.com A.-M. Slotboom e-mail: m.slotboom@vu.nl C. C. J. H. Bijleveld e-mail: Cbijleveld@nscr.nl R. Loeber : A. E. Hipwell : S. D. Stepp School of Medicine, Department of Psychiatry, University of Pittsburgh, 201 N. Craig St., 408 Sterling Plaza, Pittsburgh, PA 15213, USA R. Loeber e-mail: loeberr@upmc.edu A. E. Hipwell e-mail: hipwae@upmc.edu S. D. Stepp e-mail: steppsd@upmc.edu H. M. Koot Department of Developmental Psychology, VU University Amsterdam, Van der Boechorststraat 1, 1081 BT, Amsterdam, The Netherlands e-mail: j.m.koot@vu.nl risk and promotive factors for delinquency. ROC analyses revealed only small sex differences in delinquency thresholds, that varied by age. Accumulative risk level had a linear relationship with boys’ delinquency and a quadratic relationship with girls’ delinquency, indicating stronger effects for girls at higher levels of risk. Keywords Self-reported delinquency . Sex differences . Threshold hypothesis . Risk and promotive factors . Area under the curve Introduction Many girls involved in the juvenile justice system—those who are arrested, adjudicated or incarcerated—have been exposed to trauma or abuse, have mental health as well as academic problems, and come from multi-problem families (Chamberlain and Moore 2002; Kataoka et al. 2001; Lederman et al. 2004; Slotboom et al. 2011). Compared to arrested, adjudicated, or incarcerated boys, girls in the juvenile justice system have more problems and are exposed more to known risk factors (Belknap and Holsinger 2006; Emeka and Sorensen 2009; Gavazzi et al. 2006; Gover 2004; Johansson and Kempf-Leonard 2009). This has been interpreted as delinquent girls having a more problematic background than delinquent boys, which has also been rephrased as the ‘threshold’ hypothesis, i.e. that girls pass a higher critical ‘risk level’ in order to become delinquent. This hypothesis was initially defined for antisocial personality disorder (Cloninger and Gottesman 1987) and later expanded to other developmental disorders (Eme 1992). A threshold has been defined as the point that must be exceeded to begin producing a given effect or result (www.thefreedictionary.com). Thresholds are encountered in many areas of (social) science and generally denote a 642 critical value, under which a certain effect is not present and above which it is, such as the absolute hearing threshold in medicine, or the extinction theshold in ecology. In the manner in which the ‘threshold’-hypothesis has been phrased in criminology, it denotes the ‘risk level’ above which the probability to be delinquent is larger than the probability not to be delinquent. This ‘risk level’ that defines the risk threshold can, however, be operationalized in two ways. First, it can be operationalized as the severity or level of a single risk factor: having a problematic relationship with parents is a risk factor for delinquency, and only those youth with a very problematic parent–child relationship have a risk level that is high enough to pass the threshold to offend. The other way of operationalizing risk level is derived from the cumulative risk approach (Rutter 1979; Sameroff et al. 1987) and defines the risk level as the number of risk factors. Thus, according to this operationalization the more risk factors someone experiences, the more likely he or she is to be delinquent. There is evidence for such a dose–response relationship between the number of risk factors and the likelihood of delinquency for boys and girls (Johansson and Kempf-Leonard 2009; Loeber, Slot and Stouthamer-Loeber 2008; Van der Laan and Van der Schans 2010; Wong et al. submitted). A key issue that is unresolved in the literature and that is the focus of this study, is whether there are sex differences in the risk threshold for delinquency; differences between boys and girls in such a threshold for delinquency, while often posited, have hardly been studied empirically. Sex Difference in Risk Thresholds Alemagno et al. (2006) examined the number of risk factors of 250 detained boys and girls and found that incarcerated girls were exposed to more risk factors than their male counterparts. Van der Laan and Van der Schans (2010) showed, using a similar analytical strategy, that arrested girls were exposed to more risk factors in the family domain than arrested boys. Although the results of these studies concurred with the differential risk threshold hypothesis, they did not show that such a differential threshold exists for delinquency, since all studies investigated samples of adjudicated or incarcerated juveniles. Given that girls and women are often treated differently in the juvenile justice system, the threshold for delinquency cannot be separated from the threshold to be arrested, prosecuted or convicted (e.g., Daly 1994). Thus it is problematic to attribute sex differences in the number of risk factors in officially delinquent samples to the threshold for delinquency. This may also explain seemingly incompatible findings, such as that arrested boys have in fact a higher number of risky lifestyle factors compared to arrested girls (Van der Laan and Van der Schans 2010). Self-reported delinquency studies tend not to have the confounding effect of justice processing. J Abnorm Child Psychol (2013) 41:641–652 Wong et al. (submitted) investigated sex differences in the delinquency threshold using self-reported data of a Dutch population-based sample, and did not find support for a sex-related threshold. In contrast to the previously mentioned studies, the authors included a comparison group of nondelinquents. The use of such a comparison group is necessary, as without this group it is impossible to determine whether delinquent girls have a higher risk level than delinquent boys or vice versa. Furthermore, the authors examined, in addition to risk factors, the extent to which promotive factors influenced the risk of later delinquency. Promotive factors are those factors associated with a decreased probability of delinquency (Sameroff et al. 1998; Stouthamer-Loeber et al. 2002).1 Since promotive factors can neutralize risks (Stouthamer-Loeber et al. 2002; Van der Laan and Blom 2006), ignoring these factors might result in overstating the importance of risk factors and might make it impossible to assess any accurate threshold effect. Although the study by Wong et al. (submitted) had fewer limitations than previous studies, the authors did not investigate the threshold as such as they compared risk levels of delinquents with those of nondelinquents. The present study will improve upon previous research firstly by actually assessing the threshold itself, i.e. identifying the exact cut off value, for boys and girls. Secondly, this study will improve on previous studies by investigating whether the threshold varies with age and/or sex. Boys’ and girls’ involvement in delinquency changes with age, and criminal careers develop differently for boys and girls (Junger-Tas et al. 2003; Wong et al. 2012). Girls’ delinquency tends to peak earlier than that of boys, i.e. at age 15 versus at age 16 (Junger-Tas et al. 2003; Slotboom, et al. 2011). It remains to be seen whether delinquency thresholds vary with age for each sex. As Moffitt (1993) suggested, during puberty, it is almost normative to show some delinquent behavior. Thirdly, this study will add to previous research by incorporating sex-shared as well as sex-specific risk factors for delinquency (Wong et al. 2010; Zahn 2009). We will address the following research questions: 1) Is the age-crime curve for girls lower than that of boys? 2) Which shared and sex-specific risk and promotive factors measured in middle childhood (ages 7 to 9) and late childhood (ages 10 to 12), respectively, predict self-reported delinquency in late childhood (ages 10 to 12) and adolescence (ages 13 to 16)? 3) Are there sex differences in 1 In the literature a distinction is made between promotive and protective factors. Protective factors refer to factors that moderate the effect of risk factors on problem behavior. There should be an interaction effect with risk factors to be denoted a protective factor (see for example Rutter 1987). In our study we refer to factors that directly decrease the probability of delinquency, there is no need for interaction with risk factors. In line with previous literature, we refer to these factors as promotive factors. J Abnorm Child Psychol (2013) 41:641–652 exposure to risk and promotive factors? 4) Are there linear or quadratic differences in the relationship between cumulative risk and promotive factor score and delinquency for each sex? 5) Are there differences by sex and age in the optimal cumulative threshold to predict delinquency? The questions are addressed using data from the Pittsburgh Youth Study (PYS) and the Pittsburgh Girls Study (PGS) using self-reported delinquency as outcomes at late childhood and adolescence. The studies contain a broad array of risk and promotive factors known to predict delinquency in previous studies (e.g., Hoeve et al. 2009; Hubbard and Pratt 2002; Lipsey and Derzon 1998; Maguin and Loeber 1996; Pratt and Cullen 2005; Simourd and Andrews 1994; Wong et al. 2010; Zahn 2009). These include individual (problem) factors (i.e., birth problems, early disruptive behavior disorder, callous unemotional behavior, anxiety, early puberty), family factors (i.e., poor education of parents, single parent household, physical punishment, communication with parents, positive parenting, supervision, parent–child relationship), school factors (i.e., truancy, school motivation, school achievement), peer delinquency, and neighborhood problems. Methods Sample The PYS is a longitudinal study that started in 1987 (Loeber et al. 2008), consisting of three samples of boys who were in grades one, four, and seven, respectively, at the start of the study. Boys who attended public schools in Pittsburgh participated in the study. In the initial screening assessment, information about the boys’ antisocial behavior was collected through the boys themselves, the caretakers, and their teachers. On the basis of this information, a risk score was calculated and all of the boys with the highest scores on antisocial behavior (n0c. 250, for every sample) were selected for follow-up, while a random sample of the remaining boys (N0c. 250) were also included in the follow-ups. Only boys from the youngest sample (n0503) were included in the present study. In the first four years of the followups, interviews were conducted by trained interviewers every half year with the boys and one or both caretakers. In the same period, one of the boys’ teachers was asked to rate the boys’ behavior. Subsequently, interviews were held every year. For the current analyses, information about grades was transformed in age-specific data. The PGS is also a longitudinal study, but is based on a stratified, random sample from all households in Pittsburgh with a girl between the age of 5 and 8 (Keenan et al. 2010). Disadvantaged neighborhoods were oversampled. The final sample consists of 2,451 families. To make the samples of PGS and PYS youth comparable, the current study included 643 only girls aged 7 or 8 at the initial assessment, who attended public schools at the first assessments in 2000 (n0856). Follow-ups in the PGS consisted of yearly interviews with the girls, their caretaker and teacher ratings. Measurements To achieve comparability between the sexes, only measurements were included that were comparable across the PYS and the PGS. Delinquency Delinquency was measured at ages 11–16 through the 40-item Self-Reported Delinquency Scale (SRD; Loeber et al. 1998) which was based on an adaptation of the National Youth Survey (Elliott et al. 1985). For each of the offenses, respondents indicated whether they had committed a delinquent act, and if so, how often in the previous year. For this study we focused on moderate to serious delinquency (see details in Loeber et al. 1998), which included breaking-and-entering, stealing things worth more than 5 dollars, purse snatching, stealing from a car, dealing in stolen goods, joyriding, vehicle theft, attacking with intent to injure, forcible robbery, and gang fighting. All offences were summed and dichotomized into 0 (no offence committed—nondelinquent) and 1 (1 or more offences committed—delinquent). At age 11 the dealing in stolen goods item was accidentally not assessed in the PGS, so we did not include this item in the delinquency construct for both boys and girls. The SRD was judged to be too difficult to understand for the youngest respondents. For that reason, the Self-Reported Antisocial Behavior Scale (SRA) instead of the SRD was administered at age 10. Since boys were selected in the first wave by grade and therefore had different ages, and since the switch from SRA to SRD was made in one phase for all boys, some of the 10-year-old boys filled out the SRA en some the SRD. For girls, the switch was made after the age of 10 and therefore all 10-year-old girls reported on the SRA. The SRA consisted of 27 items of delinquent behavior that were appropriate to younger children (Loeber et al. 1998). For the current study, only those items from the SRA that were comparable to the selected SRD items were used to construct the delinquency scale: theft from building, theft from a car, and purse snatching. After the creation of the moderate and serious delinquency constructs for each age, we prepared summary constructs for age blocks in late childhood (ages 10 to 12) and adolescence (ages 13 to 16), contrasting nondelinquents with delinquents (1 or more offences committed at this age). Risk and Promotive Factors Table 1 lists all constructs used in this study based on comparable measures in the PYS and PGS. For most factors, we created two age blocks: for late Scared CBCL Highest degree of education Poor education of parents Early pubertal development Low school motivation Low school achievement Bad quality relationship with primary caretaker Truancy SRD SRD Works not hard compared to peers CBCL & TRF Child Parent–child Relationship Survey (PCRS) Parent–child Relationship Survey (PCRS) Works not hard compared to peers CBCL & TRF Child SIS SIS Child Parent and teacher Teacher 7–9 (n01223); 10–12 (n01225) 7–9 (n01212); 10–12 (n01188) 11–12 (n01273) 7–9 (n01306); 10–12 (n01282) 7–9 (n 01320); 10–12 (n01282) 7–9 (n01308); 10–12 (n01283) Parent Practices Scale (PPS) Parent Practices Scale (PPS) Child 7–9 (n01291); 10–12 (n01274) Child Supervision and Involvement Scale (SIS) Supervision and Involvement Scale (SIS) Low communication about activities with both parents Low positive parenting of both parents Low supervision 7–9 (n01304); 10–12 (n01284) Child Parent–child Conflict Tactics Scale (CTSPC) Discipline Physical punishment of both parents 8–9 (n01348); 10–12 (n01282) 9 (n01126); 12 (n01258) 7–9 (n01324); 10–12 (n01285) 7–9 (n01297); 10–12 (n01281) 7–9 (n01327); 10–12 (n01310) First assessment (n01359) Parent Child Petersen Pubertal Development Scale (PPDS) How many caretakers? Petersen Pubertal Development Scale (PPDS) How many caretakers? Single parent household Parent Parent Parent Parent Highest degree of education Child Symptom Inventory (CSI) Psychopathy Screening Device Early disruptive behavior disordera First assessment (n01177) 9 items (alpha from 0.64 to 0.71) 1 item 1 item 4 items (alpha from 0.54 to 70) 16 items (alpha from 0.83 to 0.91) 14 items (alpha from 0.71 to 0.97) 10 items (alpha from 0.64 to 0.84) 1 item 1 item 5 items (alpha from 0.56 to 0.75) 7 items (alpha from 0.54 to 0.61) 1 item 32 items (alpha from 0.90 to 0.93) ADHD: 27 items; ODD: 18 items; CD: 18 items 15 items Parent Reliability Pre and Perinatal Risk Factors Ages Boys Assessed by Girls Callous unemotional behaviorb Anxiety Boys Instruments Birth and developmental history Diagnostic Interview Schedule for Children (DISC) Child Behavioral Checklist (CBCL) Birth problems Constructs Table 1 Constructs used in this study 9 items (alpha from 0.88 to 0.97) 1 item 1 item 4 items (alpha from 0.45 to 0.61) 16 items (alpha from 0.86 to 97) 14 items (alpha from 0.71 to 0.97) 10 items (alpha from 0.52 to 0.87) 1 item 1 item 5 items (alpha from 0.50 to 0.69) 29 items (alpha from 0.90 to 0.92) 1 item 6 items (alpha from 0.56 to 0.69) ADHD: 14 items; ODD: 8 items; CD: 12 items 7 items Girls Highest 25 % Highest 25 % Truant at both ages Highest 25 % Highest 25 % Highest 25 % Highest 25 % Highest 25 % Living with one parent at all ages No diploma or a General Education Diploma (GED) for both parents at all ages Highest 25 % Highest 25 % Any pre- or perinatal birth problem At least one of the following disorders: ADHD, ODD, CD Highest 25 % Risk Lowest 25 % Lowest 25 % NA Lowest 25 % Lowest 25 % Lowest 25 % Lowest 25 % No physical punishment at all ages Living with both parents at all ages Lowest 25 % NA Lowest 25 % Lowest 25 % NA NA Promotive 644 J Abnorm Child Psychol (2013) 41:641–652 c For 7-to-9-year-olds, exactly the same offences were included (vandalism, shoplifting, stealing at school, stealing from building, violence against adult) in the PYS and the PGS. For 10-to-12-yearolds, the peer delinquency scale was similar in the PGS, but included more serious offences in the PYS. Therefore, we only took those offences of the PYS into account that were comparable to those of the PGS (and which are also similar to the offences considered at earlier ages), i.e. vandalism, stolen something up to $100, stealing from building, and hitting someone with intent to hurt. We corrected for the number of possible items. 645 b In the PYS a construct is created that measures psychopathic features in childhood, assessed by the CBCL. Examples of items are ‘lying or cheating’ ‘sudden changes in mood or feelings’, and ‘behaving irresponsibly’. In the PGS, items from the PSD were used to create a similar construct for girls. The following items are included: concerned about school or tasks, keeps promises, feels bad about doing wrong, concerned about others’ feelings, shows feelings and emotions, keeps the same friends. Due to the time of the assessment, the diagnoses of ADHD, ODD, and CD in the PYS were based on the DSM-III-R, whereas the diagnoses in the PGS were based on the DSM-IV. To make diagnoses comparable, we only included those symptoms that were assessed in both studies. For ADHD, the age of onset, that is usually part of the diagnosis, could not be taken into account since it was not assessed in the PGS. To reach the diagnosis of ADHD, boys and girls had to have 9 symptoms or more. For the diagnosis of CD, 3 or more symptoms were required, and for the diagnosis of ODD, 4 or more symptoms. a Lowest 25 % Highest 25 % 7–9 (n01312) 10–12 (n01282) Your Neighborhood Neighborhood problems Your Neighborhood Parent 17 items (alpha from 0.93 to 0.96) 7–9: 5 items (alpha from 0.78 to 0.80); 10–12: 6 items (alpha from 0.75 to 0.78) 17 items (alpha from 0.94 to 0.96) 5 items (alpha from 0.68 to 0.84) 7–9 (n01271); 10–12 (n01248) Peer Delinquency Scale (PDS) Peer delinquencyc Peer Delinquency Scale (PDS) Child Boys Boys Instruments Constructs Table 1 (continued) Girls Ages Assessed by Reliability Girls Risk Highest 25 % Promotive Lowest 25 % J Abnorm Child Psychol (2013) 41:641–652 childhood and adolescence. Birth problems and early disruptive behavior disorder were only assessed in the first assessment and regarding early pubertal development only the measurements prior to the delinquency age blocks were included (i.e. age 9 and age 12). In the PGS, no information about single parent households was available at the age of 7, so the late childhood age block regarding single parent households only contained age 8 and 9. Truancy was only measured at age 11 and 12, so the late childhood age block was not created. In creating the constructs from reported waves, missing constructs were coded as missing if more than 33 % was missing. If fewer were missing, the mean of the available responses was substituted for the missing data. In creating the age blocks, only the non-missing ages were used to calculate the age blocks for a respondent. The age block was set to be missing if the construct was missing at all ages. To identify the risk versus promotive effect of the factors we used the same method as Stouthamer-Loeber et al. (1993). All age blocks were trichotimized into a promotive, a neutral and a risk component using the sex-specific 25th and 75th percentiles of the age block distributions as cutoffs. The age blocks were recoded into two variables: a risk variable and a promotive variable. The reference category in each variable was the neutral component (the 26th to the 74th percentile of the distribution). The exceptions were birth problems, early disruptive behavior disorder, poor education of the parents, and child’s truancy, because these were inherently dichotomous. Another exception was the age block for single parent households. In this case, it was more appropriate to trichotimize according to the number of years the household consisted of a single parent (i.e. risk: single parent in all years of age block; promotive: both parents in all years of age block; neutral see Table 1). Analyses First, we established which risk and promotive factors predicted delinquency at late childhood and adolescence, respectively. These analyses were carried out separately for boys and girls and separately for the two age periods. If a factor predicted delinquency (p<0.05), this was regarded as a risk effect; if a factor predicted low or nondelinquency, this was regarded a promotive effect. If both variables were related to delinquency, this was regarded both a combined risk and a promotive effect. Some risk factors predicted delinquency in boys and girls and were labeled shared risk factors. The same applied to factors predicting nondelinquency in boys and girls and were labeled shared promotive factors. Factors that were only related to delinquency in either boys or girls were labeled sex-specific risk and promotive factors. Odds Ratios were calculated for the risk and 646 promotive factors: an Odds Ratio larger than 1 with a pvalue<0.05 indicates that the presence of the risk factor significantly increased the prediction of delinquency, while an Odds Ratio smaller than 1 with a p<0.05 indicates a promotive factor that significantly predicted nondelinquency.2 Next, we created three types of cumulative risk level indexes. The first index consisted of the number of significant risk factors in the data set. A second index indicated the number of significant promotive factors in the data set. The third, called the combined risk index indicated the number of significant risk factors minus the number of significant promotive factors. Because the three risk indexes were created by taking into account shared factors as well as sex-specific factors, each risk index consisted of slightly different risk and promotive components for boys and girls. Thresholds were studied at two levels. First, we studied whether the distribution of the relationships between cumulative risk were similar for boys and girls; for this we carried out a curve fitting analysis to see whether cumulative risk indexes predicted delinquency in a linear or quadratic way for boys and girls. If, for example, a quadratic function applied to one but not the other sex, this indicated that the risk of future delinquency accelerated faster for one sex compared to the other. In a second set of analyses, we examined whether a threshold could be empirically established by means of signal detection theory (Swets 1964). Receiver Operating Curves (ROC) were calculated with Area Under the Curve (AUC) indicating how well a cumulative risk index predicted delinquency. The analyses also allow the identification of optimal prediction thresholds in which, for every possible cut-off, the trade-off between the false negative and false positive rates is calculated. AUC values can range from 0 (total inaccuracy) to 1 (perfect accuracy). A value of 0.5 indicates that the model is not better than chance, a value between 0.5 and 0.75 is regarded as fair, between 0.75 and 0.92 as good, between 0.92 and 0.97 as very good and between 0.97 and 1 as excellent (McFall and Treat 1999). The Youden’s index, a function of sensitivity (number of true positives) and specificity (number of true negatives), was used to identify the optimal cut-off point (Youden 1950). The optimal cut-off is the value with the highest combination of sensitivity and specificity. This cut-off point is the threshold for delinquency. We carried out these analyses separately for late childhood and adolescence and for boys and girls. List wise deletion was used to deal with the missing information in the analyses. 2 The large number of tests is done to create a subset of variables on which to run a comprehensive analysis, to filter out those that are not relevant. Subsequently, boys and girls are compared. So, while this increases the risk for type I errors because of the multiple testing, this occurs for boys as well as girls. For that reason, the comparison is still valid. J Abnorm Child Psychol (2013) 41:641–652 Results Table 2 shows the descriptive results. The average number of measured risk and promotive factors are presented for boys and girls in middle and late childhood as well as the number of delinquents in late childhood and adolescence. No sex differences were found regarding the average number of measured risk and promotive factors. The prevalence of delinquency differed by gender in both late childhood as well as in adolescence. The first question we addressed was: Is the age-crime curve for girls lower than that of boys? Figure 1 shows that at age 10 there was only a small, although significant (3.6 % vs. 1.8 %; p<0.05) sex difference in the prevalence of moderate to serious delinquency, but at all other older ages the prevalence of delinquency was higher for boys than girls (for all ages p<0.01). However, the peak age of the agecrime was the same for the two sexes (age 14). The second question that we posed was: Which shared and sex-specific risk and promotive factors measured in middle childhood (ages 7 to 9) and late childhood (ages 10 to 12), respectively, predict self-reported delinquency in late childhood (ages 10 to 12) and adolescence (ages 13 to 16)? Table 3 shows the odds ratios of the risk and promotive factors for boys and girls in the two age periods. An empty cell indicates that there is no statistically significant risk (or promotive) effect of a given factor. The results showed that delinquent behavior of boys and girls is related to many different factors. As Table 3 shows, many risk and promotive factors are shared by boys and girls, but some differences were found between boys and girls, and between age periods as well. Risk and promotive factors that were shared were callous-unemotional behaviour, supervision by parents, relationship with parents, and almost all risk and promotive factors in the school and peer domain. Differences between boys and girls were found in the individual domain regarding birth problems, early disruptive behaviour and anxiety. Birth problems appeared to be a risk factor for delinquency in late childhood for girls and not for boys. Furthermore, early disruptive behaviour was a risk for delinquency at both age periods for girls, but not for boys. Also, high anxiety had a promotive effect on boys in their late childhood, but not on adolescent boys, while it had an age-invariant effect on adolescent girls. Besides, low anxiety was a risk factor for adolescent girls. Other interesting differences were found in the family domain. Living with both parents had a promotive effect on boys’ delinquency in both age periods. For girls, however, it was only promotive for delinquency in adolescence. Furthermore, not being exposed to physical punishment was a promotive factor for girls in both age periods, but not for boys. By contrast, for boys, physical punishment was a risk factor regarding delinquency in adolescence. Another remarkable difference is that communication about activities with J Abnorm Child Psychol (2013) 41:641–652 647 Table 2 Descriptive results Middle childhood Average number of risk factors (n01316) Average number of promotive factors (n01282) Late childhood Average number of risk factors (n01318) Average number of promotive factors* (n01281) % delinquent* Adolescence % delinquent* Boys (n0503) Girls (n0856) Average 3.43 (2.33) 2.92 (2.11) 3.29 (2.28) 3.00 (2.37) 3.34 (2.30) 2.97 (2.28) 3.41 (2.24) 2.95 (2.17) 24.5 % 3.19 (2.34) 3.42 (2.51) 9.7 % 3.27 (2.31) 3.24 (2.40) 15.2 % 42.6 % 21.2 % 29.2 % Standard deviations are in parentheses. With t-tests it was tested whether boys and girls differed in number of risk and promotive factors. Crosstabs were used to test the difference in delinquency prevalence *significantly different for boys and girls at p<0.05 parents only affected delinquency for girls and only during puberty, both as a risk and a promotive factor. Positive parenting was also only related to girls’ delinquency. More specifically, lack of positive parenting was a risk for girls in both age periods and a promotive factor for delinquency in adolescence. Next we asked: Are there sex differences in exposure to risk and promotive factors? Table 4 shows the average number of (significant) risk factors and (significant) risk minus promotive factors for nondelinquent and delinquent boys and girls during middle and late childhood. Delinquent boys and girls averaged higher risk scores than nondelinquent boys and girls, respectively. Furthermore, delinquent girls averaged a higher number of risk factors than delinquent boys at each age period. When averages of risk and promotive factors were considered, delinquent girls compared to delinquent boys scored higher at middle childhood only. At late childhood, average exposure to risk and promotive factors was similar for of delinquent boys and girls. The fourth question we asked was: Are there linear or quadratic differences in the relationship between cumulative risk and promotive factor score and delinquency for each sex? Curve fitting analyses showed that for both age periods positive linear relationships between the risk levels and delinquency were found for boys (with R2 of 0.07 and 0.15 respectively; other relationships had a worse fit to the data), but % delinquents 25% 20% 15% % delinquent boys 10% % delinquent girls 5% 0% positive quadratic relationships for girls (with R2 of 0.06 and 0.17 respectively, again other relationships had a worse fit to the data; see the modeled relationships in Figs. 2 and 3). This indicates that, regardless of sex, the more risk factors boys and girls were exposed to, the more likely they were to be delinquent. However, for boys the increase in likelihood for delinquency was similar across risk levels, whereas for girls the increase in likelihood was amplified at every next risk level. More specifically, because of the linear relationship for boys, every increase in the number of risk factors was associated with 5.2 % more delinquent boys in late childhood and 7.3 % more delinquent boys in adolescence. For girls, because of the quadratic relationship, this increase depended on the risk level. An increase in the risk level from 3 to 2 promotive factors (in middle and late childhood respectively), for instance, was associated with 0.6 % more delinquent girls in late childhood and to 3.3 % more in adolescence, whereas an increase in the risk level from 3 to 4 or more risk factors (in middle and late childhood) was associated with 5.4 % and 10.5 % more delinquent girls in late childhood and adolescence, respectively. Thus, for girls we see that the effect of a one-step risk increase becomes ever stronger: the higher the risk level, the larger the corresponding shift in delinquency at an increase in risk. The final question concerned: Are there differences by sex and age in the optimal cumulative threshold to predict delinquency? The results regarding the predictive power of the combined risk levels on late childhood delinquency for boys and girls are in Fig. 2: girls had slightly higher AUC values than boys (0.74 vs. 0.68). Furthermore, the optimal cut-off point for girls was higher than for boys (1 vs. 0 risk factors) 3 which indicates that girls have a higher threshold for delinquency in late childhood than boys. 10 11 12 13 14 15 16 Age Fig. 1 Age crime curve for moderate to serious delinquency by sex 3 Sensitivity and specificity at the selected threshold for late childhood delinquency were respectively 0.57 and 0.69 for boys and respectively 0.74 and 0.63 for girls. 648 J Abnorm Child Psychol (2013) 41:641–652 Table 3 Odds ratios of risk and promotive factors for delinquency at ages 10 to 12 and ages 13 to 16, by sex Factors Delinquency (10 to 12 years) Risk Boys Birth problems Disruptive behavior Callous unemotional behavior Anxiety Poor education of parents Early pubertal development Single parent household Physical punishment of parents Communication with parents Positive parenting Supervision Relationship with primary caretaker Truancy School motivation School achievement Peer delinquency Neighborhood problems Delinquency (13 to 16 years) Promotive Girls Risk Boys Girls Boys 0.35** 0.18** 2.15** 1.82** 3.22** 2.39** 3.21** Promotive Girls 3.16** 0.37** 1.88* 1.83* 2.80** 2.32** 2.20** 2.57** 1.97** 0.41** 0.35** 2.28** 3.37** 0.64* 2.59** 1.93** 2.19** Girls 2.64** 0.62* 2.20** Boys 0.41* 0.20** 0.35** 0.48* 0.32** 0.52* 0.44* 0.38* 0.12** 0.47** 0.62* 0.53** 0.64* 2.08** 0.34** 1.63* 2.31** 2.86** 0.41** 0.44** 0.55* 0.35** 0.29** 6.10** 2.13** 1.93** 4.96** 1.79** 0.46** 0.50** 0.23** 0.54** 0.28** 0.42** 0.18** 0.58* 1.82* 2.44** 2.83** 4.18** 2.29** 3.78** 1.60* *p<0.05, ** p<0.01 Next, adolescent delinquency was predicted from risk levels at the age of 10 to 12 (see Fig. 3). Girls had slightly higher AUC values (0.77 vs. 0.72), but boys had a higher optimal cut-off point than girls (1 vs. 0 risk factors).4 Boys therefore have a higher threshold than girls to become delinquent in adolescence. Thus, we see that there are no consistent differences in the delinquency threshold for boys and girls: the thresholds differ by age period. The differences are also small; however, as the threshold is a group-value and not the average of a set of individual-level values, we cannot test whether it differs significantly for boys and girls. Discussion This study examined whether boys and girls had different risk thresholds for delinquency at two age periods (late childhood and adolescence). Using data from the PYS and PGS studies, we first tested which factors (at ages 7 to 9 and 10 to 12) had a risk effect, a promotive effect, or both. Boys and girls 4 Sensitivity and specificity at the selected threshold for adolescent delinquency were respectively 0.47 and 0.85 for boys and respectively 0.67 and 0.74 for girls. appeared to share many risk and promotive factors, but sex differences and differences between age periods were found as well. This indicates that delinquent girls might need different types of interventions than delinquent boys, and that the age of the delinquent should be taken into account. Not surprisingly, boys and girls who were delinquent appeared to have higher risk levels than their nondelinquent counterparts. Within the delinquents, girls on average had higher number of risk factors than boys when only risk factors were considered. When promotive factors were taken into account as well, girls compared to boys had on average a higher risk levels in middle childhood. In late childhood, the risk level of delinquent boys and girls was similar. The relationship between the risk level and delinquency was linear for boys, indicating that every extra risk factor resulted in a similar step-wise increase regarding delinquency probability. For girls, however, this relationship turned out to be non-linear, with the increase in the probability of delinquency larger at the higher risk level ranges than in the lower part. Thus, at low risk levels, an additional risk factor gives but a small increase in the delinquency probability. However, at higher risk levels, one extra risk factor augments this probability substantially for girls. Due to this amplification, J Abnorm Child Psychol (2013) 41:641–652 649 Table 4 Means and standard deviations of risk levels for nondelinquent and delinquent boys and girls Middle childhood Average number of risk factors Average number of risk minus promotive factors Late childhood Average number of risk factors Average number of risk minus promotive factors Boys (n0503) Girls (n0856) Sex difference between delinquents Nondelinquent Delinquent Difference within boys Nondelinquent Delinquent Difference within girls 1.15 (1.27) 1.95 (1.41) t (468)05.66** 2.19 (1.76) 3.82 (1.81) t (804)07.85** t (192)08.11** −0.89 (2.41) 0.65 (2.40) t (468)04.96** 0.61 (2.61) 2.91 (2.11) t (804)07.58** t (192)06.77** 1.13 (1.25) 2.32 (1.62) t (444)08.83** 1.63 (1.50) −1.39 (2.72) 0.92 (2.69) t (444)08.91** −1.68 (3.18) 3.39 (1.89) t (747)0 t (347)05.70** 12.39** 1.43 (2.61) t (747)011.35** t (347)01.80 Standard deviations in parentheses. Means of nondelinquent and delinquent boys and of nondelinquent and delinquent girls are compared with Ttests as well as those of delinquent boys and girls *p<0.05, ** p<0.01 delinquent girls would—even with a same delinquency threshold—have higher average risk levels than boys. Therefore, previous studies that focused on the average risk level for boys and girls found higher risk levels among delinquent girls than among delinquent boys (Alemagno et al. 2006; Van der Laan and Van der Schans 2010). While higher risk levels are associated with a stronger increase in likelihood of delinquency in girls than in boys, this study implies that girls do not have a higher threshold for delinquency. Differences in the threshold are not apparent and fluctuate with age which might suggests that no actual sex difference in the threshold for delinquency exists. All in all, in this study—that was appropriately designed with a control group, and sex-specific risk as well as promotive factors—no evidence for a sex-specific delinquency threshold emerged. The threshold hypothesis was examined using two complementary approaches: curve fitting and ROC analyses. The curve estimation analyses showed a linear association between risk level and delinquency for boys and a curvilinear relationship for girls. The ROC analyses examined the location of the threshold and did not show sex differences. While there appears to be no different threshold as such, increases of the risk level beyond this threshold impact differently on girls than on boys. That is, from the threshold onwards, risks contribute more and more to the delinquency risk for girls (due to the quadratic relationship), but not for boys (due to the linear relationship). This indicates that delinquent girls might have more problematic backgrounds than their male counterparts. This has also been shown in previous research regarding characteristics of juveniles Fig. 2 Combined risk levels (number of risk factors minus number of promotive factors) at the age of 7 to 9 predicting moderate to serious delinquency at age 10 to 12, for boys and girls 650 J Abnorm Child Psychol (2013) 41:641–652 Fig. 3 Combined risk levels (number of risk factors minus number of promotive factors) at the age of 10 to 12 predicting moderate to serious delinquency at age 13 to 16, for boys and girls in the juvenile justice system (Belknap and Holsinger 2006; Emeka and Sorensen 2009). Zahn et al. (2009) showed that interventions that target multiple risk factors can reduce delinquent behavior in both boys and girls. However, given the more problematic background of girls in the juvenile justice system, for them it might be even more important to address multiple problems simultaneously. Likely, gender-specific interventions are necessary for girls. There is no clear evidence yet about the effectiveness of existing gender-specific interventions (Zahn et al. 2009). It is noteworthy that the risk level is a (much) better predictor for delinquency among girls than among boys, shown by the AUC level as well as the results regarding sensitivity and specificity. For boys, the threshold detects 57 % of the delinquents in late childhood and only 47 % in adolescence. For girls, however, these percentages were 74 % and 67 % respectively. This indicates that the risk level alone is not enough to predict delinquency, especially for boys. Differences with Previous Studies Several explanations can be put forward for the fact that most previous studies on the threshold had such different results than the present study. These explanations regard differences between previous studies and the present study regarding the sample, the definition of the threshold, and regarding the operationalization of risk. With regard to sample differences, previous studies mainly examined adjudicated or incarcerated samples. In these samples the threshold for delinquency is confounded with the threshold for criminal justice system involvement. The fact that our study showed that the threshold for delinquency differs minimally for boys and girls, these studies probably picked up on arrest, prosecution or incarceration thresholds. Concerning differences in the definition of the threshold, previous studies based their conclusions about sex different thresholds on risk levels of delinquent boys and girls (Alemagno et al. 2006; Belknap and Holsinger 2006; Emeka and Sorensen 2009; Johansson and Kempf-Leonard 2009; Van der Laan and Van der Schans 2010), whereas the current study identified the location of the threshold. Because delinquent girls had on average higher risk levels than boys and because delinquency is less prevalent in girls, previous studies concluded that girls have a higher threshold for delinquency. However, the (difference in) location of the threshold was not assessed. Regarding the operationalization of risk, there are two main differences between previous studies and the present study. First, previous studies did not include promotive factors to measure risk. However, since the number of promotive factors can buffer the influence of risk factors only (Stouthamer-Loeber et al. 2002; Van der Laan and Blom 2006), it is inadequate to examine only risk factors. To see how the results would differ if we would have considered risk factors only, the analyses of the present study were carried out as well for the risk index that only considered the number of risk factors.5 Just like in previous studies (Alemagno et al. 2006; Van der Laan, and Van der Schans 2010), we found a higher threshold for girls when we focused solely on risk factors, for both age periods. Slightly better AUC values showed, however, that models that included both risk factors and promotive factors were more adequate than models that considered risk factors only. Not including promotive factors can lead to overestimation of the risk and therefore of the threshold. This indeed turned out to be the case for girls. Second, the present study included shared as well as sexspecific factors while other studies only focused on shared 5 These results are not presented here, but are available from the first author. J Abnorm Child Psychol (2013) 41:641–652 factors (see Moffitt et al. 2001; Junger-Tas et al. 2004). Again, for the sake of comparison, the analyses of the present study were carried out as well with models that only considered shared factors.6 Models that considered both shared factors and sex-specific factors resulted in a better prediction of delinquency at puberty for girls than analyses based on shared factors only. In these latter models, that were utilized in previous studies, girls’ risks are underestimated and their risk threshold cannot be examined properly. Our study showed that girls and boys do not differ to a large extent in their delinquency ‘threshold’, i.e. the risk level beyond which the probability to be delinquent is greater than the probability to be not delinquent. It is likely that the threshold that was picked up in previous studies among criminal justice samples may actually have been a criminal justice-involvement threshold. Difference in the average risk levels of delinquent boys and girls are generated by the increasing impact of risk factors on girls beyond the delinquency threshold. Strengths and Limitations This study had several limitations. First, only moderate to serious delinquency was taken into account. It might be, however, that although no large sex differences were found in the threshold for delinquency in general, boys’ and girls’ thresholds do differ to a large extent for violent or serious delinquency. As Moffitt (1993) claimed, during puberty, delinquent behavior is more normative, which as we argued may explain the lack of a clear differential threshold. For less normative behavior, such a threshold may well emerge. This is difficult to test, however, since serious (violent) delinquent behavior is a rare phenomenon in juvenile females and therefore such analyses would have suffered from a lack of power. Another limitation is that not all factors that have an important risk or promotive effect on delinquency could be taken into account. This is because two different studies (the PYS and the PGS) were combined and we were strict in our decision not to consider factors that were not conistently measured in both studies. For instance, negative life events (i.e. crime victimization, abuse, neglect), that have been shown to be important in predicting delinquency especially for girls (Wong et al. 2010), could not be included because of assessment differences. Furthermore, delinquency in late childhood might be somewhat underrated since some of the 10-year-old boys but all of the 10-year-old girls filled out the SRA instead. The SRD was not filled out by these juveniles, and therefore the SRA was the only option to compare their delinquent behavior. However, the SRA included fewer delinquency items which might have led to underestimation of delinquency. 6 See footnote 5. 651 In line with other studies using the Pittsburgh Youth Study and the Pittsburgh Girls Study we used mean substitution in case of missing items. Even though mean substitution is in principle suboptimal, the data preparation was meant to create dichotomous variables that represented risk versus no risk (and promotive versus not promotive). These dichotomous variables were created by trichotimizing variables into a promotive, a neutral and a risk component using the 25th and 75th percentiles of the variables as cut-offs. As the mean of a variable falls most often not above the 75th percentile or below the 25th percentile, the imputed value falls mostly within the neutral category representing neither risk nor promotion. This is therefore a conservative method, but it is also in line with what one might suppose to be the case when scores on a risk factor are missing (namely that there is no marked high or low score). Therefore, imputing the variables differently might mean a likely small methodological gain at the cost of being not congruent any more with previous analyses and descriptive statistics on these data sets. Despite these limitations, the present study improved on previous studies by identifying thresholds for delinquency, and by taking into account promotive factors. Furthermore this study focused on self-reports of delinquency, and included shared as well as sex-specific risk and promotive factors, and examined thresholds longitudinally at two age periods. Moreover, we showed that some of our design improvements actually improved predictions compared to previously studies. Acknowledgments The authors thank Rebecca Stallings and Deena Battista for their help with the data preparation. 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C., Mihalic, S. F., & Tichavsky, L. (2009). Determining what works for girls in the juvenile justice system: a summary of evaluation evidence. Crime & Delinquency, 55, 266–293. Copyright of Journal of Abnormal Child Psychology is the property of Springer Science & Business Media B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Who Are At-Risk Youth? Caitlyn Meade Summer 2017 Roadmap  Who are at-risk youth?  What are trends in youth violence?  How does race relate to youth crime?  How does criminological theory relate to youth crime?  Note that this is ONLY AN OVERVIEW—you will not get the same depth on these theories as you will in your theory class.  What are the effects of justice involvement? At-Risk Youth An at-risk youth is a child who is less likely to transition successfully into adulthood. Success can include academic success and job readiness, as well as the ability to be financially independent. It also can refer to the ability to become a positive member of society by avoiding a life of crime. Risk Factors for being “At Risk”  Bad home environment  Abuse/Neglect  Bad Parenting  Poverty  Neighborhood Factors  Running Away  System involvement (Foster Care)  Poor academic performance  Victimization Youth Crime Statistics  The peak year for juvenile Violent Crime Index arrest rates was 1994.  Between 1980 and 1994, arrest rates for youth ages 15-17 increased an average of 73%.  Between 1994 and 2012, violent crime arrest rates declined for all age groups  Rates dropped an average of 64% for youth ages 15-17.  Across all juvenile age groups, age-specific Violent Crime Index arrest rates in 2012 were at their lowest level since at least 1980. Youth Crime Statistics: Age & arrests by year Demographics of Nonindex Offenses 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Female Under age 15 White Black American Indian Asian/NHPI Ages We’re Talking About Legal definition of juveniles Under the age of 18 Educational definition Prior to high school graduation Biological definition Under age ~25 Disproportionate Minority Contact “Disproportionate minority contact refers to the disproportionate number of minority youth who come into contact with the juvenile justice system.” Race and Juvenile Delinquency  Disproportionate arrests  Black youth are almost 50% more likely than Whites to have been arrested by age 18  Disparities in adjudication  Blacks more likely to be sent to secure confinement  Blacks more likely to be transferred to adult facilities  Blacks comprise 62% of the youth prosecuted in the adult criminal system and are 9 times more likely than White youth to receive an adult prison sentence Race and Juvenile Delinquency  Why are Blacks more likely to come in contact with the law?  Community Factors Disadvantaged communities have higher crime rates, which attracts more police attention  Policing strategies Hot spot policing and broken windows policing emphasize areas with high crime, increasing patrol of those areas. Why does increase patrol = increase contact between police and minorities?  Biases in policing Implicit and explicit biases held by officers lead to beliefs that minorities are more criminal Crim Theory and Racial Differences  Strain Theory  Blacks and low SES (socioeconomic status) experience different strains  Adaptation to strain may be reflective of cultural norms  Commit crime to achieve “American Dream”  Sell drugs, steal, etc. to purchase goods that increase an individual’s status  General Strain  Some people use crime as a way to cope with negative strains  Minorities are disproportionately exposed to strains related to SES, discrimination, and racism  May cope with such strains using crime—especially when they witness others coping by committing crime Crim Theory and Racial Differences  Social Disorganization Theory  Community factors affect crime  When a neighborhood exhibits certain characteristics, they are more likely to be “socially disorganized,” leading to a breakdown of informal social control. This facilitates crime in the neighborhood. Elements of Social Disorganization  Residential Mobility  Lots of people moving in and out of the neighborhood  People care less/are less invested in protecting neighbors/maintaining the neighborhood  Economic status  Low SES  The community and its residents have fewer resources to help prevent crime (such as afterschool programs, neighborhood watch, etc.)  Lack of Cohesion  When people don’t know each other, they don’t care about each other as much.  People are less likely to “call out” the neighborhood kids for doing bad stuff, less likely to call the cops if they see someone in trouble and so on  Ethnic Heterogeneity  Lots of different cultures leads to language and cultural barriers  People from different cultures may have different expectations and goals for a neighborhood  Leads to a lack of cohesion Crim Theory and Racial Differences  Social Bonding  Lack of social bonds  Attachment  Attached to other people/school/conventional aspects of society  More attached= less crime  Commitment  Committed to living lawfully  More commitment=less crime  Involvement  Involved in conventional activities  More involvement=less crime  Belief  Believe in laws and norms of societies  More belief= less crime  What are some reasons minorities may be less bonded to society?  Single parent households = less attachment to parent  Less attachment to school =lower achievement  Less time and resources to be involved in conventional activities  Norms and beliefs of society are believed to be negative due to discrimination experienced Crim Theory and Racial Differences  Self-Control  Self-control prevents an individual from committing crime.  Individual weighs the pros and cons and can hold off on impulsively committing crime.  Parenting  Parents shape an individuals level of self-control through disciplining their child correctly  Self-control is argued to be stable through the life-course  In other words, one’s level of self-control does not change after around age 8 Concept Check Stop and think about how these theories address criminal behavior. How do these theories apply to minorities? What do these theories fail to explain about minorities and crime? Barrett Article  Findings  Evidence of effects of early risk factors on recidivism  Recidivism predicted by  Gender, poverty, CPS referral, diagnosis for psych disorder, special education, early age of first offense, prosecuted for first offense  Black youth recidivism more strongly predicted by  Special education  Gender  Poverty  White youth recidivism more strongly predicted by  Psych diagnoses  Status offending  Prosecution for first offense Long-Term Effects of JJ Contact  Severity of sentencing  More severe sentences when you have a record  “On the radar”  Police know individual as trouble, more likely to arrest again, etc  Housing, education, employment, government assistance  Lose the rights to government assistance, housing, and education for some crimes  Harder to get a job or place to live with a record  Johnson Article  Effects of JJ contact on college  Black males who reported being arrested less likely to go to college  Even controlling for background characteristics Daily Review  Who are at-risk youth?  What are the demographics of youth involved in the justice system?  How does criminological theory explain minorities and crime?  What does disproportionate minority contact mean?  How does justice involvement affect juveniles going into adulthood?

Tutor Answer

School: UIUC



Criminology Research and Gender
Institution Affiliation




1. Why Criminology focuses on Males
It is a fact that criminology often focuses on male members of the society. The main
reason for this can be considered to be the fact that men are committing most crimes in the
community. To deal with the offense, most governments focus their efforts and resources on
dealing with the source of the evil. Creating such focus has both positive and negative effects as
one may deal with crime more effectively, and it can also lead to a sense of overlooking other
causes of crime.
Focusing on men also affects the knowledge that we have in general regarding issues to
do with crime and delinquency. In many cases, the collection of data centers on th...

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