hypothesis for juvenile delinquency and prevention

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Risk and protective factors and interventions for reducing juvenile delinquency: a systematic review.

1. Introduction

2. materials and methods, 2.1. inclusion criteria, 2.2. exclusion criteria, 2.3. data sources and search strategy, 2.4. risk of bias assessment, 4. discussion, 5. limitations, 6. conclusions, author contributions, informed consent statement, data availability statement, conflicts of interest.

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Share and Cite

Aazami, A.; Valek, R.; Ponce, A.N.; Zare, H. Risk and Protective Factors and Interventions for Reducing Juvenile Delinquency: A Systematic Review. Soc. Sci. 2023 , 12 , 474. https://doi.org/10.3390/socsci12090474

Aazami A, Valek R, Ponce AN, Zare H. Risk and Protective Factors and Interventions for Reducing Juvenile Delinquency: A Systematic Review. Social Sciences . 2023; 12(9):474. https://doi.org/10.3390/socsci12090474

Aazami, Aida, Rebecca Valek, Andrea N. Ponce, and Hossein Zare. 2023. "Risk and Protective Factors and Interventions for Reducing Juvenile Delinquency: A Systematic Review" Social Sciences 12, no. 9: 474. https://doi.org/10.3390/socsci12090474

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Juvenile Delinquency: Prevention, assessment, and intervention

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Juvenile offending and anti-social behaviour are enormous societal concerns. This broad-reaching volume summarizes the current evidence on prevention, diversion, causes, and rates of delinquency, as well as assessment of risk and intervention needs. A distinguished cast of contributors from law, psychology, and psychiatry describe what we know about interventions in school, community, and residential contexts, focusing particularly on interventions that are risk reducing and cost effective. Equally important, each chapter comments on what is not well supported through research, distinguishing aspects of current practice that are likely to be effective from those that are not and mapping new directions for research, policy, and practice. Finally, the volume provides a description of a model curriculum for training legal and mental health professionals on conducting relevant assessments of adolescents for the courts.

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The prevention and treatment of juvenile delinquency: A review of the research

1993, Clinical Psychology Review

Related Papers

Andrea Müller-Fabian

Sa'odah Ahmad

This paper presents an overview of the juvenile delinquency concept, trends in the delinquency problem, factors that have been linked to delinquency, governmental efforts to reduce and/or prevent the problem. Some suggestions are made to improve prevention and rehabilitation efforts to curb juvenile delinquency.

Family Court Review

Matthew Ream

David Petechuk

Criminal Justice Review

Crystal Huggard

Journal of Exploratory Studies in Law and Management

World of Researches Publication WRP

The American juvenile justice system has undergone many changes since its inception. U.S. juvenile courts initially followed the correctional model. With the increasing rate of recidivism and also the increase in the commission of violent crimes by children, especially in the 9s and 8s of the twentieth century, the juvenile justice system moved away from the model of correction and education. It tended to the "constructive justice model" after the model Correctional Education and the Punitive Justice Model. Since the beginning of the 21st century, the juvenile justice system in the United States has shifted to the restorative justice model.

Troy Allard

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Community Violence Exposure and Adolescent Delinquency: Examining a Spectrum of Promotive Factors

1 University of Chicago, Chicago, IL, USA

Dexter R. Voisin

Kristen c. jacobson.

This study examined whether promotive factors (future expectations, family warmth, school attachment, and neighborhood cohesion) moderated relationships between community violence exposure and youth delinquency. Analyses were conducted using N = 2,980 sixth to eighth graders ( M age = 12.48; 41.1% males) from a racially, ethnically, and socioeconomically diverse sample. After controlling for demographic factors, delinquency was positively associated with community violence exposure and inversely associated with each of the promotive factors. When interaction effects between all promotive factors and community violence exposure were examined simultaneously, only future expectations moderated the relationship between community violence exposure and delinquency. Specifically, community violence exposure had a weaker association with delinquency for youth reporting high versus low levels of future expectations. Results indicate that while promotive factors from family, school, and neighborhood domains are related to lower rates of delinquency, only future expectations served as a protective factor that specifically buffered youth from the risk effects of community violence exposure.

Community violence consists of violence (e.g., serious fights, gunshots, stabbing) either experienced or witnessed by individuals, which generally takes place outside the home ( Krug, Dahlberg, Mercy, Zwi, & Lozano, 2002 ). Community violence exposure tends to be more common and repetitive than other forms of violence exposure such as domestic violence or childhood sexual abuse ( Margolin & Gordis, 2000 ) and also generally occurs between individuals who are unrelated and who may or may not know each another. Community violence exposure has been characterized as a serious public health epidemic. According to national surveillance data, more than 60% of adolescents surveyed have been physically assaulted during their lifetime; additionally, 7.2% of the 10- to 13-year-olds and 10.2% of the 14- to 17-year-olds surveyed had witnessed a shooting within the past year ( Finkelhor, 2009 ).

Adolescents are at greater risk for exposure to community violence than children or adults ( Baum, 2005 ; Finkelhor, 2008 ). Moreover, the detrimental effects of community violence exposure may be especially salient during adolescence given increased levels of stress resulting from the enormous biological and social changes that take place during this developmental period ( Mrug, Loosier, & Windle, 2008 ). Likewise, adolescents, compared with children and adults, may be especially reactive to environmental influences given the uncertainty with new social roles or expectations ( Bacchini, Concetta Miranda, & Affuso, 2011 ). Although community violence exposure has been linked to higher rates of depression and anxiety in youth, a recent meta-analysis found that community violence exposure was more strongly related to externalizing than internalizing problems, and a large body of existing research has specifically focused on the effects of community violence exposure on youth delinquent and aggressive behaviors ( Fowler, Tompsett, Braciszewski, Jacques-Tiura, & Baltes, 2010 ).

While the rate of juvenile delinquency in the United States has declined since the end of the last century ( Puzzanchera, Adams, & Hockenberry, 2012 ), it is still among the highest in developed countries ( Thornberry, Huizinga, & Loeber, 2004 ) and violence among urban youth remains a significant public health concern ( Centers for Disease Control and Prevention[CDC], 2010 ). In addition, both rates of delinquent behavior ( Puzzanchera et al., 2012 ) and exposure to community violence ( Pearce, Jones, Schwab-Stone, & Ruchkin, 2003 ; Sheidow, Gorman-Smith, Tolan, & Henry, 2001 ) are higher among racial/ethnic minorities, suggesting that identifying promotive factors that protect youth from the adverse effects of community violence exposure has important implications for addressing racial/ethnic disparities in youth outcomes.

Given that reducing community violence exposure among adolescents may be difficult to achieve, especially in low-resourced communities, there has been growing interest in research exploring promotive factors that may decrease the negative sequelae associated with community violence exposure. However, there is no general consensus if and how these factors may condition the association between risk factors and youth outcomes. Researchers have proposed three major possibilities for interactions between risk and promotive factors, namely, (a) promotive factors only have main effects on maladjustment and they do not interact with risk factors to predict maladjustment (i.e., promotive factors do not alter the relationship between risk factors and maladjustment), (b) promotive factors amplify the relationship between risk factors and maladjustment (i.e., risk factors have stronger associations with maladjustment at increasing levels of promotive factors), and (c) promotive factors function as protective factors that buffer the association between risk factors and maladjustment (i.e., risk factors have weaker associations with maladjustment at higher levels of protection; Jessor, 1993 ; Luthar, Cicchetti, & Becker, 2000 ; Rutter, 1985 ). To date, findings from studies of promotive factors and community violence exposure have been inconsistent. Some studies have failed to find interaction effects of promotive factors and community violence exposure on youth problem behaviors ( Henrich, Brookmeyer, & Shahar, 2005 ; Hill, Levermore, Twaite, & Jones, 1996 ), while a more substantial body of work has reported significant interactions of promotive factors with community violence exposure, albeit with different patterns of results. Specifically, some researchers have found a stronger association between community violence exposure and problem behaviors among youth reporting higher levels of promotive factors ( Bacchini et al., 2011 ; Gorman-Smith & Tolan, 1998 ; Kliewer et al. 2004 ; Sullivan, Kung, & Farrell, 2004 ). For instance, community violence exposure has been found to be more strongly associated with antisocial behavior among adolescents reporting higher levels of parental monitoring ( Bacchini et al., 2011 ). In contrast, other findings have provided evidence for buffering effects of promotive factors on the relationship between community violence exposure and youth problem behaviors ( Brady, Gorman-Smith, Henry, & Tolan, 2008 ; Brookmeyer, Henrich, & Schwab-Stone, 2005 ; Gorman-Smith, Henry, & Tolan, 2004 ; Hardaway, McLoyd, & Wood, 2012 ; Kliewer et al., 2006 ; Mazefsky & Farrell, 2005 ; McGee, 2003 ; Pearce et al., 2003 ), with relationships among community violence exposure and adolescent problem behaviors being weaker at higher levels of promotive factors. For example, it was found that adaptive coping reduced the impact of community violence exposure on violent behavior ( Brady et al., 2008 ). These latter studies show that positive environmental and psychosocial factors can serve as protective factors that buffer the adverse effects of community violence exposure on youth outcomes.

The considerable health and social consequences of community violence exposure and its high prevalence among adolescents calls for systematic research that can identify a broad spectrum of protective factors that may restrain risk outcomes associated with community violence exposure. Given inconsistencies across prior studies, more research is needed using different samples and multiple aspects of promotive factors to determine whether there are systematic patterns of relationships between community violence exposure, promotive factors, and youth delinquency. To date, the majority of research examining interaction effects of promotive factors and community violence exposure on youth delinquent and aggressive behaviors has focused on compartmentalized domains at the individual level (e.g., prosocial cognitions or coping skills; Brady et al., 2008 ; Brookmeyer et al., 2005 ; McGee, 2003 ; Pearce et al., 2003 ) or family level (e.g., parental support or monitoring; Bacchini et al., 2011 ; Brookmeyer et al., 2005 ; Gorman-Smith & Tolan, 1998 ; Kliewer et al., 2006 ; Mazefsky & Farrell, 2005 ; Pearce et al., 2003 ; Sullivan et al., 2004 ). However, youth’s experiences in other domains such as the school and community are also likely to condition the relationship between community violence exposure and negative outcomes in adolescents. Unfortunately, only a handful of studies have considered these factors with regard to community violence exposure and youth problem behaviors ( Hardaway et al., 2012 ; Henrich et al., 2005 ; Hill et al., 1996 ; Kliewer et al., 2004 ; O’Donnell, Schwab-Stone, & Muyeed, 2002 ). In addition, few studies have examined promotive factors across individual, family, school, and community domains using a single sample. As individual differences in youth outcomes are affected by individual, family, school, and community level factors ( Voisin, DiClemente, Salazar, Crosby, & Yarber, 2006 ), it is important to identify protective factors within these domains that may promote resilience among youth exposed to community violence.

One study that excluded the school domain but did examine the significance of individual, family, and community domains ( Kliewer et al., 2004 ), reported that community violence exposure was more strongly associated with externalizing behaviors among adolescents who reported higher levels of caregiver emotion regulation (family influence). In contrast, child emotional regulation (individual trait) and neighborhood cohesion (community characteristic) did not moderate the relationship between community violence exposure and externalizing behaviors. Another study investigating the moderating effects of individual, family, and school-level promotive factors found that high levels of participation in extracurricular activities (individual behaviors) and positive parent–child relationships (family influence), but not school climate (school context), weakened the risk effect of community violence exposure on externalizing behaviors ( Hardaway et al., 2012 ). Though informative, findings from both studies were limited by relatively small homogeneous samples of African American ( Kliewer et al., 2004 ) or low-income ( Hardaway et al., 2012 ) adolescents. Furthermore, both studies examined the effects of promotive factors from each domain in separate analyses. Consequently, it is unclear if and how promotive factors from different domains of adolescent life may influence the relationship between community violence exposure and youth problem behaviors when they are assessed simultaneously.

Contribution of the Present Study

The present study uses a large socioeconomically and racially diverse community sample to identify promotive factors that may protect youth from the risk effects of community violence exposure. The study sample complements prior studies of community violence exposure, many of which have focused primarily on urban minority males. Promotive factors across individual (i.e., future expectations), family (i.e., family warmth), school (i.e., school attachment), and community (i.e., neighborhood cohesion) domains are considered simultaneously to increase our understanding of the types of environmental and psychosocial influences that are most likely to serve as protective factors. Future expectations was selected as an individual-level promotive factor given its central role in problem behavior theory ( Jessor, Turbin, Costa, Dong, Zhang & Wang, 2003 ). According to problem behavior theory, low expectations for success and a sense of hopelessness for the future increase youth vulnerability for involvement in problem behaviors. Conversely, youth who have a more optimistic view of their future are less likely to engage in delinquent behaviors, a hypothesis that has been supported in empirical research ( Blitstein, Murray, Lytle, Birnbaum, & Perry, 2005 ; Bolland, 2003 ; Caldwell, Wiebe, & Cleveland, 2006 ; Chen & Vazsonyi, 2011 ; Stoddard, Zimmerman, & Bauermeister, 2011 ). Social control theory ( Gottfredson & Hirschi, 1990 ) provides an orienting framework for the selection of family warmth, school attachment, and neighborhood cohesion to represent promotive factors from other domains. One aspect of social control theory posits that youth who are more strongly attached to prosocial agents represented by parental figures and school personnel would be more motivated to adhere to conventional norms, and thereby less likely to engage in higher rates of delinquency ( Gottfredson & Hirschi, 1990 ). In addition, high levels of neighborhood cohesion strengthen collective supervision and monitoring, which in turn function as informal social controls to prevent youth from engaging in risky behaviors (Voisin, Jenkins, & Takahashi, 2011). In support of these theoretical prepositions, family warmth ( Barnow, Lucht, & Freyberger, 2005 ; Fletcher, Steinberg, & Williams-Wheeler, 2004 ; Hipwell et al., 2008 ), school attachment ( Bond et al., 2007 ; Dornbusch, Erickson, Laird, & Wong, 2001 ; Jenkins, 1997 ), and neighborhood cohesion ( Browning, Burrington, Leventhal, & Brooks-Gunn, 2008 ; Simons, Simons, Burt, Brody, & Cutrona, 2005 ) have all been inversely associated with youth problem behaviors.

Given that youth do not exist in compartmentalized silos, the current study examining a spectrum of promotive factors across different ecological contexts can provide new information on the relative importance of different promotive factors that may further protect youth from the adverse consequences of community violence exposure. This information, in turn, can be used to develop more refined prevention and intervention programs for at-risk youth.

Study participants are from the “From Neighborhoods to Neurons and Beyond” (NNB) cohort, which is a sample of 3,350 sixth to eighth graders ( M age = 12.47, SD = .99) from 16 urban and suburban schools within 25 miles of a university located in a major city in the Midwestern United States. All youth in the NNB cohort participated in a self-report in-school survey, which obtained data on environmental and psychosocial factors related to youth problem behaviors. The National Opinion Research Center (NORC) conducted the in-school surveys. Individual schools were specifically selected to maximize racial/ethnic and socioeconomic variation in the NNB cohort. Nearly half (43.1%) of the NNB cohort were enrolled in schools with high racial/ethnic variation, 35.0% were enrolled in minority schools (including predominantly African American [16.0%] and predominantly Hispanic [19.0%] schools), and 21.9% were enrolled in predominantly White schools. Schools also differed in the percentage of students eligible for free meal programs (a marker for school poverty), ranging from 7% to 80%. All sixth-to eighth-grade students were targeted for recruitment. The consent return rate across schools was 44% and 80% of youth agreed to participate. Based on youth self-report, more than half of the respondents were non-White, including large numbers of Hispanic (22.3%), African American (20.2%), and mixed race/ethnicity (7.3%) adolescents. Missing data in study constructs from 11.0% of youth resulted in a final study sample of N = 2,980 adolescents ranging in age from 10 to 15 years old (with 98.9% of the sample between 11 and 14 years old). The study was approved by both local university and NORC IRB. Permission was obtained from school administrators/school boards and all participants granted both written parental consent and youth assent for participation. Schools received an average compensation of US$2,500 for allowing the survey to take place in the school. Youth were not individually compensated for their participation.

Measures of all main study constructs employed standardized items developed from established self-report instruments assessing similar constructs in the racially and socioeconomically diverse National Longitudinal Study of Adolescent Health ( Sieving et al., 2001 ). Demographics included age, gender, race/ethnicity, and school poverty.

Participants reported their age in years as part of the in-school survey.

Gender was coded as 1 = male ; 0 = female .

Race/ethnicity.

Participants reported their race/ethnicity. Five racial/ethnic groups were created: non-Hispanic White, Hispanic, African American, Asian, mixed race/ethnicity (i.e., participants reported more than one racial/ethnic background) and “other”. In analyses, dummy-coded variables were created for Hispanic, African American, Asian, “other”, and mixed race/ethnicity, using non-Hispanic White as the comparison group.

School poverty.

School poverty assessed by percentage of youth within a school who qualified for free meals programs was used as a proxy for youth SES. Data were retrieved from the Council of Chief State School Officers (CCSSO) public data system.

Community violence exposure.

Participants indicated whether they had ever been exposed to three violent events (seen someone shot/stabbed [9.5%], had someone pull a knife/gun on them [5.5%], been jumped [11.7%]) and whether they heard gunshots during the past month (24.3%). These four items were combined into a single yes/no index of community violence exposure (1 = yes , 0 = no ) following the strategy used in previous research ( Voisin, 2005 ; Voisin et al., 2007 ). Prior studies have shown that self-report measures of community violence exposure are correlated with objective measures of neighborhood violence, such as official neighborhood-level crime statistics ( Hastings & Kelley, 1997 ; Selner-O’Hagan, Kindlon, Buka, Raudenbush, & Earls, 1998 ), supporting the external validity of this measure. In addition, community violence exposure was positively correlated with school poverty in the current study ( r = .21, p < .001), offering further evidence for the ecological validity of this measure.

Delinquency.

Youth delinquency was measured with 16 items assessing frequency of a broad range of illegal (e.g., stealing something worth more than US$50), norm-violating (e.g., skipping school without permission), and aggressive (e.g., getting into a serious physical fight) behaviors within the past 12 months. Responses were given on a 3-point scale, ranging from 0 = never to 3 = 5 or more times , and were recoded into 0 = never and 1 = 1 or more times . A composite score of the number of delinquent behaviors endorsed was computed by summing the recoded responses to the 16 items (α = .79; M = 1.96, SD = 2.41). The composite delinquency score was positively skewed (skewness = 1.71) and was transformed using a square root transformation for analyses.

Future expectations.

Participants were asked to rate 4 statements assessing their perceived likelihood of future events (i.e., living to age 35, being killed by age 21, graduating from high school, graduating from college) in a 5-point scale ranging from 1 = little or no chance to 5 = it will happen . Responses to the items were averaged to create a score of future expectations, with the item assessing the likelihood of being killed by age 21 reverse coded (α = .61). As the scale score of future expectations was highly skewed with 51% of the participants having a score of 5, it was recoded into 0 = low future expectations (participants with a scale score that was below 5) and 1 = high future expectations (participants with a scale score of 5) for analyses. We also repeated the analyses using the continuous scale score of future expectations and findings were consistent with those reported in the article.

Family warmth.

Participants responded to 5 items assessing family warmth (e.g., how much do you feel that people in your family understand you) with a 5-point scale ranging from 1 = not at all to 5 = very much . A family warmth score was created using the mean of the responses to the 5 items (α = .80; M = 4.20, SD = .76). The family warmth score was negatively skewed (skewness = −1.22) and was transformed using a square transformation for analyses.

School attachment.

School attachment was assessed with 6 items. Five items asked participants how much they agreed with statements describing their attachment to school (e.g., I feel like I am part of this school). Responses ranged from 1 = strongly disagree to 5 = strongly agree . A sixth item assessed how much participants felt that their teachers cared about them, with responses ranging from 1 = not at all to 5 = very much . A score of school attachment was computed by averaging the responses to all 6 items (α = .84; M = 3.87, SD = .69).

Neighborhood cohesion.

Neighborhood cohesion was measured with 11 items (e.g., people in my neighborhood look out for each other). Responses ranged from 1 = strongly disagree to 4 = strongly agree and were averaged to create a score of neighborhood cohesion (α = .88; M = 3.05, SD = .57)

Analytical Plan

Multilevel modeling using SPSS was implemented to account for nonindependence between participants in the same school. Four hierarchical models were specified to examine the main effects of community violence exposure and promotive factors and their interaction effects on delinquency, including an unconditional means model estimating the proportion of variability in delinquency that exists between individuals and between schools (Model 1), a model with all the demographic control variables (Model 2), a model considering main effects of community violence exposure and promotive factors (Model 3), and a model examining all of the interaction effects between community violence exposure and promotive factors simultaneously (Model 4; i.e., community violence exposure × future expectations, community violence exposure × family warmth, community violence exposure × school attachment, community violence exposure × neighborhood cohesion). Standardized scores of all continuous variables were used in analyses so that standardized coefficients could be compared across measures. Comparisons across models were based on differences in −2LL between models (Δ−2LL), which is distributed as a chi-square statistic with degrees of freedom equal to the difference in degrees of freedom between the models compared. Significant interactions were plotted and interpreted using methods outlined by Preacher, Curran, and Bauer (2006) for calculation of the regression coefficient between community violence exposure and delinquency at low and high levels of potential moderators.

Missing Data Analyses

Multivariate logistic regression analyses were conducted to compare the demographic characteristics of youth included in the final sample with youth who were excluded due to missing data in main study constructs. Findings indicated that excluded youth were more likely to be younger adolescents ( b = −.23, p < .001) and male ( b = .48, p < .001). In addition, African American adolescents ( b = .37, p < .05) and adolescents with mixed racial/ethnic background ( b = .60, p < .01) were more likely, and Asian adolescents ( b = −1.22, p < .05) less likely, to be excluded from the current analyses than White adolescents. Other racial comparisons were not statistically significant. No significant differences were found between included and excluded youth in levels of school poverty.

Descriptive Statistics and Correlations

The mean age of participants included in the present analysis was 12.48 ( SD = .98) and 58.9% of the sample was female. Almost half of study participants (42.1%) self-identified as non-Hispanic White, 22.4% as Hispanic, 19.8% as African American, 4.2% as Asian, 6.9% as mixed race/ethnicity, and 3.6% as “other.” The average percentage of youth who qualified for free meals across schools was 42.44% ( SD = 20.77%). About one third of study participants (34.3%) reported exposure to community violence. Chi-square tests showed significant differences in rates of community violence exposure across the different racial/ethnic groups (χ 2 = 229.02, df = 5, p < .001). Rates of community violence exposure were highest among youth from African American (54.4%), Hispanic (42.9%), and mixed (37.7%) racial/ethnic backgrounds, compared with rates of community violence exposure in “other” (26.9%), White (21.8%), and Asian (21.8%) youth.

Table 1 shows the prevalence of each of the 16 delinquent behaviors in the present study sample as well as the average composite score for delinquency calculated separately for the subgroups of youth who endorsed each of the 16 delinquency items. Consistent with other studies using community-based samples, the most commonly endorsed behaviors represented minor delinquency (e.g., being disruptive in a public place; lying to parents). However, more than 10% of the sample also participated in vandalism, theft, and aggressive behaviors. In addition, although endorsement of the most serious delinquent behaviors was relatively low (e.g., using a weapon in a fight, selling marijuana or other drugs), youth who endorsed these behaviors had the highest levels of overall delinquency. Indeed the data presented in Table 1 show a positive relationship between severity of behaviors and total number of delinquent behaviors, indicating that our delinquency composite score represented both severity and variety of delinquent activities.

Descriptive Statistics for Each Delinquent Behavior Assessed

Findings from one-way ANOVAs indicated that youth exposed to community violence reported significantly ( p < .001) higher levels of delinquency than unexposed youth (exposed youth: M [ SD ] = 3.37 [2.93]; unexposed youth: M [ SD ] = 1.23 [1.66]). They also reported lower levels of family warmth (exposed youth: M [ SD ] = 3.93 [.86]; unexposed youth: M [ SD ] = 4.34 [.65]), school attachment (exposed youth: M [ SD ] = 3.60 [.72]; unexposed youth: M [ SD ] = 4.01 [.63]), and neighborhood cohesion (exposed youth: M [ SD ] = 2.85 [.60]; unexposed youth: M [ SD ] = 3.15 [.52]). Differences in future expectations between youth exposed and unexposed to community violence were examined using chi-square. Results indicated that a significantly higher proportion of exposed youth reported low levels of future expectations (59.6%) in comparison to unexposed youth (46.5%; χ 2 = 46.76, df = 1, p < .001). Table 2 shows correlation statistics among main study constructs. Measures of promotive factors were positively correlated with each other and negatively correlated with delinquency and community violence exposure. Community violence exposure was positively correlated with delinquency.

Correlation Statistics Among Main Study Variables ( N = 2,980)

Note: Tetrachoric correlation coefficient was reported for the correlation between community violence exposure and future expectations.

Main Effects of Community Violence Exposure and Promotive Factors

Results for the main effects of community violence exposure and promotive factors on delinquency are shown in Table 3 . Findings from Model 1 revealed statistically significant variability in delinquency between individuals (σ 2 = .94, p < .001) and between schools (τ 00 = .06, p < .001), supporting the use of multilevel modeling to correct for sample nonindependence. Model 2 testing the effects of demographic control variables fit significantly better than Model 1. Findings indicated that age and school poverty were significantly and positively related to levels of delinquency and that males reported higher levels of delinquency than females. In addition, African American and Hispanic adolescents, as well as youth from the “other” and mixed racial/ethnic groups, were more likely to show higher levels of delinquency than White adolescents.

Multilevel Regression Predicting Adolescent Delinquency by Community Violence Exposure and Promotive Factors ( N = 2,980)

Model 3, which examined the main effects of community violence exposure and promotive factors net the effects of control variables, had a significantly better model fit than Model 2. Adolescents who were exposed to community violence exhibited higher levels of delinquency than unexposed youth. Furthermore, future expectations, family warmth, school attachment, and neighborhood cohesion all had significantly negative associations with delinquency. The association with delinquency was relatively stronger for family warmth ( b = −.22) and school attachment ( b = −.19) than for future expectations ( b = −.14), while neighborhood cohesion ( b = −.05) had the weakest association with delinquency when all of the promotive factors were examined simultaneously.

Interaction Effects Between Promotive Factors and Community Violence Exposure

Findings on the interaction effects between promotive factors and community violence exposure on delinquency are also exhibited in Table 3 . Model 4 tested all of the interaction effects between community violence exposure and promotive factors simultaneously. Although Model 4 had a significantly better model fit than Model 3, findings from this model indicated that among all of the promotive factors examined, only future expectations interacted with community violence exposure to predict adolescent delinquency, suggesting variations in associations between community violence exposure and delinquency for youth with low and high levels of future expectations. Therefore, a final model (Model 5) considering the main effects of community violence exposure and promotive factors, as well as the interaction effect between future expectations and community violence exposure, was tested. Model 5 also had a significantly better model fit than Model 3. Results from this model indicated that controlling for everything else in the model, family warmth, school attachment, and neighborhood cohesion were significantly and negatively associated with adolescent delinquency although they did not interact with community violence exposure to predict delinquency. To explore the significant interaction effect between future expectations and community violence exposure, we plotted the predicted differences in levels of delinquency between exposed and unexposed youth by levels of future expectations in Figure 1 . Although exposed youth consistently exhibited higher levels of delinquency than unexposed youth, differences in delinquency between exposed and unexposed youth were smaller among youth reporting high levels of future expectations in comparison to those with low levels of future expectations. In other words, the association between community violence exposure and delinquency was weaker at high levels of future expectations (high future expectations: b community violence exposure = .45, p < .001; low future expectations: b community violence exposure = .66, p < .001).

Moderating effect of future expectations on the association between community violence exposure and adolescent delinquency

Note: Delinquency is shown as standardized ( z score) values.

Community violence exposure has consistent and marked effects on youth externalizing behaviors ( Fowler et al., 2010 ). Identifying protective factors that may reduce the risk effects of community violence exposure has important implications for preventing youth delinquency, especially among urban minority and poor youth, who are at increased risk for community violence exposure ( Pearce et al., 2003 ; Sheidow et al., 2001 ). The present study is one of the first community studies using a large racially, ethnically, and socioeconomically diverse sample of adolescents to examine the moderating effects of a spectrum of promotive factors across multiple ecological domains on the relationship between community violence exposure and adolescent delinquency. By incorporating multidomain promotive factors simultaneously into the analysis of relationships between community violence exposure and adolescent delinquency, we were able to rigorously examine their independent associations with adolescent delinquency and to test how they independently moderated the relationship between community violence exposure and delinquency. We found that all promotive factors examined in the present study (i.e., future expectations, family warmth, school attachment, and neighborhood cohesion) were significantly and independently associated with lower levels of adolescent delinquency. Consistent with prior empirical work, our findings document that positive individual traits, family processes, and school and community characteristics are associated with lower levels of youth delinquency ( Deković, 1999 ; Garmezy, 1985 ; Luthar et al., 2000 ). Additionally, the present study provided evidence for stronger associations between family warmth and school attachment with adolescent delinquency than future expectations and community cohesion. These findings are also consistent with previous work, which has highlighted the critical roles played by family and school in promoting child competence ( Crosnoe, Erickson, & Dornbusch, 2002 ; Englund, Levy, Hyson, & Sroufe, 2000 ; Luthar, 2006 ; O’Donnell et al., 2002 ).

Importantly, our findings provided support for a moderating effect of future expectations on the relationship between community violence exposure and adolescent delinquency, when interactions between all promotive factors and community violence exposure were examined simultaneously. Specifically, differences in delinquency between exposed and unexposed youth were smaller among adolescents reporting high levels of future expectations in comparison to those with low levels of future expectations, implying that future expectations buffered the relationship between community violence exposure and adolescent delinquency. In contrast, the relationship between community violence exposure and adolescent delinquency did not vary across different levels of family warmth, school attachment, or neighborhood cohesion. This finding suggests that promotive processes within the individual, such as future expectations, are more likely to function as protective factors that support adolescents’ resilience in the presence of risk environments compared to more distal promotive factors that occur in youth’s families, schools, and communities. The assumption that proximal versus distal promotive factors have a greater effect on resiliency has been posited by prior researchers ( Bronfenbrenner & Ceci, 1994 ; Rutter, 1985 ; Voisin et al., 2006 ).

Our findings advance the extant literature on interactions between risk and promotive factors in several ways. Previous theoretical and empirical work ( Bacchini et al., 2011 ; Brady et al., 2008 ; Brookmeyer et al., 2005 ; Gorman-Smith et al., 2004 ; Gorman-Smith & Tolan, 1998 ; Jessor, 1993 ; Kliewer et al., 2004 , 2006 ; Luthar et al., 2000 ; Mazefsky & Farrell, 2005 ; McGee, 2003 ; Rutter, 1985 ; Sullivan et al., 2004 ) has not reached agreement as to whether and how promotive factors may interact with risk factors, such as community violence exposure, to predict adolescent adjustment. Our results indicate that high levels of future expectations mitigate the negative influences of environmental risk on youth delinquency and therefore add support to the “buffering hypothesis” about relationships between risk and promotive factors. However, our findings further suggest variations in interaction patterns of promotive factors across various ecological strata, consistent with more recent conceptual thought ( Luthar et al., 2000 ). Specifically, our findings indicate that the relationship between community violence exposure and adolescent delinquency did not vary as a function of promotive factors measured at the family, school, and community level, although these promotive factors were directly associated with lower levels of delinquency. The different pattern of results observed for promotive factors from different domains suggests that inconsistencies in previous research on protective factors may be partly due to differences in the types of promotive factors examined.

Limitations

The current study has several strengths, including the use of a large, racially, ethnically, and socioeconomically diverse sample of youth and the consideration of promotive factors from multiple domains. However, a number of study limitations should be noted. First, our study used a cross-sectional design, and therefore causal relationships between study constructs cannot be made. Several of the relationships assessed in this study could be bidirectional. For instance, delinquency might result in higher levels of community violence exposure and vice versa. Cross-sectional studies are often criticized for their inability to tease out temporal ordering. However, such designs do provide important preliminarily evidence for relationships examined, a contribution which this study offers, which can then form the basis for more costly longitudinal studies. Second, measures of community violence exposure were based on responses that combined both witnessed violence and victimization, and it is possible that community violence exposure assessed in the present study overlaps with other similar constructs (e.g., exposure to family violence). However, we note that these items were specifically selected to capture exposure to violence that most likely occurs outside the family context. Third, based on our measure of future expectations, the majority of our sixth- to eighth-grade participants reported positive expectations toward the future. However, we note that patterns of results were the same using both dichotomous and continuously measured definitions of future expectations. Moreover, the limited variation in the measure of future expectations likely attenuated its interaction with community violence exposure. Fourth, although one of the strengths of the present study is our racially/ethnically diverse sample of adolescents, this diversity also restricted our ability to test racial/ethnic differences in the moderating effects of promotive factors on the community violence exposure and delinquency link (i.e., three-way interactions among race/ethnicity, promotive factors, and community violence exposure), especially given the small sample sizes of the participants from the Asian, “other”, and mixed racial/ethnic groups. However, we note that additional analyses (available from authors) revealed a similar pattern of results for two of our three largest racial/ethnic groups (i.e., Whites and African Americans), suggesting that the interactions between community violence exposure and future expectations do generalize to both minority and nonminority youth. Fifth, the current sample only included students who were enrolled in public schools. Rates of homelessness, incarceration, institutionalization, school dropout, and expulsion are low among this age group ( Child Trends Data Bank, 2012 ; Molino, 2007 ; Whitted, Takiff, & Ali, 2011 ), suggesting that our results are not likely to be biased by sampling method. However, we note that the present results may not generalize to youth outside the school system, who may be exposed to higher levels of community violence and are likely to be disproportionately involved in more serious forms of delinquency. Finally, although the present study examined the interaction patterns between promotive factors with community violence exposure at multiple ecological levels, it only considered one promotive factor at each level of influence. It is possible that different processes within domains may show different patterns of interactions with risk factors. For example, a study of aggressive behavior in a sample of urban minority males reported a significant interaction between community violence exposure and a composite measure of family organization, support, and intolerance of antisocial values, but did not find a significant interaction between community violence exposure and family cohesion ( Gorman-Smith & Tolan, 1998 ). Thus our finding that individual factors but not family, school, or neighborhood factors acted as a protective factor against the risk effects of community violence exposure may not generalize to studies investigating other individual and environmental processes.

Clinical and Policy Implications

Despite the limitations described above, our study results have several clinical and policy implications. First, our results indicate that successful intervention and prevention programs should be multifaceted, as we found independent main effects for promotive factors across several domains. Thus, programs emphasizing promotive factors in any one domain may have limited effects on reducing delinquency. On the other hand, our finding that future expectations was the only measure that attenuated the risk effects of community violence exposure on delinquency indicates that individual characteristics may play a critical role in protecting youth from adverse effects of community violence exposure. Thus a second implication of our study highlights the need to develop programs that can promote positive future expectations, especially among youth exposed to community violence. Other studies have shown that effective coping strategies reduce the risk effects of community violence exposure on behavioral problems ( Brady et al., 2008 ; McGee, 2003 ) and that optimistic thinking about the future triggers active coping behaviors and thereby predicts positive adjustment ( Nes & Segerstrom, 2006 ). Many existing coping intervention and prevention programs are designed to be implemented in group settings, such as schools and community centers. Especially exciting are recent results from a follow-up analysis of youth enrolled in the Child Coping Power intervention study, a group-based intervention given to adolescents in fifth and sixth grade ( Lochman, Wells, Qu, & Chen, 2012 ). While youth in the intervention group showed greater reductions in aggressive behavior 3 to 5 years later than control youth, the strongest effect of the intervention was found for youth who lived in more socially disadvantaged neighborhoods. These results parallel the results of the current study, which indicate that at-risk youth may derive the greatest benefits from interventions aimed at changing individual psychosocial and cognitive characteristics. Finally, the effects of policies aimed at ameliorating external risk factors that are correlated with community violence exposure and delinquency may be mediated by indirect effects on positive youth cognitive and socioemotional abilities. For example, antipoverty programs focused on increasing parental employment have been shown to enhance youth’s educational and occupational expectations ( Huston et al., 2005 ; McLoyd, Kaplan, Purtell, & Huston, 2011 ). Likewise, children’s involvement in before- and after-school programs and other adult-supervised structured activities can promote youth’s self-confidence and optimism ( McLoyd et al., 2011 ). Therefore, there are several avenues that could be pursued to increase more positive future expectations for at-risk youth.

In summary, findings from the present study and prior work highlight the importance of building individual competence in prevention/intervention programs targeted toward reducing the deleterious influences of community violence to promote positive youth development. This information is critical for clinicians and other service providers who work with diverse samples of youth who are at risk for exposure to community violence.

Acknowledgments

We would like to acknowledge the National Opinion Research Council (NORC) at the University of Chicago who conducted the data collection for the current study. In addition, we acknowledge current and former staff at the University of Chicago Clinical Neuroscience & Psychopharmacology Research Unit (CNPRU), especially Ms. Crystal Johnson, Ms. Kristen Jezior, and Ms. Bing Chen, for their assistance with this project. Finally, we are grateful for the participation of the schools in the Chicago area that allowed this study to take place and thank the individuals of the Neighborhoods to Neurons and Beyond cohort for participating in this research.

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was funded by the National Institutes of Health through the NIH Director’s New Innovator Award Program, Grant number DP2-OD-003021 to Dr. Jacobson. Information on the New Innovator Award Program is at http://grants.nih.gov/grants/new_investigators/innovator_award/ .

Pan Chen is a postdoctoral scholar in the Department of Psychiatry and Behavioral Neuroscience at the University of Chicago. Her research broadly focuses on adolescent problem behaviors, with a particular emphasis on the interaction effects of psychological and social-contextual factors on adolescent adjustment.

Dexter R. Voisin is an Associate Professor at the University of Chicago, School of Social Service Administration. His research broadly focuses on the social determinants of drug use and risky sex among racial and sexual minorities and the role of gender in relation to risk pathways.

Kristen C. Jacobson is an Associate Professor in the Department of Psychiatry and Behavioral Neuroscience at the University of Chicago. Dr. Jacobson’s current research uses interdisciplinary approaches to examine the joint interplay of environmental, social, psychological, and biological influences on adolescent socioemotional development, with a particular emphasis on youth antisocial behaviors.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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Tests of three hypotheses regarding the predictors of delinquency

Affiliation.

• 1 Department of Psychology, Carleton University, Ottawa, Ontario, Canada.
• PMID: 7822628
• DOI: 10.1007/BF02168937

Three hypotheses regarding the predictors of criminal activity in children and adolescents were assessed. These dealt with family, peer, and attitudinal variables, and they were explored in relation to indices based on seriousness of criminal activity and reoffending. The data were based on a sample of 338 youths who had been convicted of crimes and received probation or custody dispositions. The results provided general support for a model implicating family, peer, and attitudinal variables in youthful criminal activity. They did not, however, provide support for hypothesized interactions between family relationship and family structuring dimensions or between family relationship and peer association variables. The results did support an hypothesis regarding the independent contribution of an antisocial attitudes variable to the prediction of criminal activity.

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The history of the 1974 Juvenile Justice and Delinquency Prevention Act

During a May 1971 hearing held before the U.S. Senate Subcommittee to Investigate Juvenile Delinquency, advocates testified on the grim challenges faced by children in the juvenile justice system.

News coverage of the hearing recounted “stories of  brutality, corruption and flouting of the law by juvenile judges , parole officers and those who run detention homes for children.” 

Reporters wrote that “witnesses told of children being sent for months to jails and reformatories without a court hearing or an attorney, having committed no ‘crime’ other than being runaways or being described as ‘uncontrollable’ by a parent.”

The 1971 hearing was part of a series of hearings organized by lawmakers in the early 1970s  to study juvenile justice across the country and investigate the conditions faced by youth in jails and detention centers.

At the time, children could be placed in adult or juvenile corrections facilities that lacked adequate medical services and education or rehabilitation programs. Children were often treated like adults no matter how minor the offense.

With concerns over the treatment of children in the justice system, rising juvenile crime rates and doubts surrounding the implementation of a 1968 juvenile justice bill, Indiana Senator Birch Bayh first introduced the Juvenile Justice and Delinquency Prevention Act  in February 1972 . 

Bayh proposed safeguards to  prevent young people from entering the justice system and coordinated federal assistance for communities to develop more appropriate approaches for youth accused of delinquency or crime.

In the midst of Watergate, Congress would consider and later pass the Juvenile Justice and Delinquency Prevention Act in 1974. President Gerald Ford  signed the JJDPA into law on September 7, 1974 – just one month after President Richard Nixon resigned and the day before Ford announced his pardon of the former president.

The passage of the JJDPA was a landmark  federal effort to address juvenile delinquency. It set basic standards for state juvenile justice systems, established core protections for young people in the system and created the Office of Juvenile Justice and Delinquency Prevention. 

Protecting Justice-Involved Children

The most comprehensive piece of juvenile justice legislation ever passed by Congress, the JJDPA substantially revised existing federal laws and agency responsibilities related to juvenile delinquency.

The JJDPA created a federal-state partnership for the administration of juvenile justice and delinquency prevention,  placing the principal responsibility for federal juvenile delinquency resources in the U.S. Department of Justice. By establishing OJJDP, the JJDPA created a federal office to serve as a partner with the states on issues related to juvenile justice. OJJDP is the only federal office whose primary charge is to lead juvenile justice efforts.

Under the JJDPA, states and territories were required to meet certain standards for how youth were treated in the justice system in order to receive federal funding for their juvenile justice systems. 

Initially, the JJDPA required keeping youth separate from adults in adult facilities with the ultimate goal of removing youth from adult jails – which it required in the 1980 reauthorization. The JJDPA also called for preventing the detainment of youth for status offenses, actions that would not be a crime if committed by an adult, such as truancy and underaged drinking or smoking.

For states that met these requirements, the JJDPA provided federal funding for an array of juvenile justice approaches and programs, with a focus on community-based programs.  Since 1974, it has helped states fund innovative programs that support children and make communities safer.

Federal Efforts on Juvenile Delinquency Reforms

The rate of crimes committed by youth was a concern long before the JJDPA was passed in 1974. Some of the earliest efforts to address juvenile delinquency can be traced back to 1912 when Congress created the children’s bureau to investigate and report on all matters pertaining to the welfare of children, including juvenile courts.

However, juvenile delinquency became a pressing issue for lawmakers in the 1960s as juvenile crime rates increased over the decade.

Between 1960 and 1968 the number of juvenile court cases increased by 76.4% and the FBI reported that arrests of those under 18 doubled during that same timeframe.

In 1961, President John F. Kennedy signed the Juvenile Delinquency and Youth Offenses Control Act which provided federal resources to local communities for initiatives to reduce juvenile delinquency. Administration of these resources was assigned to the U.S. Department of Health, Education, and Welfare, the agency that would eventually become today’s Department of Health and Human Services .

Years later, President Lyndon Johnson’s Commission on Law Enforcement and Administration of Justice described reducing juvenile delinquency and youth crime as “America’s best hope for reducing crime” when they published  The Challenge of Crime in a Free Society in 1967.

The 1961 Juvenile Delinquency and Youth Offenses Control Act expired in 1967 and the next federal effort to address youth crime issues came the following year when Congress passed the Juvenile Delinquency Prevention and Control Act along with the Omnibus Crime Control and Safe Streets Act.

Under the Juvenile Delinquency Prevention and Control Act of 1968, the Department of Health, Education, and Welfare was once again assigned responsibility for national leadership in developing new approaches to juvenile delinquency problems and the coordination of federal efforts.

However, lawmakers believed that efforts from the 1968 juvenile delinquency legislation could be  better coordinated between federal agencies . 

In response, Senator Bayh proposed the first version of the JJDPA in 1972 to strengthen federal resources that addressed juvenile justice reforms.

Originally, Bayh proposed  a coordinating body in the White House for juvenile justice issues, but lawmakers decided the Law Enforcement Assistance Administration – an OJP predecessor which had just been established within the Department of Justice in 1968 – was best suited to lead these programs.

In 1974, the JJDPA was overwhelmingly passed by both chambers of Congress and it became  the first juvenile justice legislation presented to President Ford after he ascended to the presidency.

The JJDPA replaced the 1968 Juvenile Delinquency Prevention and Control Act and moved ongoing juvenile justice efforts from HEW over to the newly created Office of Juvenile Justice and Delinquency Prevention within LEAA. HEW maintained responsibility for programs on runaway youth.

As chairman of the Senate Subcommittee to Investigate Juvenile Delinquency, Bayh presided over the passage of the JJDPA in the early 1970s. He was the author, sponsor, and chief architect of the historic reforms to the juvenile justice system. 

Since its passage in 1974, Congress has  updated and reauthorized the JJDPA with strong bipartisan support to meet the changing needs of juvenile justice across the country.

Learn more about the OJJDP's 50 th anniversary

About the author

Blair Ames is the historian at the OJP Office of Communications. He has supported OJP program offices as a writer since 2015. Over that time, he’s worked with every OJP program office, helping to produce blogs, web content, newsletters, email messaging campaigns, journal articles, speeches, talking points and more.

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• Highlighting three years of OJP achievements
• Mission in action: OJP focuses on community as co-producer of safety and justice

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Racial and Ethnic Disparity in Juvenile Justice Processing

Data have shown that youths of color are more likely than white youths to be arrested and subsequently go deeper into the juvenile justice system (e.g., Puzzanchera, 2021; Puzzanchera and Hockenberry, 2013; Sickmund et al., 2021; Sickmund, Sladky, and Kang, 2021). Researchers have examined the contributing factors to these racial and ethnic disparities for decades, often testing hypotheses and theoretical frameworks related to differential offending and system biases (Leiber and Fix, 2019; Pope and Feyerherm, 1990; Pope, Lovell, and Hsia, 2002; Zane and Pupo, 2021). Most scholars acknowledge there are numerous factors at work and that this complex social problem cannot be reduced to either differential offending or differential treatment alone ( National Research Council, 2013). Much of the work to address racial and ethnic disparities in the juvenile justice system has been driven by amendments to the federal Juvenile Justice and Delinquency Prevention Act (JJDPA) through the federal Office of Juvenile Justice and Delinquency Prevention (OJJDP). Although some progress has been made and overall involvement in the juvenile justice system has been decreasing nationally, disparities continue to exist today, especially for Black and American Indian/Alaska Native youths (see Figures 1, 2a, and 2c).

This literature review covers racial and ethnic disparities in the juvenile justice system. It begins with definitions related to racial and ethnic disparities, which are followed by how disparities can be measured and a description of the scope of the problem. A brief history of the Core Requirement to address racial and ethnic disproportionality in the JJDPA is then presented, followed by a description of a large body of empirical studies that attempt to explain why there are disparities in juvenile justice. A brief overview is provided on some of the efforts to address racial and ethnic disparities that have been captured by research literature, followed, finally, by examples of programs related to the reduction of these disparities.

Definitions 

Terminology related to racial and ethnic disparity has changed over time. According to the JJDPA, amended in 2018, racial and ethnic disparity means minority youth populations are involved at a decision point in the juvenile justice system at disproportionately higher rates than nonminority youth at that decision point (Pub. L. 115–385, title I, §   102) and is often written as R.E.D., RED, R/ED, or ERD. From 2002 to 2018, OJJDP referred to this as disproportionate minority contact ( DMC ). Before that, DMC stood for disproportionate minority confinement . Confinement was changed to contact in 2002 because of disproportionality throughout all stages of the juvenile justice system (e.g., arrest, diversion, probation), and not merely at confinement (OJJDP, 2009a). 

The terms disproportionality and disparity often are used interchangeably to refer to rates of contact with any point of the juvenile justice system that are not the same among different races or ethnicities, regardless of the cause. However, their meanings differ slightly: disproportionality refers to the state of being out of proportion, while disparity refers to a state of being unequal (Abrams, Mizel, and Barnert, 2021; Dettlaff et al., 2011).

The term minority overrepresentation is still used by some organizations but increasingly has been replaced by either the term disparity or disproportion since minority youths often are underrepresented in receiving more - lenient outcomes such as diversion from court and probation placement after a finding of delinquency.

Juvenile justice contact points or decision points are terms used to refer to different points where youths have “contact” with the juvenile justice system (e.g., arrest, detention, petition). These two terms are frequently used interchangeably but referring to these stages as decision points shifts greater attention on the juvenile justice system decisionmakers who determine whether the youths will become involved in the system at that point.

Discrimination denotes between-group differences in outcomes based on the consideration of extralegal or illegitimate factors (Bishop, 2005). T he terms discrimination and bias are used when disparities appear to be caused by some intent on the part of the decisionmaker or when a system’s design puts minority youth at a disadvantage. Both individual and system bias can be intentional but often are unintentional or implicit (Fix, 2020; Goff et al., 2014; Gove, 2011; Tomaskovic–Devey and Warren, 2009).

Measuring Racial and Ethnic Disparity and Disproportionality

Disproportionality can be measured using various approaches, such as comparing proportions or using rates. Each of these measures identifies levels of disproportionality in a specific way.

• Proportions . When using proportions, the racial breakdown of youths in the general population is usually compared with the racial breakdown of youths at a certain point in the juvenile justice system. For example, someone may explain that while only 15 percent of all youths in the United States are Black, 41 percent of juveniles in the population committed to residential placement are Black, indicating racial disparities (Rovner, 2021a). Proportions at one point can also be compared with the proportions in the preceding point (or points) to see incremental changes. For example, one publication compared the representation of Black youth in the general population with five stages of the justice system (arrests, referrals to court, detention, residential placement, admission to adult prison), demonstrating their increasing involvement in the justice system in Pennsylvania (Shoenberg, 2012). In this case, Black youths made up less than 20 percent of the youth population but more than 25 percent of the arrests, more than 30 percent of the referrals, slightly less than 40 percent of the detained and placed youths, and almost 60 percent of the youths admitted to adult prison. There are some limitations to using proportions. It can be difficult to use proportions to compare disparities in different jurisdictions or to examine trends over time when the composition of the youth population changes (Feyerherm and Butts, 2002; Feyerherm, Snyder, and Villarruel, 2009). Also, when minority groups are in the majority (i.e., when most youths in a population are nonwhite), disparities may appear less evident than when using rates.
• Relative Rates. Another approach to measuring disproportionality is to use the relative rate index (RRI). The RRI compares the rates of processing for minority youth with the rates of processing for white youth. The RRI method describes the volume of activity from one contact point to the next and how it differs between w hite and minority youths, thereby isolating disproportionality at a particular point (e.g., comparing secure detention rates among the population of youth referred to court) (Feyerherm and Butts, 2002; Feyerherm, Snyder, and Villarruel, 2009). The RRI can also be based on the general youth population (e.g., comparing the incarceration rate based on the general youth population). Thus, as with using proportions, the RRI can consider the rates of processing at the previous point or compare rates from the general youth population.
• Rates . Rates and relative rates can show different aspects of disproportionality. For example, the Census of Juveniles in Residential Placement provides the counts, percentages, and rates of youths in custody per 100,000 in the population. In the most recent census, Massachusetts had one of the lowest rates of residential placement for Black juveniles (133 per 100,000). However, because the rate for white juveniles in Massachusetts (19 per 100,000) was much lower than the Black rate, the Massachusetts' RRI is higher than the national average of 4.4 (see Figure 2a), indicating high levels of disproportionality in the state. By contrast, Indiana had one of the lowest population-based RRIs for Black youth, even though they had higher residential placement rates for Black youth than Massachusetts. Since the residential placement rate for Black youths in Indiana (298 per 100,000) was closer to the rate for white youths in Indiana (138 per 100,000), it had a much lower RRI than Massachusetts (RRI of 2.2 in Indiana compared with 7.0 in Massachusetts) but still a higher placement rate for Black youths than Massachusetts (Sickmund et al., 2021).

Counts, rates, proportions, and RRIs all direct policymakers and practitioners to the points of the juvenile justice system that may need more examination, but none of these measures identifies contributing mechanisms for this disproportionality (Hsia et al., 2006). Each of these measurement approaches has been used at different times by OJJDP (Harp, 2018; Leiber and Fix, 2019) and within the research literature (e.g., Leiber and Fix, 2019; Abrams, Mizel, and Barnert, 2021; Rovner, 2021b), as each measure provides unique information that is valuable for recognizing and monitoring disproportionality. As of 2019, OJJDP no longer accepts RRI to demonstrate compliance with the Core Requirement (see "Federal Legislation" section below).

Scope of the Problem

National data show that Black youths and other youths of color are more likely than white youths to be arrested, referred to court, petitioned after referral (i.e., handled formally), and placed in an out-of-home facility after being adjudicated (Hockenberry and Puzzanchera, 2020; Sickmund, Sladky, and Kang, 2021.).

In 2019, compared to white youths, Black youths were 2.4 times more likely and American Indian youths were 1.5 times more likely to be arrested. On the other hand, Asian youths were less likely than white youths to be arrested (OJJDP, 2020).

Juvenile court data generally provide more detail than arrest data, including information for Hispanic youths. In 2018, 52 percent of delinquency cases involving white youths in juvenile court were handled formally (instead of being handled informally—that is, without filing a petition for adjudication, such as through diversion), compared with 64 percent of cases involving Black youths, 58 percent of cases involving American Indian youths, 55 percent of cases involving Hispanic youths, and 54 percent of cases involving Asian youths. Also, after being adjudicated delinquent, cases involving Black juveniles and Hispanic juveniles were more likely to result in out-of-home placements (32 percent each) than cases involving youth of all other races/ethnicities (27 percent of cases involving American Indian juveniles, 23 percent of cases involving white juveniles, 20 percent of cases involving Asian juveniles) (Hockenberry and Puzzanchera, 2020:58–59).

However, although Black youths tend to be pushed further into the system at most juvenile justice decision points than youths of other races/ethnicities, this is not always the case. Among cases handled formally in juvenile court, American Indian youths were the most likely to be adjudicated delinquent (59 percent), followed by Hispanic youths (57 percent), white youths (52 percent), Asian youths (49 percent), and finally Black youths (49 percent) [Hockenberry and Puzzanchera, 2020]. Similarly, a systematic review of empirical studies examining racial disparities in juvenile justice found that the adjudication decision was the least likely to show disadvantage toward youth of color, including Black youth (Spinney et al., 2018).

The previous two paragraphs describe disparities at each point, as youths move from one juvenile justice contact point to another. Point-in-time estimates at the deep end of the system can also demonstrate the prevalence of disparities relative to the whole population. For example, in 2019, the Census of Juveniles in Residential Placement showed a rate of 315 Black youths in custody per 100,000 in the population, compared with 72 white youths per 100,000—a ratio of approximately 4.4 to 1.0 (Sickmund et al., 2021), which is the same as a population-based RRI of 4.4 (see Figure 2a). Data collected for Hispanic and American Indian youths have also indicated higher levels of placement than white youth, although these disparities have lessened for Hispanic youths (see Figure 2b) over time (Sickmund et al. 2021).

Nationally, the rate of juveniles in residential placement decreased from 356 per 100,000 in 1997 to 114 per 100,000 in 2019 (Sickmund et al., 2021). During this time, the residential placement rates decreased for all youth races (see Figure 1). However, disparities have not decreased in the same way for all. While it appears that disproportionality in residential placement has decreased for Hispanic youths, compared with white youths (as measured with a decreasing RRI from 2.3 in 1997 to 1.3 in 2019; see Figure 2b), it has remained relatively steady for Black youths (ranging from 3.9 to 5.0 since 1997; see Figure 2a). Disproportionality for American Indian youths appears to be increasing (see Figure 2c). Asian youths were less likely than white youths to be in a residential placement each year from 1997 to 2019, and this relative rate has deceased consistently from almost 1.0 in 2007 to about 0.25 in 2017 and 2019 (see Figure 2d).

Although these national rates provide an important snapshot, disparities vary from state to state, jurisdiction to jurisdiction, among different offense types, and by other demographics. For example, the Census of Juveniles in Residential Placement provides juvenile placement rates by state and race, which demonstrate large differences in disproportionality: the population-based RRI for Black youth in New Jersey, New Hampshire, and Wisconsin is over 10.0, while it is less than 3.0 in Alabama, Indiana, New Mexico, and Wyoming; the population-based RRI for Native American youth is more than 10.0 in Nebraska and less than 1.0 in New Mexico, Nevada, and Texas (Sickmund et al. 2021).

State studies also find differences in disproportionality by jurisdiction. For example, Michigan data collected by the Michigan Committee on Juvenile Justice show that Black youths were statistically significantly less likely to be securely detained than white youths in Kent County, but there was no statistically significant difference in Oakland County (Michigan Committee on Juvenile Justice, 2021). Similarly, a DMC assessment study of Tennessee found that two major metropolitan areas had statistically significantly higher levels of disparities for Black youth, compared with rural areas of the state (Tennessee Commission on Children and Youth, 2012:64).

In terms of gender, state and national data show some differences in levels of disparity, although consistent trends have not emerged. For example, data from Florida show that statewide racial disparities are greater for Black boys than for Black girls at the arrest stages (RRI of 3.3 for Black boys and 2.6 for Black girls) and at the diversion stage (RRI of 0.7 for Black boys and 0.9 for Black girls [ 1 ] ) [Florida Department of Juvenile Justice, n.d.]. But racial disparities were the same for boys and girls in Florida at detention (RRI of 1.4 for both genders). Also, a Virginia study found that gender composition of racial/ethnic groups in Norfolk County varied among youth referred to juvenile court: 46 percent of the Hispanic youths referred to court were girls, compared with 39 percent of white youths and 36 percent of Black youths (Harig et al., 2012). Finally, a Nebraska study found that for white, Black, and Hispanic youth, males were statistically significantly more likely to be taken into custody than females, but for Native American youth, females were more likely to be taken into custody than males (Hobbs et al., 2012).

National data showing racial and ethnic compositions at various points of the juvenile justice system also demonstrate some differences by gender. For example, in the population of boys in residential placement in 2019, 33 percent were white, and 42 percent were Black; in the population of girls, 38 percent were white, and 35 percent were Black (Sickmund et al., 2021). Similarly, juvenile court data demonstrate differences in gender composition for certain charges. For example, among girls with drug charges in 2019, 61 percent were white, while 39 percent were minority; among boys with drug charges, 51 percent were white, while 49 percent were minority (Sickmund, Sladky, and Kang, 2021). However, for person offenses, the portion of the sample that was minority was higher for the boys than for the girls (58 percent, compared with 61 percent).

Finally, racial disparities vary by offense type. Arrest data from the Federal Bureau of Investigation (FBI) show that both arrest rates and relative rates differ by offense. For example, Black youths were eight times more likely than white youths to be arrested for stolen property (buying, receiving, possessing) and seven times more likely to be arrested for robbery. However, they were less likely than white youths to be arrested for drunkenness, liquor laws, and driving under the influence. American Indian youths were six times more likely than white youths to be arrested for offenses against the family and children and five times more likely to be arrested for drunkenness, but they were less likely than white youths to be arrested for gambling, robbery, embezzlement, prostitution and commercialized vice, and forgery and counterfeiting. Offense types among the residential population also differ by race. Further, according to the Census of Juveniles in Residential Placement , although all races/ethnicities were most likely to be in residential placement for a person offense (35.7 percent) or a property offense (26.0 percent), white and Native American youths were overrepresented among status offenders (Sickmund et al., 2021).

[1] At the diversion stage, an RRI lower than 1 indicates disproportionality disadvantaging minority youth, since being diverted is a positive option.

Federal Legislation

Over the years, amendments to the federal  JJDPA of 1974 and OJJDP compliance requirements for states applying for and/or receiving JJDPA Formula Grant funding have provided direction on how states address racial and ethnic disparities. These amendments occurred in 1988, 1992, 2002, and 2018. 

First, the 1988 JJDP Act amendment contained a requirement that states address DMC (which at this point meant disproportionate minority confinement ) in their state plans. Then, in the 1992 amendment, the identification of DMC became a C ore R equirement, tying state compliance to future funding through the Formula Grants Program (OJJDP, 2013; OJJDP, n .d.a; OJJDP, n.d.b). Requirements in subsequent amendments were also tied to Formula Grant funding. 

Amendments in 2002 resulted in a requirement that states, "address juvenile delinquency prevention efforts and system improvement efforts designed to reduce, without establishing or requiring numerical standards or quotas, the disproportionate number of juvenile members of minority groups, who come into contact with the juvenile justice system" ( Pub. L. No. 107–273, 116 Stat. 1878 (23)). Between 2002 and 2018, states were required to submit data to OJJDP on the numbers of youths by race/ethnicity who came into contact with nine juvenile justice system points statewide , and for at least three targeted counties in the state. The nine juvenile justice points were 1) a rrest (law enforcement referral), 2) referral to court, 3) diversion, 4) secure detention, 5) petition filed (charged), 6) adjudication (delinquent, guilty finding), 7) probation supervision, 8) secure confinement, and 9) transfer to adult court (waiver).

OJJDP outlined a five-stage process for states to follow: 1) identify the extent to which DMC exists, 2) assess the reasons for DMC, 3) develop an intervention plan to address DMC, 4) evaluate the effectiveness of interventions, and 5) monitor DMC trends (OJJDP, 2009a). During this time, DMC was measured using the RRI.

In December 2018, the Juvenile Justice Reform Act was signed into law, again reauthorizing the JJDPA and amending certain parts of the Act. The amendments became effective on Oct. 1, 2019 (OJJDP, 2019b). The requirement to "address DMC" was changed to "identifying and reducing racial and ethnic disparities" (OJJDP, 2019a).  Other changes included a reduction in the number of decision points where states are required to track data, from 9 points to 5 points (OJJDP, 2019c), "where research has shown that potential disparity may occur":

• Diversion [filing of charges]
• Pretrial detention
• Disposition commitments
• Adult transfer [OJJDP, n.d.c]

OJJDP also began requiring that states measure disparities by using proportions instead of relative rates and that they submit plans with three-pronged strategies. The three prongs are to 1) submit statewide data for at least four of the five juvenile justice contact points (indicated above), by providing the percentage distribution of race or ethnic groups compared with the general population distribution, 2) develop an action plan to reduce racial and ethnic disparities, and 3) conduct an outcome-based evaluation by tracking changes in numbers, addressing whether goals were met, indicating what worked and what drove that success, identifying barriers to success, indicating how OJJDP can help, explaining how they will protect the public and hold offenders accountable, and forming goals for the following year ( OJJDP, n.d.c ).

Empirical Studies of Racial and Ethnic Disparities

Numerous national and jurisdiction-specific studies on racial and ethnic disparities have been conducted. These empirical studies differ from those that focus solely on rates, counts, and proportions because empirical studies attempt to better understand why the disproportionality is occurring. Between 2002 and 2018, OJJDP distinguished between these two stages, with the former being the "identification stage" and the latter being the "assessment stage."

Many of these empirical studies examine whether race had an effect on one or more juvenile justice decision points after controlling for other variables (e.g., offense severity, prior record, age, gender). Many of these studies are guided by research interests and are published in scholarly journals (e.g., Abrams, Mizel, and Barnert, 2021; Rodriguez, 2007; Leiber, Brubaker, and Fox, 2009; Freiburger and Burke, 2010; Zane, Mears, and Welsh, 2020), while another group of studies resulted from the OJJDP mandate for states to conduct DMC assessment studies and are generally published as reports available to the public (Donnelly and Asiedu, 2021).

Several large-scale, comprehensive efforts have been conducted that analyzed the body of research literature on racial and ethnic disparities in juvenile justice (Pope and Feyerherm, 1990; Pope, Lovell, and Hsia, 2002; Engen, Steen, and Bridges, 2002; Bishop, 2005; Bishop and Leiber, 2012; Spinney et al., 2018; Zane and Pupo, 2021). For example, one of these reviews (Spinney et al., 2018) was an OJJDP–funded review of articles from 2002 to 2014 evaluating the percentage of studies that found disparities, by decision point and by race/ethnicity. This study found that, while the picture that emerges collectively is complex, effects of race that disadvantage minority youths were found to exist at all decision points. This finding is similar to the results of other reviews, which found that race affects decisionmaking to some extent but also that other legal variables (such as prior offense and offense seriousness) and extralegal variables (such as age) also play key roles (Pope and Feyerherm, 1990; Pope, Lovell, and Hsia, 2002; Engen, Steen, and Bridges, 2002; Bishop, 2005; Bishop and Leiber, 2012; Zane and Pupo, 2021). The degree of these disparities can vary considerably by both decision point and race/ethnicity.

First, the extent of disparity varies across points in the process. For example, in the 2018 review by Spinney and colleagues described above, studies that included analysis of earlier decision points in the juvenile justice system (e.g., arrest, secure detention, and referral to court) overwhelmingly found there was some disadvantage to minority youths. However, fewer studies of later decision points (e.g., adjudication, probation, secure confinement, and disposition in adult court for transferred youths) found racial disadvantage to minority youths.

Second, levels of disparity at each point in the system vary by racial and ethnic group. A more-recent systematic review used meta-analytic techniques to analyze the data from studies of racial disparities. This review found there was a small average effect for some outcomes (e.g., detention) and no discernible average effect on others (e.g., petition, waiver, adjudication). Specifically, the authors found that

• For Black/white comparisons, there was evidence of small average race effects on detention and placement, a slight average effect on intake, and no average effects on petition, waiver, or adjudication.
• For Hispanic/white comparisons, there was evidence of a small average race effect on detention; slight average effects on petition, adjudication, and placement; and no average effects on intake or waiver.
• For nonwhite/white comparisons, there was evidence of small average effects on detention, intake, and waiver; a slight average effect on placement; and no average effect on petition or adjudication. [Zane and Pupo, 2021]

However, even small average race effects can make a large impact over the course of the many decisions in the juvenile justice system through cumulative disadvantage (Kurlychek and Johnson, 2019; Pope and Feyerherm, 1990; Zane, 2018). Cumulative disadvantage can be displayed in at least two different ways. First, simple accumulation occurs when a higher rate of arrest for minority youth is subsequently followed by a lower rate of diversion, higher rates of formal processing as delinquent, and so forth (Pope and Feyerherm, 1990; Spinney et al., 2018). Thus, although the differential treatment at any particular stage may appear "small," the cumulative impact across the entire juvenile justice system may be relatively large. Second, decisions made at earlier stages, such as detention, can affect outcomes at later stages—in particular, judicial disposition (Leiber and Fox, 2005; Mendel, 2014; Rodriguez, 2010). For example, one study of predictors of formal disposition in a large southern state found that the number of days spent in secure detention predicted formal disposition even after controlling for offense type, gang affiliation, weapon carrying, and extralegal factors (Caudill et al., 2013). However, minority youths are more likely to be detained than their white counterparts. Thus, although minority youths and white youths who have been detained may be treated similarly, because the minority youths are more likely to be detained, they are also more likely than to receive more severe dispositions than do their white counterparts.

An emerging body of literature has examined additional discretionary decisions. For example, a systematic review of 26 studies examining racial disparities among referrals to mental health and substance misuse services from within the juvenile justice system found that the majority of studies showed at least some race effects in the decision to refer youths (Spinney et al., 2016). Another study (Ogle, 2019) examined whether there were racial and ethnic disparities in the use of solitary confinement among pre-adjudicatory youth in juvenile detention centers in Florida, finding that Black youths had 68.8 percent greater odds of being placed in solitary confinement than white youths, even after incorporating statistical controls for relevant factors such as risk to reoffend. Researchers also have examined other decision points, including failure to appear for court hearings (Walker et al., 2019), probation violations ( Gale–Bentz, 2019; Leiber and Peck, 2013), and being written up for new offenses while institutionalized (Oglesby–Neal and Peterson, 2021). Similarly, some researchers have examined racial disparities in pathways into the juvenile justice system, specifically in referrals from schools (Blad and Harwin, 2017; Hughes, Raines, and Malone, 2020).

Contributing Factors to Racial and Ethnic Disparities

Often racial and ethnic disparity is presented as being caused by differential offending (i.e., youths of color commit more crimes or commit more serious crimes) or differential treatment (i.e., the juvenile justice system treats youths of color differently). Differential offending is also referred to as differential involvement or differential behavior , and differential treatment is also referred to as differential selection or systems factors . These two theoretical frameworks have largely helped frame discussions and studies (Bishop, 2005), for these key theoretical distinctions suggest independent causal mechanisms that account for racial and ethnic disparities (Zane and Pupo, 2021). 

The differential offending framework centers on the individual and refers to differing rates at which youths from various racial and ethnic subgroups are involved in delinquent activity. Differential behavior results when minority youths are involved in more serious crime, participate more deeply in gang activity, begin delinquent activity at earlier ages, and are involved in other social service– or justice-related systems such as the child welfare system (Leiber, Richetelli, and Feyerherm, 2009). This perspective requires that causes of differential involvement be sought outside the court system by looking at individual, family, and neighborhood factors that are related to offending (e.g., Piquero, Moffitt, and Lawton, 2005; Tracy, 2005). For example, Fite, Wynn, and Pardini (2009) found that much of the difference in arrest rates between white and Black boys was attributable to higher levels of both individual and contextual risk factors for Black boys across multiple domains.

In this framework, legal factors are often related to "minority-centered contexts of risk" (National Research Council, 2013:224), such as

• Economically disadvantaged and unstable communities and neighborhood social contexts (Fite, Wynn, and Pardini, 2009; Sampson, Morenoff, and Raudenbush, 2005; Moak et al., 2012)
• Low-performing institutions, especially public schools ( Hirschfield, 2018; Sharkey and Sampson, 2010)
• Delinquent peers (Fite, Wynn, and Pardini, 2009; Haynie and Payne, 2006)
• Family risk factors such as unmarried or single parents, incarcerated parents, poor parent– child communication, death of a parent, and harsh, lax, or inconsistent discipline (Fite, Wynn, and Pardini, 2009; Jarjoura et al., 2013; Maguire–Jack, Lanier, and Lombardi, 2020; Sampson, Morenoff, and Raudenbush, 2005; Vespa, Lewis, and Kreider, 2013)
• Greater exposure to violence (Kilpatrick, Saunders, and Smith, 2003; Maguire–Jack, Lanier, and Lombardi, 2020) 

Further, the allocation of prevention and treatment resources within communities is seldom uniform or universally accessible across an entire community. In some instances, those allocations create a disadvantage for minority youth (Leiber, Richetelli, and Feyerherm, 2009). For example, effective programs may be geographically inaccessible to minority youth in a jurisdiction, or existing programs may be designed for white, suburban youth. Thus, retention and outcomes for minority urban youth are weak. The National Research Council concluded that the “totality of these risk factors is such that minority youths are born into and raised in severely compromised familial, community, and educational environments that set the stage for a range of adverse behaviors and outcomes, including problems in school, relationships, and engaging in prosocial behavior” (2013:224). 

The differential treatment framework perspective, by contrast, generally concentrates on the structure of justice decisionmaking acts that can disadvantage minority youth (e.g., Leiber, 2003; Pope and Feyerherm, 1990). This perspective, also known as bias theory , argues that minority youths are more likely than w hite youths to suffer harsher consequences at each stage of the juvenile justice decisionmaking process because the system treats minority youths differently (and more punitively). This theoretical orientation expects to find differential treatment of minority youth even after accounting for legal, and often extralegal (e.g., age, socioeconomic status, school status), factors (e.g., Mallett and Stoddard–Dare, 2010). The differential treatment framework centers on the juvenile justice system to explain disparities and is the approach that most frequently characterizes empirical studies of racial and ethnic disparities (e.g., Leiber, 2003; Leiber, Brubaker, and Fox, 2009; Richetelli, Hartstone, and Murphy, 2009). 

A contributing factor related to differential treatment is justice by geography (Leiber, Richetelli, and Feyerherm, 2009) . Minority youths may live in jurisdictions that have stricter law enforcement or harsher judges, compared with jurisdictions where white youths live (Bray, Sample, and Kempf–Leonard, 2005; Leiber, Richetelli, and Feyerherm, 2009; Taylor et al., 2012). For example, a Massachusetts DMC assessment study found that police tend to patrol urban minority neighborhoods more aggressively than suburban areas where fewer minorities reside. Thus, the likelihood of arrest is much higher for minority youth than white youth in this state (Kaufman, 1997). 

Another explanation for differential treatment includes legislation, policies, and legal factors (Leiber, Richetelli, and Feyerherm, 2009). Policies enacted through legislation or administrative action may sometimes contain elements that create a disadvantage for minority youth. For example, statutes that define drug offenses tend to treat crack cocaine more seriously than powdered cocaine, which, given the usage patterns for the two forms of cocaine, creates a disadvantage for minority youth (Birckhead, 2017; Leiber, Richetelli, and Feyerherm, 2009). Zero-tolerance policies and other harsh discipline practices in school also adversely affect students of color (Dunbar and Villarruel, 2004; Hirschfield, 2018). 

Differential processing or inappropriate decisionmaking is another contributing mechanism that can explain differential treatment . Differential processing or inappropriate decisionmaking results when the criteria used to make decisions in the system are either not applied consistently across all groups of youth or when the criteria are structured in a manner that disadvantages some groups. One example of differential processing or inappropriate decisionmaking is the use of the term gang related , which is cited frequently as a factor in decisions about how to handle juveniles. To assess gang-related impact, it is important to know how a jurisdiction defines the term and whether the “gang related” question is asked only of youth from certain communities. If so, then use of this criterion likely will place minority youth at some disadvantage relative to white youth—especially w hite youth from community areas not believed to be gang affiliated (Birckhead, 2017; Leiber, Richetelli, and Feyerherm, 2009). Another example is related to parenting structure. Some courts, fearing lack of supervision, may be more likely to use secure detention if the child is from a single-parent home. If minority youths are more likely to live in single-parent homes (Vespa, Lewis, and Kreider, 2013), these decisions will contribute to disparities (Leiber, Richetelli, and Feyerherm, 2009), regardless of the family’s ability to supervise their child. 

Another contributing factor that has increasingly gotten more attention is implicit bias and its role in the many decisions made about juveniles as they move through (or are diverted from) the juvenile justice system (Darling–Hammond, 2017; Glenn, 2019; Marsh, 2009; National Juvenile Justice Network, 2017). Whereas explicit bias is a conscious preference (positive or negative) for a social category, implicit bias is a preference (positive or negative) for a social category that operates outside of awareness (Marsh, 2009). Although the research focused on exploring the link between implicit bias and racial and ethnic disparities in juvenile justice is limited (Glenn, 2019), many of the interventions aimed at reducing discretion in judicial decisionmaking are based on the belief that this discretion is influenced by bias, and more specifically by implicit bias. These interventions include two main approaches: 1) the use of risk assessment instruments (see below) and 2) trainings designed to reduce implicit bias among justice system decisionmakers by targeting implicit bias itself (e.g., Fix, 2020; Worden et al., 2020). 

In addition to these examples of how differential treatment may occur, there are several related academic theories that may also explain differential treatment. The racial or symbolic threat theory (Ousey and Lee, 2008; Moak et al., 2012), within the differential treatment framework, focuses on the social–psychological processes behind decisions that disadvantage one or more racial/ethnic groups compared with others (Kurtz, Linnemann, and Spohn, 2008). In this framework, decisionmakers are influenced by emotions driven by the perception of minority youth as threatening to middle-class standards and public safety (Leiber and Fox, 2005). Reference is often made to the work of scholars such as Tittle and Curran (1988), who explored how negative perceptions of Black youth and stereotypes affect decisionmakers. A recent study expanded the definition of “threat” and found that higher rates of county-level homicide prosecutions and racial differences in unemployment were associated with secure detention and placement of youth (Fix et al., 2021). The authors concluded that racial threat and other theories aiming to explain racial disparities should be reexamined and modified to include markers of violent and sexual offenses. 

Similarly, labeling theory posits that dominant groups maintain their status by using labels to define deviant or criminal behavior and disenfranchise certain other groups (Tapia, 2010). One example of labeling theory is when youths who experience police stops align their identities with the delinquent label and subsequently engage in illegal activities (McGlynn–Wright et al., 2020). For example, one recent study found that being stopped or arrested not only increased future delinquency but also amplified deviant attitudes (Wiley and Esbensen, 2016). 

Other theories from the differential treatment framework include individual-level approaches such as attribution theory , which posits that decisionmakers may rely on internal and external factors they perceive to be linked to blameworthiness and delinquent behavior (Lowery and Burrow, 2019; Rodriguez, 2007:633), and focal concerns theory , which examines the factors that guide actors’ decisions in the justice system and the mechanisms by which these focal concerns shape final case outcomes (Harris, 2009). 

In terms of attribution theory, researchers have demonstrated that juvenile justice decisionmakers are more likely to assign negative internal attributes (e.g., personality, attitude, cooperativeness) to youths of color and negative external attributes (e.g., delinquent peers, family conflict, school issues) to white juveniles; this is an important finding, for researchers have found that decisions are influenced more by negative internal attributes than by negative external attributes (Bridges and Steen, 1998; Beckman and Rodriguez, 2021). To empirically test the negative attributions theory, a recent study of diversion decisions found that youths of color were more likely to be linked to negative internal attributions in their files, in comparison with white youths, and that negative internal attributions in turn decreased the probability of receiving diversion (Beckman and Rodriguez, 2021). Another recent study examined the effects and intersections of race, legal characteristics, and macro-level community characteristics on juvenile institutionalization through the lens of attribution theory, concluding that race does influence confinement decisions (Lowery and Burrow, 2019). 

Several studies have applied a focal-concerns framework to explain racial disparities in juvenile justice by examining the differences in the focal concerns of decisionmakers at different points of the system (Bishop, Leiber, and Johnson, 2010; Ericson and Eckberg, 2016). A key assertion of the focal concerns framework is that decisionmakers have limited time and information to make decisions, so they develop “perceptual shorthand,” which is often conditioned by stereotypes, extralegal factors, and legal cues (Hartley, Maddan, and Spohn 2007; Hawkins 1981; Ishoy and Dabney, 2018). The juvenile system consists of a several different independent bureaucracies that are responsible for decisions at different points of the process, and each set of bureaucracies contributes some outcome or information that pertains to the next point. Bishop, Leiber, and Johnson (2010) hypothesized that focal concerns would influence outcomes at loosely coupled points (intake, detention, disposition), but not at tightly coupled points (petition, adjudication), and found that their findings were generally consistent with these expectations. 

Another explanation under the differential treatment framework is the liberation hypothesis ( Guevara et al., 2011; Spohn and Cederblom, 1991). This hypothesis posits that in less-serious cases and when evidence is less conclusive, there is more ambiguity for decisionmakers, thus decisions are more likely to be influenced by race or other extralegal factors. In other words, the decisionmakers are “liberated” from legal constraints and therefore individualize the decision on a variety of factors, including racial and ethnic biases. Though limiting decisionmaker discretion using culturally competent, standardized decisionmaking tools is a main component of most approaches designed to reduce racial and ethnic disparities (e.g., Cabaniss et al., 2007; Center for Children’s Law and Policy, 2015; Hinton Hoytt et al., 2003; Nellis, 2005), some studies have failed to find support for the liberation hypothesis, which posits that this discretion is a contributing factor to disparities. In their study of juvenile court referrals in a northeastern state, Beaudry–Cyr and colleagues (2020) failed to find support for their hypothesis that extralegal factors would have a diminishing effect on case outcomes as the severity of the case increased. Similarly, in their study of factors that influence pre-adjudication and disposition outcomes between an urban and suburban county, Taylor and colleagues (2012) found there were more varying effects of legal and extralegal factors across race in the urban county than in the suburban county. Their interpretation of the liberation hypothesis was that there would be more of a due-process orientation in the urban locations, which would result in greater reliance on legal factors; their findings did not support this hypothesis. 

Various scholars have identified shortcomings in looking exclusively at either the differential offending framework or the differential treatment framework (e.g., Tracy, 2005; Pope and Feyerherm, 1990; Bishop, 2005). With a complex social problem such as racial and ethnic disparity, numerous factors are likely at work, including poverty, segregation, educational challenges, residential instability, and the broader “racialized society” in which many institutional practices, public policies, and cultural representations operate (National Research Council, 2013). Thus, racial/ethnic disparities are “not reducible to either differential offending or differential selection” (National Research Council, 2013). 

In addition to differential involvement and differential treatment, Engen and colleagues (2002) proposed two other perspectives: macro-contextual explanations and structural–processual explanations. Both mention that differential treatment may take place in some contexts but not in others (Zane and Pupo, 2021). The key issue for the structural–processual perspective is the separate and interrelated decisions of system processing, while the macro-contextual explanations focus on larger societal and community-levels factors (Rodriguez, 2007; Sampson and Wilson, 1995).

Outcome Evidence

The current literature measuring the effectiveness of interventions to reduce racial and ethnic disparities generally involves comparing numbers, percentages, rates, or relative rates before and after the implementation of an intervention. Changes in disparities can happen at the local, state, or federal level. Thus, researchers must be clear on how and where changes in disparities are targeted and measured.

Several frameworks and strategies for reducing racial and ethnic disparities in juvenile justice have been developed, promoted, implemented, and evaluated. Leiber and Fix (2019) examined the effect of three of these large-scale initiatives: 1) the OJJDP requirement to address racial disparities in the JJDPA, 2) the Annie E. Casey Foundation’s Juvenile Detention Alternatives Initiative (JDAI) model (often implemented in partnership with the W. Haywood Burns Institute), and 3) the MacArthur Foundation's Models for Change initiative. Overall, the study found that these three efforts were often ineffective, though some practices had mixed support. They concluded that the common factors found to effectively reduce racial and ethnic disparities included

• Access to data collection and utilization.
• Stability in terms of employment for those receiving services.
• Collaboration among various agencies.
• Affiliation with other efforts to prevent delinquency and racial and ethnic disparities.
• System change (most notably in the form of developing and implementing racially and ethnically neutral objective decisionmaking tools).
• Cultural competence training.
• Commitment to disparity reduction in the short and long terms.
• State and local leadership.
• Long-term partnerships with universities and/or people trained in methodologies to aid in the study, implementation, and evaluation of strategies and interventions.

Before the Leiber and Fix study, an OJJDP–funded study identified nine jurisdictions that were able to decrease racial disparities as measured by the RRI and conducted case study research to describe the interventions that led to these reductions (Spinney et al., 2014). The researchers found that jurisdictions that successfully reduced disparities in their systems used nine primary strategies, several of which were identified by Leiber and Fix (above). In addition to the strategies identified by Leiber and Fix, the Spinney and colleagues (2014) case study research identified the following additional strategies: shifting the institutional culture from a punitive or procedural focus toward a focus on what was best for the youths and the community; creation of alternatives to secure detention, secure confinement, and formal system involvement; directing reduction interventions at the system (and not at the youths); and changing policies, procedures, and laws.

One example of a successful jurisdiction in the Spinney and colleagues (2014) study was Bernalillo County, NM, a jurisdiction that was able to decrease disproportionality (as measured by the RRI) in arrests, [ 2 ] referrals to court, and diversions from the juvenile justice system for Black, Hispanic, and Native American youths. For example, in 2004, the arrest rate for Black youth was 16.4 per 100 youths while the white arrest rate was 8.8 per 100 youths, resulting in an RRI of 1.9. By 2010, the Black arrest rate had declined to 7.1, while the white arrest rate declined to 6.6, resulting in an RRI of 1.1. [ 3 ] Bernalillo County’s sustained reductions in racial disparities at multiple stages of the juvenile justice system for Black, Hispanic, and Native American youths was likely a result of multiple strategies designed primarily around systems reform, attention to data, and increasing community-based services for court-involved youth. Strategies included implementation of the JDAI framework, emphasis on reducing the number of youths in secure detention, enhanced services for detained youths after returning to the community, establishment of a unit to increase access to diversion, and involvement of multiple partners over long periods of time in their efforts, even when individuals moved to new positions.

Several other publications describe reductions in racial and ethnic disparities (Hinton Hoytt et al., 2003; Nellis and Richardson, 2010; Shoenberg, 2012; Spinney et al., 2014). For example, a study of an intervention to reduce failures to appear in court in one jurisdiction was evaluated to identify whether there was a reduction in disparities as a result (Walker et al., 2019). The authors found that although the program significantly reduced the likelihood of youths failing to appear in court at the first court hearing following a summons (arraignment), it did not affect subsequent hearings and had no effect on reducing racial disparities. Another study that examined the use of a risk assessment instrument (RAI) in a midsized county in the Midwest found that the instrument did not eliminate racial and ethnic disparity in secure detention placements; however, that study suggested that the use of an RAI may reduce the effect of race on detention placement decisions (Mallett and Stoddard–Dare, 2010).

At least two evaluations examined the effect of multifaceted juvenile justice reforms at the state level. Donnelly (2019) examined changes in racial and ethnic disparities at secure detention and placement decisions in three Pennsylvania counties after the implementation of several juvenile justice reforms. Reforms included development of alternatives to secure detention and placement, revision of a RAI to inform detention proceedings, modification of the placement decisionmaking guidelines and process, and partnership with the Models for Change initiative. The author of the study found that the reforms resulted in a greater reliance on legal factors in decisionmaking, which should moderate the effect of race on processing outcomes.

Zane (2021) examined whether racial and ethnic disparities declined in Connecticut between 2000 and 2010, after the state had made substantial reforms, which included police training for working effectively with youth, development of a model memorandum of understanding for police officers and schools to use to reduce school-based arrests and referrals to court, funding for projects to build relationships between youth and police in local jurisdictions, and establishing two informational campaigns: Just.Start, which focused on addressing disparities in the juvenile justice system, and Right Response CT, which concentrated on schools and police knowing the “right response” to youth misbehavior. During this period, there was steady leadership from the Juvenile Justice Specialist, and the State Advisory Group later contributed to developing and executing these strategies (Spinney et al, 2014). Zane (2021) found that Black–white disparities in detention decreased over time. However, Black–white disparities increased for petition, adjudication, and waiver, and Hispanic–white disparities increased for adjudication (while not changing for other outcomes). Another analysis of changes in racial disparities in Connecticut found that during 2006–12 the RRI values at referral to court declined from 2.9 to 1.6 for Hispanic youth and from 6.3 to 4.7 for African American youth (Spinney et al., 2014).

Given the methodological challenges of evaluating comprehensive interventions to reduce racial and ethnic disparities, most of the more rigorous program evaluations examine the effect of specific, direct services to reduce differential offending among youths of color, which is just one of many plausible contributing factors.

A few programs are designed specifically for youths of color. For example, Protecting Strong African American Families (ProSAAF) is designed to improve family functioning and enhance youth development by targeting parents’ relationships and parenting skills. One study found that families who participated in ProSAAF saw statistically significant improvements in parental monitoring, self-concept, conduct problems, and substance-use initiation (Beach et al., 2016). Project Venture is a prevention program designed for at-risk Native American youths. This outdoor experiential program resulted in statistically significant reductions in the growth of substance use, including alcohol, marijuana, and other illicit substances (Carter, Straits, and Hall, 2007).

In addition to programs designed specifically for youths of color, mainstream programs can also result in positive outcomes. A meta-analysis of 350 studies of programs addressing juvenile delinquency found no evidence that mainstream delinquency intervention programs yield poorer outcomes for minority youth than for white youth (Wilson, Lipsey, and Soydan, 2003). Thus, targeting those interventions to youths of color may reduce disparities in a jurisdiction. Some examples of evidence-based intervention programs from the Model Programs Guide include the following:

The Child–Parent Center Program is a school- and family-based early intervention program that provides comprehensive educational and family support services to economically disadvantaged children. A longitudinal study that followed more than 1,500 predominantly Black children growing up in a high-poverty area of Chicago, IL, found that this intervention resulted in statistically significant declines in substance use, incarceration rates, and felony arrest rates at age 24 (Reynolds and Ou, 2011).

The Little Village Gang Violence Reduction Project is a comprehensive gang violence reduction program with five core elements: 1) community mobilization, 2) social intervention, 3) provision of social opportunities, 4) suppression, and 5) organization change and development of local agencies and groups. An evaluation of the project in the Little Village neighborhood of Chicago, which is predominantly Hispanic, found that the intervention resulted in statistically significant reductions in total violent crime, serious violent crime, and drug crime arrests (Spergel et al., 2003).

Project BUILD (Broader Urban Involvement and Leadership Development, now the BUILD Violence Intervention Curriculum), is a violence prevention curriculum designed to help youths in detention overcome problems they may face in their communities, such as gangs, drugs, and crime. The program is designed to intervene in the lives of youths who have come into contact with the juvenile justice system to reduce recidivism and diminish the prospects that they will become adult offenders. A 2000 study by Lurigio and colleagues found that youths who participated in Project BUILD had statistically significantly lower rates of recidivism, compared with nonparticipants.

However, these interventions do not address community-level and systems-level contributing factors to racial disparities, which many practitioners, policymakers, and advocates identify as the most important to address.

[2] Bernalillo County refers to arrests as "law enforcement referrals to probation."

[3] An RRI of 1.0 would indicate no disproportionality.

The existence of racial and ethnic disparities in the U.S. juvenile justice system is a complex issue. Its causes are multifaceted, and methodologically rigorous studies linking interventions to systemwide decreases in these disparities are not available (National Research Council, 2013:234–235). The evaluations that do exist find mixed results. Exacerbating the difficulty of addressing this issue is the fact that disparities exist well before contact with the juvenile justice system has occurred—in child welfare, the foster care system, school readiness, school performance, and school suspensions and expulsions (HHS, 2021; Knott and Giwa, 2012; Morris and Perry, 2016). Youths of color are more likely to live in single-parent families, in poverty, in disadvantaged communities with low performing schools, and in high-crime areas (Hirschfield, 2018; Moak et al., 2012; National Research Council, 2013). Given the problem’s extent and complexity, this issue is difficult to address. 

The 2013 National Research Council report on reforming juvenile justice summarized the continued need to address this complex issue: 1) the existence of racial and ethnic disparities in the juvenile justice system raises questions of bias, fairness, and legitimacy regarding its functioning; and 2) these disparities raise questions about the larger life-course trajectories of many youths in minority communities who may become marked by criminal records early in life (2013:211). 

Since 1988, OJJDP has mandated that states participating in the federal Title II Formula Grant Program address racial and ethnic disparities, and jurisdictions across the United States have made attempts to reduce these disparities. Although there is no conclusive evidence of what works to eliminate racial disparities, appropriate responses most likely require a multifaceted approach (Cabaniss et al., 2007; Center for Children’s Law and Policy, 2015; Donnelly, 2019; OJJDP, 2009b; Pope, Lovell, and Hsia, 2002; Spinney et al., 2014; Spinney et al., 2018).

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About this Literature Review

Suggested Reference: Development Services Group, Inc. 2022. "Racial and Ethnic Disparity (R/ED) in Juvenile Justice Processing."  Literature review. Washington, DC: Office of Juvenile Justice and Delinquency Prevention.  https://ojjdp.ojp.gov/model-programs-guide/literature-reviews/racial-and-ethnic-disparity

Prepared by Development Services Group, Inc., under Contract Number: 47QRAA20D002V.

Last Update: March 2022

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Five Things About Juvenile Delinquency Intervention and Treatment

Juvenile delinquency intervention and treatment programs have the broad goals of preventing crime and reducing recidivism by providing treatment and services to youth who have committed crimes.

The five statements below are based on practices and programs rated by CrimeSolutions. [1]

1. Juvenile awareness programs may be ineffective and potentially harmful.

Juvenile awareness programs — like Scared Straight — involve organized visits to adult prison facilities for adjudicated youth and youth at risk of adjudication. Based on the review and rating by CrimeSolutions of two meta-analyses of existing research , youth participating in these types of programs were more likely to commit offenses in the future than adjudicated youth and youth at risk of adjudication who did not. Consequently, recidivism rates were, on average, higher for participants compared to juveniles who went through regular case processing.

The results suggest that not only are juvenile awareness programs ineffective at deterring youth from committing crimes, but youth exposed to them are more likely to commit offenses in the future.

Read the practice profile Juvenile Awareness Programs (Scared Straight) to learn more.

2. Cognitive behavioral therapy can effectively reduce aggression in children and adolescents.

Cognitive behavioral therapy (CBT) is a problem-focused, therapeutic approach that attempts to help people identify and change the dysfunctional beliefs, thoughts, and patterns that contribute to their problem behaviors. CBT programs are delivered in various settings, including juvenile detention facilities. Based on the review and rating by CrimeSolutions of two meta-analyses of existing research , a variant of CBT focused specifically on children and adolescents who have anger-related problems is effective for reducing aggression and anger expression, and for improving self-control, problem-solving, and social competencies.

Read the practice profile Cognitive Behavioral Therapy for Anger-Related Problems in Children and Adolescents to learn more.

3. Multisystemic therapy for juveniles reduces recidivism, rearrests, and the total number of days incarcerated.

Multisystemic therapy is a family- and community-based treatment program for adolescents with criminal offense histories and serious antisocial, delinquent, and other problem behaviors. Based on the review and rating by CrimeSolutions of three randomized controlled trials (each evaluating a program in a different state), the program effectively reduced rearrests and number of days incarcerated.

Read the program profile Multisystemic Therapy to learn more.

4. Intensive supervision of juveniles — the conditions of which may vary — has not been found to reduce recidivism.

This practice consists of increased supervision and control of youth on probation in the community, compared with those on traditional community supervision. Intensive supervision programs have three primary features: 1) smaller caseloads for juvenile probation officers, 2) more frequent face-to-face contacts, and 3) strict conditions of compliance with stiffer penalties for violations. Other conditions may vary, but they can include electronic monitoring, drug/urinalysis testing, and participation in programming (such as tutoring, counseling, or job training). Based on the review and rating by CrimeSolutions of three meta-analyses of existing research , the practice does not reduce recidivism.

Read the practice profile Juvenile Intensive Supervision Programs to learn more.

5. Incarceration-based therapeutic communities for juveniles with substance use disorders have not been found to reduce recidivism after release.

Incarceration-based therapeutic communities employ a comprehensive, residential drug-treatment program model for youth in a detention facility who have substance use disorders. Therapeutic communities are designed to foster changes in attitudes, perceptions, and behaviors related to substance use and to reduce subsequent criminal offending. Based on the review and rating by CrimeSolutions of two meta-analyses of existing research , incarceration-based therapeutic communities have not been found to reduce recidivism after release for those who participate.

Read the practice profile Incarceration-Based Therapeutic Communities for Juveniles to learn more.

[note 1] As defined by CrimeSolutions, a practice is a general category of programs, strategies, or procedures that share similar characteristics with regard to the issues they address and how they address them. Practice profiles tell us about the average results from multiple evaluations of similar programs, strategies, or procedures. A program is a specific set of activities carried out according to guidelines to achieve a defined purpose. Program profiles on CrimeSolutions tell us whether a specific program was found to achieve its goals when it was carefully evaluated.

Practice ratings do not take into account variations in implementation or other program-specific factors which may impact the effectiveness of a specific program. Practices may be rated differently on outcomes not included here.

CrimeSolutions helps practitioners and policymakers understand what works in justice-related programs and practices. CrimeSolutions is funded by the National Institute of Justice and the Office of Juvenile Justice and Delinquency Prevention (OJJDP). Programs and practices profiles related to juveniles also appear on OJJDP’s Model Programs Guide .

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Parenting Styles and Youth’s Externalizing and Internalizing Behaviors: Does Self-Control Matter?

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• Published: 06 September 2024

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• Diana Almeida 1 &
• Gilda Santos   ORCID: orcid.org/0000-0002-8699-0126 1 , 2  

The externalizing and internalizing behaviors of children and youth have been the object of extensive criminological research, mainly due to the potentially harmful impact on these individuals' future development and adjustment. The current study aimed to explore the influence of parenting styles on the emergence of children and youth’s externalizing and internalizing behaviors and to understand the influence of self-control in this relationship. Following a quantitative self-report approach and using a sample of 472 Portuguese middle-school children, this study found that the children’s sex, low self-control, and authoritative parenting style significantly predicted externalizing and internalizing behaviors. The data also revealed that children's age and the permissive parenting style significantly predicted externalizing but not internalizing behaviors and that the authoritarian parenting style significantly predicted internalizing behaviors. Low self-control partially mediated the relationship between parenting styles and externalizing and internalizing behaviors in most tested models. Implications for theory and practice are discussed.

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Introduction

Over the last decades, there has been a growing interest in the study of children and youth’s problem behavior, mainly due to its relation to difficulties in later behavioral, emotional, cognitive, and social adjustment (Calkins et al., 2007 ; Campbell et al., 2000 ; Keane & Calkins, 2004 ; Lier et al., 2012 ; Liu et al., 2011 ; Min et al., 2018 ; Rinaldi & Howe, 2012 ; Sommer, 2010 ).

A common approach is the one that classifies children and youth’s problem behaviors into two major categories, namely externalizing and internalizing behaviors (Akhter et al., 2011 ; Alizadeh et al., 2011 ; Rinaldi & Howe, 2012 ), acknowledging that adolescence is one of the most critical developmental periods for the emergence and development of such behaviors (Braza et al., 2015 ; Lorber & Egeland, 2009 ; Risper, 2012 ).

Externalizing behaviors are complex and can cause severe consequences for the child and the community as whole in the immediate or long term (Georgiou & Symeou, 2018 ). Largely, these behaviors involve actions and include disruptive ( e.g., hyperactivity, anger, frustration, attention problems, impulsivity), antisocial, aggressive, and/or delinquent behaviors (Alizadeh et al., 2011 ; Braza et al., 2015 ; Rose et al., 2018 ), being, therefore, characterized by their visibility and exteriority (Georgiou & Symeou, 2018 ; Liu, 2004 ; Rinaldi & Howe, 2012 ; Sommer, 2010 ). In other words, externalizing behaviors constitute an evident type of behavior, based on which the child interacts negatively with the environment that surrounds him and adopts inadequate behaviors, such as defiance, verbal aggression, restlessness (Rinaldi & Howe, 2012 ), destruction of property, among others (Keil & Price, 2006 ).

On the other hand, internalizing behaviors are intrapersonal, that is, turned inside out (Achenbach & Edelbrock, 1978 ) and expressed at the child's psychological and emotional levels. Internalizing behaviors include anxiety ( e.g., worry, fear), distress ( e.g., difficulty being calm), shyness and/or social isolation, withdrawal, depression, and somatization, among others (Alizadeh et al., 2011 ; Braza et al., 2015 ; Georgiou & Symeou, 2018 ; Liu, 2004 ; Rinaldi & Howe, 2012 ). These behavioral problems have a more pronounced and negative impact on the child's psychological functioning than on their exterior environment since, in most situations, the behavior is covert and difficult to detect (Georgiou & Symeou, 2018 ; Rose et al., 2018 ).

When comparing the expression of such behaviors as a function of the child’s sex, previous studies have been consistently demonstrating that girls tend to express more internalizing behaviors, such as anxiety and depression, while boys tend to adopt more externalizing behaviors, such as anger and aggressive behavior (Bongers et al., 2003 ; Campos et al., 2014 ; Chaplin & Aldao, 2013 ; Crijnen et al., 1997 ; Moral et al., 2012).

The importance of studying these behaviors is related to the fact that they are usually associated with several negative outcomes over the life course. For example, previous studies have revealed that externalizing behaviors increase the risk of juvenile delinquency, adult crime, violent and antisocial behaviors, and substance abuse. Also, internalizing behaviors appear to be associated with a greater risk of depression, anxiety, suicide in adolescence and adulthood, and school failure (Farrington, 2003 ; Liu, 2004 ; Lorber & Egeland, 2009 ; Min et al., 2014 ; Moffitt, 1993 ). As such, the importance of understanding the origin of these behaviors (Georgiou & Symeou, 2018 ) and their developmental patterns is acknowledged, reinforcing the need to adopt intervention strategies suitable for preventing those behaviors and their iatrogenic effects (Liu, 2004 ).

Prior research has focused on the influence that individual, familiar, or contextual factors play in the emergence and development of such behaviors (Min et al., 2018 ). Particularly, focusing on the individual and family levels, previous studies have consistently shown that parenting styles and self-control might play a central role in the emergence and development of children and youth’s disruptive behaviors, such as externalizing, internalizing and even delinquent behaviors (Georgiou & Symeou, 2018 ; Farrington, 2003 ; Hoeve et al., 2009 ; Lorber & Egeland, 2009 ; Liu, 2004 ; Min et al., 2014 ; Moffitt, 1993 ; Steinberg et al., 2006 ; Cauffman et al., 2005 ; Moffitt et al,. 2011 ; Pratt & Cullen, 2000 ).

Thus, the current study seeks to explore the influence that permissive, authoritarian, and authoritative parenting styles have on the development of externalizing and internalizing behaviors, as well as to understand the potential indirect effects that self-control can have on this relationship, using a sample of 472 Portuguese middle school children and youth.

Parenting Styles

Parenting is a complex task, particularly when considering the different elements, processes, and dynamics it comprises (Rose et al., 2018 ). Over the years, there has been extensive theoretical and empirical research into the influence of parenting on children’s socialization (Baumrind, 1967 , 1971 , 1978 ), which has commonly followed two distinct theoretical approaches (Darling & Steinberg, 1993 ): either dimensional or typological. The former focuses on the individual dimensions of parenting, i.e., parenting practices ( e.g., affection, monitoring, and parental discipline). The latter labels parents with a particular parenting style according to different dimensions (Baumrind, 1967 ; Pinquart, 2017 ). There are a series of reasons based upon which the typological approach has been considered preferable for a comprehensive understanding of the influence parenting exert on children and youth behavior. First, it provides a holistic, interactive, and dynamic understanding of the processes and environments on which the family context is based (O’Connor, 2002 ). Furthermore, it possesses an " increased ecological validity " (Pereira et al., 2009 , p. 455) to the extent that it can capture the interaction effects of the different dimensions and how they affect and influence each other (Steward & Bond, 2002 ). Finally, this approach captures more comprehensively the multiple aspects underlying a child’s upbringing and, as such, provides a broader understanding of the role that behaviors, interactions, and emotions play in shaping children and youth’s behavior (Hoeve et al., 2011 ).

Baumrind ( 1967 ) proposes a typological approach based on two dimensions: parental control/demand and parental warmth/involvement and responsiveness. The first concerns the “ active role that parents play in promoting respect for rules and social conventions ” (Akhter et al., 2011 , p.24) and is related to high expectations, the definition of behavioral limits, and the application of rules and standards of conduct, including monitoring child’s behavior. The second is focused on responding to the child's needs, being available to talk, and providing a safe environment for learning and integral development. According to Baumrind ( 1967 ), combining the above-mentioned dimensions allows the conception of three distinct parenting styles: authoritative, authoritarian, and permissive (Baumrind, 1967 ).

The authoritative parenting style is rational and issue-oriented and is characterized by a parental attitude particularly oriented toward the child's activities and behaviors. Usually, parents adopting such a style are highly responsive, affectionate, and cognitive, establishing and encouraging flexible networks and communication with their children. Authoritative parents tend to exert firm control and set clear limits and boundaries in the face of disagreements between themselves and their children, motivating obedience but not limiting them incessantly (Baumrind, 1978 ). Empirical evidence has shown that children exposed to this parenting style present lower levels of externalizing and internalizing behaviors (Akhter et al., 2011 ; Alizadeh et al., 2011 ; Pinquart, 2017 ; Rose et al., 2018 ), mainly because these are parents who are warm, providing the child with structured environments, but who are also capable of adapting this environment to the child's needs. This parenting style is perceived as the most suitable for promoting the child’s behavioral and psychological development and adjustment (Rose et al., 2018 ).

On the other hand, authoritarian and permissive parenting styles have been systematically associated with increased rates of externalizing and internalizing behaviors (e.g., Baumrind et al., 2010 ; Pinquart, 2017 ; Rinaldi & Howe, 2012 ). The authoritarian parenting style embodies parental efforts to shape, control, and evaluate the child's conduct according to pre-established behavioral standards. These standards are usually absolute, theologically motivated, and directed by a figure of authority and superiority. This limits the child's individuality since punitive measures and strict rules are used when the child adopts any behavior that goes against what the parents think is the appropriate way to behave. Authoritarian parents do not encourage dialog or the debate of ideas, believing that the child must comply with what the parental figure imposes (Baumrind, 1978 ), thus contributing to the development of children’s negativity and tension in terms of family dynamics and communications, which, in turn, has been associated with children’s lower levels of attachment to their parents and higher rates of disruptive behavior, such as externalizing or internalizing (Amran & Basri, 2020 ).

A permissive parenting style refers to parents who do not set standards, limits, and behavioral expectations for their children despite being warm and affectionate with them. Permissive parents tend to be unable to enforce consistent discipline thus leaving the children free to satisfy their impulses, actions, and desires (Baumrind, 1978 ), which, in turn, has been associated with higher rates of children’s externalizing and internalizing behaviors.

Considering this, it is relatively easy to assume that the relationship between parenting styles and children’s externalizing and internalizing behaviors is robust and well-documented. Furthermore, previous studies have demonstrated that these relationships are sustained regardless of the child sex (Akhter et al., 2011 ; Alizadeh et al., 2011 ; Braza et al., 2015 ; Pinquart, 2017 ; Rinaldi & Howe, 2012 ).

However, less is known about the processes, mechanisms, or variables underlying such a relationship. This reinforced the need to research further the influence that other factors might exert at individual, familial, or contextual levels. In this regard, research has been conducted to understand self-control’s role in this relationship. These studies have revealed that self-control plays an important role in the relationship between parenting styles and children’s externalizing and internalizing behaviors, as it is explored below (Bai et al., 2020 ; Finkenauer et al., 2005 ; Özdemir et al., 2013 ; Pan et al., 2021 ; Tehrani & Yamini, 2020 ; Van Prooijen et al., 2018 ; Zhang & Wang, 2022 ).

Self-Control

Self-control is a widely used and researched construct, and a considerable number of definitions can be found throughout the literature (e.g., Finkenauer et al., 2005 ; Gottfredson & Hirschi, 1990 ; Moffitt et al., 2011 ; Pan et al., 2021 ; Tangney et al., 2004 ). For example, Moffitt et al., ( 2011 , p. 2693) see self-control as an "umbrella construct" encompassing concepts and measures from different areas such as impulsivity, delay of gratification, inattention, conscientiousness, and timeless choice. On the other hand, at the heart of the concept of self-control proposed by Tangney et al. ( 2004 ) is the ability to override or modify internal responses, suspend undesirable tendencies (e.g., impulses), and refrain from acting on them.

The current study follows Gottfredson and Hirschi’s ( 1990 ) conceptualization, according to which self-control constitutes an individual factor that takes the form of “ the tendency to avoid acts whose long-term costs exceed their momentary advantages ” (Hirschi & Gottfredson, 1994 , p. 3). According to the authors, there are six core elements of low self-control: (i) impulsiveness and the inability to delay gratification, i.e., an attitude and behavior focused on the immediate and the present; (ii) lack of persistence or tenacity, which means that individuals with low self-control have a tendency to avoid complex tasks, little enthusiasm for work or persistence to finish a task already started; (iii) participation in risk-seeking activities, i.e., involvement in risky, exciting, and arousing activities; (iv) a low appreciation of intellectual ability, in other words, a person that lacks self-control prefers to engage in physical and risky activities rather than cognitive and mental ones; (v) egocentrism, i.e., being unable to take into account the perspective of others or caring to their needs; and (vi) volatile temperament, which means minimal tolerance for frustration and little ability to respond to conflicts using verbal rather than physical means (Gottfredson & Hirschi, 1990 ).

Previous studies have also explored whether self-control manifests itself differently as a function of sex. In their General Theory of Crime , Gottfredson and Hirschi ( 1990 ) refer that women develop higher levels of self-control, which is corroborated by Duckworth et al. ( 2015 ). Furthermore, previous studies have demonstrated that the influence of low self-control on behavior occurs in the same way, regardless of sex (Botchkovar et al., 2015 ; Ivert et al., 2018 ). However, several other studies showed that the influence of self-control on behavior varies depending on the sex of the individuals (Chui & Chan, 2016 ; De Ridder et al., 2012 ; Flexon et al., 2016 ), reinforcing the need for further research (Pechorro et al., 2021 ).

Empirical evidence has shown a robust association between deviance, crime, and self-control, and criminologists have focused on exploring the factors responsible for the differences in the levels of self-control, particularly those most commonly associated with low self-control (Beaver et al., 2010 ). A fundamental theoretical assumption from the General Theory of Crime (Gottfredson & Hirschi, 1990 ) concerns the fact that it proposes that self-control develops during the first years of a child's life and becomes stable around ten years of age, even though this stability is not absolute, but rather between individuals (Vazsonyi & Jiskrova, 2018 ). In this sense, Gottfredson and Hirschi ( 1990 ) attribute the greatest weight to parenting in developing self-control. Parents are usually responsible for monitoring and supervising their children's behavior, recognizing inappropriate behavior, and punishing it when it occurs, thus instilling the development of self-control. Based on these theoretical premises, several empirical studies have tested this hypothesis and verified that parenting is important in developing children’s self-control ( e.g., Marcone et al., 2020 ; Özdemir et al., 2013 ; Tehrani & Yamini, 2020 ). Overall, these studies highlighted family stability, positive parenting, good parent–child relationships, monitoring, affection, emotional support, consistent discipline, and an authoritative parenting style as the most important aspects of parenting for developing self-control. Particularly, it was found that ineffective parenting practices in which authoritarian and/or permissive parenting styles prevail, combined with poor family stability, negative parenting, and poor relations and interactions between parents and children, are associated with lower levels of self-control in children (Marcone et al., 2020 ; Tehrani & Yamini, 2020 ).

Indirect Effects Between Parenting Styles, Externalizing and Internalizing Behaviors, and Self-Control

Over the years, several studies and theoretical assumptions have highlighted the importance of self-control as a mediator of the relationship between parenting and children’s externalizing, internalizing, antisocial, or even delinquent behaviors. For example, Tehrani and Yamini’s ( 2020 ) meta-analysis explored the relationship between effective parenting practices, low self-control, and antisocial behavior. The results showed that parenting practices indirectly affected antisocial behavior through low self-control and directly affected antisocial behavior regardless of low self-control, thus suggesting the mediation effect of self-control on the relationship between parenting practices and children’s antisocial behavior (Tehrani & Yamini, 2020 ). Van Prooijen et al. ( 2018 ) found no interaction effects between self-control and concerning externalizing and internalizing problems. Nevertheless, it was found that higher levels of children’s self-control, reported by both the mother and the father, were associated with lower levels of externalizing behaviors. In comparison, higher levels of self-control reported by the mother were also associated with fewer internalizing behaviors. Overall, positive parenting practices by both parents were associated with fewer externalizing and internalizing behaviors.

In their study, Özdemir et al. ( 2013 ) explored the direct and indirect relationships between parenting practices, such as closeness, monitoring and affection, low self-control, and aggression. The results revealed that parental measures of closeness and monitoring were significantly and negatively correlated with low self-control and aggressive behavior. In addition, the authors analyzed the role of self-control in this relationship. They concluded that parental measures were directly correlated with aggressive behavior and indirectly through low self-control. Specifically, monitoring by parents had significant direct and indirect effects on aggression through low self-control, suggesting that adolescents whose parents monitored their behaviors were more likely to develop greater self-control, which, in turn, led to the adoption of fewer aggressive behaviors.

Rezaei et al. ( 2019 ) sought to explore the relationship between parenting styles and the capacity for self-control in delinquent adolescents. The results showed that juvenile delinquents with a higher perception of authoritative parenting style and a lower perception of permissive parenting style had higher levels of self-control. Regression analyses show that an increase in the perception of authoritative parenting style and a decrease in the perception of authoritarian parenting style was associated with higher levels of self-control, thus suggesting that parenting styles “ can predict self-control capacity of juvenile delinquents ” (p. 61). Specifically, authoritative parenting creates favorable socialization conditions for developing self-control, while authoritarian parenting reduces juvenile delinquents’ ability to exercise self-control (Rezaei et al., 2019 ).

Similarly, Finkenauer et al. ( 2005 ) showed that both self-control and some parenting features, such as psychological control, poor parental monitoring and supervision, were independently associated with higher rates of emotional problems ( e.g., depression, stress, and low self-esteem) and behavioral problems ( e.g., aggression) in adolescents between 10 and 14 years. Also, low levels of self-control reported by the youth were strongly associated with behavioral and emotional problems, regardless of gender. Additionally, perceiving parents as restrictive and psychologically controlling was associated with higher emotional and behavioral problems. On the other hand, perceived parental receptivity, solidarity, and proper monitoring of adolescents’ activities and whereabouts were associated with youth’s lower emotional and behavioral problems. The results also indicated that the link between parenting and behavioral and emotional problems was partially mediated by self-control.

Recently, using a sample of 611 Chinese adolescents, Zhang and Wang ( 2022 ) examined the mediating role of self-control in the relationship between parenting styles, namely paternal and maternal rejection, affection and overprotection, and externalizing and internalizing behaviors. In addition, they also sought to explore if there were gender differences in the abovementioned relationships. The results showed that parenting variables had different influences on adolescent behavior. Specifically, paternal rejection was positively associated with externalizing behaviors, while maternal rejection was positively correlated with internalizing behaviors. Paternal affection, not maternal affection, was negatively correlated with internalizing behaviors. Maternal overprotection was positively associated with externalizing and internalizing behaviors. Adolescents’ self-control was significantly and negatively correlated with externalizing and internalizing behaviors. Also, adolescents’ self-control significantly mediated the effect of maternal rejection on internalizing behaviors and paternal rejection on externalizing behaviors (Zhang & Wang, 2022 ).

The above-mentioned studies have shown that positive parenting, such as the authoritative parenting style, contributes to lower levels of externalizing and internalizing behaviors through its influence on reducing low self-control. In turn, children exposed to negative parenting, such as authoritarian and permissive parenting styles, have more externalizing and internalizing behaviors due to the influence of these parenting styles on higher levels of low self-control (Liu et al., 2019 ; Pan et al., 2021 ; Tehrani & Yamini, 2020 ; Van Prooijen et al., 2018 ; Zhang & Wang, 2022 ).

Given the empirical evidence described above, it should be noted that externalizing and internalizing behaviors have a central influence on the development of children and youth and that parenting styles and self-control play an important role in developing these behaviors. Despite this, the development of this study is essential since, to the best of our knowledge, no other study has yet explored the relationship between parenting styles and externalizing and internalizing behaviors, neither in the Portuguese context nor with this specific population. In addition, few studies have allowed us to understand the role of self-control in this relationship. Those carried out have shown mixed results, thus reinforcing the need for further research to understand how the parenting styles developed by Baumrind ( 1971 , 1978 ) influence the externalizing and internalizing behaviors of children and youth, as well as the role of self-control in this relationship.

Current Study

Given the theoretical and empirical considerations presented, this exploratory cross-sectional study sought to explore and compare the relative influence of the parenting styles proposed by Baumrind ( 1971 , 1978 ) on the emergence and development of children and youth’s externalizing and internalizing behaviors. Furthermore, it aimed to analyze self-control’s potential mediating role in this relationship. Following this goal and considering the theoretical rationale underlying this subject, the following hypotheses were tested: (i) authoritarian and permissive parenting styles positively influence children and youth’s externalizing and internalizing behaviors; (ii) authoritative parenting style negatively influences children and youth’s externalizing and internalizing behaviors; (iii) as age increases, the levels of externalizing and internalizing behaviors increase; (iv) female children report higher levels of internalizing behaviors while male children report higher levels externalizing behaviors; (v) children and youth’s low self-control is positively associated with externalizing and internalizing behaviors; (vi) children and youth’s low self-control has a mediating effect on the relationship between parenting styles and externalizing and internalizing behaviors, that is, each of the parenting styles influences externalizing and internalizing behaviors, through their influence on increasing and/or decreasing low self-control.

Participants

The study was conducted with a non-clinical convenience sample. The participants ( n  = 472) were children and youth between 12 and 15 years old, attending the 7th ( n  = 161), 8th ( n  = 144), and 9th ( n  = 167) grades of middle school. The sample consisted of 57% males ( n  = 268), with an average age of 13.30 (SD = 0.983).

The data were collected during 2022. The participating schools were selected based on the school years administrated and their availability and willingness to participate in the study. To ensure the school’s participation, the researchers contacted each principal to obtain consent for the research development. From the twelve schools in the district of Porto that were invited to participate in the study, only four agreed to participate (the remaining eight either formally declined to participate or did not provide any kind of response). The schools that agreed to collaborate in the study were then contacted for the joint selection of the specific classes that would be sampled, considering the eligibility criteria defined, namely the participant’s age and grade. Students with special educational needs were not considered eligible for participation in the research. This contact also allowed the outline of the procedures needed to contact the parents/legal guardians to obtain their informed consent and authorization for their children’s participation in the research, considering all participants were under-aged. Furthermore, the consent of the participating children and youth was also requested before the data collection. Finally, it should also be mentioned that before the data collection procedure, the research project was submitted to the Faculty of Law of the University of Porto’s Ethics Committee, which approved the current study’s execution. All participants completed a paper and pencil self-report measure after the researchers explained and provided the necessary study details and instructions on completing the forms.

Parenting styles (permissive, authoritarian, and authoritative) were assessed using the Parental Authority Questionnaire (PAQ; Buri, 1991 , adapted for the Portuguese population by Morgado et al., 2006 ), which is a self-report measure directed at children and youth. This measure comprises 30 items that reflect parents’ educational strategies and perspectives during their children’s childhood and adolescence. Children and youth are asked to express the degree of agreement with each one of the statements presented using a five-point Likert scale, ranging from (1) “totally disagree” to (5) “totally agree.” The items are grouped into three sub-scales of 10, each corresponding to the specific parenting style under study. Each subscale is scored between 10 and 50 points. The subscale with the highest score represents the parenting style predominantly adopted by the parent (Buri, 1991 ; Morgado et al., 2006 ). Concerning reliability, the PAQ consistency analyses conducted in this study revealed an adequate internal consistency (permissive parenting style α  = 0.64; authoritarian parenting style α  = . 82; authoritative parenting style α  = 0.83).

Externalizing and Internalizing Behaviors

Externalizing and internalizing behaviors were measured using the Youth Self Report (YSR/11–18; adapted and validated for the Portuguese population by Fonseca & Monteiro, 1999). Being part of the Achenbach System of Empirically Based Assessment (Achenbach & Rescorla, 2001 ), YSR is a high-quality diagnostic self-report measure for emotional and behavioral problems and social skills of children and adolescents, whose standard classification period is the last six months (Achenbach & Rescorla, 2001 ). The internalizing syndrome scale, which measures emotional problems, comprises three subscales: anxious/depressed, withdrawn/depressed, and somatic complaints. The externalizing syndrome scale assesses behavioral problems and comprises the subscales of rule-breaking behavior and aggressive behavior. For each item presented, respondents are requested to indicate the frequency of each behavior on a scale ranging from (0) “ not true ,” (1) “ somewhat or sometimes true, ” and (2) “ very true or often true ” (Achenbach & Rescorla, 2001 ). In the current study, the YSR showed satisfactory internal consistency indexes, specifically α  = 0.67 for the externalizing and α  = 0.72 for the internalizing syndrome scales.

• Self-control

Self-control was assessed using the Low Self-Control Scale (LSCS) by Grasmick et al. ( 1993 ). The original Grasmick LSCS is an attitudinal and self-report measure comprising 24 items, corresponding to the six dimensions of self-control proposed in Gottfredson and Hirschi’s ( 1990 ) General Theory of Crime , namely impulsivity, preference for simple tasks, risk-seeking, preference for physical activities, being self-centered, and having trouble controlling one’s temper. The children and youth were asked to rate their degree of agreement for each of the items, using a 4-point Likert scale, ranging from (1) “ totally disagree ” to (4) “ totally agree.” The items are aggregated to form a total score; the higher this score, the lower the levels of self-control. In the current study, good levels of internal consistency were found for the total scale ( α  = 0.82).

Data Analysis

Descriptive statistics and reliability analyses of the scales were used to assess the psychometric features of the sample. Independent samples t -tests and Cohen’s d (effect size measure) were used to explore gender differences. Pearson’s r correlations coefficients were used to analyzed the relationships between the variables under study. Additional data analysis procedures explored the direct and indirect effects of parenting styles, externalizing and internalizing behaviors, and self-control. Multiple linear regression analysis was conducted to identify significant predictors of children and youth’s externalizing and internalizing behaviors. The Ordinary Least Squares method was used to obtain the Beta values ( β ) and the adjusted r 2 . In addition, to assess the quality of the model's fit, the Coefficient of Determination ( R 2 ) and the F -test were calculated to check the overall significance of the regression. In turn, the assumptions of the linear regression were validated using the Durbin-Watson Test for the Independence of Random Terms (ui). Finally, the Variance Inflation Factor (VIF) was used to check for multicollinearity. Values greater than 5 would indicate multicollinearity (Field, 2013 ). In addition, SPSS PROCESS MACRO 4.3 was used to examine the indirect effects between the variables under study (Hayes, 2012 ). Briefly, this tool allows us to (1) estimate the total effect of the Independent Variable (IV) on the Dependent Variable (DV); (2) to understand the effect of the IV on the DV by controlling for the Mediating Variable (MV); and (3) to analyze the indirect effect of the IV on the DV through the MV. In addition, PROCESS also makes it possible to test mediation and moderation models by estimating the coefficients of linear or logistic regressions, regardless of the nature of the variables under analysis, calculating the direct and indirect effects in mediation and moderation models (Hayes, 2012 ).

Sample Descriptive Statistics for Externalizing and Internalizing Behaviors, Parenting Styles and Self-Control, Both for the Total Sample and by Gender

Table 1 presents the descriptive statistics of the main study variables: externalizing and internalizing behaviors, parenting styles and low self-control, for the total sample and for females and males, separately. Girls presented significantly higher mean scores for internalizing behaviors ( M  = 22.64; SD = 11.04) than boys ( M  = 13.26; SD = 8.86). Concerning parenting styles, the results revealed that the authoritative parenting style is the most prevalent in the sample ( M  = 37.39; SD = 6.51). Furthermore, although higher mean levels for all parenting styles were observed for boys, compared with girls, significant differences were only found for the authoritarian parenting style ( p  < 0.00; d  = 0.33). Lastly, regarding self-control, the majority of participants presented moderate to high levels of low self-control ( M  = 55.43; SD = 9.63; Gottfredson & Hirschi, 1990 ). No significant differences were found between boys and girls.

Correlations Between Externalizing Behaviors, Internalizing Behaviors, Parenting Styles and Self-Control

Table 2 reports the Pearson’s correlations between studied variables. The results revealed that externalizing and internalizing behaviors are significantly correlated ( r  = 0.504**), indicating that higher levels of externalizing behaviors are associated with higher levels of internalizing behaviors. Furthermore, authoritarian parenting style is positively correlated with externalizing behaviors ( r  = 0.246**) and internalizing behaviors ( r  = 0.182**), suggesting that the higher the frequency of authoritarian parenting style perceived by the children, the higher the rates of externalizing and internalizing behaviors. In line with this, the authoritative parenting style is negatively correlated with externalizing behaviors ( r  = − 0.410*) and internalizing behaviors ( r  = − 0.379**), demonstrating that the more prevalent this parenting style is, the lower the rates of children and youth’s externalizing and internalizing behaviors. Regarding self-control, it is important to note it is positively associated with externalizing behaviors ( r  = 0.518**) and internalizing behaviors ( r  = 0.241**). Finally, it should also be mentioned that low self-control is positively correlated with permissive ( r  = 0.127**) and authoritarian ( r  = 0.264**) parenting styles, as well as negatively correlated with authoritative parenting style ( r  = − 0.281**), thus suggesting that children exposed to an authoritarian and permissive parenting style have higher levels of low self-control, and that children exposed to an authoritative parenting style have lower levels of low self-control.

Regression Models for Internalizing and Externalizing Behaviors

Table 3 presents the final regression models developed for children and youth’s externalizing and internalizing behaviors. As displayed in the table, the regression model for the externalizing behaviors is significant, explaining around 37% ( p  < 0.001) of the total variance of the dependent variable. The children’s sex ( β  = 0.084; p  = 0.041), age ( β  = 0.093; p  = 0.021), and low self-control ( β  = 0.461; p  < 0.001) significantly predicted externalizing behaviors, suggesting that girls are less likely to adopt externalizing behaviors; that as age increases, so do the levels of externalizing behaviors; and those higher levels of low self-control contribute to explaining higher levels of externalizing behavior. In turn, the permissive ( β  = − 0.097; p  = 0.023) and authoritative parenting styles ( β  = − 0.242; p  < 0.001) significantly integrate the model but in a negative manner, thus suggesting that greater exposure to each one of these parenting styles leads to lower levels of children and youth’s externalizing behaviors.

As for internalizing behaviors, the regression model executed is statistically significant and explains around 35% (p < 0.001) of the total variability of the dependent variable. Considering the predictors introduced in the model, the results revealed that the children’s sex ( β  = 0.438; p  < 0.001), authoritarian parenting style ( β  = 0.130; p  < 0.005), and low self-control ( β  = 0.179; p  < 0.001) integrate the model in a positive and statistically significant way, thus indicating that female children are more likely to present higher levels of internalizing behaviors; that the more the children are exposed to an authoritarian parenting style, the higher the rates of internalizing behaviors; and that, similarly to what was found for externalizing behaviors, higher levels of low self-control predicted more internalizing behaviors. In turn, the authoritative parenting style is the only statistically significant variable ( β  = − 0.266; p  < 0.001), which suggests that the more children are exposed to this parenting style, the lower the levels of internalizing behaviors.

Indirect Effects of Low Self-Control

Mediation models were tested to explore the indirect effects of low self-control in the relationship between parenting styles and externalizing and internalizing behaviors. Three models were generated to analyze the mediation processes associated with predicting externalizing behaviors, as presented in Table  4 .

Figure  1 illustrates the first model that tested the mediating effect of low self-control on the relationship between permissive parenting style and externalizing behaviors. The results show that the independent variable, permissive parenting style, has a positive and statistically significant effect on the mediating variable low self-control (direct effect = 0.215; p  = 0.025) and that the mediating variable has a positive and statistically significant effect on the dependent variable, externalizing behaviors (direct effect = 0.390; p  < 0.000).

Mediation models: permissive, authoritarian and authoritative parenting styles, externalizing behaviors, and low self-control

In turn, the permissive parenting style negatively and significantly predicted externalizing behaviors (direct effect = − 0.218; p  = 0.000). However, as far as indirect effects are concerned, these were tested using bootstrapping procedures, which showed that the standardized effect was 0.047 with a 95% confidence interval ranging from − 0.010 to 0.177, including a value of 0. This indicates that although a permissive parenting style has a negative and significant direct effect on externalizing behaviors, the indirect effect through low self-control is insignificant.

The second model, shown in Fig.  1 , tested the mediating effect of low self-control on the relationship between authoritarian parenting style and externalizing behaviors to analyze whether authoritarian parenting style increases levels of low self-control, and these, in turn, lead to higher rates of externalizing behaviors. As can be seen, the independent variable authoritarian parenting style has a positive and statistically significant effect on the mediating variable low self-control (direct effect = 0.363; p  = 0.000), and the mediating variable also has a positive and significant effect on the dependent variable (direct effect = 0.348; p  = 0.000). As for the independent variable, it has a positive and statistically significant effect on the dependent variable (direct effect = 0.133; p  = 0.004). However, there was a standardized indirect effect of 0.029 with a 95% confidence interval ranging from 0.075 to 0.187, indicating that the indirect effect was statistically significant. This indicates a partial mediation relationship between the variables because, despite the direct and significant effect between the independent and dependent variables, the authoritarian parenting style indirectly influences increasing levels of externalizing behavior through its positive influence on low self-control.

As for model 3, the aim was to understand the mediating effect of low self-control on the relationship between authoritarian parenting style and externalizing behaviors to understand whether authoritarian parenting style reduces low self-control, which in turn leads to a reduction in externalizing behaviors.

As shown in Fig.  1 , the independent variable has a negative and significant effect on the mediating variable (direct effect = − 0.429; p  = 0.000), and the mediating variable has a positive and significant effect on the dependent variable (direct effect = 0.317; p  = 0.000). As for the independent variable, it has a negative and significant effect on the dependent variable (direct effect = − 0.303; p  = 0.000). Despite this, there was a standardized effect of 0.033, with a 95% confidence interval of − 0.206 to − 0.077, which suggests that the indirect effect tested is significant. This indicates a partial mediation relationship between the variables since, despite the significant direct effect between the dependent and independent variables, the authoritarian parenting style reduces externalizing behavior by reducing low self-control.

On the other hand, to analyze the mediation processes underlying the prediction of internalizing behaviors, the three models shown in Table  5 were processed.

The fourth model tested the mediating effect of low self-control on the relationship between the independent variable, permissive parenting style, and the dependent variable, internalizing behaviors. The results shown in Fig.  2 indicate that the independent variable has a positive and significant effect on the mediating variable (direct effect = 0.233; p  = 0.012) and that the mediating variable influences the dependent variable in a positive and statistically significant way (direct effect = 0.325; p  = 0.000). In turn, the independent variable has a negative and significant effect on the dependent variable (direct effect = − 0.248; p  = 0.024). Despite this, there is a standardized indirect effect of 0.040, with a 95% confidence interval of 0.000 to 0.158, which means a partial mediation relationship exists. In other words, despite the significant direct effect recorded between the dependent and independent variables, the permissive parenting style contributes to the increase in levels of internalizing behaviors through its influence on the increase in levels of low self-control.

Mediation models: permissive authoritarian and authoritative parenting styles, internalizing behaviors, and low self-control

The fifth model investigated the mediating effect of low self-control on the relationship between authoritarian parenting style and internalizing behaviors. As can be seen in Fig.  2 , it is possible to understand that the independent variable has a positive and statistically significant effect on the mediating variable (direct effect = 0.368; p  = 0.000) and that the mediating variable has a positive and significant effect on the dependent variable (direct effect = 0.251; p  = 0.000).

The independent variable positively and significantly affects the dependent variable (direct effect = 0.229; p  = 0.007). Finally, as in the previous model, there is a standardized indirect effect of 0.040 for a 95% confidence interval of 0.037 to 0.153). This indicates a partial mediation relationship because, despite the significant direct effect between the dependent and independent variables, the authoritarian parenting style contributes to an increase in internalizing behaviors through its positive influence on low self-control (Fig.  2 ).

The sixth model focused on analyzing the mediating effects of low self-control on the relationship between authoritative parenting style and internalizing behaviors. The results shown in Fig.  2 indicate that authoritarian parenting style has a statistically significant negative effect on the mediating variable (direct effect = − 0.431; p  = 0.000) and that the mediating variable has a positive and significant effect on the dependent variable (right effect = 0.160; p  = 0.007). In turn, the authoritative parenting style has a negative and significant effect on internalizing behaviors (direct effect = − 0.580; p  = 0.000). Finally, there is a standardized indirect effect of 0.031 for a 95% confidence interval of − 0.133 to − 0.012). This suggests that, although the authoritarian parenting style contributes to a decrease in internalizing behaviors, there is a partial mediation relationship in that the authoritarian parenting style affects the decrease of internalizing behaviors by decreasing low self-control.

The main goal of this research was to analyze and compare the relative influence of authoritative, authoritarian, and permissive parenting styles on children and youth’s externalizing and internalizing behaviors and explore the indirect effects of self-control on this relation.

Thus, concerning the first research hypothesis, it was defined that authoritarian and permissive parenting styles positively influence children and youth’s externalizing and internalizing behaviors. As for the authoritarian parenting style, the hypothesis was partially confirmed since this variable is a significant predictor only of internalizing behaviors. Nevertheless, this is a result that finds empirical support in different studies ( e.g., Alizadeh et al., 2011 ; Akhter et al., 2011 ; Braza et al., 2015 ) which have shown cross-sectionally and longitudinally, that children exposed to a parenting style based on levels of authority and behavioral demands, and little freedom of expression, present higher levels of internalizing behaviors (Akhter et al., 2011 ). Thus, the results observed in the current study might be related to the fact that parents who adopt this parenting style do not establish an interactive dialogue with their children and are, in most situations and life contexts, strict, rigid, and inflexible, both in terms of limits and in terms of the behavioral expectations they impose, not responding to their children's emotional and affective needs. As Amran and Basri ( 2020 ) suggest, this type of parenting incites certain negativity in children, leading to higher levels of internalizing behaviors, as demonstrated in this study, because when parents do not respond to their children's needs and emotions, tensions are created in terms of communication and family dynamics. This leads to what Rose et al. ( 2018 , p. 1482) describe as " parenting stress and child-rearing stress.”, leading children to look for opportunities to release their tensions when they enter other socialization contexts, and in many of these situations, internalizing behaviors occur.

On the other hand, the permissive parenting style variable is statistically significant being a statistically significant predictor of externalizing behaviors, suggesting that children's greater exposure to this parenting style leads to lower externalizing behaviors. This was one of the results that did not follow the same direction as previous studies (e.g., Akhter et al., 2011 ; Alizadeh et al., 2011 ; Braza et al., 2015 ), nor the research hypothesis defined for this study. This result might be explained by the fact that this parenting style has fewer direct and immediate consequences on this type of behavior in children during this development period (Rinaldi & Howe, 2012 ). On the other hand, this parenting style was the least reported by the children, so given the low levels of this style in the sample, it is possible to understand why this relationship exists.

As for the second research hypothesis, it was defined that authoritative parenting style negatively influences the externalizing and internalizing behaviors of children and young people. This hypothesis was confirmed since the authoritarian parenting style variable was statistically significant, indicating that children exposed to this style have lower externalizing and internalizing behaviors. These results align with others from previous studies ( e.g., Pinquart, 2017 ; Rinaldi & Howe, 2012 ). As such, the results found in this study might be explained by what Rose et al. ( 2018 ) propose, i.e., authoritative parents are warm from an affective point of view, set clear and structured limits for their children's actions and behaviors, and can adapt them to their needs. This makes children develop greater levels of affection for their parents and feel safe and understood in the relationships they establish with them, leading to lower levels of internalizing and externalizing behaviors. As Baumrind et al. ( 2010 ) argue, childhood is a period of development in which children begin to create their independence and capacity for autonomy, and authoritarian parenting is ideal for providing children with the right support for this development.

The third research hypothesis states that the externalizing and internalizing behaviors increase as age increases. This hypothesis was partially confirmed, considering that the age of the children was a significant predictor only of internalizing behaviors. There have been mixed results in the literature ( e.g., Bishop et al., 2020 ; Bongers et al., 2003 ; Crijnen et al., 1997 ). However, the results obtained in the current study might be explained because the children and youth who took part in the study were starting puberty, a developmental period in which various hormonal, social, and behavioral changes occur, which may make this behavior more likely to occur. This reality was tested in the study by Bishop et al. ( 2020 ), which found that levels of externalizing behaviors increased between the ages of 11 and 15 and decreased when the children were between 16 and 20. In this sense, longitudinal studies are needed to understand better the evolution of these behaviors over different age groups and the individual, community, and social factors that can affect their development.

Furthermore, the fourth research hypothesis states that girls report more internalizing behaviors, and boys are more likely to report more externalizing behaviors. This hypothesis was partially confirmed, as being female significantly predicts both externalizing and internalizing behaviors, which also contradicts the results from previous studies ( e.g., Bongers et al., 2003 ; Campos et al., 2014 ; Chaplin & Aldao, 2013 ; Crijnen et al., 1997 ). Regarding internalizing behaviors, according to Chaplin and Aldao ( 2013 ), this result could be explained by the tendency of girls, especially during adolescence, to be more emotionally expressive. On the other hand, Brown ( 1999 ) states that the expression of externalizing behaviors has become increasingly common among adolescents and is more prevalent in female children, potentially reflecting a change in gender roles in today's society. However, longitudinal research would be necessary to analyze how the expression of these behaviors changes from childhood to adulthood. The result regarding internalizing behaviors aligns with previous studies’ findings ( e.g., Bongers et al., 2003 ; Campos et al., 2014 ; Crijnen et al., 1997 ) and with the hypothesis defined in the study. As shown by Bongers et al. ( 2003 ), the prevalence of internalizing behaviors between boys and girls in childhood does not differ. However, with the onset and entry into adolescence, an increase in internalizing behaviors in girls is common, which can be explained by the fact that girls struggle earlier with physical, hormonal, and behavioral changes that can lead to a greater expression of this type of behavior due to the uncertainty and instability typical of this period of development (Bongers et al., 2003 ).

The fifth research hypothesis tested in the current study which proposes that low self-control in children and youth is positively associated with externalizing and internalizing behaviors, was fully confirmed. Other studies have widely documented the relationship between low self-control and externalizing and internalizing behaviors (e.g., Bai et al., 2020 ; Van Prooijen et al., 2018 ; Zhang & Wang, 2022 ). Thus, children with low self-control are characterized by being more impulsive, egocentric, preferring simple, physical, and risky activities, and having a difficult temperament (Gottfredson & Hirschi, 1990 ). As such, the results of the current study might be understood in light of what Zhang and Wang ( 2022 ) propose since children with low self-control have greater difficulty in redirecting their attention away from impulses, and if this tendency continues, they are more likely to adopt externalizing behaviors. As for internalizing behaviors, the authors highlight the attentional component of self-control, in that children with low self-control have difficulty shifting their attention from negative to positive aspects and may develop more internalizing behaviors (Eisenberg et al., 2001 ). In turn, the fact that low self-control is a stronger predictor of externalizing behavior can be understood from the research carried out by Krueger et al. ( 1996 ). The authors analyzed low self-control as a specific risk factor for externalizing behaviors. Through laboratory tasks, they concluded that children with externalizing behaviors tended to seek immediate gratification more than children with internalizing behaviors.

The last hypothesis states that low self-control in children and youth has a mediating effect on the relationship between parenting styles and externalizing and internalizing behaviors, i.e., each parenting style influences externalizing and internalizing behaviors through its influence on increasing and/or decreasing low self-control. The hypothesis was partially confirmed because low self-control significantly mediated the relationship between parenting styles and externalizing and internalizing behaviors in only five of the six models tested. Thus, each parenting style influences externalizing and internalizing behaviors, and parenting styles also influence low self-control, which influences externalizing and internalizing behaviors.

As for the partial mediation models that were confirmed, the results of the analyses follow the same direction as those found in other empirical research that has analyzed the relationship between several aspects of parenting, self-control, and behavior problems, including externalizing and internalizing behaviors ( e.g., Liu et al., 2019 ; Pan et al., 2021 ; Tehrani & Yamini, 2020 ; Van Prooijen et al., 2018 ; Zhang & Wang, 2022 ). The results of the current study might be explained by the fact that positive parenting, as the authoritative parenting style, contributes to lower levels of externalizing and internalizing behaviors through its influence on reducing low self-control. On the other hand, children exposed to negative parenting, such as authoritarian and permissive parenting styles, show more externalizing and internalizing behaviors due to the influence of these parenting styles on higher levels of low self-control.

In this regard, there is a debate in the scientific literature about whether it is more appropriate to talk about the role of low self-control in terms of total or partial mediation, and there are some gaps in the literature due to the mixed results found. The study by Tehrani and Yamini ( 2020 ) set out to fill this gap based on the idea that low self-control may not be able to "absorb" all the effects of parental practices and styles on externalizing and internalizing behaviors, so in the light of this study, as in the present research, the partial mediation model is the most appropriate to describe the relationship between these dimensions and variables under analysis. Thus, these results, in the light of what Tehrani and Yamini ( 2020 ) explain based on the General Theory of Crime (Gottfredson & Hirschi, 1990 ), allow us to argue that children exposed to ineffective or inadequate parenting styles are not exposed to the necessary parenting and socialization practices that allow them to develop adequate levels of self-control, which in turn explain the emergence of externalizing and internalizing behaviors.

The opposite is also possible, i.e., when parents do not fail to emotionally support the child, monitor their behavior, and exert effective discipline and control. From authoritative parents, children learn, for example, to control their impulses, postpone their immediate gratification, be less egocentric, develop adequate self-control, and, as such, are less likely to adopt externalizing and internalizing behaviors. In short, externalizing and internalizing behaviors could be prevented if parents adopted appropriate socialization and education strategies, such as those typical of an authoritative parenting style (Pan et al., 2021 ; Tehrani & Yamini, 2020 ; Zhang & Wang, 2022 ). Therefore, the results presented reinforce the importance of studying self-control and different parenting styles in the emergence and development of externalizing and internalizing behaviors while explaining the mechanisms by which this influence occurs.

Limitations and Directions for Future Research

Despite the added value of the current study, it is not immune to some limitations. First, this study used a convenience sample of middle-school children from a restricted geographical area, thus impeding the generalization of the results. Future research should consider using a probabilistic sample of children from different geographical areas and cultural backgrounds. In line with this, it would be interesting that future research explores, in greater detail, the specific influence that some cultural features might exert in the explanation of the observed results (e.g., education, values, beliefs). In fact, previous studies have suggested that child-rearing (e.g., parenting styles) might be influenced by cultural values and that its impact on children’s behavior and adjustment might vary, depending on whether the adopted parenting strategies are considered more or less usual and accepted (e.g., Bornstein, 2013 ; Gershoff et al., 2010 ; Tehrani & Yamini, 2020 ).

Also, it would be interesting that future studies explore and analyze the potential maintenance of the results found with samples with low, medium, and high levels of antisocial and delinquent behavior, and not just normative ones, as the one used in the current study, while exploring further the gender differences for the relationships analyzed in the current study (particularly considering the mixed results found in previous studies, as described above; e.g., Braza et al., 2015 ; Chui & Chan, 2016 ; Pechorro et al., 2021 ; Pinquart, 2017 ).

In addition, there might have been a margin of bias in the data due to the self-report nature of the questionnaires. This bias may have occurred due to the children's reduced ability to remember past behaviors and/or events since for externalizing and internalizing behaviors, the children were asked to refer to behaviors adopted over the last six months. On the other hand, one can exclude the possibility of distortions or difficulties in understanding some of the questions, which might have influenced the results observed.

Finally, it is important to note that this was a correlational study, thus limiting the possibility of understanding the bidirectional influences of the variables and dimensions under study. This is important since other authors and previous studies have shown that the influence of parenting styles on externalizing and internalizing behaviors is a relationship that can be bidirectional because children with certain levels of externalizing and internalizing behaviors can trigger the adoption of specific parenting styles in their parents (Pardini et al., 2008 ; Pinquart, 2017 ). Thus, although this study demonstrated that parenting styles have a transversal and important influence in explaining externalizing and internalizing behaviors and the role of low self-control in this relationship, it does not allow us to understand whether externalizing and internalizing behaviors and low self-control explain parenting styles. As such, future studies should, using a multi-informant and multi-method approach, seek to understand the cumulative influences of parenting styles on externalizing and internalizing behaviors over different developmental periods, which is only possible through a longitudinal research design.

Implications

Despite the above-mentioned limitations and its exploratory nature, this study has several strengths and important theoretical and practical implications. First, this study extends previous research into the influence of parenting styles on the externalizing and internalizing behaviors of children and youth while also helping to understand the variables that predict these behaviors. In addition, the mediation analyses contributed to the scarce evidence and mixed results regarding the specific role that low self-control plays in the relationship between parenting styles and externalizing and internalizing behaviors.

Moreover, this study provides critical insights for developing prevention and intervention strategies targeting parents, children, and youth. By emphasizing the importance of specific factors consistently identified as crucial predictors of externalizing and internalizing behaviors, this research informs the design of targeted interventions. Specifically, it enhances our understanding of which parenting styles are most likely to contribute to the emergence, prevention, or reduction of these behaviors (Pinquart, 2017 ). This knowledge is essential for crafting prevention programs and intervention strategies that are not only grounded in theory but also supported by robust empirical evidence (Akhter et al., 2011 ; Hoeve et al., 2009 ; Kawabata et al., 2011 ; Kazdin, 2001 ).

Data Availability

Not applicable.

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Almeida, D., Santos, G. Parenting Styles and Youth’s Externalizing and Internalizing Behaviors: Does Self-Control Matter?. Int Criminol (2024). https://doi.org/10.1007/s43576-024-00137-1

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Received : 08 May 2024

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DOI : https://doi.org/10.1007/s43576-024-00137-1

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