The author(s) shown below used Federal funding provided by the U.S. 

Department of Justice to prepare the following resource: 

Document Title: Exposure to Violence, Trauma, and 

Juvenile Court Involvement: A Longitudinal 

Analysis of Mobile Youth and Poverty 

Study Data (1998-2011) 

Author(s): Anneliese Bolland, Ph.D., John Bolland, 

Ph.D. 

Document Number: 254496 

Date Received: January 2020 

Award Number: 2016-MU-MU-0068 

This resource has not been published by the U.S. Department of 

Justice. This resource is being made publically available through the 

Office of Justice Programs’ National Criminal Justice Reference 

Service. 

Opinions or points of view expressed are those of the author(s) and 

do not necessarily reflect the official position or policies of the U.S. 

Department of Justice. 
Research Projects Final Technical Report 

Exposure to Violence, Trauma, and Juvenile Court Involvement: A Longitudinal 

Analysis of Mobile Youth and Poverty Study Data (1998-2011) 

Principal Investigators: 

Dr. Anneliese Bolland (PI), Institute for Communication and Information Research, The 

University of Alabama, Box 870172, Tuscaloosa, AL 35487-0172; tel: 205-348-1235; email: 

acbolland@ua.edu 

Dr. John Bolland (co-PI), Institute for Social Science Research, The University of 

Alabama, Box 870216, Tuscaloosa, AL 35487-0216; tel: 205-348-1235; email: 

john.m.bolland@gmail.com 

This project was supported by Grant # 2016-MU-MU-0068, awarded by the Office of 

Juvenile Justice and Delinquency Prevention, Office of Justice Programs, U.S. Department of 

Justice. The opinions, findings, and conclusions or recommendations expressed in this 

publication/program/exhibition are those of the author(s) and do not necessarily reflect those 

of the Department of Justice. 

1 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

TABLE OF CONTENTS 

1: Abstract.................................................................................................................................................... vi 

2: Introduction............................................................................................................................................... 1 

A: Poverty, Exposure to Violence, and Trauma ...................................................................................... 1 

B: Objectives............................................................................................................................................ 2 

C: Model and Research Questions ........................................................................................................... 2 

3: Methods .................................................................................................................................................... 6 

A: Study Location .................................................................................................................................... 6 

B: Recruitment and Data Collection Procedures...................................................................................... 6 

C: Initial MYS Sample............................................................................................................................. 7 

D: Data Sources ..................................................................................................................................... 12 

E: Exclusion Criteria.............................................................................................................................. 12 

F: Study Sample ..................................................................................................................................... 12 

G: Measures ........................................................................................................................................... 12 

H: Analytic Procedures .......................................................................................................................... 21 

4: Results..................................................................................................................................................... 26 

A: Research Question 1.......................................................................................................................... 26 

B: Research Question 2.......................................................................................................................... 30 

C: Research Question 3.......................................................................................................................... 36 

5: Discussion............................................................................................................................................... 83 

A: Summary of Exposure to Violence Juvenile Justice Involvement Results ................................... 83 

B: Summary of results that suggest promising intervention points ....................................................... 87 

C: Addressing Exposure to Violence: Approaches to Intervention ....................................................... 90 

D: Strengths and Limitations ............................................................................................................... 108 

E: Future Research ............................................................................................................................... 109 

F: Conclusions...................................................................................................................................... 112 

6: References............................................................................................................................................. 115 

i 

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TABLE OF CONTENTS 

Tables and Figures 

TABLES 

1: Description of MYS Target Neighborhoods: 2000 Census ................................................................. 10 

2: Mobile Youth Survey Multiple Cohort Design ................................................................................... 11 

3: Descriptive Statistics for MYS Derived Measures.............................................................................. 16 

4: Exposure to Violence Descriptive Statistics........................................................................................ 19 

5: Normative Academic Progress Descriptive Statistics ......................................................................... 20 

6: Juvenile Court Involvement Descriptive Statistics.............................................................................. 21 

7: Exposure to Violence Predicting Crimes Against a Person................................................................. 27 

8: Exposure to Violence Predicting Court Outcome Severity ................................................................. 29 

9: Sources of Victimization Predicting Crimes Against a Person ........................................................... 30 

10: Sources of Victimization Predicting Court Outcome Severity.......................................................... 31 

11: Instances of Victimization Predicting Crimes Against a Person ....................................................... 32 

12: Instances of Victimization Predicting Court Outcome Severity........................................................ 33 

13: Sources and Instances of Victimization Predicting Crimes Against a Person................................... 34 

14: Sources and Instances of Victimization Predicting Court Outcome Severity ................................... 35 

15: SEM Estimates: Determinants of Hopelessness at Time t................................................................. 37 

16: SEM Estimates: Determinants of Street Code at Time t ................................................................... 43 

17: SEM Estimates: Determinants of Behavioral Self-Worth at Time t.................................................. 48 

18: SEM Estimates: Determinants of Global Self-Worth at Time t ........................................................ 52 

19: SEM Estimates: Determinants of Worry at Time t............................................................................ 57 

20: SEM Estimates: Determinants of Traumatic Stress at Time t ........................................................... 61 

21: SEM Estimates: Determinants of Academic Progress at Time t ....................................................... 64 

22: SEM Estimates: Determinants of Court Outcome Severity at Time t + 1 ......................................... 71 

23: SEM Estimates: Indirect Effects on Court Outcome Severity and Academic Progress .................... 80 

24: SEM Estimates: Sum of Psychological Adjustment Indirect Effects on Court Outcome Severity ... 82 

FIGURES 

1: Model depicting direct and indirect effects of lagged exposure to violence on court outcomes........... 5 

2a: Witnessing Violence as a Function of Age........................................................................................ 18 

2b: Violent Victimization as a Function of Age ...................................................................................... 18 

3: Crimes Against a Person as a Function of Violent Victimization, Gender, and Age .......................... 26 

4: Court Outcome Severity as a Function of Violent Victimization, Gender, and Age........................... 28 

5: Hopelessness as a Function of Age ..................................................................................................... 38 

6: Hopelessness as a Function of Maternal Warmth and Age ................................................................. 39 

7: Hopelessness as a Function of Witnessing Violence and Age ............................................................ 39 

8: Hopelessness as a Function of Violent Victimization, Gender, and Age ............................................ 40 

9: Hopelessness as a Function of Positive Neighborhood Connectedness and Gender........................... 41 

10: Hopelessness as a Function of Positive Neighborhood Connectedness, Gender, and Age ............... 41 

11: Hopelessness as a Function of Negative Neighborhood Connectedness and Gender ....................... 42 

12: Hopelessness as a Function of Negative Neighborhood Connectedness and Age ............................ 42 

13: Hopelessness as a Function of Negative Neighborhood Connectedness, Gender, and Age.............. 42 

14: Street Code as a Function of Age ...................................................................................................... 44 

15: Street Code as a Function of Positive Neighborhood Connectedness and Gender............................ 45 

16: Street Code as a Function of Negative Neighborhood Connectedness and Gender .......................... 45 

17: Street Code as a Function of Witnessing Violence and Age ............................................................. 46 

18: Street Code as a Function of Positive Neighborhood Connectedness and Age................................. 46 

19: Behavioral Self-Worth as a Function of Age .................................................................................... 48 

ii 

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Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

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20: Behavioral Self Worth as a Function of Maternal Warmth and Gender............................................ 49 

21: Behavioral Self-Worth as a Function of Maternal Warmth, Gender, and Age.................................. 49 

22: Behavioral Self Worth as a Function of Positive Neighborhood Connectedness and Gender .......... 50 

23: Behavioral Self Worth as a Function of Positive Neighborhood Connectedness and Age ............... 50 

24: Behavioral Self-Worth as a Function of Negative Neighborhood Connectedness, Gender, and 

Age .................................................................................................................................................... 51 

25: Global Self-Worth as a Function of Age ........................................................................................... 53 

26: Global Self-Worth as a Function of Violent Victimization and Gender ........................................... 54 

27: Global Self-Worth as a Function of Violent Victimization, Gender, and Age.................................. 54 

28: Global self-worth as a Function of Positive Neighborhood Connectedness and Gender .................. 55 

29: Global self-worth as a Function of Positive Neighborhood Connectedness, Gender, and Age ........ 55 

30: Worry as a Function of Age .............................................................................................................. 57 

31: Worry as a Function of Witnessing Violence and Gender ................................................................ 58 

32: Worry as a Function of Witnessing Violence and Age ..................................................................... 58 

33: Worry as a Function of Violent Victimization, Gender, and Age ..................................................... 58 

34: Worry as a Function of Positive Neighborhood Connectedness and Gender.................................... 59 

35: Worry as a Function of Positive Neighborhood Connectedness, Gender, and Age .......................... 59 

36: Worry as a Function of Negative Neighborhood Connectedness and Gender .................................. 60 

37: Worry as a Function of Negative Neighborhood Connectedness, Gender, and Age......................... 60 

38: Traumatic Stress as a Function of Age .............................................................................................. 62 

39: Traumatic Stress as a Function of Witnessing Violence and Age ..................................................... 62 

40: Academic Progress as a Function of Street Code and Gender .......................................................... 66 

41: Academic Progress as a Function of Traumatic Stress and Gender .................................................. 66 

42: Academic Progress as a Function of Worry and Parental Monitoring .............................................. 67 

43: Academic Progress as a Function of Worry and Negative Neighborhood Connectedness ............... 67 

44: Academic Progress as a Function of Global Self-Worth and Negative Neighborhood 

Connectedness................................................................................................................................... 68 

45: Academic Progress as a Function of Global Self Worth, Negative Neighborhood Connectedness, 

and Gender ........................................................................................................................................ 68 

46: Academic Progress as a Function of Behavioral Self Worth, Negative Neighborhood 

Connectedness, and Gender .............................................................................................................. 69 

47: Court Outcome Severity as a Function of Age .................................................................................. 73 

48: Court Outcome Severity as a Function of Street Code and Curfew ................................................. 74 

49: Court Outcome Severity as a Function of Street Code and Age ....................................................... 74 

50: Court Outcome Severity as a Function of Street Code, Parental Monitoring, and Gender ............... 74 

51: Court Outcome Severity as a Function of Traumatic Stress and Negative Neighborhood 

Connectedness................................................................................................................................... 75 

52: Court Outcome Severity as a Function of Traumatic Stress, Negative Neighborhood 

Connectedness, and Gender .............................................................................................................. 75 

53: Court Outcome Severity as a Function of Violent Victimization and Age ....................................... 76 

54: Court Outcome Severity as a Function of Violent Victimization, Gender, and Age......................... 76 

55: Court Outcome Severity as a Function of Behavioral Self-Worth and Age...................................... 77 

56: Court Outcome Severity as a Function of Global Self-Worth and Age ............................................ 78 

57: Court Outcome Severity as a Function of Global Self-Worth, Gender, and Age.............................. 78 

iii 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

TABLE OF CONTENTS 

Appendices 

A: Mobile Youth and Poverty Study (MYPS) Archive ............................................................................ 136 

1: Mobile Youth Survey (MYS, 1998-2011) ....................................................................................... 136 

2: Adult and Family Dynamics Questionnaires ................................................................................... 136 

3: Substance Use Decision Making Survey ......................................................................................... 136 

4: Sleep Survey .................................................................................................................................... 136 

5: Gene by Environment Neighborhood Interaction Survey................................................................ 137 

6: Mobile County Public School System Student Records .................................................................. 137 

7: Mobile County Juvenile court Records............................................................................................ 137 

8: Mobile Housing Board Records....................................................................................................... 137 

9: Mobile Police Department Crime and Service Reports ................................................................... 138 

10: MYPS Geodatabase ....................................................................................................................... 138 

11: Uniqueness of the MYPS Archive ................................................................................................. 138 

12: Procedures to Access MYPS Data ................................................................................................. 138 

B: Mobile Youth Survey Data Collection Procedures .............................................................................. 140 

1: MYS Questionnaire ......................................................................................................................... 140 

2: IRB Protocol .................................................................................................................................... 140 

3: Data Collection Procedures.............................................................................................................. 140 

4: Missing Data and Sample Representativeness................................................................................. 144 

C: MYS Measures..................................................................................................................................... 146 

D: Mobile County Public School System Measures ................................................................................. 166 

E: Mobile County Juvenile Court Measures ............................................................................................. 171 

F: Biographies of Study Personnel............................................................................................................ 174 

APPENDICES TABLES 

B1: Detailed Analysis of Retention, Attrition, and Recapture for 1998 MYS Sample ......................... 143 

C1: Race Items on the MYS .................................................................................................................. 146 

C2: Age Item on the MYS..................................................................................................................... 146 

C3: Gender Item on the MYS................................................................................................................ 146 

C4: Witnessing Violence Items on the MYS......................................................................................... 147 

C5: Violent Victimization (Weapon Brandishment) Item on the MYS ................................................ 147 

C6: Violent Victimization (Cut or Shot at) Item on the MYS............................................................... 148 

C7: Hopelessness Items on the MYS..................................................................................................... 149 

C8: Reliability: Hopelessness Scale ...................................................................................................... 150 

C9: Worry Items on the MYS................................................................................................................ 151 

C10: Reliability: Worry Scale ............................................................................................................... 152 

C11: Self-Worth Items on the MYS ...................................................................................................... 153 

C12: Reliability: Self-Worth Scale ........................................................................................................ 154 

C13: Street Code Items on the MYS ..................................................................................................... 155 

C14: Reliability: Street Code Scale ....................................................................................................... 156 

C15: Traumatic Stress Items on the MYS ............................................................................................. 157 

C16: Reliability: Traumatic Stress Scale ............................................................................................... 158 

C17: Maternal Warmth Items on the MYS............................................................................................ 159 

C18: Reliability: Maternal Warmth Scale ............................................................................................. 160 

C19: Neighborhood Connectedness Items on the MYS ........................................................................ 161 

C20: Reliability: Neighborhood Connectedness Scale .......................................................................... 162 

C21: Curfew Items on the MYS ............................................................................................................ 163 

C22: Reliability: Curfew Scale.............................................................................................................. 163 

iv 

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C23: Parental Monitoring Items on the MYS........................................................................................ 164 

C24: Reliability: Parental Monitoring Scale.......................................................................................... 165 

C25: Ancillary MYS Questions Necessary to Create the Parental Monitoring Scale ........................... 165 

D1: Normative Academic Grade Progress............................................................................................. 166 

D2: Normative Academic Progress Scale.............................................................................................. 167 

E1: Court Referral Date, MYS Date Wave Correspondence................................................................. 171 

E2: Categories of Offense...................................................................................................................... 172 

E3: Court Outcome Severity.................................................................................................................. 173 

v 

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Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
 

 

 

Abstract 

An examination of predominantly African American adolescents who live in extreme poverty 

suggests that exposure to violence is positively related to involvement in the juvenile court 

system, and partially mediated by psychological factors, particularly hopelessness; thus, 

practitioners should take care to target more than just traumatic stress as a result of exposure to 

violence in African American impoverished youth. 

Research Overview 

The uncertainty associated with poverty creates existential challenges for day-to-day survival of children 

growing up poor. One area where this is particularly the case is exposure to violence and how it may portend future 

involvement in the juvenile court system. Additionally, exposure to violence is related to psychological and 

behavioral maladjustment. This study explores these factors as well as identifies approaches to intervention. 

Research Design 

Data used in this study were collected as part of the Mobile Youth and Poverty Study, a community-based 

longitudinal cohort study of adolescents living in extreme poverty in the Mobile, AL, Metropolitan Statistical Area 

(MSA). Data sources include annual surveys of adolescents living in the most impoverished neighborhoods in the 

MSA between 1998 and 2011; school records for these youth available between 1998 and 2011; and juvenile court 

records for these youth, also available between 1998 and 2011. Factors used in this study include exposure to 

violence, psychological adjustment, social support, family control, juvenile court involvement, and academic 

progress. 

Because the data were nested within respondent, they were analyzed using linear mixed models and structural 

equation models. In analyzing juvenile court outcomes, statistical models used a Granger causal framework, in 

which the outcome measured at time t+1 was estimated as a function of both independent variables and the outcome 

variable measured at time t. Although this does not guarantee causality, it provides stronger causal inference than 

would otherwise be possible. 

Research Findings 

Results from this study indicate that exposure to violence (both witnessing violence and violent victimization) 

does influence juvenile court involvement for adolescents who live in extreme and concentrated poverty. Additional 

findings show that (a) simple exposure to violence (one incident or source) does not differ from more complex 

exposure to violence with respect to court outcomes; (b) psychological adjustment and academic progress partially 

mediate the relationship between exposure to violence and court outcome severity; (c) the mediating effects of 

psychological adjustment affect boys and girls differently, but these differences are not consistent across types of 

psychological adjustment; and (d) the mediating effects of psychological maladjustment differ as a function of age, 

but again these differences are not consistent across types of psychological adjustment. 

Policy/Practice Implications 

Findings from this research study suggest that interventions targeting negative outcomes associated with 

exposure to violence potentially should occur at multiple levels: as policy changes by governmental and quasigovernmental units; at the neighborhood level in the form of community building programs; at the school level, in 

the form of policy changes and primary prevention programs; at the family level, in the form of creating greater 

household stability; and at the individual level through therapeutic treatment. However, the lack of clear pattern for 

age or gender effects suggest that programs must be tailored to specific needs and circumstances. The results also 

suggest that there is not a single program or approach that has the potential to effectively address the problem, and 

that given the lack of evidence-based programs effectively address the most important psychological adjustment 

mediators (i.e., hopelessness), solutions should involve new and innovative approaches. 

vi 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
 

 

Introduction 

Poverty, Exposure to Violence, and Trauma 

Violence, particularly among youth, has been termed a scourge on American society 

(Hogeveen, 2007). However, its consequences are not randomly distributed through the 

population. Rather, they tend to be concentrated in poor, minority urban neighborhoods 

(Peterson & Krivo, 2009); indeed, poverty has been implicated as a major cause of violence 

(Bensing & Schroeder, 1060; Blum et al., 2000; Bullock, 1955; Coulton, Korbin, Su, & Chow, 

1995; Kposowa, Breault, & Harrison,1995; Limber & Nation, 1998; Oberwittler, 2007), and poor 

neighborhoods have been likened to war zones (Garbarino, 1995). This is particularly troubling 

because poverty in the US is at one of its highest levels since the late 1960s, and the 

concentration of African American and Latino poverty is considerably higher than it was in 1970 

(Jargowsky, 1997; Jargowsky, 2003; Kneebone, 2014). Moreover, poverty rates (particularly 

concentrated and extreme poverty) are unlikely to change (other than at the margins). 

The uncertainty associated with poverty creates existential challenges for day-to-day 

survival of children growing up poor. One area where this is particularly the case is exposure to 

violence and the psychological and behavioral adjustment that it causes. While exposure to 

violence is not a particularly prevalent source of trauma for middle-class youth, it is an important 

source of trauma for economically disadvantaged youth (Dubrow & Garbarino, 1989; Mathews, 

Dempsey, & Overstreet, 2009; Ruchkin, Henrich, Jones, Vermeiren, & Schwab-Stone, 2007), 

and it is relatively common in impoverished urban neighborhoods (McGill et al., 2014). 

Numerous studies show that exposure to violence is a prominent aspect of life in 

economically disadvantaged neighborhoods (Buka, Stichick, Birdthistle, & Earles, 2001; 

Cedeno, Elias, Kelly, & Chu, 2010; Kaynak, Lepore, & Kliewer, 2011). Survey data from the 

Mobile Youth Survey (MYS), which was collected annually between 1998 and 2011 from a 

large sample of impoverished adolescents living in Mobile, AL, and informs this study, are 

consistent with these other studies. Across years, 26.8% of respondents reported witnessing 

someone being cut, stabbed, or shot during the previous three months; 33.8% reported that a 

friend or family member had been cut, stabbed, or shot during the previous year; and 14.1% 

reported that a knife or gun had been brandished against them during the previous three months. 

All three were statistically significant predictors of growth in reported trauma symptoms (ps < 

.0001). Additionally, these three measures of violence are significant predictors of (a) growth in 

carrying, brandishing, and using a knife or gun and (b) alcohol and marijuana use (ps < .0001). 

It is therefore surprising that exposure to traumatic events is not considered more often in 

studies of pathways to juvenile justice involvement. This is not strictly true: a large number of 

studies (Abram et al., 2004; Ford, Chapman, Hawke, & Albert, 2007; Shufelt & Cocozza, 2006; 

Wasserman & McReynolds, 2011) show that large proportion of justice-involved youth had been 

exposed to traumatic events prior to their justice involvement. However, these studies have an 

inherent bias, in that the sample is not representative of the population. In contrast, prospective 

studies showing the prevalence of trauma-exposed youth (Herrera & McCloskey, 2001), or youth 

with psychiatric disorders (Copeland, Miller-Johnson, Keeler, Angold, & Costello, 2007), who 

later become justice-involved are rare. Moreover, these studies are largely limited to showing 

that a relationship exists rather than why. 

1 

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Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

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Objectives 

This study has three objectives: (a) better understand how traumatic events increase the risk 

of juvenile justice involvement for vulnerable adolescents; (b) identify developmental points 

where interventions can most effectively reduce this risk; and (c) identify classes (i.e., types) of 

intervention that might be most effective for these adolescents. Our study links data from 

multiple sources (e.g., longitudinal survey responses, juvenile court and school records) of over 

8,000 adolescents living in impoverished neighborhoods living in Mobile, AL between 1998 and 

2011. Unlike most other studies, we use a prospective population-based design. 

Prospective, population-based studies of factors leading to juvenile justice involvement are 

rare, for two reasons. First, only a portion of youth ever become involved in the juvenile justice 

system; so, if the purpose of a study is to examine juvenile justice involvement as an outcome, 

the size of the sample must be very large. Second, it is difficult to obtain juvenile justice records 

for specific cases, because these records are generally sealed. Yet, administrative records provide 

a more accurate measure of both the assignation and timing of events (e.g., arrest) than are 

available through self-report; on the other hand, administrative records typically are unable to 

provide experiential information, such as exposure to traumatic events and psychological or 

behavioral responses to these events. We have combined administrative and self-reported survey 

data to develop a more complete understanding of how contextual factors and events influence 

psychological and behavioral change (Winokur, Li, & McEntire, 2002), and ultimately court 

involvement. 

Specifically, we explored the direct and mediated pathways among exposure to violence, 

psychological adjustment (including, but not limited to, traumatic stress), academic progress, and 

juvenile justice involvement while controlling for social support, family control, and 

demographic factors. The longitudinal nature of the data, coupled with effective control of 

socioeconomic status through study design, allow much stronger causal inference than would 

otherwise be possible. By studying developmental factors and psychological adjustment across a 

range of adolescent years (10-17), we are able to suggest when and how programmatic 

interventions can be most successful. 

Model and Research Questions 

To address gaps in the literature, we must explore why exposure to violence results in risk 

behaviors among impoverished adolescents and how this leads to juvenile justice involvement. 

Figure 1 presents a model showing the pathways among these constructs and outcomes. Gender, 

both as a main effect and as a moderator, is ubiquitous in this model; however, it is not explicitly 

listed in Figure 1. To make this figure less cluttered, the effects of gender are implicitly 

expressed as simple arrows affecting constructs and relationships, but without a specific 

origination referent. Thus, the short arrow with no variable-of–origin influencing the exposureto-violence – court-involvement relationship in Figure 1 indicates the effect of gender on the 

relationship, or more specifically, an exposure-to-violence × gender interaction affecting court 

outcome severity. The first and most important hypothesized pathway involves the direct 

relationship between exposure to violence and juvenile justice involvement, and motivates the 

first research question: Does exposure to violence affect juvenile court involvement among 

adolescents living in high-poverty neighborhoods? The reasons underlying this relationship are 

explored by other pathways in the model and additional research questions. 

Given the level of violence in economically disadvantaged neighborhoods, we might expect 

exposures to violence to not be isolated events. In this study, while we expect that 

2 

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polyvictimization (see Finkelhor, Ormrod, & Turner, 2007) is indeed widespread, we are more 

interested in multiple exposures to violence (MEV). Several researchers have found that 

increased exposure to violence was indeed associated with increases in psychological 

maladjustment (e.g., Margolin, Vickerman, Oliver, & Gordis, 2010; Suliman et al, 2009). 

Additionally, multiple studies of cumulative risk suggest that as risks increase, negative 

outcomes, including increased likelihood of externalizing behavior (e.g., Fleckman, Drury, 

Taylor, & Theall, 2016), violent behavior (e.g., Stoddard et al., 2013), and stress symptoms (e.g., 

Heinze, Stoddard, Aiyer, Eisman, & Zimmerman, 2017) also increase. This leads us to dig 

deeper into the relationship between exposure to violence and juvenile justice involvement with 

the second research question: Does exposure to multiple violent events lead to greater juvenile 

justice involvement than exposure to single violent events? 

By definition, a traumatic event is one that provokes feelings of fear, helplessness, or horror 

(American Psychiatric Association, APA, 2013) and has been further defined by the National 

Child Traumatic Stress Network (2003) as an acute or chronic life event that adversely affects 

physical or emotional well-being. Exposure to traumatic events may result in post-traumatic 

stress disorder (PTSD), with symptoms that include difficulty sleeping, difficulty concentrating, 

irritability, re-experiencing the event through nightmares or flashbacks, and attempts to avoid 

reminders of the event (APA, 2013). While relatively few traumatic events result in a PTSD 

diagnosis (Yehuda, 2002), such events can disrupt normal development during childhood and 

adolescence (Margolin, 2005) and may result in major depression, panic disorder, and 

generalized anxiety disorder (Kessler, Sonnega, Bromet, Hughes, & Nelson, 1995). It is therefore 

clear that trauma is not a single psychological response to a traumatic event but rather a cluster 

of psychological (mal)adjustments to a traumatic event which includes traumatic stress, feelings 

of hopelessness (Bolland, 2003; Joiner & Wagner, 1995; Lorion & Saltzman, 1993; Nurius, 

Russell, Herting, Hooven, & Thompson, 2009), worry and anxiety (Ladouceur, Blais, Freeston, 

& Dugas, 1998; Martinez & Richters, 1993; Putwain, 2007; Pynoos & Nader, 1988; Richters & 

Martinez, 1993; Schwab-Stone et al., 1995, and low self-worth (Copeland-Linder, Lambert, & 

Iolongo, 2010; Harter & Whitsell, 2003; McMahon, Felix, Halpert, & Petropoulos, 2009; Turner, 

Finkelhor, & Ormrod, 2010), and belief in the code of the street (Anderson, 1999; Brezina, 

Agnew, Cullen, & Wright, 2004; Drummond, Bolland, & Harris, 2011; Stewart & Simons, 

2006). We hypothesize that these adjustments, and the risk behaviors they generate, mediate the 

relationship between exposure to violence and juvenile justice involvement. 

Additionally, exposure to violence is associated with negative school outcomes, both 

academic (Bowen & Bowen, 1999; Delaney-Black et al., 2002; Henrich, Schwab-Stone, Fanti, 

Jones, & Ruchkin, 2004; Hurt, Malmud, Brodsky, & Giannetta, 2001; McEvoy & Welker, 2000; 

Schwartz & Gorman, 2003) and behavioral (Bowen & Bowen, 1999; Hurt et al., 2001; Kennedy 

& Bennett, 2006; Mathews et al., 2009; Ramirez et al., 2012; Stoddard et al., 2013; Wright, 

Fagan, & Pinchevsky, 2013). Further, children who live in economically disadvantaged 

communities are more likely to repeat a grade (or not have normative academic progress) or drop 

out of high school compared to their more economically advantaged peers (Child Trends, 2012; 

Rumberger, 1995). Those who have a lack of normative academic progress are more at risk for 

dropping out of high school (Allensworth & Easton, 2005; Andrew, 2014). Two important 

factors related to normative academic progress are (a) school grades (Bowers, Sprott & Taff, 

2012) and (b) behavior (Balfanz, Herzog, & Mac Iver, 2007; Pressler, Raver, Friedman-Krauss, 

& Roy, 2016). And a number of studies demonstrate that poor school outcomes (both academic 

3 

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and behavioral) lead to juvenile justice involvement (Balfanz, Spiridakis, Neild, & Legters, 

2003; Buka et al., 2001; Heitzeg, 2009; Losen, Hewitt, & Kim, 2010). 

Finally, all of these hypothesized relationships are inevitably moderated by risk and 

protective factors associated with the individual child and his or her environment. In fact, Perfect 

and colleagues (2016) suggest that additional analysis of the mediators and moderators 

associated with trauma affect outcomes. The relationship between exposure to violence and 

psychological adjustment and behavior is affected by development and gender (Crick, Ostrov, & 

Werner, 2006). For example, exposure to violence is more likely to predict depressive symptoms 

in girls than boys (Moses, 1999); and girls who were exposed to violence are more likely to 

report anxiety than boys (White, Bruce, Farrell, & Kliewer, 1998). On the other hand, boys may 

be more likely to embrace a “code of the street” mentality than girls (Latzman & Swisher, 2005). 

Psychological response to trauma exposure is also moderated by family and neighborhood 

support (Bacchini, Esposito, & Affuso, 2009; Jain & Cohen, 2013; Ludwig & Warren, 2009). 

Washington and colleagues (2017) found in a review of literature that generally positive 

parenting in the African American community is related to fewer symptoms of depression and 

anxiety in children and adolescents. On the other hand, Williamson and colleagues (2017) found 

inconsistent results in their review of literature on parenting and PTSD in childhood. Further, 

Hair, Moore, Garrett, Ling, & Cleveland (2008) found that the relationship between 

psychological (mal)adjustment and risk behaviors is moderated by parental support. Finally, 

Chung and Steinberg (2006) studied how neighborhood disorder was related to youth offending 

behaviors and found a complex system where community connectedness is related to both 

prosocial and antisocial outcomes related to adolescents. Similarly, when neighborhoods 

function as true communities, where there are feelings of support, residents can be empowered 

individually and collectively (Hawkins & Catalano, 1992), resulting in hope (Cohen & Phillips, 

1997). It is therefore not unreasonable to hypothesize that the relationships between exposure to 

violence, psychological adjustment, and educational outcomes on the one hand and juvenile 

justice involvement on the other are moderated by demography and social support. This leads to 

our final research questions: 

(3a) How does psychological adjustment mediate the direct pathway between exposure to 

violence and juvenile justice involvement? 

(3b) How does normative academic progress mediate the relationship between psychological 

adjustment and juvenile justice involvement? 

(3c) How are the direct and indirect paths in the model moderated by demography, social 

support, and family control? 

4 

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5 

Figure 1. Model depicting direct and indirect effects of lagged exposure to violence on court outcome severity 

Note: arrows without originating referents indicate gender effects 

lagged 

exposure to 

violence

lagged 

psychological 

adjustment

lagged 

support/

control

academic 

progress

court 

outcome 

severity

lagged court outcome severity

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Methods 

Study Location 

The study was conducted using data collected in the Mobile, Alabama, Metropolitan 

Statistical Area (MSA). The City of Mobile (2015 population = 195,111; 2014 poverty rate = 

23.4%) anchors the MSA. In 1990, 42% of African Americans in the MSA lived in high-poverty 

census tracts, placing Mobile third in the nation in this measure of concentrated poverty 

(Jargowsky, 1997). Despite court-ordered busing and full implementation of a consent decree in 

1998, the county-wide school district has remained largely segregated (Frankenberg, 2009). 

Recruitment and Data Collection Procedures 

We began the Mobile Youth Survey (MYS) in 1998 by sampling adolescents from 13 

neighborhoods representing 23 block groups in 14 census tracts. These neighborhoods were 

selected because they had the lowest median household income in the MSA (based on the 1990 

Census). The median 1990 poverty rate for these neighborhoods was 77.25, and the median 

household income that year was $5,190; the population in the typical target neighborhood was 

100% African American. Seven of the neighborhoods were public housing developments; the 

other six consisted of privately-owned housing. Five of the neighborhoods were located in 

Prichard, and eight were located in Mobile. 

Selected demographic characteristics of these 13 neighborhoods, derived from the 2000 

census, are reported in Table 1 (several neighborhoods share census tracts). The targeted 

neighborhoods were overwhelmingly African American (median = 97%). Poverty rates in the 13 

neighborhoods ranged between 31.5% and 81.4%, with a median poverty rate of 57.2% and a 

median extreme poverty (i.e., <50% of the poverty level) rate of 30%. The increase no doubt 

reflects a true economic improvement in the lives of neighborhood residents. But it also reflects a 

change in the boundaries of several census tracts and block groups. 

During the summer of 1998 (Wave 1), the Mobile Housing Board provided us with names of 

adolescents who lived in public housing units and their addresses; in addition, the Prichard 

Housing Authority provided us with addresses of public housing unit where adolescents lived. 

We randomly selected approximately half the units in the seven public housing neighborhoods in 

Mobile and Prichard for recruitment. In the six private housing neighborhoods, approximately 

half the houses and apartments were randomly selected for recruitment. We attempted to make 

contact with residents in each of these targeted residences, explain the study, determine whether 

eligible youth lived in the residences and, if so, obtain parental consent for them to participate 

and schedule a time and place for them to be surveyed. Additionally, we posted notices in each 

of the 13 study neighborhoods inviting neighborhood youth to come to survey sites if they 

wanted to participate; this plus word of mouth, resulted in a number of participants who had not 

been actively recruited. Because we had not obtained parental consent for these youth, study 

personnel accompanied them to their homes to explain the study to parents and obtain consent. 

Surveys were conducted in community centers, churches, schools, and other buildings in the 

study neighborhoods. Study participants were scheduled to come to the survey sites on particular 

days and at particular times; on arrival, they were checked in and assigned to rooms, such that no 

room contained more than 20 participants. The survey questions were read aloud to the group, 

who were asked to follow along in their response booklets and bubble in appropriate answers to 

each question. Individual participants who had difficulty keeping up were invited to work in 

6 

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even smaller groups with study personnel, who were able to provide them with more attention 

and work with them at a more leisurely pace. In some cases, when participants were not able to 

come to the survey site, they were surveyed in their homes. 

In 1999 (Wave 2), we attempted to resurvey each of the 1998 participants (who were still 

under 19 years of age), and we recruited a new cohort (including youth who had been contacted 

the previous year but who had not been surveyed). After conducting the survey in 1998 without 

incident (e.g., no drug busts had occurred the day after we had surveyed youths about, including 

many other things, their drug use behavior), we had gained legitimacy and trust, and word had 

spread among neighborhood residents. Thus, the response rate increased among actively 

recruited youth, and the proportion of passively recruited youth increased as well. During each 

subsequent year between 2000 and 2011, we engaged in a similar procedure. By 2011, the vast 

majority of the MYS participants lived in 50 identifiable low-income neighborhoods. 

Response rate is difficult to determine because the exact sampling frame is unknown. 

However, we estimate that in 1998, the active recruitment response rate in public housing 

neighborhoods ranged between 59% and 67%, and the active recruitment response rate for 

nonpublic housing neighborhoods ranged between 64% and 72% (see Bolland, 2007, and 

Appendix B for a discussion of how these figures were obtained). Since 1998, the active 

recruitment response rate has approached 90% each year. 

Initial MYS Sample 

The initial MYS sample consisted of approximately 12,500 participants who produced 

approximately 36,000 annual data points (see Appendix A). However, we were forced to 

eliminate observations, and sometimes entire cases, because we could not confirm their 

identities. This is a problem for almost all community-based longitudinal studies, particularly 

those involving youth, because cases cannot be definitively and independently confirmed. 

Our first challenge was to identify youths who actually existed in Mobile and match MYS 

participants to these people. The first step in this process was to identify the people, which we 

did using several databases: Mobile County Public School System (MCPSS) records, Mobile 

County Juvenile Court (MCJC) records, Mobile Housing Authority records, records from a 

Mobile Police Department prevention program (Family Intervention Team or FIT Program) that 

served the areas we studied, and the LexisNexis public records database (however, only people 

aged 18 and older are included in this database). We assumed that anyone who was included in 

these databases was a real person. These databases provided date of birth, address, and some 

other demographic information (although in some cases, data were missing, e.g., the LexisNexis 

database provided data of birth only for some people). 

The second step was to match MYS participants with people in the databases. The MYS 

coversheet asked participants to print their names, their addresses, and their birthdates. We 

assumed that if the name and date of birth matched a name in one of the databases, a match had 

occurred. In cases where we found only a slight discrepancy (e.g. date of birth did not match but 

was very close to correct; e.g., 03/05/1994 vs. 04/05/1994, 08/19/1998 vs. 08/19/1999), we also 

assumed a match. In cases where a larger discrepancy occurred (e.g., 11/14/1987 vs. 

12/15/1988), we assumed a match if we could confirm and match a different piece of information 

(e.g., address). We were able to match the vast majority of cases based on these criteria. 

However, in cases where we found a more egregious discrepancy in birth date, or where we 

found a minor discrepancy in birth date and could not match a different piece of information, we 

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deferred our match decision. 

Like almost all other longitudinal studies, and particularly like other community (rather than 

clinical) based longitudinal studies, the MYS had a high degree of dropout, with approximately 

one third of all participants contributing only a single data point. In some cases, dropout was 

structurally imposed: participants could continue in the study only until they were 19.25 years of 

age. Thus, approximately 10% of the sample aged out of the study each year rather than dropped 

out. But even with this relatively high level of attrition, two thirds of MYS participants 

contributed data during two or more years. For those participants who were not already matched, 

we had additional opportunities to match them to people in the databases. So, for example, an 

MYS participant in 2001 might have given an egregiously discrepant birth date, but in 2002 the 

birth date did match. In this case, we confirmed the case but only if the handwriting during the 

two years was sufficiently similar for us to confirm that the same person completed both cover 

sheets. 

A third possibility exists for confirming the identity of a MYS participant. Assume an 

adolescent was home schooled (therefore did not show up in the MCPSS database), had never 

been arrested (therefore did not show up in the juvenile court database), did not live public 

housing (therefore did not show up in the housing authority database), had not been targeted for 

prevention by the FIT Program (therefore did not show up in its database), and was born in late 

2001 (therefore did not show up in the LexisNexis database). This person exists, but he or she 

does not show up in any of our available databases. We would confirm him or her if the 

information provided on two-or-more cover sheets match and the handwriting on those cover 

sheets is similar. 

All MYS participants who could not be confirmed in one of these ways were eliminated from 

the MYS data for analysis. Altogether, this resulted in the elimination of over 1,000 cases; the 

vast majority of these had only a single observation. These were obviously real people; most 

often, they likely misrepresented themselves because they were skeptical of our promise of 

anonymity, or they claimed a different name so that they could participate twice and be paid 

twice, or because they were just goofing off. No doubt, we also eliminated some cases that were 

not bogus, but we suspect that these were few and far between. Our decision rules were 

conservative, which meant more cases were eliminated from the analysis but greater confidence 

in the cases that were retained. 

In addition to eliminating entire cases, sometimes observations were eliminated if we had 

doubts about the authenticity of the observation. Observations were flagged for further 

examination if there was egregious discrepancy in date of birth or in spelling of the name, 

particularly the last name. When this occurred, we compared the handwriting in the discrepant 

observation; in a few cases, where we concluded that the handwriting was the same, we assumed 

that it was due to a mental error or goofing off. Most of the time, however, the handwriting did 

not match and we eliminated the observation. To this point, the total MYS sample consists of 

11,582 youths who produced 35,295 annual data points. 

Table 2 shows the initial size of each cohort, and the number of respondents from each 

cohort who contributed two, three, ..., twelve annual data points (footnote: the maximum 

number of data points a participant could legitimately contribute is 10, so more than 10 data 

points from a given person is only possible if that person misrepresented his or her age), as well 

as demographic statistics for each cohort. Retention rates are higher than Table 2 might initially 

suggest, because during any given year the number of participants who age out of the sample 

averages 10.2% (i.e., 100% of respondents age out over an 11-year period). The mean annual 

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follow-up rate, discounting those who aged out of the study, equals .65; not surprisingly, as the 

number of active enrollees increases each year and they move to new neighborhoods, the followup rates generally decline over time (from 74.2% in wave 2 to 63.1% in wave 14). 

The sample is very homogeneous in several areas: poverty, municipal jurisdictions, and court 

jurisdiction. By any measure, MYS participants were impoverished. Table 2 shows that each 

year, between 80.6% and 90.2% of participants received free school lunches. Examination of a 

sample of free/reduced cost lunch applications from the Mobile County Public School System 

(MCPSS) shows the monthly earned household incomes of MYS households who qualified for 

free lunch in 2005-06 (M = $415, SD = $682) was lower than those for non-MYS participants (M 

= $814, SD = $837). Methodological challenges related to measuring and controlling for SES 

plague much of social science research: SES affects most of the social science constructs we 

examine, but the measurement and meaning of SES is ethereal and poorly understood, leading it 

typically to be measured incompletely, mismeasured, or measured not at all (Braveman et al., 

2005; Oakes & Rossi, 2003; Shavers, 2007). In these cases, residual confounding occurs 

(Kaufman & Cooper, 2001; Kaufman & Poole, 2000), with resulting misinterpretation of results. 

As an alternative to statistical control, in this study, we limit the impact of residual confounding 

by severely restricting heterogeneity of SES (Geronimus & Korenman, 1993; Rosenbaum, 2005). 

Certainly, municipalities and court jurisdictions are better understood than SES, but which 

characteristics make them similar or different are not well understood. We resolve this by 

restricting the study to three municipal jurisdictions and a single court jurisdiction. 

9 

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Table 1 

Description of MYS Target Neighborhoods: 2000 Census 

Census tracts Population African- Poverty rate Extreme Median 

(Block groups) American (individuals) poverty household 

population rate income 

(individuals) 

Non-public housing 

Plateaua 12 2,511 88% 56.7% 28.3% $13,810 

Harlemb 39.02 (1) 1,203 85.6% 47.1% 11.2% $18,426 

Martin Luther 4.01 (2, 3, 4) 2,827 97.2% 49.5% 30.6% $12,157 

Kinga 

5 (1) 

Snug Harborb 43 (1) 535 100.0% 65.2% 24.2% $11,597 

Alabama Villageb 47 (1) 2,565 84.5% 70.7% 39.0% $10,793 

48 (1, 2) 

Trinity Gardensa 39.01 (1, 2, 3) 2,479 97.9% 31.5% 12.2% $18,374 

Public housing 

Orange Grovea 4.01 (1, 2) 3,517 98.7% 76.3% 59.2% $6,696 

4.02 (1, 2) 

Josephine Allen 12 2,511 88.8% 56.7% 28.3% $13,810 

Homesa 

Roger Williams 6 (2, 3) 2,326 97.2% 56.7% 30.3% $11,236 

Homesa 

Oaklawn Homesa 13.02 (2) 1,816 98.2% 44.2% 22.9% $14,648 

R.V. Taylor Plazaa 15.01 (2, 4) 3,139 95.6% 64.6% 36.9% $9,963 

15.02 (1) 

Gulf Villageb 48 (1) 943 94.7% 81.4% 44.1% $8,783 

Bessemer 40 (4) 1,487 98.0% 57.7% 30.3% $11,950 

Apartmentsb 

a

Mobile; bPrichard 

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Table 2 

Mobile Youth Survey Multiple Cohort Design 

Cohort 

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 Total 

Ntotal 1,717 2,372 2,129 2,389 2,175 2,220 2,263 2,583 2,324 3,009 2,819 3,078 3,241 2,976 35,295 

Nnew cohort 1,717 1,137 561 806 604 603 490 692 565 989 747 1,006 988 677 11,582 

Data points 

1 331 297 136 207 148 145 79 136 137 241 217 337 473 677 3,561 

2 346 195 84 122 98 109 68 116 85 213 161 322 515 0 2,434 

3 254 148 68 103 77 70 67 97 107 214 179 347 0 0 1,731 

4 238 124 62 96 63 54 65 104 82 187 190 0 0 0 1,265 

5 171 119 54 62 50 68 55 82 90 134 0 0 0 0 885 

6 166 84 40 67 40 46 57 97 64 0 0 0 0 0 661 

7 87 66 56 55 47 53 62 60 0 0 0 0 0 0 486 

8 79 39 25 42 39 37 37 0 0 0 0 0 0 0 298 

9 31 39 26 39 34 21 0 0 0 0 0 0 0 0 190 

10 9 21 7 11 8 0 0 0 0 0 0 0 0 0 56 

11 4 5 3 2 0 0 0 0 0 0 0 0 0 0 14 

12 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 

Descriptive statistics 

Mean age 13.50 13.06 12.45 12.65 12.21 12.30 12.2 12.66 12.73 12.91 12.92 13.08 13.00 12.83 

% Male 50.4 53.0 51.3 49.6 50.3 51.7 48.5 53.9 50.1 50.2 49.7 48.6 52.2 50.9 

% African American 94.1 92.3 91.6 91.7 88.3 91.5 93.9 92.3 94.4 92.6 91.8 90.9 92.9 92.8 

% Multiracial 4.4 6.2 6.1 6.7 9.2 7.1 5.0 7.2 5.0 6.6 8.0 8.0 6.6 6.5 

% Public housing 62.0 57.2 55.8 44.7 49.3 50.8 52.7 31.4 25.7 19.1 18.8 15.5 a a 

% Free lunch 88.5 87.2 86.2 84.6 82.3 85.6 86.1 84.8 80.6 82.2 84.1 82.1 87.6 90.2 

a

not available 

11 

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Data Sources 

Availability. All data analyzed in this study are part of the Mobile Youth and Poverty Study 

(MYPS) archive (see Appendix A). As such, they have been collected, cleaned, and stored in 

electronic files that can be merged at the individual, school, and/or neighborhood level based on 

based on established identification numbers. The data used for this study include MYS data; 

administrative and student records from the MCPSS; and administrative records from the MCJC. 

Electronic files were obtained from MCPSS (1998-2011 and MCJC (1999-2011) under terms of 

signed partnership agreements between The University of Alabama (UA) and each agency. 

Quality. MYS data are self-reported, and therefore subject to limitations of any self-reported 

data (see Strengths and Limitations). All administrative records (MCJC, MCPSS) meet State 

and Federal standards of quality control. 

Exclusion Criteria 

MYS observations were excluded from analyses for this study if they did not meet an internal 

consistency criterion. The format of the questions about 20 risk behaviors asked each year by the 

MYS allows us to check consistency of responses. Each behavior was assessed by two or more 

questions: the first asked whether the respondent ever engaged in the behavior; the other 

question(s) asked whether the respondent engaged in the behavior during increasingly more 

recent time periods (e.g., 90 days, 30 days, 7 days). A response pattern is inconsistent if the 

respondent reported that he or she had never engaged in the behavior in response to the first 

question and that he or she had engaged in the behavior during at least one recent time period in 

response to other question(s). Inconsistent responses may be an indication that respondents were 

impaired or fatigued, or that they misinterpreted the questions, or that they did not take the task 

seriously. Any of these explanations would raise questions about the validity of the data of 

respondents who were consistently inconsistent in their response patterns across behaviors. We 

excluded a respondent’s entire wave of data if he or she was inconsistent across more than three

waves of data. This resulted, on average, in the exclusion of 7.32% of the observations each year. 

The second exclusion criterion concerns age. In Alabama, adolescents are transferred to adult 

court at age 18. Thus, their data were excluded for any year when they were 18 or older. The 

third exclusion criterion involved enrollment in the MCPSS. Our computation of academic 

progress is dependent on records from the MCPSS; therefore academic progress for any 

adolescent who was never enrolled in the school system is undefined. To solve this problem, all 

data for adolescents who were never enrolled in the MCPSS were excluded. 

Study Sample 

After exclusions, the sample for the study consists of 9,215 adolescents (aged 9-17) who 

participated in the MYS. Respondents provided an average of 2.56 annual data points, for a total 

of 23,569 data points. 

Measures 

All measures used in these analyses, except for those describing academic progress and court 

involvement, are based on questions from the MYS questionnaire. Information about the scales 

used in the analyses for this study (e.g., items that comprise each scale, reliability coefficients for 

the scales, sources of the scales, and notes about the scales) are included in Appendix C. The 

SPSS code used to generate each scales is available in the Data Archive. 

12 

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Short-term stability coefficients (five-week test-retest reliability, from a study using the same 

questions with approximately 50 adolescents comparable to the MYS sample conducted in 

Huntsville, AL in 1998) are provided for some of the MYS measures used in this study (see 

Appendix C). Internal reliability coefficients (Cronbach’s α, specified across waves of MYS 

data) are provided for all MYS scales used in this study (see Appendix C). 

Psychometric properties are not as robust as we might expect; but two factors may explain 

this. First, given the homogeneity of the MYS sample, correlations tend to be attenuated (e.g., 

they are not artificially inflated by uncontrolled or poorly controlled SES, Umlauf, Bolland, 

Bolland, Tomek, & Bolland, 2015). Moreover, many of the questions on the MYS use a twocategory response option (agree–disagree), acknowledging the cognitive challenges for many of 

the MYS participants. The resulting lack of variance at the individual item level also leads to 

attenuated correlations (Muthen, 1990; Vargha, Rudas, Delayney, & Maxwell, 1996). 

Demographic measures. Demographic characteristics were measured using data from the 

MYS (see Appendix C, Tables C1, C2, and C3). We include measures of race, age, and gender in 

analyses. Descriptive statistics for this study are reported in Table 3. These and all other 

descriptive statistics include only observations that are not excluded based on the exclusion 

criteria. 

Psychological adjustment. Psychological adjustment was measured by items on the MYS. 

Six psychological adjustment scales were included in analyses in this project: (a) hopelessness, 

(b) worry, (c) traumatic stress, (d) global self-worth, (e) behavioral self-worth, and (f) street 

code. All items, response options, as well as reliability information are presented in Appendix C. 

Descriptive statistics for each psychological adjustment scale are reported in Table 3. 

Hopelessness. Hopelessness was measured using six items adapted from Kazdin and 

colleagues (1983) and DuRant and colleagues (1994) where respondents were asked to agree (= 

1) or disagree (= 0) with items such as “all I see ahead of me are bad things, not good things” 

(see Appendix C, Tables C7 and C8)1

. Resulting is a scale ranging from 0 to 6 where higher 

scores indicate higher levels of hopelessness. 

Street code. Street code was measured using eight items adapted from Bandura (1973). 

Although these items were originally designed to measure attitudes about violence, they reflect 

the street code construct developed by Anderson (1999) and are used to measure that construct. 

Respondents were asked to agree (= 0) or disagree (= 1) with items such as “it is not possible to 

avoid fights in my neighborhood” (see Appendix C, Tables C13 and C14). The resulting scale 

ranges from 0 to 8, with higher scores indicating higher levels of street code. 

Self-worth. Self-worth was measured using nine items adapted from Harter (1982) resulting 

in two scales: (a) global self-worth and (b) behavioral self-worth. Global self-worth was 

measured using four items and behavioral self-worth was measured using five items. Participants 

were asked to select one of two statements that was most like them (e.g., “I usually don’t like the 

way I behave” (= 0) or “I usually like the way I behave,” (= 1) see Appendix C, Tables C11 and 

C12). Resulting are a (a) global self-worth scale ranging from 0 to 4 where higher scores indicate 

1 Response option coding for all MYS measures does not match the response options provided in Appendix C. 

Response options values in Appendix C represent the values as they appear on the MYS Questionnaire. These 

values have been recoded for analysis purposes. For ease of interpretation, response options of 1 and 2, for 

example, were recoded as 0 and 1. Syntax for this recoding and in some cases, reverse coding, is provided in 

the Data Archive. 

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higher levels of global self-worth and (b) a behavioral self-worth scale ranging from 0 to 5 where 

higher scores indicate higher levels of behavioral self-worth. 

Worry. Worry was measured using nine items adapted from Small and Rogers (1995) where 

respondents were asked how much they worried about various things such as getting good grades 

and getting a good job (a) not at all (= 0), (b) some (= 1), or (c) very much (= 2). For one item, 

“how much do you worry about getting good grades?,” respondents were also given a choice to 

respond that they were not in school (see Appendix C, Tables C9 and C10). Resulting is a scale 

ranging from 0 to 18 where higher scores indicate higher levels of worry. 

Traumatic stress. Traumatic stress was measured using seven items developed for the MYS 

where respondents were asked how much they experienced stress (reflected in, for example, 

trouble sleeping at night when bad things happen to a family member or friend). Responses are 

(a) almost never (= 0), (b) sometimes (= 1), or (c) very often (= 2) (see Appendix C, Tables C15 

and C16). The resulting scale ranges from 0 to 14, with higher scores indicating higher levels of 

traumatic stress. 

Social support. Social support was measured by items on the MYS. Three social support 

scales were included in analyses in this project: (a) maternal warmth; (b) positive neighborhood 

connectedness, (c) negative neighborhood connectedness. All items, response options, as well as 

reliability information are presented in Appendix C. Descriptive statistics for all social support 

scales are reported in Table 3. 

Maternal warmth. Maternal warmth was measured using six items on the MYS adapted from 

Lamborn and colleagues (1991) where respondents were asked to think about the person most 

like a mother to them and then to agree (= 1) or disagree (= 0) with items such as “I can usually 

count on her to help me out if I have some kind of problem” (see Appendix C, Tables C17 and 

C18). Respondents were also able to respond “I don’t have anyone who is like a mother to me”

to each of the items. Resulting is a scale ranging from 0 to 6 where higher scores indicate higher 

levels of maternal warmth. A response of “I don’t have anyone who is like a mother to me” was 

treated as missing. Because other MYS questions asked about mother figure, we created a single 

variable called NoMom, and if it were missing for a particular case, all responses to questions 

about mother figure were considered to be missing. 

Neighborhood connectedness. Neighborhood connectedness was measured using 11 items 

on the MYS adapted from Glynn (1981) and Perkins and colleagues (1990); because some of the 

questions were worded positively and some negatively, two subscales were created: 

neighborhood connectednesspositive was measured using six positively worded items, and 

neighborhood connectednessnegative was measured using five negatively worded items. 

Participants were asked to agree (= 1) or disagree (= 0) with items such as “I feel I am an 

important part of my neighborhood” (see Appendix C, Tables C19 and C20). Negative items 

were reverse coded to create the neighborhood connectednessnegative scale. The neighborhood 

connectednesspositive scale ranges from 0 to 6, with higher scores indicating more positive feelings 

about the neighborhood. The neighborhood connectednessnegative scale ranges from 0 to 5, with 

higher scores indicating greater absence of negative feelings about the neighborhood. Thus, 

higher scores on both neighborhood connectedness scales reflect higher levels of neighborhood 

connectedness. 

Family control. Family control was measured by items on the MYS. Two family control 

scales were included in analyses in this project: (a) curfew and (b) parental monitoring. All 

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items, response options, as well as reliability information are presented in Appendix C. 

Descriptive statistics for all family control scales are reported in Table 3. 

Curfew. Curfew was measured using four items on the MYS adapted from Lamborn and 

colleagues (1991) where respondents were asked to indicate whether they were not (= 1) or were 

(= 0) allowed to stay out in various circumstances (e.g., after dark on school nights, see 

Appendix C, Tables C21 and C22). Resulting is a scale ranging from 0 to 4 where higher scores 

indicate more curfew rules. 

Parental monitoring. Parental monitoring was measured using six items on the MYS 

adapted from Lamborn and colleagues (1991) (see Appendix C, Tables C23 and C24). 

Participants were asked two questions about whether their parent(s) knew who they hung out 

with and where they were outside of school hours (no = 0, yes = 1). Then, they were asked how 

much their parent(s) really new about what they did or where they were (a) outside of school 

hours, (b) at night, and (c) generally how they spend their time with response options (a) they 

don’t know (= 0), they know a little (= 1), or they know a lot (= 2). Participants were also able to 

respond that they do not go out at night (coded as “2” or the same as “they know a lot). Finally, 

participants were asked how much their parent(s) tried to find out how they spend their time with 

response options (a) they don’t try (= 0), (b) they try a little (= 1), or (c) they try a lot (= 2). 

Resulting is a scale ranging from 0 to 12 where higher scores indicate higher levels of parental 

monitoring. 

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Table 3 

Descriptive Statistics for MYS Derived Measures 

Demographic variables 

Race 

African American 

White 

Multiracial 

Other 

Gender 

Boy 

Girl 

Age 

Age groups 

Frequency 

21,846 

121 

1,133 

49 

11,473 

11,863 

Percentage 

94.40 

0.50 

4.90 

0.20 

49.20 

50.80 

M 

13.09 

SD 

2.00 

1 (9-11 year olds) 6,178 26.21 

2 (12-13 year olds) 6,773 28.74 

3 (14-16 year olds) 10,618 45.05 

Psychological adjustment 

Hopelessness 

Code of the street 

Behavioral self-worth 

Global self-worth 

Worry 

Traumatic stress 

1.328 

3.586 

3.142 

3.259 

7.008 

6.640 

1.680 

2.188 

1.351 

0.990 

4.166 

3.194 

Social support 

Maternal warmth 

Neighborhood connectednesspositive 

Neighborhood connectednessnegative 

5.266 

4.014 

2.962 

1.160 

1.824 

1.520 

Family control 

Curfew 

Parental monitoring 

2.402 

9.224 

1.315 

2.746 

Note. Descriptive statistics are based on observations rather than cases. Because observations are 

not independent, care should be exercised in interpreting these results. 

Exposure to violence. Exposure to violence was measured by items on the MYS. Two 

exposure to violence scales were included in this study: (a) witnessing violence and (b) violent 

victimization. Descriptive statistics for exposure to violence scales are presented in Table 4. 

Witnessing violence. An MYS question asked respondents “During the past 3 months (90 

days), did you see someone being cut, stabbed, or shot?” Response options were (a) no; (b) yes, 

just once; and (c) yes, more than once. The witnessing violence scale was constructed based on 

these responses, with scale values equal to 0 = no, 1 = yes, just once, and 2 = yes, more than 

once (see Appendix C, Table C4). 

Violent victimization. The violent victimization scale was constructed from three MYS 

questions. First, respondents were asked “In the past 3 months (90 days), did someone pull a 

knife or a gun on you?” Response options were (a) no; (b) yes, just once; and (c) yes, more than 

once; this reflects weapon brandishment. Second, respondents were asked “In the past year (12 

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months), did someone cut or stab you bad enough that you had to see a doctor? Third, 

respondents were asked “In the past year (12 months), did someone shoot a gun at you?” 

Response options for the second and third questions were the same as for the first (see Appendix 

C, Tables C5 and C6). 

In constructing the violent victimization scale, we began by combining the second and third 

questions, such that cut/shot at = 2 if the respondent answered yes, more than once to either 

question; cut/shot at = 1 if the respondent answered yes, just once to either question and yes, 

more than once to neither question; cut/shot = 0 if the respondent answered no to both questions. 

Finally, the violent victimization scale was created by combining the weapon brandishment and 

the cut/shot at measures, such that violent victimization = 4 if cut/shot at = 2; violent 

victimization = 3 if cut/shot at = 1; violent victimization = 2 if weapon brandishment = 2 and 

cut/shot at = 0; violent victimization = 1 if weapon brandishment = 1 and cut/shot at = 0; violent 

victimization = 0 if weapon brandishment = 0 and cut/shot at = 0. 

We acknowledge the non-alignment of the timing in the response options for the weapon 

brandishment and cut/shot at variables. With additional analysis, we have found that this nonalignment is not negligible, however should not invalidate the results (see Appendix C). 

Multiple exposures to violence. To assess multiple exposures to violence (MEV), three 

scales were created from the exposure to violence variables. First, sources of MEV during the 

previous time period (MEVs) was measured from the witnessing violence and violent 

victimization scales, such that (a) MEVs = 0 if neither witnessing violence nor violent 

victimization occurred; (b) MEVs = 1 if either witnessing violence or violent victimization 

occurred; and (c) MEVs = 2 if both witnessing violence and violent victimization occurred. 

Second, incidents of MEV during the previous time period (MEVi) was measured from the same 

two scales, such that MEVi = 0 if neither witnessing violence nor violent victimization occurred; 

(b) MEVi = 1 if a single incident of witnessing violence or violent victimization occurred; and 

(c) MEVi = 2 if more than one incident of witnessing violence or more than one incident of 

violent victimization occurred. Finally, combined MEV during the previous time period (MEVc) 

was measured using MEVs and MEVi, such that MEVc = 0 if both MEVs = 0 and MEVi = 0; (b) 

MEVc = 1 if either MEVs = 1 or MEVi = 1; (c) MEVc = 2 if either MEVs = 2 or MEVi = 2; and 

(d) MEVc = 3 if both MEVs = 2 and MEVi = 2. Lower designations of MEV are always 

superseded if conditions for a higher designation hold. Descriptive statistics are presented in 

Table 4. 

An analysis was conducted to determine the effect of age on witnessing violence, using a 

linear mixed model as implemented in SAS PROC MIXED, with restricted maximum likelihood, a 

first-order autoregressive covariance structure, and random intercepts. Degrees of freedom were 

estimated using a procedure developed by Satterthwaite (1941) coupled with an inflation of the 

estimated variance-covariance matrix of the fixed and random effects, as detailed by Kenward 

and Roger (1997). Results showed a nonsignificant linear effect, but a significant quadratic effect 

(b = 0.003, se = 0.001, t = 2.84, p = .005, Figure 2a). Witnessing violence is at its highest level 

for nine year olds and decreases through age 13 before rising again at age 14 and 15, and then 

decreases again at age 16. A similar analysis was also conducted to determine the effect of age 

on violent victimization. Results showed a significant linear effect. While witnessing violence 

decreases from nine years old to ten years old, it then increases with year of age thereafter (b = 

0.063, se = 0.004, t = 17.75, p < .001, Figure 2b). 

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Witnessing Violence 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

0 

9 10 11 12 13 14 15 16 

Age 

Figure 2a. Witnessing Violence as a Function of Age. 

0.8 

0.7 

Violent Victimization 0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

0 

9 10 11 12 13 14 15 16 

Age 

Figure 2b. Violent Victimization as a Function of Age. 

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Table 4 

Exposure to Violence Descriptive Statistics 

Frequency Percentage M SD 

Exposure to violence 

Witnessing violence 0.31 0.591 

0: not in the past 90 days 17,600 75.5 

1: once in the past 90 days 4,148 17.8 

2: more than once in the past 90 days 1,568 6.7 

Weapon brandished against 0.13 0.392 

0: not in the past 90 days 20,939 89.6 

1: once in the past 90 days 1,906 8.2 

2: more than once in the past 90 days 513 0.2 

Cut, shot, or shot at 0.13 0.411 

0: not in the past year 20,752 89.4 

1: once in the past year 1,833 7.9 

2: more than once in the past year 623 2.7 

Violent victimization 0.41 1.024 

0: not in the past 90 days/year (qualified with 19,586 83.8 

time frame) 

1: brandishment one time (90 days) 1,153 4.9 

2: brandishment more than once (90 days) 188 0.8 

3: cut or shot once (year) 1,833 7.2 

4: cut or shot more than once (year) 623 2.7 

Multiple exposures to violence (MEV) 

MEVsources 0.41 0.653 

0: No victimization 15,610 67.8 

1: One source of victimization 5,291 23.0 

2: More than one source of victimization 2,111 9.2 

MEVinstances 0.41 0.653 

0: No victimization 15,610 67.8 

1: One instance of victimization 5,280 22.9 

2: More than one instance of victimization 2,122 9.20 

MEVcombination 0.51 0.837 

0: No victimization 15,610 67.8 

1: One source or instance of victimization 4,119 17.9 

2: More than one source or more than one 2,333 10.1 

instance of victimization 

3: More than one source and more than one 950 0.4 

instance of victimization 

Note. Descriptive statistics are based on observations rather than cases. Because observations are 

not independent, care should be exercised in interpreting these results. 

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Normative academic progress. Normative academic progress was measured using the 

Mobile County Public School System records (see Appendix D). For this project, one variable 

was used to measure academic progress. Descriptive statistics for normative academic progress 

are reported in Table 5. 

Table 5 

Normative Academic Progress Descriptive Statistics 

Frequency Percentage M SD 

Academic progress 4.03 1.109 

0: Dropout 869 3.8 

1: 4+ years behind normative progress 28 0.1 

2: 3 years behind normative progress 497 2.1 

3: 2 years behind normative progress 3,222 14.2 

4: 1 year behind normative progress 9,507 42.0 

5: Normative progress or high school graduate 8,501 37.6 

Note. Descriptive statistics are based on observations rather than cases. Because observations are 

not independent, care should be exercised in interpreting these results. 

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Juvenile court involvement. Juvenile court involvement was measured using the Mobile 

County Juvenile Court records (see Appendix E). For this project, particular attention was paid to 

(a) court offenses identified as Crimes against a Person (CAP) and to (b) the severity of 

disposition and/or court action after offense. With respect to the court outcomes, the highest level 

of severity is noted as residential placement. Descriptive statistics for court involvement 

variables are reported in Table 6. It is important to note that the descriptive statistics reflect 

observations. 

Table 6 

Juvenile Court Involvement Descriptive Statistics 

Court outcomes Frequency Percentage M SD 

Number of crimes against a person 

offenses within wave 

0.04 0.246 

0 22,395 96.5 

1 674 2.9 

2 120 0.5 

3 25 0.1 

4 3 0.00 

5 1 0.00 

Court outcome severity 0.5 1.544 

0: No offense within wave 21,070 89.4 

1: Released without transfer or referral 274 1.2 

2: Nolle Prosse, dismissed with 4 0.00 

conditions 

3: Lecture and release 259 1.1 

4: Informal adjustment 593 2.5 

5: Released with transfer or referral 115 0.5 

6: Probation supervision or fined 1,112 4.7 

7: Residential placementa 142 0.6 a

This category also includes other, but less frequent, severe outcomes including transfer to criminal 

court (see Appendix E). 

Note. Descriptive statistics are based on observations rather than cases. Because observations are 

not independent, care should be exercised in interpreting these results. 

Analytic Procedures 

Before discussing specific procedures used to analyze data to address the three research 

questions, several general issues regarding our analysis should be discussed. First, in addressing 

all of the research questions, primary variables of interest involve exposure to violence: for 

research questions 1 and 3, these primary variables are witnessing violence and violent 

victimization, and for research question 2, the primary variable is multiple exposures to violence. 

In addition to the primary variables, models also include demographic variables. Analyses for the 

first two research questions include race, gender, and age as demographic variables. Race is a 

categorical variable with four levels. Gender is also categorical; but because it is treated as 

dichotomous, it is treated as ordered rather than categorical in all the analyses (this has the effect 

of simplifying interaction effects). Age is more complex in that adolescent development is 

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generally recognized as a nonlinear function of age (e.g., Nook, Sasse, Lambert, McLaughlin, & 

Somerville, 2018). Thus, we decomposed age into two components: agelin and agequad, where 

quadratic age = age × age. In addition to treating demographic variables as main effects, they are 

also considered in interaction with the primary variables. 

Research question 3 requires a more complicated structural equation model (SEM) to analyze 

the data. As with the first two research question, the model includes primary variables and 

demographic variables; but it also includes psychological adjustment variables and academic 

progress as mediators, and social support and family control variables as moderators of these 

mediators. Thus, in order to simplify the analysis, race was excluded as a demographic factor, for 

two reasons. First, it was not statistically significant in any of the analyses that addressed the first 

two research questions; second, because it is categorical, it would have added considerably to the 

number of model parameters (increasing the number of parameters by over 150). Additionally, 

we excluded age from the SEM analysis. Based on the way that academic progress is calculated, 

it is correlated with age (r = .340); by including it in the analysis, it would have attenuated results 

associated with education. However, we ran a supplemental analyses for each of the individual 

model components; for those where academic progress was not an outcome variable, we 

included agelin and agequad as predictors. Presentation of the supplemental analyses are included 

in the results section. 

Again because of the complexity of the analyses for research question 3, we did not consider 

CAP as an outcome. This outcome reflects the behavior of adolescents and charges brought 

against them as a result of these behaviors.2 It therefore has little to do with the workings of the 

court. Due to the sheer volume of the SEM results, we only examined court outcome severity. 

In all analyses, we used all available MYS data. Because MYS participants typically 

participated in more than one year of data collection, observations are not independent, and any 

analysis that does not acknowledge this non-independence is inappropriate. Thus, in all analyses 

we controlled for non-independence, typically by using a linear mixed model or a generalized 

linear mixed model. 

Further, in all of the analyses, outcome variables (i.e., CAP and court outcome severity for 

the first two research questions, academic progress and court outcome severity for research 

question 3) were measured at time t+1, while predictor variables were measured at time t. For all 

analyses predicting CAP or court outcome severity, we employed a Granger causality (Granger, 

1969) framework (although not a strict Granger causality test). Strictly speaking, a variable X 

Granger causes another variable Y if Y can be better predicted from previous values of X and Y 

than from previous values of Y alone (Freeman, 1983). Typically, previous values of X and Y 

reflect a time series, and econometric statistical tests consider the autoregressive structure of the 

prior data. In contrast, we consider only a single prior data point for X and Y, which simplifies 

the analysis greatly (but also limits its power). In this way, our approach resembles a crosslagged panel design (Shingles, 1976). 

Specifically, we consider the general regression equation 

Ŷt+1 = a + b1Yt + b2Xt 

2

The correspondence is not perfect, however, in that (a) not all criminal behaviors are detected by police and 

perpetrators apprehended, and (b) charges may be adjusted by the court. 

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where Yt+1 = the dependent variable at time t+1, Yt = the dependent variable at time t, and Xt = the 

dependent variable at time t. We can conclude that X Granger causes Y if b2 is statistically 

significant. By including b1Yt on the right hand side of the equation, b2 reflects the slope of Xt on 

the residualized value of Y, or in effect, change in Y between t and t+1 and acceleration in the 

trajectory of Y. Van Meter (1974) concluded that this measure of change was preferable to either 

simple gain scores (i.e., Yt+1 – Yt) or percentagized gain scores (i.e., (Yt+1 – Yt) · Yt+1-1). 

Despite the name, Granger Causation does not reflect the law-like necessity that theoretical 

causation requires (Pearl, 2000); rather, it should be viewed as a form of statistical association in 

which temporal precedence—one of the strongest requirements, but not the only requirement, for 

causation—is demonstrated. Any causal interpretation is subject to several limitations: (a) 

temporal precedence does not guarantee causality; (b) any contemporaneous causal relationship 

between X and Y will be overlooked; (c) in the presence of measurement error, feedback loops 

between prior values of X and Y may be mistaken for causation; and (d) some omitted variable Z 

may, in fact, cause both X and Y. 

This latter concern is somewhat mitigated, however, by the likelihood that Xt is correlated 

with Xt+1, that Yt is correlated with Yt+1, and that any unobserved variable, Zt is correlated with 

Zt+1. Thus, if Zt+1 is correlated with Yt+1, Zt is correlated with Yt; as a result, the potential 

confounding effects of Zt+1 is at least partially controlled through Yt. For more discussion on how 

this is particularly relevant for poverty research, see Duncan and colleagues (1998) and Raver 

(2004). 

Two final notes are of importance. First, this approach is inherently more conservative than 

one using only contemporaneously-observed variables (Keele & Kelly, 2006). The essential 

reason is as noted previously: Yt is likely correlated with Yt+1, and as this correlation increases, 

modeled effects become increasingly attenuated. Second, and perhaps most important, model 

estimates for CAP and court outcome severity essentially represent year-to-year changes in the 

outcome variables as a function of the predictor variables. As a result, estimates are often small, 

in that they do not reflect absolute levels of outcome variables but rather their residualized 

change. 

Finally, interaction terms calculated multiplicatively potentially confound results, because a 

× b is correlated with both a and b. Aiken, West, and Reno (1991) show that this 

multicollinearity can be eliminated by centering each of the interaction terms at its mean. This 

was done for all variables except gender: its dichotomous quality (0, 1) resolves the 

multicollinearity problem without the need for centering. In creating figures to help 

interpretation of significant interaction effects, uncentered variables were used in the analyses. 

When significant interactions occurred, typically the results were plotted so that the direction 

of the interaction was clear. To determine plot points, analyses were run estimating the outcome 

variable as a function of one of the interaction variables in its uncentered form and other 

interaction variables dichotomized (or in the case of age, trichotomized). For gender, 

dichotomization was obvious; for other variables, cutpoints were determined based on a median 

split (or a value approximating the median as closely as possible). Thus, if we found a three-way 

interaction (e.g., between hopelessness, gender, and age), we estimated the model using only 

hopelessness as an independent variable for each of the 2 × 3 levels of gender × age; the 

intercept and slope derived from each analysis were used to calculate a regression line predicting 

the outcome variable as a function of levels of the independent variable (e.g., hopelessness) for 

each gender × age condition. We deviated from this plan only in the case of plotting (a) CAP and 

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(b) court outcome severity, where as an additional control variable we included (a) lagged CAP 

and (b) lagged court outcome severity in the model. 

In estimating the models to obtain plot coordinates, we estimated linear mixed models for all 

analyses involving variables other than CAP and court outcome severity. Model specifics are 

provided for each analysis. For these models, and for the figures derived from them, the 

estimated outcome describes the actual level of the outcome variable. For analyses involving 

CAP and court outcome severity, we estimated generalized linear mixed models to obtain plot 

coordinates. These are confounded by two factors, however. First, they represent Grangeradjusted estimates, which essentially reflect residualized change rather than exact actual values 

of the outcome variable. Second, because the generalized model estimates are logarithms, they 

cannot be interpreted in the same way as estimates using linear mixed models. For example, the 

slope coefficients (b) must be exponentiated to obtain values that reflect the actual relationship 

between the independent and dependent variables in the original units of each. But in doing so, 

we obtain values that are centered on 1.0, such that the estimate of Y | Xi+1 = (Y | Xi ) • exp(b). 

Thus, 

1. if exp(b) = 1, the regression line is flat. 

2. if exp(b) < 1, the regression line is negative; for example, if exp(b) = .95, then a one unit 

increase in X results in a 5% decrease in Y (i.e., Y | Xi+1 = (Y | Xi ) • .95).

3. if (exp(b) > 1, the regression line is positive; for example, if exp(b) = 1.10, then a one 

unit increase in X results in a 10% increase in Y (i.e., Y | Xi+1 = (Y | Xi ) • 1.1).

Because both the exponentiated value of b and the Granger-adjustment reflect change units, it is 

not entirely clear how to interpret the actual value of Y in the presented figures. 

Research questions 1 and 2. Two models were run: (a) with CAP as the outcome variable; 

and (b) with court outcome severity as the outcome variable. CAP was computed as a count 

variable, and therefore should roughly follow a Poisson distribution; however, the 0 value was 

severely inflated (approximately 96% of the cases). These models were estimated using a 

Laplace approximation to maximum likelihood (as implemented in SAS PROC GLIMMIX) with 

repeated measures and random intercepts. The models fit unstructured covariance matrices, 

parameterized through their Cholesky roots; the error distribution was assumed to be negative 

binomial (which is similar to, but theoretically better justified than a zero-inflated Poisson 

distribution) with a log link function. Degrees of freedom were determined using containment. 

Court outcome severity is heavily skewed right, reflecting the fact that during any given year, the 

vast majority of MYS participants did not have any court contact. These models also used a 

Laplace approximation to maximum likelihood with repeated measures and random intercepts. 

The models fit unstructured covariance matrices, parameterized through their Cholesky roots; the 

error distribution was again assumed to be negative binomial, with a log link function. Degrees 

of freedom were determined using containment. 

Research question 3. The three components of research question 3 can best be examined 

simultaneously using a single structural equations model. The SEM was estimated using MPlus 

(Version 8). All predictor variables were centered on their grand means. Although the data are 

nested within respondents, the research questions do not involve cross-level relationships. 

Therefore, the nesting was most easily acknowledged using TYPE = COMPLEX, wherein 

nonindependence of observations is treated as an artifact of survey sampling strategy. Model 

estimation used maximum likelihood with robust standard errors. In this analysis, court outcome 

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severity is treated as a count variable with a negative binomial distribution; all other variables are 

treated as if normally distributed. The latter assumption is no-doubt violated for several of the 

variables; however, in a separate study (Bolland, Besnoy, Tomek, & Bolland, 2019), analyses 

treating the error distribution for hopelessness as negative binomial produced almost identical 

results to analyses that treated it as normal. We assume that the same holds for the other 

psychological adjustment variables and for academic progress. 

The estimated model generally conforms to the model in Figure 1, with two caveats. First, 

the ellipses in the figure, which generally correspond to latent variables, actually reflect specific 

variables that comprise the construct. MPlus does not allow use of latent variables, particularly 

as interaction effects, in the analyses that we ran. Second, the social support/family control 

ellipse reflects two constructs, which are generally used together. However, we expect that 

family control should have little impact on psychological adjustment, and therefore the 

support/control→ psychological adjustment relationship is limited to support→psychological 

adjustment in the estimated model. 

25 

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Results 

Presented in this section are comprehensive results responding to Research Questions 1, 2, 

and 3. Recognizing that these results are comprehensive and complex with many factors 

included, summaries of the results are presented at the beginning of the Discussion section 

following the results section. 

Research Question 1: Does exposure to violence affect juvenile court involvement among 

adolescents living in high-poverty neighborhoods? 

Crimes against a person. Using the framework specified in the Analytic Procedures 

section, and controlling for lagged (time t) crimes against a person (CAP) and demographic 

measures, witnessing violence was a statistically significant and positive predictor of 

contemporary (time t+1) CAP (Table 7). Additionally, the violent victimization × gender × agelin 

interaction statistically predicted CAP. Analyses were run to estimate CAP for each of the six 

gender × age groups for each point along the X axis. To accomplish this, generalized linear 

mixed models were estimated using procedures very similar to those for the main analysis. For 

each gender × age group, an intercept (a) and a slope (b) for violent victimization were obtained. 

To obtain Y coordinates for each of the i values of X, we calculated Y = exp(a + bXi); the linearity 

assumption is used in all subsequent analyses to determine plot coordinates for interaction 

effects, except as explicitly noted. The number of crimes against a person increased as a function 

of violent victimization for all groups. The oldest group of boys (exp(bo) = 1.08) and youngest 

group of girls (exp(by) = 1.00) showed the smallest change in CAP. The two middle groups 

showed slight increases, with girls exhibiting more of an increase (exp(bm) = 1.24) than boys 

(exp(bm) = 1.16) relative to their intercept. Finally, the youngest age group of boys (exp(bm) = 

1.33) and oldest group of girls (exp(bm) = 1.68) exhibited the largest increases in CAP. 

0.12 

0 

0.02 

0.04 

0.06 

0.08 

0.1 

Crimes Against a Person 

None 365 days Brandishment: 1 Brandishment: >1 Cut/shot: 1 time Cut/shot: >1 time 

time 90 days time 90 days 365 days 365 days 

Violent Victimization 

Boy: Age 9-11 Boy: Age 12-13 Boy: Age 14-16 

Girl: Age 9-11 Girl: Age 12-13 Girl: Age 14-16 

Figure 3. Crimes Against a Person as a Function of Violent Victimization, Gender, and Age. 

26 

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Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
Table 7 

Crimes Against a Person as a Function of Exposure to Violence and Demographic Characteristics 

Effect Racea b SE df t p exp(b) 

Intercept -1.6200 1.0148 8865 -1.60 .1104 0.1979 

Crimes against a persona 0.8598 0.1460 13337 5.89 <.0001 2.36269 

Witnessing violenceb 0.3882 0.1635 13337 2.37 .0176 1.47432 

Violent victimizationb 0.04685 0.08577 13337 0.55 .5849 1.04796 

Gendera -0.5428 0.1111 13337 -4.88 <.0001 0.58112 

Race 1 -1.1776 1.0130 13337 -1.16 .2451 0.30802 

Race 2 -1.2030 1.2470 13337 -0.96 .3347 0.30029 

Race 3 -1.0489 1.0374 13337 -1.01 .3120 0.35032 

Race 4 0 . . . . 1 

Ageb -0.00189 0.03201 13337 -0.06 .9530 0.99811 

b Agequad -0.1732 0.01723 13337 -10.05 <.0001 0.84097 

Witnessing violence × gender -0.2161 0.2473 13337 -0.87 .3821 0.80565 

Witnessing violence × age 0.09218 0.06422 13337 1.44 .1512 1.09656 

Witnessing violence × agequad 0.006265 0.03582 13337 0.17 .8611 1.00628 

Witnessing violence × gender × age -0.05391 0.09935 13337 -0.54 .5874 0.94752 

Witnessing violence × gender × agequad 0.000261 0.05494 13337 0.00 .9962 1.00026 

Violent victimization × gender 0.2745 0.1489 13337 1.84 .0654 1.31587 

Violent victimization × age -0.06667 0.03459 13337 -1.93 .0539 0.9355 

Violent victimization × agequad 0.005900 0.01858 13337 0.32 .7508 1.00592 

Violent victimization × gender × age 0.1509 0.07578 13337 1.99 .0464 1.16288 

Violent victimization × gender × agequad -0.06014 0.04010 13337 -1.50 .1337 0.94163 a

Lagged variable, bLagged and centered variable 

27 

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Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
Court outcome severity. A similar model was estimated for court outcome severity. The 

relationship between witnessing violence and court outcome severity was positive and 

statistically significant (Table 8). The main effect for violent victimization was not statistically 

significant, but the violent victimization × gender × age interactions (i.e., for both the linear and 

quadratic components of age) were statistically significant. To determine the plot coordinates for 

the six gender × age groups, models similar to those used to determine plot coordinates for CAP 

were run; results are specified in Figure 3. The relationship between violent victimization and 

court outcome severity is positive for all six groups. Court outcome severity increased as a 

function of violent victimization for all groups, with the middle age group of boys (exp(bm) = 

1.243) and girls (exp(bm) = 1.205) showing large absolute increases, but smaller increases 

relative to the magnitude of their intercepts. The oldest age group of boys (exp(bo) = 1.146) 

showed the least change as a function of their intercept and the youngest age group of girls 

(exp(by) = 1.469) showed the highest change in court outcome severity as a function of their 

intercept. Finally, the youngest boys (exp(by) = 1.286) and the oldest girls (exp(bo) = 1.389) 

showed increases generally between these two. The middle group and youngest group of boys 

showed similar changes as a function of their intercepts, though the middle group of boys started 

and finished at higher levels of court outcome severity than the youngest group of boys. The 

youngest group of girls demonstrated the largest absolute change in court outcome severity as a 

function of violent victimization, however, remained at the lowest level of court outcome 

severity throughout the levels of victimization. Court Outcome Severity 

1 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

0 

None 365 days Brandishment: 1 Brandishment: >1 Cut/shot: 1 time 365 Cut/shot: >1 time 

time 90 days time 90 days days 365 days 

Violent Victimization 

Boy: Age 9-11 Boy: Age 12-13 Boy: Age 14-16 

Girl: Age 9-11 Girl: Age 12-13 Girl: Age 14-16 

Figure 4. Court Outcome Severity as a Function of Violent Victimization, Gender, and Age. 

28 

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necessarily reflect the official position or policies of the U.S. Department of Justice.
 

 

 

 

Table 8 

Court Outcome Severity as a Function of Exposure to Violence and Demographic Characteristics 

Effect Racea b SE df t p exp(b) 

Intercept -1.620 1.015 8865 -1.60 .110 0.198 

Court outcome severitya 0.860 0.146 13337 5.89 <.0001 2.363 

Witnessing violenceb 0.388 0.164 13337 2.37 .018 1.474 

Violent victimizationb 0.047 0.086 13337 0.55 .585 1.048 

Gendera -0.543 0.111 13337 -4.88 <.0001 0.581 

Race 1 -1.178 1.013 13337 -1.16 .245 0.308 

Race 2 -1.203 1.247 13337 -0.96 .335 0.300 

Race 3 -1.049 1.037 13337 -1.01 .312 0.350 

Race 4 0.000 . . . . 1.000 

b Age -0.002 0.032 13337 -0.06 .953 0.998 

b Agequad -0.173 0.017 13337 -10.05 <.0001 0.841 

Witnessing violence × 

gender -0.216 0.247 13337 -0.87 .382 0.806 

Witnessing violence × age 0.092 0.064 13337 1.44 .151 1.097 

Witnessing violence × 

agequad 

0.006 0.036 13337 0.17 .861 1.006 

Witnessing violence × 

gender × age -0.054 0.099 13337 -0.54 .587 0.948 

Witnessing violence × 

gender × agequad 

0.000 0.055 13337 0.00 .996 1.000 

Violent victimization × 

gender 0.275 0.149 13337 1.84 .065 1.316 

Violent victimization × age -0.067 0.035 13337 -1.93 .054 0.936 

Violent victimization × 

agequad 

0.006 0.019 13337 0.32 .751 1.006 

Violent victimization × 

gender × age 

0.151 0.076 13337 1.99 .046 1.163 

Violent victimization × 

gender × agequad -0.060 0.040 13337 -1.50 .134 0.942 

a

Lagged variable, bLagged and centered variable 

Research Question 2: Does exposure to multiple violent events lead to greater juvenile justice 

involvement (either directly or indirectly) than exposure to single violent events? 

To address this question, three models were estimated for each of the two outcomes (CAP, 

court outcome severity); each used the lagged outcome variable as a statistical control on the 

right side of the equation. The first pair of models considered multiple sources of exposure to 

violence, as specified in the definition of the MEVs. The second pair of models considered 

multiple incidents of exposure to violence, as specified in the definition of MEVi. The third pair 

of models considered a combination of sources and incidents, as specified in the definition of 

MEVc. 

29 

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Multiple sources of exposure to violence. An analysis was conducted to determine how 

sources of multiple exposures to violence (MEVs) affect CAP, treating MEVs as a linear 

variable. A main effect for MEVs was statistically significant (b = 0.256, se = 0.088, df =13,207, 

t = 2.90, p = .004). However, this assumes that the relationship between MEVs and CAP is 

linear. To test this assumption, we conducted a supplemental analysis to test whether this 

assumption is valid. Specifically, we re-estimated the previous model, but treated MEVs as a 

categorical variable (Table 9); if the results for MEV were statistically significant, we also 

estimated least-squares means for each category of MEVs and conducted post-hoc comparisons 

among categories of MEV using Tukey’s HSD procedure as adjusted by Kramer (1956) for 

unbalanced designs. This analysis showed a significant main effect for MEVs treated as a 

categorical variable (F = 3.29, p = .037). The post-hoc comparison of means shows that CAP 

when MEVs = 0 differs from CAP when MEVs = 1 (b = -0.304, se = 0.127, t = -2.39, p = .044) 

and when MEVs = 2 (b = -0.654, se = 0.204, t = -3.20, p = .004), but that CAP does not differ for 

MEVs = 1 versus MEVs = 2 (b = -0.350, se = 0.221, t = -1.58, p = .253). This shows that there is 

an effect for no victimization versus victimization, but that multiple sources of victimization do 

not contribute more to CAP than a single source. Thus, for CAP, conclusions about MEVs 

simplify to those obtained previously, as reported in Research Question 1. 

Table 9 

Crimes Against a Person as a Function of Sources of Victimization and Demographic 

Characteristics 

Effect df F p 

Crimes against a persona 1, 13199 36.97 <.0001 

Multiple Exposures to Violence (MEV) a

s 2, 13199 3.29 .037 

Gendera 1, 13199 6.96 .008 

b Age 1, 13199 0.21 .650 

b Agequad 1, 13199 46.52 <.0001 

Race 3, 13199 0.45 .718 

MEVs × gender 2, 13199 0.78 .459 

MEVs × age 2, 13199 0.02 .981 

MEVs × agequad 2, 13199 1.29 .276 

MEVs × gender × age 3, 13199 0.76 .516 

MEVs × gender × agequad 3, 13199 1.85 .135 a

Lagged variable, bLagged and centered variable 

Next, an analysis was conducted to determine how MEVs affect court outcome severity, 

treating MEVs as a linear variable. A main effect for MEVs was statistically significant (b = 

0.204, se = 0.077, df =13,341, t = 2.65, p = .008). However, this too assumes that the relationship 

between MEVs and court outcome severity is linear. To test this assumption, we conducted a 

supplemental analysis, treating MEVs as a categorical variable (Table 10). This analysis showed 

a significant main effect for MEVs treated as a categorical variable, was statistically significant 

(F = 4.18, p = .015). The post-hoc comparison of means shows that court outcome severity when 

MEVs = 0 differs from court outcome severity when MEVs = 1 (b = -0.461, se = 0.066, t = -6.99, 

p < .0001) and when MEVs = 2 (b = -0.568, se = 0.107, t = -5.33, p < .0001), but that court 

outcome severity does not differ for MEVs = 1 versus MEVs = 2 (b = -0.107, se = 0.115, t = -

0.93, p = .620). This again shows that there is an effect for no victimization versus victimization, 

30 

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but that multiple sources of victimization do not contribute more to court outcome severity than a 

single source. Thus, for court outcome severity, conclusions about MEVs simplify to those 

obtained previously as reported in Research Question 1. 

Table 10 

Court Outcome Severity as a Function of Sources of Victimization and Demographic 

Characteristics 

Effect df F p 

Court Outcome Severitya 1, 13333 313.48 <.0001 

Multiple Exposures to Violence (MEV) a

s 2, 13333 4.18 .015 

Gendera 1, 13333 4.01 .045 

b Age 1, 13333 0.05 .817 

b Agequad 1, 13333 89.53 <.0001 

Race 3, 13333 0.43 .733 

MEVs × gender 2, 13333 1.64 .194 

MEVs × age 2, 13333 1.15 .317 

MEVs × agequad 2, 13333 2.88 .056 

MEVs × gender × age 3, 13333 1.17 .318 

MEVs × gender × agequad 3, 13333 3.37 .018 a

Lagged variable, bLagged and centered variable 

31 

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Multiple incidents of exposure to violence. Results are similar for the relationship between 

instances of multiple exposures to violence (MEVi) and CAP. A main effect for MEVi (treated as 

a linear variable) was statistically significant (b = 0.212, se = 0.091, df =13,208, t = 2.32, p = 

.020). To test the linearity assumption, we re-estimated the previous model, treating MEVi as a 

categorical variable (Table 11); we also estimated least-squares means for each category of 

MEVi and compared them using a post-hoc analysis. This shows a non-significant main effect 

when MEVi is treated as a categorical variable (F= 2.76, p = .063). Even so, given the 

conservative nature of these analyses and the fact that this omnibus test produced a result where 

p < .10, we chose to conduct post-hoc tests. The post-hoc comparison shows that CAP when 

MEVi = 0 differs from CAP when MEVi = 1 (b = -0.382, se = 0.128, t = -2.99, p = .008) and 

when MEVi = 2 (b = -0.615, se = 0.174, t = -3.53, p = .001), but that CAP does not differ for 

MEVi = 1 versus MEVi = 2 (b = -0.233, se = 0.194, t = -1.20, p = .453). This shows that there is 

an effect for no victimization versus victimization, but that multiple instances of victimization do 

not contribute more to CAP than a single instance. Thus, as with sources of MEV, the analysis of 

instances of MEV for CAP simplifies to results reported in Research Question 1. 

Table 11 

Crimes Against a Person as a Function of Instances of Victimization and Demographic 

Characteristics 

Effect df F p 

Crimes against a persona 1, 13199 36.56 <.0001 

Multiple Exposures to Violence (MEV) a

i 2, 13199 2.76 .063 

Gendera 1, 13199 7.29 .007 

b Age 1, 13199 0.19 .661 

b Agequad 1, 13199 47.08 <.0001 

Race 3, 13199 0.48 .697 

MEVi × gender 2, 13199 0.59 .556 

MEVi × age 2, 13199 0.07 .94 

MEVi × agequad 2, 13199 1.57 .209 

MEVi × gender × age 3, 13199 0.45 .720 

MEVi × gender × agequad 3, 13199 1.53 .204 a

Lagged variable, bLagged and centered variable 

32 

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necessarily reflect the official position or policies of the U.S. Department of Justice.
 

 

 

 

 

 

Next, an analysis was conducted to determine how MEVi affect court outcome severity, 

treating MEVi as a linear variable. A main effect for MEVi (treated as a linear variable) was 

statistically significant (b = 0.255, se = 0.080, df =13,341, t = 3.20, p = .001). A subsequent 

analysis, treating MEVi as categorical (Table 12), also showed a significant main effect (F = 

6.30, p = .002). A post-hoc comparison of means shows that court outcome severity when MEVi 

= 0 differs from court outcome severity when MEVi = 1 (b = -0.465, se = 0.067, t = -6.93, p < 

.0001) and when MEVi = 2 (b = -0.567, se = 0.097, t = -5.86, p < .0001), but that court outcome 

severity does not differ for MEVi = 1 versus MEVi = 2 (b = -0.102, se = 0.107, t = -0.96, p = 

.605). This shows that there is an effect for no victimization versus victimization, but that 

multiple instances of victimization do not contribute more to court outcome severity than a single 

instance. Thus, again, conclusions about MEVi simplify to those obtained previously as reported 

in Research Question 1. 

Table 12 

Court Outcome Severity as a Function of Instances of Victimization and Demographic 

Characteristics 

Effect df F p 

Court Outcome Severitya 1, 13333 315.51 <.0001 

Multiple Exposures to Violence (MEV) a

i 2, 13333 6.30 .002 

Gendera 1, 13333 7.54 .006 

b Age 1, 13333 0.05 .824 

b Agequad 1, 13333 97.40 <.0001 

Race 3, 13333 0.39 .764 

MEVi × gender 2, 13333 2.02 .108 

MEVi × age 2, 13333 0.90 .405 

MEVi × agequad 2, 13333 4.69 .009 

MEVi × gender × age 3, 13333 2.02 .108 

MEVi × gender × agequad 3, 13333 3.60 .013 a

Lagged variable, bLagged and centered variable 

33 

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Multiple sources and incidents of exposure to violence. Finally, an analysis was conducted 

to determine how sources and instances of multiple exposures to violence (MEVc) affect CAP 

and court outcome severity, treating MEVc as linear. First, with respect to CAP, a main effect for 

MEVc was statistically significant (b = 0.20, se = 0.068, df =13,207, t = 2.97, p = .003). The 

model was re-estimated with MEVc as a categorical variable (Table 13). Specifically, we reestimated the previous model, but treated MEVc as a categorical variable. This analysis shows a 

non-significant main effect when MEVc is treated as a categorical variable (F= 2.46, p = .061). 

Even so, given the conservative nature of these analyses and the fact that this omnibus test 

produced a result where p < .10, we chose to conduct post-hoc tests. A post-hoc comparison of 

means shows that CAP when MEVc = 0 does not differ from CAP when MEVc = 1 (b = -0.240, 

se = 0.148, t = -1.62, p = .366) and when MEVc = 3 (b = -0.634, se = 0.305, t = -2.08, p = .160), 

but that CAP does differ when MEVc = 2 (b = -0.631, se = 0.170, t = -3.71, p = .001). Further, 

CAP does not differ for MEVc = 1 versus MEVs = 2 (b = -0.390, se = 0.204, t = -1.92, p = .221), 

or between MEVc = 1 versus MEVs = 3 (b = -0.394, se = 0.325 t = -1.21, p = .620), or finally 

between MEVc = 2 versus MEVc = 3 (b = -0.003, se = 0.335, t = -0.01, p = 1.0). This shows that 

there is an effect for multiple sources or instances of victimization versus no victimization, but 

no other comparison was significant. As before, the linearity assumption does not hold, but the 

relationship between combined MEV and CAP is more complex than other relationships we 

explored in this section. 

Table 13 

Crimes Against a Person as a Function of Sources and Instances of Victimization and 

Demographic Characteristics 

Effect df F p 

Crimes against a persona 1, 13194 35.68 <.0001 

Multiple Exposures to Violence (MEV) a

c 2, 13194 2.46 .061 

Gendera 1, 13194 5.17 .023 

b Age 1, 13194 0.16 .694 

b Agequad 1, 13194 4.08 <.0001 

Race 3, 13194 0.47 .707 

MEVc × gender 2, 13194 0.61 .607 

MEVc × age 2, 13194 0.11 .956 

MEVc × agequad 2, 13194 1.32 .266 

MEVc × gender × age 3, 13194 0.87 .481 

MEVc × gender × agequad 3, 13194 1.50 .199 a

Lagged variable, bLagged and centered variable 

34 

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With respect to court outcome severity, as before, a preliminary analysis, treating MEVc as a 

linear variable, was conducted. The main effect for MEVc was statistically significant (b = 0.182, 

se = 0.061, df =13,341, t = 3.00 p = .003). A further analysis, treating MEVc as categorical 

(Table 10), showed a statistically significant main effect for MEV (F = 3.51, p = .015). A posthoc comparison of means shows that court outcome severity when MEVc = 0 differs from court 

outcome severity when MEVc = 1 (b = -0.451, se = 0.073, t = -6.21, p < .001), when MEVc = 2 

(b = -0.477, se = 0.094, t = -5.06, p < .0001), and when MEVc = 3 (b = -0.653, se = 0.149, t = -

4.39, p < .0001). Court outcome severity does not differ for MEVc = 1 versus MEVs = 2 (b = -

0.027, se = 0.108, t = -0.25, p = .995), or between MEVc = 1 versus MEVc = 3 (b = -0.202, se = 

0.158 t = -1.28, p = .577), or finally between MEVc = 2 versus MEVc = 3 (b = -0.175, se = 0.169, 

t = -1.04, p = .726). This, again, shows an effect for no victimization versus victimization, but 

not between different levels of victimization. Thus, for court outcome severity, conclusions 

about MEVc simplify to those obtained previously as reported in Research Question 1. 

Table 14 

Court Outcome Severity as a Function of Sources and Instances of Victimization and 

Demographic Characteristics 

Effect df F p 

Court Outcome Severitya 1, 13327 314.36 <.0001 

Multiple Exposures to Violence a (MEV)c 2, 13327 3.51 .015 

Gendera 1, 13327 4.26 .039 

b Age 1, 13327 0.02 .892 

b Agequad 1, 13327 62.44 <.0001 

Race 3, 13327 0.41 .743 

MEVc × gender 2, 13327 1.40 .240 

MEVc × age 2, 13327 0.67 .569 

MEVc × agequad 2, 13327 2.30 .076 

MEVc × gender × age 3, 13327 1.38 .240 

MEVc × gender × agequad 3, 13327 3.37 .009 a

Lagged variable, bLagged and centered variable 

35 

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Research Question 3: (3a) How does psychological adjustment mediate the direct pathway 

between exposure to violence and juvenile justice involvement?; (3b) How does normative 

academic progress mediate the relationship between psychological adjustment and juvenile 

justice involvement?; (3c) How are the direct and indirect paths in the model moderated by 

demography, social support, and family control? 

Psychological Adjustment. The first set of results (Tables 15-20) shows the effects of 

exposure to violence (witnessing violence, violent victimization) and social support (maternal 

warmth, neighborhood connectedness) on the six psychological adjustment variables 

(hopelessness, street code, behavioral self-worth, global self-worth, worry, and traumatic stress). 

Hopelessness. Table 15 shows that both exposure to violence variables are positively and 

statistically associated with hopelessness (i.e., as exposure to violence increases, hopelessness 

increases). Gender was negatively associated with hopelessness, indicating greater hopelessness 

for boys than for girls. Maternal warmth and both neighborhood connectedness variables were 

statistically significant predictors of hopelessness, as were the gender interactions with the two 

neighborhood support variables. The main effects indicate that maternal warmth helps offset the 

effect that exposure to violence has on hopelessness, as does neighborhood connectedness. The 

two interaction effects are shown in Figures 9 and 11. For the first, the relationship between 

neighborhood connectedness (positive feelings toward neighborhood) and hopelessness is 

essentially flat for girls (b = 0.005) but negative for boys (b = -0.017). For the second, the 

relationship between neighborhood connectedness (absence of negative feelings toward 

neighborhood) and hopelessness negative for both girls (b = -0.274) and boys (b = -0.356); the 

decline is steeper for boys than for girls. A comparison of the two figures shows that the effect of 

negative neighborhood connectedness is stronger than the effect of positive neighborhood 

connectedness. 

A supplemental analysis was conducted using a linear mixed model (LMM) as implemented 

in SAS PROC MIXED, with restricted maximum likelihood, a first-order autoregressive covariance 

structure, and random intercepts. Degrees of freedom were estimated using a procedure 

developed by Satterthwaite (1941) coupled with an inflation of the estimated variancecovariance matrix of the fixed and random effects, as detailed by Kenward and Roger (1997). 

Results showed similar main effects as the SEM analysis. Additionally, we found significant 

linear (b = -0.064, se = 0.008, t = -8.25, p < .001) and quadratic (b = 0.024, se = 0.004, t = 5.95, 

p < .001) age effects. Hopelessness decreased monotonically as a function of age, with the 

steepest decreases occurring during the earliest ages (Figure 5 shows unsmoothed means). We 

found several significant interaction effects involving age. 

1. Witnessing violence × agelin (b = -0.033, se = 0.009, t = -3.64, p < .001; Figure 7). 

Hopelessness as a function of witnessing violence is most rapid for the youngest (by = 

0.299) and middle (bm = 0.266) age groups, and slowest for the oldest age group (bo = 

0.188). 

2. Violent victimization × gender × agelin (b = 0.030, se = 0.012, t = 2.49, p < .05; Figure 8). 

Hopelessness among the three male age groups (by =0.179, bm = 0.190, bo = 0.147) and for 

the two older female age groups (bm = 0.206, bo = 0.165) increases at a similar rate as a 

function of violent victimization. For the youngest group of girls, the rate of increase is 

considerably lower, approaching zero (by = 0.045). 

36 

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3. Maternal warmth × agelin (b = 0.019, se = 0.006, t = 2.98, p < .01; Figure 6). Hopelessness 

declines at identical rates as a function of maternal warmth for the two younger age 

groups (by = -0.180, bm = -0.180); for the oldest age group, the descent occurs at a less 

rapid rate (bo = -0.112). 

4. Neighborhood connectednesspositive × gender × agequad (b = 0.007, se = 0.003, t = 2.13, p < 

.05; Figure 10). For all three male age groups, hopelessness shows a marginal and similar 

decline as a function of positive neighborhood connectedness (by = -0.012, bm = -0.030, bo 

= -0.028; the youngest group of girls shows a similar decline (by = -0.013). For the oldest 

group of girls, the relationship is inconsequently positive (bo = 0.006). Only for the middle 

group of girls does the relationship, which is negative, attain even modest proportions (bm 

= -0.056.). 

5. Neighborhood connectedessnegatve× agequad (b = -0.009, se = 0.003, t = 3.45, p < .001; 

Figure 12). Hopelessness as a function of negative neighborhood connectedness declines 

sequentially from the youngest age group to the oldest age group (by = -0.400, bm= 0.330, 

bo = -0.300). 

6. Neighborhood connectedessnegatve× gender × agelin (b = 0.023, se = 0.007, t = 3.38, p < 

.001; Figure 13). Among all six age × gender groups, hopelessness declines as a function 

of negative neighborhood connectedness. Among boys, the decline is most steep for the 

youngest age group (by = -0.426), while for the two older groups of boys it is similar but 

less rapid (bm =-0.362, bo = -0.369). For girls, the decline is less steep than for boys; the 

negative slope is most rapid for the youngest group of girls (by = -0.376), and is 

sequentially less rapid for each increasing age group (bm = -0.293, bo = -0.229). Notably, 

for adolescents who have negative feelings about their neighborhoods, there exist large 

gender and age differences in levels of hopelessness. However, for adolescents who do not 

have negative feelings about their neighborhoods, these gender and age differences almost 

completely disappear. 

Table 15 

SEM Estimates: Determinants of Hopelessness at Time t 

Gender (G) 

Estimate S

-0.310 

Eestimate S

0.026 

Estimate

Eestimate

-11.817 

p

<.001 

Exposure to violence 

Witnessing violence (WV) 0.151 0.029 5.150 <.001 

Violent victimization (VV) 0.081 0.016 5.145 <.001 

G × WV 0.031 0.041 0.746 .455 

G × VV 0.004 0.027 0.149 .882 

Social support 

Maternal warmth (MOM) -0.115 0.015 -7.678 <.001 

Neighborhood connectednesspositive (NHP) -0.028 0.011 -2.606 .009 

Neighborhood connectednessnegative (NHN) -0.377 0.012 -30.972 <.001 

G × MOM -0.026 0.021 -1.211 .226 

G × NHP 0.057 0.014 3.962 <.001 

G × NHN 0.087 0.017 5.187 <.001 

37 

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Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
Hopelessness 

2.95 

2.45 

1.95 

1.45 

0.95 

0.45 

9 10 11 12 13 14 15 16 

Age 

Figure 5. Hopelessness as a Function of Age. 

38 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
 

Hopelessness 

2.95 

2.45 

1.95 

1.45 

0.95 

0.45 

0 1 2 3 4 5 

Maternal Warmth 

Age: 9-11 Age: 12-13 Age: 14-16 

Figure 6. Hopelessness as a Function of Maternal Warmth and Age. 

2.95 

2.45 

Hopelessness 1.95 

1.45 

0.95 

0.45 

0 times 1 time >1 time 

Frequency of Witnessing Violence: Past 90 Days 

Age: 9-11 Age: 12-13 Age: 14-16 

Figure 7. Hopelessness as a Function of Witnessing Violence and Age. 

39 

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Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

6 
0.45 

0.95 

1.45 

1.95 

2.45 

2.95 

Hopelessness 

None/365 days Brandishment: Brandishment: Cut/shot: 1 time Cut/shot: >1 time 

1 time 90 days >1 time 90 days 365 days 365 days 

Violent Victimization 

Boy: Age 9-11 Boy: Age 12-13 Boy: Age 14-16 

Girl: Age 9-11 Girl: Age 12-13 Girl: Age 14-16 

Figure 8. Hopelessness as a Function of Violent Victimization, Gender, and Age. 

40 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
2.95 

2.45 

1.95 

1.45 

0.95 

0.45 

Positive Neighborhood Connectedness 

Boy Girl 

0 1 2 3 4 5 6 

Hopelessness 

Figure 9. Hopelessness as a Function of Positive Neighborhood Connectedness and Gender. 

2.95 

2.45 

0 1 2 3 4 5 6 

Hopelessness 

1.95 

1.45 

0.95 

0.45 

Positive Neighborhood Connectedness 

Boy: Age 9-11 Boy: Age 12-13 Boy: Age 14-16 

Girl: Age 9-11 Girl: Age 12-13 Girl: Age 14-16 

Figure 10. Hopelessness as a Function of Positive Neighborhood Connectedness, Gender, and 

Age. 

41 

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Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
Hopelessness 

2.95 

2.45 

1.95 

1.45 

0.95 

0.45 

Negative Neighborhood Connectedness 

Boy Girl 

0 1 2 3 4 5 

Figure 11. Hopelessness as a Function of Negative Neighborhood Connectedness and Gender. 

2.95 

Hopelessness 2.45 

1.95 

1.45 

0.95 

0.45 

0 1 2 3 4 5 

Negative Neighborhood Connectedness 

Age: 9-11 Age: 12-13 Age: 14-16 

Figure 12. Hopelessness as a Function of Negative Neighborhood Connectedness and Age. 

2.95 

Hopelessness 

2.45 

1.95 

1.45 

0.95 

0.45 

0 1 2 3 4 5 

Negative Neighborhood Connectedness 

Boy: Age 9-11 Boy: Age 12-13 Boy: Age 14-16 

Girl: Age 9-11 Girl: Age 12-13 Girl: Age 14-16 

Figure 13. Hopelessness as a Function of Negative Neighborhood Connectedness, Gender, and 

Age. 

42 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
 

 

 

 

 

 

 

 

 

 

 

Street code. Table 16 shows a similar result for street code, with both exposure to violence 

variables predicting increases in street code beliefs. Also like the results for hopelessness, 

maternal warmth and neighborhood connectednessnegative are protective factors against street code 

beliefs. However, neighborhood connectednesspositive is a risk factor for street code beliefs. This 

may reflect the potential confounding effect of neighborhood gangs. Gang membership can 

create a sense of neighborhood belonging and positive feelings of neighborhood connectedness 

for adolescents. But this could be a negative rather than a positive influence on their behavior. 

Gang connections may be particularly relevant for the development of street code beliefs. 

Neighborhood connectednessposative and neighborhood connectednessnegative both interact with 

gender to affect street code. Figure 15 shows that for boys the relationship between positive 

neighborhood connectedness and street code is stronger than for girls (b = 0.155 versus b = 

0.098). Figure 16 shows that, for negative network connectedness, the effect for boys is also 

greater than the effect for girls (b = -0.368 versus b = -0.306). 

A supplemental analysis showed statistically significant linear (b = 0.026, se = 0.010, t = 

2.52, p < .05) and quadratic (b = -0.019, se = 0.005, t = -3.60, p < .001) for age. Further 

examination shows that street code beliefs increase monotonically though not linearly through 

age 13, then decrease monotonically albeit slowly through age 14 (Figure 16). Two age 

interactions were also statistically significant. 

1.Witnessing violence × agelin (b = 0.034, se = 0.012, t = 2.80, p < .01); Witnessing violence × 

agequad (b = -0.015, se = 0.007, t = -2.37, p < .05; Figure 17). The effect of witnessing 

violence on street code increases as a function of age (by = 0.534, bm = 0.755, bo = 0.833 

2.Neighborhood connectednesspositive × agelin (b = 0.011, se = 0.006, t = 2.02, p < .05; Figure 

18). Street code as a function of positive neighborhood connectedness increases most rapidly 

for the oldest age group and least rapidly for the youngest age group by = 0.122, bm = 0.124. 

bo = 0.145). The effect size appears to be quite small, however. 

Table 16 

SEM Estimates: Determinants of Street Code at Time t 

Estimate SEestimate

Estimate

SEestimate

p

Gender (G) -0.286 0.034 -8.415 <.001 

Exposure to violence 

Witnessing violence (WV) 0.464 

Violent victimization (VV) 0.305 

G × WV 0.016 

G × VV 0.003 

Social support 

0.037 

0.020 

0.053 

0.034 

12.418 

15.591 

0.295 

0.099 

<.001 

<.001 

.768 

.921 

Maternal warmth (MOM) -0.137 0.019 -7.199 <.001 

Neighborhood connectednesspositive (NHP) 0.143 0.013 10.680 <.001 

Neighborhood connectednessnegative (NHN) -0.326 0.016 -21.027 <.001 

G × MOM 0.022 0.026 0.833 .405 

G × NHP -0.022 0.018 -1.227 .220 

G × NHN 0.024 0.021 1.100 .271 

43 

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Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
Street Code 

4.6 

4.1 

3.6 

3.1 

2.6 

9 10 11 12 13 14 15 16 

Age 

Figure 14. Street Code as a Function of Age. 

44 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
Street Code 

4.6 

4.1 

3.6 

3.1 

2.6 

0 1 2 3 4 5 6 

Positive Neighborhood Connectedness 

Boy Girl 

Figure 15. Street Code as a Function of Positive Neighborhood Connectedness and Gender. 

4.6 

4.1 

Street Code 3.6 

3.1 

2.6 

0 1 2 3 4 5 

Negative Neighborhood Connectedness 

Boy Girl 

Figure 16. Street Code as a Function of Negative Neighborhood Connectedness and Gender. 

45 

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Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
 

 

Street Code 

4.6 

4.1 

3.6 

3.1 

2.6 

0 times 1 time >1 time 

Frequency of Witnessing Violence: Past 90 Days 

Age: 9-11 Age: 12-13 Age: 14-16 

Figure 17. Street Code as a Function of Witnessing Violence and Age. 

4.6 

Street Code

4.1 

3.6 

3.1 

2.6 

0 1 2 3 4 5 

Positive Neighborhood Connectedness 

Age: 9-11 Age: 12-13 Age: 14-16 

Figure 18. Street Code as a Function of Positive Neighborhood Connectedness and Age. 

46 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

6 
 

 

 

 

 

 

 

 

 

 

 

Behavioral self-worth. Table 17 shows SEM results for behavioral self-worth. Gender had a 

significant positive main effect on behavioral self-worth, indicating that girls had higher levels of 

behavioral self-worth than boys. Both witnessing violence and violent victimization had a 

negative effect on behavioral self-worth. All three of the social support variables have a positive 

and significant effect on behavioral self-worth. In addition, we found a positive maternal warmth 

by gender interaction, and a positive neighborhood connectednesspositive by gender interaction. 

The former result (Figure 20) reflects a stronger positive maternal warmth effect for girls (b = 

0.168) than for boys (b = 0.137), while the latter (Figure 22) reflects a stronger positive 

neighborhood connectednesspositive effect for boys (b = 0.057) than for girls (b = 0.027). 

A supplemental LMM analysis showed a significant positive effect for agelin (b = 0.062, se = 

0.006, t = 9.54 p < .001) and for agequad (b = 0.010, se = 0.005, t = 2.13, p < .05); behavioral selfworth remains more-or-less constant through age 14, then begins increasing at age 15 (Figure 

19). The analysis also yielded several significant age interactions. 

1. Maternal warmth × gender × agequad (b = -0.009, se = 0.004, t = -2.25, p < .05; Figure 

21). Behavioral self-worth as a function of maternal warmth increases at a very similar 

rate for all three age groups of boys (by =0.128, bm = 0.133, bo = 0.141). For all three 

female age groups, the slope is greater than for any male age group. It is highest for the 

middle age group (bm = 0.208), lower for the youngest group of girls (by = 0.176) and the 

oldest group of girls (bo = 0.145). The trend lines for the oldest group of boys and the 

oldest group of girls are nearly coincident. 

2. Neighborhood connectednesspositive × agelin (b = -0.008, se = 0.004, t = -2.37, p < .05; 

Figure 23. The relationship between behavioral self-worth and positive neighborhood 

connectedness is positive for all three age groups. The steepest increase occurs for the 

youngest age group and the magnitude of the effect decreases sequentially with age (by = 

0.067, bm = .046, bo = .027). The effect of age on the relationship between positive 

neighborhood connectedness and age is moderate for youths who have no positive 

feelings about their neighborhood, but it becomes inconsequential for adolescents who 

feel very positive about their neighborhood. 

3. Neighborhood connectednessnegative × gender × agequad (b = -0.014, se = 0.006, t = -2.45, p 

< .05; Figure 24). Absence of negative feelings about the neighborhood is positively 

related to behavioral self-worth for all six gender × age groups. For boys, the strength of 

the relationship increases as a function of age (by = 0.064, bm = 0.076, bo = 0.104); for 

girls, the strength of the relationship decreases as a function of age (by = 0.127, bm = 

0.100, bo = 0.089. 

47 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
Table 17 

SEM Estimates: Determinants of Behavioral Self-Worth at Time t 

Estimate SEestimate

Estimate

SEestimate

p

Gender (G) 0.075 0.024 3.158 .002 

Exposure to violence 

Witnessing violence (WV) -0.140 0.023 -5.987 <.001 

Violent victimization (VV) -0.106 0.012 -8.677 <.001 

G × WV -0.011 0.035 -0.326 .744 

G × VV -0.043 0.022 -1.932 .053 

Social support 

Maternal warmth (MOM) 0.110 0.012 9.141 <.001 

Neighborhood connectednesspositive (NHP) 0.057 0.008 7.125 <.001 

Neighborhood connectednessnegative (NHN) 0.084 0.009 9.353 <.001 

G × MOM 0.041 0.018 2.349 .019 

G × NHP -0.051 0.011 -4.532 <.001 

G × NHN 0.012 0.013 0.899 .369 Behavioral Self-Worth 

3.5 

3.3 

3.1 

2.9 

2.7 

2.5 

2.3 

2.1 

1.9 

9 10 11 12 13 14 15 16 

Age 

Figure 19. Behavioral Self-Worth as a Function of Age. 

48 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
Behavioral Self-Worth 

3.5 

3.3 

3.1 

2.9 

2.7 

2.5 

2.3 

2.1 

1.9 

0 1 2 3 4 5 

Maternal Warmth 

Boy Girl 

Figure 20. Behavioral Self-Worth as a Function of Maternal Warmth and Gender. 

3.5 

3.3 

Behavioral Self-Worth 

3.1 

2.9 

2.7 

2.5 

2.3 

2.1 

1.9 

0 1 2 3 4 5 

Maternal Warmth 

Boy: Age 9-11 Boy: Age 12-13 Boy: Age 14-16 

Girl:Age 9-11 Girl: Age 12-13 Girl: Age 14-16 

Figure 21. Behavioral Self-Worth as a Function of Maternal Warmth, Gender, and Age 

49 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.

6 
Behavioral Self-Worth 

3.5 

3.3 

3.1 

2.9 

2.7 

2.5 

2.3 

2.1 

1.9 

0 1 2 3 4 5 6 

Boy Girl 

Positive Neighborhood Connectedness 

Figure 22. Behavioral Self Worth as a Function of Positive Neighborhood Connectedness and 

Gender. Behavioral Self-Worth 3.5 

3.3 

3.1 

2.9 

2.7 

2.5 

2.3 

2.1 

1.9 

0 1 2 3 4 5 6 

Positive Neighborhood Connectedness 

Age: 9-11 Age: 12-13 Age: 14-16 

Figure 23. Behavioral Self-Worth as a Function of Neighborhood Positive Connectedness and 

Age 

50 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
3.5 

3.3 

3.1 

2.9 

2.7 

2.5 

2.3 

2.1 

1.9 

0 1 2 3 4 5 

Negative Neighborhood Connectedness 

Boy: 9-11 Boy: 12-13 Boy: 14-16 

Girl: 9-11 Girl: 12-13 Girl: 14-16 

Figure 24. Behavioral Self-Worth as a Function of Negative Neighborhood Connectedness, 

Gender, and Age. Behavioral Self-Worth 

51 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
 

 

 

 

 

 

 

 

 

 

Global self-worth. Table 18 shows the SEM results for global self-worth. As with behavioral 

self-worth, girls have a higher level of global self-worth than boys; moreover, both witnessing 

violence and violent victimization are negatively associated with global self-worth, and all three 

of the social support variables are positively related to global self-worth. Results also show a 

significant interaction between violent victimization and gender (Figure 26): girls had a slightly 

greater loss of global self-worth as a function of violent victimization than boys (b = -0.127 

versus b = -0.094). Results also showed a significant neighborhood connectednesspositive × gender 

interaction (Figure 28): positive feelings about one’s neighborhood had a stronger effect on 

global self-worth for boys than for girls. 

The supplemental LMM analysis showed significant positive main effects for agelin (b = 

0.026, se = 0.005, t = 5.41, p < .001) but not for agequad. Global self-worth generally increases 

throughout the age range (Figure 25); except for the change between ages 14 and 15, the increase 

is monotonic. We found two statistically significant age interactions. 

1.Violent victimization × gender × agelin (b = -0.014, se = 0.007, t = -1.99, p < .05; Figure 27). 

Global self-worth decreased as a function of violent victimization for all six gender × age 

groups. The steepest decline occurred for the youngest group of boys (by = -0.127) and for 

the oldest group of girls (bo = -0.144). The older two groups of boys (bm = -0.105, bo = -

0.094) and the two younger groups of girls (by = -0.114, bm = -0.111) showed similar 

declines. The size of the effect appears to be limited, however. 

2.Neighborhood connectednesspositive × gender × agelin (b = -0.014, se = 0.006, t = -2.45, p < 

.05; Figure 29). Global self-worth increased as a function of positive neighborhood 

connectedness at a more-or-less consistent rate for all six gender × age groups. For boys, the 

largest increases occurred for the younger age groups (by = 0.060, bm = 0.060), while the 

smallest increase occurred for the oldest age group (bo = 0.049). For girls, the steepest 

increase occurred for the youngest age group (by = 0.05), less so for the other two age groups 

(bm = 0.043, bo = 0.048). All of these effects appear to be very small, however. 

Table 18 

SEM Estimates: Determinants of Global Self Worth at Time t 

Estimate SEestimate

Estimate

SEestimate

p

Gender (G) 0.039 0.017 2.357 .018 

Exposure to violence 

Witnessing violence (WV) -0.052 0.017 -3.090 .002 

Violent victimization (VV) -0.068 0.009 -7.270 <.001 

G × WV -0.025 0.025 -0.985 .325 

G × VV -0.037 0.018 -2.032 .042 

Social support 

Maternal warmth (MOM) 0.082 0.009 8.806 <.001 

Neighborhood connectednesspositive (NHP) 0.048 0.006 7.998 <.001 

Neighborhood connectednessnegative (NHN) 0.104 0.007 15.864 <.001 

G × MOM 0.023 0.013 1.688 .091 

G × NHP -0.018 0.008 -2.192 .028 

G × NHN -0.004 0.009 -0.378 .705 

52 

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Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
Global Self-Worth 

3.5 

3.4 

3.3 

3.2 

3.1 

3 

2.9 

2.8 

2.7 

2.6 

2.5 

9 10 11 12 13 14 15 16 

Age 

Figure 25. Global Self-Worth as a Function of Age. 

53 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
Global Self-Worth 

3.5 

3.4 

3.3 

3.2 

3.1 

3 

2.9 

2.8 

2.7 

2.6 

2.5 

None/365 days Brandishment: 1 

time 90 days 

Brandishment: >1 

time 90 days 

Cut/shot: 1 time 

365 days 

Cut/shot: >1 time 

365 days 

Violent Victimization 

Boy Girl 

Figure 26. Global Self-Worth as a Function of Violent Victimization and Gender. 

3.5 

3.4 

3.3 

Global Self-Worth 

3.2 

3.1 

3 

2.9 

2.8 

2.7 

2.6 

2.5 

None/365 days Brandishment: Brandishment: Cut/shot: 1 time Cut/shot: >1 time 

1 time 90 days >1 time 90 days 365 days 365 days 

Violent Victimization 

Boy: Age 9-11 Boy: Age 12-13 Boy: Age 14-16 

Girl: Age 9-11 Girl: Age 12-13 Girl: Age 14-16 

Figure 27. Global Self-Worth as a Function of Violent Victimization, Gender, and Age. 

54 

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Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
 

Global Self-Worth 

3.5 

3.4 

3.3 

3.2 

3.1 

3 

2.9 

2.8 

2.7 

2.6 

2.5 

0 1 2 3 4 5 6 

Positive Neighborhood Connectedness 

Boy Girl 

Figure 28. Global Self-worth as a Function of Positive Neighborhood Connectedness and 

Gender. 

3.5 

3.4 

3.3 

3.2 

0 1 2 3 4 5 6 

Global Self-Worth 3.1 

3 

2.9 

2.8 

2.7 

2.6 

2.5 

Positive Neighborhood Connectedness 

Boy: Age 9-11 Boy: Age 12-13 Boy: Age 14-16 

Girl: Age 9-11 Girl: Age 12-13 Girl: Age 14-16 

Figure 29. Global Self-worth as a Function of Positive Neighborhood Connectedness, Gender, 

and Age. 

55 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
 

 

 

 

 

 

 

 

 

 

 

 

Worry. Table 19 shows SEM results for worry. Gender had a negative main effect on worry, 

indicating that boys worry more than girls. We also found a significant and positive main effect 

for witnessing violence, and a significant and negative main effect for violent victimization; thus, 

witnessing violence is associated with higher levels of worry, but violent victimization is 

associated with lower levels of worry. A similar conundrum occurred for neighborhood 

connectedness, where positive connectedness was associated with greater worry but absence of 

negative connectedness was associated with greater worry. We found a significant witnessing 

violence × gender interaction (Figure 31): witnessing violence has a greater effect on worry for 

girls (b = 0.640) than for boys (b = 0.280). Similarly, we found significant neighborhood 

connectedness by gender interactions (Figures 34, 36). Even though the positive neighborhood 

connectedness × gender interaction was significant in the MPlus results, the interaction effect is 

severely attenuated when only positive neighborhood connectedness is included in the model: the 

slope for boys (b = 0.083) and the slope for girls (b = 0.084) are virtually identical. In contrast, 

the absence of negative neighborhood connectedness has a stronger ameliorative effect on worry 

for girls (b = -0.614) than for boys (b = -0.527). 

The supplemental LMM analysis showed a significant negative main effect on worry for 

agelin (b = -0.667, se = 0.019, t = -35.83, p < .001), but not for agequad. Further examination 

shows that worry decreases monotonically as a function of age (Figure 30). Several interactions 

involving age were statistically significant. 

1. Witnessing violence × agelin (b = -0.047, se = 0.021, t = -2.19, p < .05; Figure 32). The 

effects of witnessing violence on worry are greatest for the two younger age groups (by = 

0.560, bm = 0.575), and less substantial for the oldest age group (bo = 0.330). 

Interestingly, the effect for the two younger age groups is greater than for the oldest age 

group, even though the intercepts for the two younger age groups are higher than for the 

oldest age group for the youngest age group. Effects are statistically significant for all age 

groups. 

2. Violent victimization × gender × agelin (b = 0.062, se = 0.028, t = 2.21, p < .05; Figure 

33). For boys, the strongest relationship between violent victimization and worry occurs 

in the middle age group (bm = 0.145), with increases in worry as a function of violent 

victimization occurring as a slower rate for the other two age groups (by = 0.087, bo = 

0.052). For girls, the increase occurs most rapidly among the two older age groups (bm = 

0.220, bo = 0.257). For the youngest girls, worry decreases as a function of violent 

victimization (by = -0.081). 

3. Neighborhood connectednesspositive × gender × agelin (b = -0.28, se = 0.014, t = -2.02, p < 

.05; Figure 35). For the youngest age group, the relationship between positive 

neighborhood connectedness and worry are similar (for girls, by 0.192; for boys, by = 

0.148). For the oldest age group, the slopes for boys (bo = -0.013) and girls (bo = -0.016) 

are identical and flat. For the middle age group, the relationship between positive 

neighborhood connectedness and worry differ for boys (bm = 0.091) and girls (bm = 

0.002), with the former accelerating at a faster pace than the latter. 

4. Neighborhood connectednessnegative × gender × agequad (b = 0.13, se = 0.006, t = 2.01, p < 

.05; Figure 37). For all six gender × age groups, the relationship between absence of 

negative feelings about the neighborhood and worry is negative. Among boys, the 

relationship is strongest for the middle age group (bm = -0.490), and it is less strong but 

approximately equal for the other two age groups (by = -0.406, bo = -0.409). For girls, the 

56 

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Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
 

strongest relationship also occurs for the middle age group (bm = -0.605), and it is less 

strong for the other age groups (by = -0.479, bo = -0.472). 

Table 19 

SEM Estimates: Determinants of Worry at Time t 

Estimate SEestimate

Estimate

SEestimate

p

Gender (G) -0.348 0.075 -4.613 <.001 

Exposure to violence 

Witnessing violence (WV) 0.323 

Violent victimization (VV) -0.206 

G × WV 0.250 

G × VV 0.111 

Social support 

0.076 

0.037 

0.110 

0.065 

4.255 

-5.594 

2.270 

1.712 

<.001 

<.001 

.023 

.087 

Maternal warmth (MOM) 0.024 0.037 0.650 .516 

Neighborhood connectednesspositive (NHP) 0.067 0.025 2.619 .009 

Neighborhood connectednessnegative (NHN) -0.537 0.029 -18.335 <.001 

G × MOM -0.009 0.053 -0.168 .867 

G × NHP 0.074 0.035 2.106 .035 

G × NHN -0.111 0.042 -2.645 .008 Worry 

10.4 

9.4 

8.4 

7.4 

6.4 

5.4 

4.4 

9 10 11 12 13 14 15 16 

Age 

Figure 30. Worry as a Function of Age. 

57 

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necessarily reflect the official position or policies of the U.S. Department of Justice.
 

 

 

Worry 

10.4 

9.4 

8.4 

7.4 

6.4 

5.4 

4.4 

0 times 1 time >1 time 

Frequency of Witnessing Violence: Past 90 Days 

Boy Girl 

Figure 31. Worry as a Function of Witnessing Violence and Gender. 

10.4 

9.4 

8.4 

7.4 

Worry 

6.4 

5.4 

4.4 

0 times 1 time >1 time 

Frequency of Witnessing Violence: Past 90 Days 

Age: 9-11 Age: 12-13 Age: 14-16 

Figure 32. Worry as a Function of Witnessing Violence and Age. 

4.4 

5.4 

6.4 

7.4 

8.4 

9.4 

10.4 

Worry 

None/365 days Brandishment: Brandishment: Cut/shot: 1 time Cut/shot: >1 time 

1 time 90 days >1 time 90 days 365 days 365 days 

Violent Victimization 

Boy: Age 9-11 Boy: Age 12-13 Boy: Age 14-16 

Girl: Age 9-11 Girl: Age 12-13 Girl: Age 14-16 

Figure 33. Worry as a Function of Violent Victimization, Gender, and Age. 

58 

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necessarily reflect the official position or policies of the U.S. Department of Justice.
 

10.4 

9.4 

8.4 

7.4 

6.4 

5.4 

4.4 

Positive Neighborhood Connectedness 

Boy Girl 

0 1 2 3 4 5 6 

Worry 

Figure 34. Worry as a Function of Positive Neighborhood Connectedness and Gender. 

10.4 

9.4 

8.4 

0 1 2 3 4 5 6 

Worry 7.4 

6.4 

5.4 

4.4 

Positive Neighborhood Connectedness 

Boy: Age 9-11 Boy: Age 12-13 Boy: Age 14-16 

Girl: Age 9-11 Girl: Age 12-13 Girl: Age 14-16 

Figure 35. Worry as a Function of Positive Neighborhood Connectedness, Gender, and Age. 

59 

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necessarily reflect the official position or policies of the U.S. Department of Justice.
 

Worry 

10.4 

9.4 

8.4 

7.4 

6.4 

5.4 

4.4 

0 1 2 3 4 

Negative Neighborhood Connectedness 

Boy Girl 

5 

Figure 36. Worry as a Function of Negative Neighborhood Connectedness and Gender. 

10.4 

9.4 

8.4 

Worry 

7.4 

6.4 

5.4 

4.4 

0 1 2 3 4 5 

Negative Neighborhood Connectedness 

Boy: Age 9-11 Boy: Age 12-13 Boy: Age 14-16 

Girl: Age 9-11 Girl: Age 12-13 Girl: Age 14-16 

Figure 37. Worry as a Function of Negative Neighborhood Connectedness, Gender, and Age. 

60 

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necessarily reflect the official position or policies of the U.S. Department of Justice.
 

 

 

 

 

 

Traumatic stress. Table 20 shows SEM results for traumatic stress. Gender and witnessing 

violence both have positive main effects on traumatic stress, the former indicating that girls have 

higher levels of traumatic stress than boys. All three of the social support variables are 

statistically significant: maternal warmth and positive neighborhood connectedness are both 

positively associated with traumatic stress, and absence of negative neighborhood connectedness 

is negatively associated with traumatic stress. 

The supplemental LMM analysis showed main effects for agelin (b = -0.380, se = 0.015, t = -

24.81, p < .001) and for agequad (b = 0.029, se = 0.008, t = 3.67, p < .001). Traumatic stress 

increased modestly between ages 9 and 10, and thereafter it decreased monotonically as a 

function of age (Figure 38). Only one significant age-related interaction occurred. 

1.Witnessing violence × agelin (b = -0.046, se = 0.018, t = -2.58, p < .01; Figure 39). 

Witnessing violence is associated with an increase in traumatic stress for all three age groups. 

The sharpest increase occurs for the two younger age group (by = 0.710, bm = .715, with the 

increase for the oldest age group not as substantial (bo = 0.550). 

Table 20 

SEM Estimates: Determinants of Traumatic Stress at Time t 

Estimate SEestimate

Estimate

SEestimate

p

Gender (G) 0.352 0.054 6.494 <.001 

Exposure to violence 

Witnessing violence (WV) 0.553 

Violent victimization (VV) -0.036 

G × WV 0.142 

G × VV 0.091 

Social support 

0.061 

0.030 

0.083 

0.051 

9.095 

-1.201 

1.704 

1.802 

<.001 

.230 

.088 

.072 

Maternal warmth (MOM) 0.091 0.030 3.034 .002 

Neighborhood connectednesspositive (NHP) 0.213 0.021 10.290 <.001 

Neighborhood connectednessnegative (NHN) -0.197 0.023 -8.427 <.001 

G × MOM 0.070 0.043 1.629 .103 

G × NHP -0.032 0.028 -1.143 .253 

G × NHN -0.053 0.033 -1.618 .106 

61 

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Ttrumatic Stress 

8.7 

8.2 

7.7 

7.2 

6.7 

6.2 

5.7 

9 10 11 12 13 14 15 16 

Age 

Figure 38. Traumatic Stress as a Function of Age. 

8.7 

8.2 

Traumatic Stress 7.7 

7.2 

6.7 

6.2 

5.7 

0 times 1 time >1 time 

Frequency of Witnessing Violence: Past 90 Days 

Age: 9-11 Age: 12-13 Age: 14-16 

Figure 39. Traumatic Stress as a Function of Witnessing Violence and Age. 

62 

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Academic progress. Table 21 shows direct effects, assessed through SEM, of variables 

predicting academic progress. As in all the other analyses, gender has a significant effect on 

academic progress, with girls faring better than boys. Both of the exposure to violence variables 

have significant negative effects on academic progress; neither of the exposure to violence by 

gender interaction is significant. Maternal warmth is a significant predictor of academic 

progress; greater maternal warmth is associated with lower academic progress. None of the 

social support by gender interactions is significant. Parental monitoring and curfew are both 

associated with improved normative academic progress, but neither of the family control by 

gender effects is significant. Hopelessness and street code are negatively associated with 

academic progress, while global self-worth, worry, and traumatic stress are all positively 

associated with academic progress. Several psychological adjustment interactions are associated 

with academic progress. When a continuous variable is dichotomized in the figures, groupings 

are based on median splits. 

1. Street code × gender (Figure 40). Street code had negative effect on academic progress 

for boys (b = -0.042); its effect on academic progress for girls was also negative but 

smaller (b = -0.021). 

2. Traumatic stress × gender (Figure 41). Traumatic stress had a positive, albeit modest, 

effect on academic progress for boys (b = .019); it had virtually no effect on academic 

progress for girls (b = .001). 

3. Self-worthglobal × neighborhood connectednessnegative (Figure 44). Global self-worth had a 

larger positive effect on academic progress for adolescents who felt an absence of 

negative connectedness to their neighborhood (b = 0.141) than for adolescents who had 

high levels negative feelings about their neighborhood (b = 0.088). 

4. Worry × neighborhood connectednessnegative (Figure 43). Worry had a modest positive 

effect on academic progress for adolescents who had negative feelings about their 

neighborhood (b = 0.014); for adolescents who did not have negative feelings about their 

neighborhoods, the relationship was essentially flat (b = 0.004). 

5. Worry × parental monitoring (Figure 42). A simplified analysis showed that worry had 

essentially no effect on academic progress either adolescents who experienced low levels 

of parental monitoring (b = 0.008) or for adolescents who experienced high levels of 

parental monitoring (b = 0.001), despite the results from the SEM analysis. 

6. Self-worthbehavioral × neighborhood connectednessnegative × gender (Figure 46). For boys, 

those who had negative feelings about their neighborhood showed a weaker relationship 

between behavioral self-worth and academic progress (b = 0.032) than boys who had no 

negative feelings about their neighborhood (b = 0.052). Girls showed a similar pattern, 

with those holding negative feelings about their neighborhood showing a weaker 

relationship between behavioral self-worth and academic progress (b = 0.039) than those 

with no negative feelings about their neighborhood (b = 0.073). 

7. Self-worthglobal × neighborhood connectednessnegative × gender (Figure 45). Boys for 

whom negative feelings about their neighborhood were absent displayed a stronger 

relationship between global self-worth and academic progress (b = 0.152) compared with 

boys for whom such negative feelings were present (0.065). For girls, the relationship 

between global self-worth and academic progress was strongest among those with no 

negative feelings about their neighborhood (b = 0.112) compared with those who had 

negative feelings about their neighborhood (b = 0.089). The difference in slopes between 

63 

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the two groups of boys was greater than the difference in slopes between the two groups 

of girls. 

Table 21 

SEM Estimates: Determinants of Academic Progress at Time t 

Estimate SEestimate

Estimate

SEestimate

p

Gender (G) 0.122 0.023 5.193 <.001 

Exposure to violence 

Witnessing violence (WV) 

Violent victimization (VV) 

G × WV 

G × VV 

Social support 

-0.057 

-0.078 

0.013 

-0.007 

0.021 

0.013 

0.030 

0.021 

-2.672 

-6.153 

0.418 

-0.347 

.008 

<.001 

.676 

.728 

Maternal warmth (MOM) 

Neighborhood connectednesspositive (NHP

Neighborhood connectednessnegative (NH

G × MOM 

G × NHP 

G × NHN 

Family control 

-0.035 

) 0.007 

N) 0.016 

0.016 

-0.002 

0.020 

0.011 

0.007 

0.008 

0.016 

0.010 

0.012 

-3.178 

0.990 

1.933 

0.999 

-0.237 

1.621 

.001 

.322 

.053 

.318 

.813 

.105 

Parental monitoring (PM) 

Curfew (CF) 

G × PM 

G × CF 

Psychological adjustment 

0.023 

0.076 

0.000 

-0.023 

0.005 

0.010 

0.008 

0.014 

4.401 

7.600 

0.008 

-1.627 

<.001 

<.001 

.994 

.104 

Hopelessness (HPL) 

Street code (SC) 

Self-worthbehavioral (SWB) 

Self-worthglobal (SWG) 

Worry (WOR) 

Traumatic stress (TS) 

G × HPL 

G × SC 

G × SWB 

G × SWG 

G × WOR 

G × TRS 

-0.033 

-0.013 

-0.018 

0.070 

0.009 

0.014 

0.002 

0.025 

-0.001 

0.027 

-0.005 

-0.014 

0.008 

0.005 

0.009 

0.014 

0.003 

0.004 

0.012 

0.009 

0.005 

0.014 

0.020 

0.006 

-4.183 

-2.392 

-1.858 

5.048 

2.948 

3.794 

0.179 

2.864 

-0.240 

1.917 

-0.260 

-2.445 

<.001 

.017 

.063 

<.001 

.003 

<.001 

.858 

.004 

.810 

.055 

.795 

.014 

HPL × MOM 

HPL × NHP 

HPL × NHN 

HPL × PM 

HPL × CF 

0.007 

0.001 

0.006 

-0.000 

-0.006 

0.007 

0.004 

0.005 

0.003 

0.006 

0.988 

0.263 

1.236 

-0.178 

-1.085 

.323 

.793 

.216 

.858 

.278 

SC × MOM 

SC × NHP 

SC × NHN 

S C × PM 

SC × CF 

-0.004 

-0.005 

0.001 

0.003 

-0.004 

0.004 

0.003 

0.003 

0.002 

0.004 

-0.896 

-1.638 

0.204 

1.399 

-0.965 

.370 

.102 

.838 

.162 

.335 

SWB × MOM 0.005 0.008 0.581 .562 

64 

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SWB × NHP 0.000 0.005 0.043 .966 

SWB × NHN -0.009 0.006 -1.548 .122 

SWB × PM 0.001 0.004 0.266 .790 

SWB × CF -0.011 0.007 -1.415 .157 

SWG × MOM -0.003 0.011 -0.289 .773 

SWG × NHP 0.007 0.007 0.984 .325 

SWG × NHN 0.038 0.009 4.404 <.001 

SWG × PM -0.005 0.005 -1.044 .296 

SWG × CF -0.002 0.010 -0.148 .882 

WOR × MOM 0.001 0.002 0.476 .634 

WOR × NHP 0.001 0.002 0.424 .672 

WOR × NHN -0.005 0.002 -2.670 .008 

WOR × PM -0.002 0.001 -2.094 .036 

WOR × CF 0.001 0.002 0.639 .523 

TS × MOM 0.000 0.003 0.008 .993 

TS × NHP 0.002 0.002 1.054 .292 

TS × NHN 0.001 0.002 0.562 .574 

TS × PM -0.002 0.001 -1.622 .105 

TS × CF -0.004 0.003 -1.363 .173 

G × HPL × MOM 0.004 0.010 0.433 .665 

G × HPL × NHP 0.006 0.006 0.939 .348 

G × HPL × NHN -0.010 0.007 -1.387 .166 

G × HPL × PM -0.002 0.004 -0.410 .682 

G × HPL × CF -0.004 0.008 -0.434 .664 

G × SC × MOM 0.005 0.007 0.630 .529 

G × SC × NHP -0.003 0.004 -0.662 .508 

G × SC × NHN -0.000 0.005 -0.076 .940 

G × SC × PM -0.005 0.003 -1.614 .106 

G × SC × CF 0.008 0.006 1.225 .221 

G × SWB × MOM 0.009 0.012 0.733 .464 

G × SWB × NHP -0.002 0.007 -0.233 .816 

G × SWB × NHN 0.025 0.009 2.856 .004 

G × SWB × PM 0.001 0.005 0.188 .851 

G × SWB × CF 0.003 0.011 0.260 .795 

G × SWG × MOM 0.018 0.015 1.147 .251 

G × SWG × NHP -0.013 0.010 -1.236 .216 

G × SWG × NHN -0.046 0.012 -3.747 <.001 

G × SWG × PM -0.003 0.007 -0.347 .729 

G × SWG × CF -0.001 0.015 -0.059 .953 

G × WOR × MOM -0.001 0.004 -0.358 .721 

G × WOR × NHP -0.000 0.002 -0.210 .833 

G × WOR × NHN 0.005 0.003 1.928 .054 

G × WOR × PM 0.002 0.002 0.872 .383 

G × WOR × CF -0.001 0.003 -0.384 .701 

G × TS × MOM 0.007 0.005 1.545 .122 

G × TS × NHP -0.003 0.003 -0.941 .347 

G × TS × NHN -0.006 0.003 -1.743 .081 

G × TS × PM 0.001 0.002 0.536 .592 

65 

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necessarily reflect the official position or policies of the U.S. Department of Justice.
Academic Progress 

4.5 

4.3 

4.1 

3.9 

3.7 

3.5 

3.3 

0 1 2 3 4 

Street Code 

Boy 

5 6 

Girl 

7 8 

Figure 40. Academic Progress as a Function of Street Code and Gender. 

4.5 

Academic Progress 

4.3 

4.1 

3.9 

3.7 

3.5 

3.3 

Traumatic Stress 

Boy Girl 

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 

Figure 41. Academic Progress as a Function of Traumatic Stress and Gender. 

66 

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Academic Progress 

4.5 

4.3 

4.1 

3.9 

3.7 

3.5 

3.3 

Worry 

Low Parental Monitoring High Parental Monitoring 

0 1 2 3 4 5 6 7 8 9 10 11 12 

Figure 42. Academic Progress as a Function of Worry and Parental Monitoring. 

4.5 

Academic Progress 

4.3 

4.1 

3.9 

3.7 

3.5 

3.3 

Worry 

Low Neighborhood Connectedness High Neighborhood Connectedness 

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 

Figure 43. Academic Progress as a Function of Worry and Negative Neighborhood 

Connectedness. 

67 

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Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
Academic Progress 

4.5 

4.3 

4.1 

3.9 

3.7 

3.5 

3.3 

0 1 2 3 4 

Global Self-Worth 

Low Neighborhood Connectedness High Neighborhood Connectedness 

Figure 44. Academic Progress as a Function of Global Self-Worth and Negative 

Neighborhood Connectedness 

4.5 

Academic Progress 

4.3 

4.1 

3.9 

3.7 

3.5 

3.3 

0 1 2 3 4 

Global Self-Worth 

Boy - Low Neighborhood Connectedness Boy - High Neighborhood Connectedness 

Girl - Low Neighborhood Connectedness Girl - High Neighborhood Connectedness 

Figure 45. Academic Progress as a Function of Global Self Worth, Negative Neighborhood 

Connectedness, and Gender. 

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Academic Progress 

4.5 

4.3 

4.1 

3.9 

3.7 

3.5 

3.3 

0 1 2 3 4 5 

Behavioral Self-Worth 

Boy - Low Neighborhood Connectedness Boy - High Neighborhood Connectedness 

Girl - Low Neighborhood Connectedness Girl - High Neighborhood Connectedness 

Figure 46. Academic Progress as a Function of Behavioral Self Worth, Negative 

Neighborhood Connectedness, and Gender. 

Court outcome severity. The effects of gender, exposure to violence, psychological 

adjustment, social support and family control, and academic progress on court outcome severity 

are shown in Table 22. In this analysis, court outcome severity is included on both sides of the 

equation, as a dependent variable at time t+1 and as an independent variable at time t. Thus, the 

case for inferring causation from statistically significant relationships is strengthened, although 

by no means absolute. 

Both measures of exposure to violence are statistically significant and positive predictors of 

court outcome severity, as expected based on the evidence from Research Question 1. Unlike 

many of the other outcome variables, gender does not predict court outcome severity. Among the 

psychological adjustment variables, only hopelessness, behavioral self-worth, and global selfworth are statistically significant predictors of court outcome severity. Among the social support 

variables, maternal warmth and positive feelings of neighborhood connectedness are statistically 

significant predictors of court outcome severity. And among the family control variables, 

parental monitoring is a significant predictor of court outcome severity. Academic progress has a 

negative (expected) effect on court outcome severity. As with academic progress, several 

interactions were statistically significant predictors of court outcome severity. 

1. Street code × curfew (Figure 48). The relationship between street code and court outcome 

severity is negative for adolescents with a strict curfew (exp(b) = 0.995). That is, for 

every unit of increase in street code, court outcome severity decreases by 0.005 (i.e., 1-

.995). For adolescents with a lax curfew, the relationship is positive (exp(b) = 1.007); that 

is, for every unit of increase in street code, court outcome severity increases by 0.007. 

2. Traumatic Stress × neighborhood connectednessnegative (Figure 51). For adolescents who 

had negative feelings about their neighborhood, the relationship between traumatic stress 

and court outcome severity was positive (exp(b) = 1.013); for adolescents who had fewer 

69 

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negative feelings about their neighborhood, the relationship between traumatic stress and 

court outcome severity was negative (exp(b) = 0.991). 

3. Street code × parental monitoring × gender (Figure 50). The relationship between street 

code and court outcome severity is positive and strongest for boys (exp(b) = 1.115) and 

girls (exp(b) = 1.155) who reported high levels of parental monitoring. It is also positive, 

but not as strong, for boys (exp(b) = 1.029) and girls (exp(b) = 1.093) who reported low 

levels of parental monitoring. 

4. Traumatic Stress × neighborhood connectednessnegative × gender (Figure 52). For girls 

without negative feelings about their neighborhood, the relationship between traumatic 

stress and court outcome severity is positive (exp(b) = 1.018) and nearly identical to the 

relationship for girls with negative feelings about their neighborhood (exp(b) = 1.021). 

For boys with negative feelings about their neighborhood, the relationship between 

traumatic stress and court outcome severity is also positive (exp(b) = 1.013). However, 

for boys without negative feelings about their neighborhood, the relationship is negative 

(exp(b) = -0.976). 

A supplemental GLMM analysis was run to determine the effects of linear and quadratic age, 

both as main effects and in interaction with other variables, on court outcome severity. Because 

the distribution of court outcome severity had zero-inflated Poisson characteristics, the analysis 

treated the error distribution as negative binomial with log link function. The model was 

estimated using maximum likelihood estimation based on Laplace approximation. Intercept was 

treated as a random effect, and the variance-covariance matrix was allowed to be unstructured, 

parameterized through its Cholesky root. Denominator degrees of freedom were determined 

using containment. The first result of interest was a statistically significant quadratic age effect 

(b = -0.158, se = .008, t = -19.07, p < .001). Figure 47 shows how age affect court outcome 

severity. Court outcome severity remains very low through age 10, then increases through age 13 

before beginning to decline at age 14. Several age interactions also affect court outcome severity. 

1. Violent victimization × agequad (b = 0.020, se = 0.009, t = 2.26, p < .05; Figure 53). The 

relationship between violent victimization and court outcome severity is positive for all 

three age groups. (Note that this result is different from the RQ1 results, likely because of 

the greater complexity of this model.) This relationship is strongest for the youngest age 

group (exp(bo) = 1.827) and weakest for the middle age group (exp(bm) = 1.195, exp(bo) 

= 1.266). 

2. Violent victimization × agequad × gender (b = -0.037, se = 0.18, t = -2.04, p < .05; Figure 

54). This is essentially the same analysis that was conducted to address Research 

Question 1 (Figure 4). For all six gender × age groups, the relationship between violent 

victimization and court outcome severity is positive. The relationship is stronger for girls 

than for boys, with the youngest girls showing the sharpest increase in court outcome 

severity as a function of violent victimization (exp(by) = 1.469), followed by the oldest 

girls (exp(bo) = 1.389) and the middle female age group (exp(bm) = 1.205). Among boys, 

the relationship between violent victimization and court outcome severity is strongest for 

the youngest age group (exp(by) = 1.286), followed sequentially by the middle age group 

(exp(bm) = 1.243) and the oldest age group (exp(bo) = 1.146). 

3. Street code × agelin (b = -0.022, se = 0.007, t = -3.17, p < .01; Figure 49). The increase in 

court outcome severity as a function of street code is most rapid for the youngest age 

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group (exp(by) = 1.568), followed in intensity by the oldest age group (exp(bo) = 1.083) 

and the middle age group (exp(by) = 1.044). 

4. Self-worthbehavioral × agequad (b = -0.019, se = 0.007, t = -1.99, p < .01; Figure 55). For all 

three age groups, the relationship between behavioral self-worth and court outcome 

severity was negative. The decrease is most rapid for the youngest age group (exp(bm) = 

0.570), followed by the oldest (exp(bo) = 0.765) and the middle (exp(bm) = 0.872). 

5. Self-worthglobal × agequad (b = 0.025, se = 0.009, t = 2.80, p < .01; Figure 56). The 

youngest age group shows the steepest decline in court outcome severity as a function of 

global self-worth (exp(by) = 0.521), with the other two age groups showing less steep and 

similar declines (exp(bm) = 0.81; exp(bo) = 0.800). 

6. Self-worthglobal × agequad × gender (b8 = 0.068, se = 0.029, t = 2.33, p < .05; Figure 57). 

All six gender × age groups showed a decline in court outcome severity as a function of 

global self-worth; this decline tended to be steeper for girls than for boys. For girls, the 

steepest decline occurred for the youngest age group (exp(by) = 0.415), becoming less 

steep as age increased (exp(bm) = 0.7301, exp(bo) = 0.833). For boys, steepness of decline 

inversely followed age order (exp(by) = 0.844) (exp(bm) = 0.822) (exp(by) = 0.790), 

although differences among the three age groups were small. 

Table 22 

SEM Estimates: Determinants of Court Outcome Severity at Time t + 1 

Estimate SEestimate

Estimate

SEestimate

p

Gender (G) -0.260 0.229 -1.136 .256 

Lagged court outcome severity 0.252 0.010 26.519 <.001 

Exposure to violence 

Witnessing violence (WV) 

Violent victimization (VV) 

G × WV 

G × VV 

Social support 

0.131 

0.097 

0.058 

-0.043 

0.052 

0.026 

0.083 

0.050 

2.516 

3.752 

0.703 

-0.860 

.012 

<.001 

.482 

.390 

Maternal warmth (MOM) 

Neighborhood connectednesspositive (NHP

Neighborhood connectednessnegative (NH

G × MOM 

G × NHP 

G × NHN 

Family control 

0.057 

) 0.036 

N) 0.010 

-0.004 

-0.001 

0.004 

0.036 

0.021 

0.024 

0.054 

0.030 

0.037 

1.594 

1.733 

0.392 

-0.067 

-0.031 

0.109 

.111 

.083 

.695 

.947 

.975 

.913 

Parental monitoring (PM) 

Curfew (CF) 

G × PM 

G × CF 

Psychological adjustment 

-0.031 

-0.050 

-0.009 

0.034 

0.013 

0.027 

0.021 

0.041 

-2.329 

-1.893 

-0.444 

0.838 

.020 

.058 

.657 

.402 

Hopelessness (HPL) 0.073 0.022 3.399 .001 

Street code (SC) 0.013 0.016 0.809 .419 

Self-worthbehavioral (SWB) -0.061 0.030 -2.051 .040 

Self-worthglobal (SWG) -0.035 0.042 -0.836 .403 

Worry (WOR) -0.007 0.009 -0.799 .424 

71 

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Traumatic stress (TS) 

-0.006 0.011 -0.566 .572 

G × HPL 0.016 0.026 0.627 .531 

G × SC -0.004 0.014 -0.274 .784 

G × SWB -0.039 0.044 -0.883 .377 

G × SWG -0.062 0.060 -1.040 .298 

G × WOR 0.009 0.017 0.520 .603 

G × TRS -0.004 0.054 -0.067 .947 

HPL × MOM 0.006 0.018 0.314 

.754 

HPL × NHP 

-0.011 0.010 -1.079 .280 

HPL × NHN 0.009 0.012 0.758 

.449 

HPL × PM 0.001 0.007 0.136 

.892 

HPL × CF 

-0.003 0.014 -0.232 .817 

SC × MOM 0.019 0.014 1.323 

.186 

SC × NHP 0.006 0.008 0.800 

.424 

SC × NHN 0.001 0.010 0.145 

.885 

SC × PM 0.006 0.005 1.118 

.264 

SC × CF 0.023 0.010 2.221 

.026 

SWB × MOM 0.017 0.023 0.754 

.451 

SWB × NHP 0.001 0.016 0.035 

.972 

SWB × NHN 0.001 0.018 0.054 

.957 

SWB × PM 

-0.001 0.010 -0.155 .877 

SWB × CF 

-0.003 0.021 -0.158 .875 

SWG × MOM 0.002 0.030 0.060 

.952 

SWG × NHP 0.024 0.022 1.095 

.274 

SWG × NHN 

-0.027 0.024 -1.151 .250 

SWG × PM 0.007 0.013 0.545 

.586 

SWG × CF 0.027 0.028 0.964 

.335 

WOR × MOM 0.011 0.007 1.509 

.131 

WOR × NHP 

-0.004 0.005 -0.708 .479 

WOR × NHN 0.004 0.006 0.722 

.470 

WOR × PM 

-0.004 0.003 -1.300 .194 

WOR × CF 

-0.011 0.006 -1.767 .077 

TS × MOM 

-0.009 0.009 -1.005 .315 

TS × NHP 

-0.010 0.006 -1.781 .075 

TS × NHN 

-0.019 0.007 -2.951 .003 

TS × PM 0.001 0.003 0.326 

.744 

TS × CF 

-0.005 0.008 -0.614 .539 

G × HPL × MOM -0.031 0.028 -1.113 .266 

G × HPL × NHP -0.011 0.017 -0.675 .500 

G × HPL × NHN 0.001 0.019 0.074 .941 

G × HPL × PM -0.011 0.011 -0.986 .324 

72 

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G × HPL × CF 0.003 0.022 0.119 .905 

G × SC × MOM 0.014 0.022 0.647 .518 

G × SC × NHP 0.000 0.012 0.012 .990 

G × SC × NHN -0.021 0.015 -1.378 .168 

G × SC × PM -0.018 0.009 -2.093 .036 

G × SC × CF -0.011 0.017 -0.603 .546 

G × SWB × MOM 0.026 0.039 0.678 .498 

G × SSWB × NHP -0.005 0.025 -0.182 .856 

G × SWB × NHN 0.009 0.028 0.336 .737 

G × SWB × PM -0.018 0.016 -1.140 .254 

G × SWB × CF 0.020 0.033 0.608 .543 

G × SWG × MOM -0.028 0.050 -0.554 .580 

G × SWG × NHP -0.028 0.033 -0.873 .383 

G × SWG × NHN -0.033 0.037 -0.903 .366 

G × SWG × PM -0.032 0.020 -1.594 .111 

G × SWG × CF 0.001 0.044 0.025 .980 

G × WOR × MOM 0.010 0.012 0.793 .428 

G × WOR × NHP -0.002 0.008 -0.223 .823 

G × WOR × NHN -0.000 0.009 -0.056 .955 

G × WOR × PM 0.000 0.005 0.086 .932 

G × WOR × CF 0.010 0.010 0.975 .330 

G × TS × MOM 0.013 0.014 0.894 .371 

G × TS × NHP 0.003 0.009 0.340 .734 

G × TS × NHN 0.021 0.010 2.003 .045 

G × TS × PM 0.006 0.006 0.965 .335 

G × TS × CF 0.004 0.013 0.303 .762 

Education 

Academic progress (EDUC) 

G × EDUC 

-0.107 

-0.011 

0.034 

0.056 

-3.096 

-0.203 

.002 

.839 

Court Outcome Severity 

1 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

0 

9 10 11 12 13 14 15 16 

Age 

Figure 47. Court Outcome Severity as a Function of Age. 

73 

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Court Outcome Severity 

1 

0.8 

0.6 

0.4 

0.2 

0 

Street Code 

Lax Curfew Strict Curfew 

0 1 2 3 4 5 6 7 8 

Figure 48. Court Outcome Severity as a Function of Street Code and Curfew. Court Outcome Severity 

1 

0.8 

0.6 

0.4 

0.2 

0 

Street Code 

Age:9-11 Age:12-13 Age: 14-16 

0 1 2 3 4 5 6 7 8 

Figure 49. Court Outcome Severity as a Function of Street Code and Age. Court Outcome Severity 

1 

0.8 

0.6 

0.4 

0.2 

0 

Street Code 

Boy - Low Parental Monitoring Boy - High Parental Monitoring 

Girl - Low Parental Monitoring Girl - High Parental Monitoring 

0 1 2 3 4 5 6 7 8 

Figure 50. Court Outcome Severity as a Function of Street Code, Parental Monitoring, and 

Gender. 

74 

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necessarily reflect the official position or policies of the U.S. Department of Justice.
 Court Outcome Severity 

1 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

0 

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 

Traumatic Stress 

Low Neighborhood Connectedness High Neighborhood Connectedness 

Figure 51. Court Outcome Severity as a Function of Traumatic Stress and Negative 

Neighborhood Connectedness. 

1 

Court Outcome Severity 0.8 

0.6 

0.4 

0.2 

0 

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 

Traumatic Stress 

Boy - Low Neighborhood Connectedness Boy - High Neighborhood Connectedness 

Girl - Low Neighborhood Connectedness Girl - High Neighborhood Connectedness 

Figure 52. Court Outcome Severity as a Function of Traumatic Stress, Negative Neighborhood 

Connectedness, and Gender. 

75 

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Court Outcome Severity 

1 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

0 

None/365 days Brandishment: 1 Brandishment: >1 Cut/shot: 1 time Cut/shot: >1 time 

time 90 days time 90 days 365 days 365 days 

Violent Victimization 

Age:9-11 Age:12-13 Age: 14-16 

Figure 53. Court Outcome Severity as a Function of Violent Victimization and Age. 

0 

0.1 

0.2 

0.3 

0.4 

0.5 

0.6 

0.7 

0.8 

0.9 

1 

Court Outcome Severity 

None/365 days Brandishment: 1 Brandishment: >1 Cut/shot: 1 time Cut/shot: >1 time 

time 90 days time 90 days 365 days 365 days 

Violent Victimization 

Boy: Age 9-11 Boy: Age 12-13 Boy: Age 14-16 

Girl: Age 9-11 Girl: Age 12-13 Girl: Age 14-16 

Figure 54. Court Outcome Severity as a Function of Violent Victimization, Gender, and Age. 

76 

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1 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

0 

0 1 2 3 4 5 

Behavioral Self-Worth 

Age:9-11 Age:12-13 Age: 14-16 

Figure 55. Court Outcome Severity as a Function of Behavioral Self-Worth and Age. Court Outcome Severity 

77 

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Court Outcome Severity 

1 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

0 

0 1 2 3 4 

Global Self-Worth 

Age:9-11 Age:12-13 Age: 14-16 

Figure 56. Court Outcome Severity as a Function of Global Self-Worth and Age. 

0 

0.1 

0.2 

0.3 

0.4 

0.5 

0.6 

0.7 

0.8 

0.9 

1 

Court Outcome Severity 

0 1 2 

Global Self-Worth 

3 4 

Boy: Age 9-11 

Girl: Age 9-11 

Boy: Age 12-13 

Girl: Age 12-13 

Boy: Age 14-16 

Girl: Age 14-16 

Figure 57. Court Outcome Severity as a Function of Global Self-Worth, Gender, and Age. 

78 

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Indirect effects. Table 23 shows direct effects, indirect effects, and total effects originating 

from the two exposure to violence variables. The direct effect of witnessing violence on court 

outcome severity is statistically significant, accounting for 82.5% of its total effect; even so, the 

indirect effect (through academic progress and hopelessness directly, and through hopelessness, 

global self-worth, and traumatic stress indirectly via academic progress) is statistically 

significant. Therefore, we would conclude that the effect of witnessing violence on court 

outcome severity is partially mediated by psychological adjustment and academic progress. 

Like witnessing violence, the direct effect of violent victimization on court outcome severity 

is statistically significant, accounting for 76.5% of the total effect; however, the indirect effect is 

again statistically significant, manifest through academic progress, hopelessness, and both 

behavioral directly, and through hopelessness, global self-worth, and worry indirectly (via 

academic progress). As before, the conclusion should be that the effect of violent victimization 

on court outcome severity is partially mediated by psychological adjustment and academic 

progress. 

Table 23 also shows the indirect effects of exposure to violence on academic progress. The 

overwhelming bulk of the effects are direct (98.3%), and the total contribution of indirect effects 

is not statistically significant. However, this is confounded by the fact that negative indirect 

effects of hopelessness, street code, and global self-worth largely offset the positive indirect 

effects of worry and traumatic stress, each of which is statistically significant in its own right. 

Therefore, we should again conclude that the effect of witnessing violence on academic progress 

is partially mediated by psychological adjustment. Finally, the direct effects of violent 

victimization account for 86.7% of its total effects on academic progress. But again, the 

combined indirect effects of psychological adjustment, notably through hopelessness, street 

code, global self-worth, worry, and traumatic stress are statistically significant. Again, we should 

conclude that the relationship between violent victimization and academic progress is partially 

mediated by psychological adjustment. 

Finally, Table 23 shows the indirect effects of gender on court outcome severity, through the 

psychological adjustment mediators, academic progress, and the psychological adjustment → 

academic progress mediational path. Overall, the indirect effect is statistically significant, even 

though neither the direct effect of gender nor the total effect of gender on court outcome severity 

achieves statistical significance. Thus, girls have less severe court outcomes than boys, but this 

relationship is manifest only indirectly. Significant mediators are hopelessness, behavioral selfworth, academic progress, and the hopelessness → academic progress, the worry → academic 

progress, and the traumatic stress → academic progress paths.

79 

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Table 23 

SEM Estimates: Indirect Effects on Court Outcome Severity and Academic Progress 

Estimate

Estimate SEestimate SEestimatep

Witnessing violence → Court outcome severity 

Total effects 0.160 0.052 3.079 .002 

Direct effects 0.131 0.052 2.516 .012 

Indirect effects 0.028 0.011 2.664 .008 

WV → HPL → COURT 0.011 0.004 2.821 .005 

WV → SC → COURT 0.006 0.008 0.809 .418 

WV → SWB → COURT 0.009 0.004 1.932 .053 

WV → SWG → COURT 0.002 0.002 0.800 .424 

WV → WOR → COURT -0.002 0.003 -0.789 .430 

WV → TRS → COURT -0.003 0.006 -0.565 .572 

WV → EDUC → COURT 0.006 0.003 2.019 .043 

WV → HPL → EDUC → COURT 0.001 0.000 2.186 .029 

WV → SC → EDUC → COURT 0.001 0.000 1.865 .062 

WV → SWB → EDUC → COURT -0.000 0.000 -1.534 .125 

WV → SWG → EDUC → COURT 0.000 0.000 2.023 .043 

WV → WOR → EDUC → COURT -0.000 0.000 -1.902 .057 

WV → TRS → EDUC → COURT -0.001 0.000 -2.378 .017 

Violent victimization → Court outcome severity 

Total effects 0.128 0.026 4.819 <.001 

Direct effects 0.097 0.026 3.752 <.001 

Indirect effects 0.030 0.007 4.256 <.001 

VV → HPL → COURT 0.006 0.002 2.890 .004 

VV → SC → COURT 0.004 0.005 0.807 .420 

VV → SWB → COURT 0.006 0.003 2.010 .044 

VV → SWG → COURT 0.002 0.003 0.829 .407 

VV → WOR → COURT 0.001 0.002 0.792 .428 

VV → TRS → COURT 0.000 0.000 0.507 .612 

VV → EDUC → COURT 0.008 0.003 2.777 .005 

VV → HPL → EDUC → COURT 0.000 0.000 2.146 .032 

VV → SC → EDUC → COURT 0.000 0.000 1.876 .061 

VV → SWB → EDUC → COURT -0.000 0.000 -1.575 .115 

VV → SWG → EDUC → COURT 0.001 0.000 2.512 .012 

VV → WOR → EDUC → COURT 0.000 0.000 2.003 .045 

VV → TRS → EDUC → COURT 0.000 0.000 1.093 .274 

Witnessing violence → Academic progress 

Total effects -0.058 0.021 -2.736 .006 

Direct effects -0.057 0.021 -2.672 .008 

Indirect effects -0.001 0.004 -0.355 .722 

WV → HPL → EDUC -0.005 0.002 -3.243 .001 

WV → SC → EDUC -0.006 0.003 -2.348 .019 

WV → SWB → EDUC 0.002 0.001 1.777 .076 

80 

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WV → SWG → EDUC -0.004 0.001 -2.655 .008 

WV → WOR → EDUC 0.003 0.001 2.439 .015 

WV → TRS → EDUC 0.008 0.002 3.536 <.001 

Violent victimization → Academic progress 

Total effects -0.090 0.013 -7.057 <.001 

Direct effects -0.078 0.013 -6.153 <.001 

Indirect effects -0.012 0.002 -5.019 <.001 

VV → HPL → EDUC -0.003 0.001 -3.216 .001 

VV → SC → EDUC -0.004 0.002 -2.358 .018 

VV → SWB → EDUC 0.002 0.001 1.821 .069 

VV → SWG → EDUC -0.005 0.001 -4.090 <.001 

VV → WOR → EDUC -0.002 0.001 -2.615 .009 

VV → TRS → EDUC -0.001 0.000 -1.153 .249 

Gender → Court outcome severity 

Total effects -0.307 0.231 -1.329 .184 

Direct effects -0.260 0.229 -1.136 .256 

Indirect effects -0.047 0.011 -4.251 <.001 

G → HPL → COURT -0.023 0.007 -3.252 .001 

G → SC → COURT -0.004 0.005 -0.804 .421 

G → SWB → COURT -0.005 0.003 -1.720 .085 

G → SWG → COURT -0.001 0.002 -0.794 .427 

G → WOR → COURT 0.002 0.003 0.791 .429 

G → TRS → COURT -0.002 0.004 -0.562 .574 

G → EDUC → COURT -0.013 0.005 -2.754 .006 

G → HPL → EDUC → COURT -0.001 0.000 -2.348 .019 

G → SC → EDUC → COURT -0.000 0.000 -1.852 .064 

G → SWB → EDUC → COURT 0.000 0.000 1.413 .158 

G → SWG → EDUC → COURT -0.000 0.000 -1.790 .074 

G → WOR → EDUC → COURT 0.000 0.000 1.941 .052 

81 

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Table 24 shows the combined indirect effect (directly and indirectly via academic progress) of 

each of the psychological adjustment variables in the two exposure to violence → court outcome 

severity paths. The indirect effects of hopelessness and behavioral self-worth have the most 

consistent and strongest combined indirect effects, although the total indirect effect of global 

self-worth trends toward statistical significance, for the paths leading from exposure to violence 

to court outcome severity. 

Table 24 

SEM Estimates: Sum of Psychological Adjustment Indirect Effects on Court Outcome Severity 

Estimate

Estimate SEestimate p SEestimate

Witnessing violence 

Hopelessness 0.012 0.004 2.909 .004 

Street code 0.007 0.008 0.892 .372 

Self-worthbehavioral 0.008 0.004 1.876 .061 

Self-worthglobal 0.002 0.002 0.951 .342 

Worry -0.003 0.003 -0.892 .372 

Traumatic stress -0.004 0.006 -0.702 .483 

Violent victimization 

Hopelessness 0.006 0.002 2.977 .003 

Street code 0.004 0.005 0.889 .374 

Self-worthbehavioral 0.006 0.003 1.947 .052 

Self-worthglobal 0.003 0.003 0.999 .318 

Worry 0.002 0.002 0.898 .369 

Traumatic stress 0.000 0.000 0.602 .547 

82 

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Discussion 

The relationship between exposure to violence and involvement in the juvenile justice system 

has been previously established (e.g., Gorman-Smith, Henry, & Tolan, 2004; Hong, Huang, 

Upton Patton, & Washington, 2014; Patchin, Huebner, McCluskey, Varano, & Bynum, 2006; 

Salzinger, Rosario, Feldman, & Ng-Mak, 2008). In this study, we sought to extend findings 

about the exposure-to-violence to juvenile court relationship to an extremely impoverished and 

vulnerable population of urban, predominantly African American adolescents, for whom both 

exposure to violence and court contact are common experiences. Further, we explored factors 

that may mediate or moderate the exposure-to-violence to juvenile court relationship. Because 

this population has disproportionately high levels of court contact. We used longitudinal data 

from the Mobile Youth and Poverty Study to explore how exposure to violence impacts juvenile 

court involvement in a sample of economically disadvantaged adolescents living in Mobile, 

Alabama. In this discussion, first a summary of main results is first presented, followed by 

implications for policy and practice, leading to strengths, limitations, and future research 

possibilities. 

Summary of Exposure to Violence  Juvenile Justice Involvement Results 

Our first objective was to better understand how traumatic events increase the risk of 

juvenile justice involvement for vulnerable adolescents. Of primary importance in addressing this 

objective is an exploration of the outcome variables used in the model: (a) crimes against a 

person and (b) court outcome severity. While research has previously been conducted to 

investigate whether exposure to violence is related to juvenile court contact (e.g., Voisin, Patel, 

Hong, Takahashi, & Gaylord-Harden, 2016), this study goes further to investigate this path. 

Research question 1. A brief summary of the results for Research Question 1 indicates that 

exposure to violence is positively associated with both outcome variables. Witnessing violence 

significantly predicted a change in crimes against a person offense (i.e., more likely to be 

charged with a crime against a person) from time t to time t+1 as well as significantly predicted a 

change in court outcome severity (i.e., more adjudication, more likely to be assigned residential 

placement, put on probation, etc.) from time t to time t+1. That is, witnessing violence did have 

an effect on behaviors that may lead to court contact, and to an even greater extent, the severity 

of outcomes associated with these contacts. The analyses that produced these conclusions were 

particularly rigorous, in that they statistically controlled for previous levels of crimes against a 

person and court outcome severity; thus, these analyses demonstrated not only a relationship 

between exposure to violence and court involvement, but also, given its temporal quality, that 

exposure to violence is associated with change in court involvement. Although strong causality 

cannot ever be definitively established in statistical analysis, the analyses we performed allowed 

us to infer a weak form of causality (Granger causality). 

Research question 2. Research question 2 treats the question of exposure to violence in a 

more nuanced way, specifically by considering how a simple exposure to violence may differ 

from more complex exposure in predicting crimes against a person and court outcome severity. 

Specifically, complex exposure reflected (a) multiple incidents of exposure, (b) multiple sources 

of exposure, and (c) multiple incidents and multiple sources of exposure. This is an important 

distinction, because complex exposure to violence is common in court-involved youth: Stimmel 

83 

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and colleagues (20014) found that within a sample of detained youth, more than half reported 

more than one traumatic event over their lifetime and that more exposure to violence was 

associated with higher levels of trauma. Although analyses associated with Research Question 1 

showed clear differences between no exposure and exposure in predicting juvenile justice 

variables, the analyses for Research Question 2 showed that overall, complex exposure did not 

differ from simple exposure in predicting the two outcome variables. 

Research question 3. Research question 3 considers factors that mediate and moderate the 

relationship between exposure to violence and court involvement, using a structural equation 

model. First, we considered how exposure to violence might be associated with six different 

psychological adjustment variables. Second, we considered how exposure to violence might be 

associated with social support and family control variables. Third, we considered how exposure 

to violence is related to court outcome severity. 

Psychological Adjustment. Consistent with prior research (see Salzinger, Feldman, 

Stockhammer, & Hood, et al., 2002 for a review), our results suggest that exposure to violence is 

indeed related to psychological adjustment. Findings show that witnessing violence was 

positively associated with feelings of hopelessness, street code, worry, and traumatic stress, and 

negatively associated with behavioral self-worth and global self-worth. Similarly, violent 

victimization is positively associated with hopelessness and street code, and negatively 

associated with behavioral self-worth, global self-worth, and worry; it is not, however, associated 

with traumatic stress. This latter finding may be due to the wording of the traumatic stress items: 

they all ask about symptoms that occur when bad things happen to a family member of friend 

rather than oneself. Nonetheless, unless there is a complete bifurcation between emotional 

response to events affecting self and other, this finding seems strange. It is also worth noting that 

not all youth exposed to trauma will display symptoms of post-traumatic stress disorder 

according to the DSM V, but they may still have notable outcomes related to that trauma (Kerig 

& Becker, 2012). Additionally, the negative relationship between violent victimization and 

worry seems, on its face, implausible. 

However, in high-poverty inner-city neighborhoods, many adolescents worry about being 

victimized (Bagley, Tu, Buckhalt, & El-Sheikh, 2016; Goldman-Mellor, Margerison-Zilko, 

Allen, & Cerda, 2016). When this happens, it may remove the uncertainty of victimization and 

result in lower levels of overall worry. There also may be a desensitization effect. Mrug and 

colleagues (2016) concluded that “emotional desensitization to violence in early adolescence 

contributes to serious violence in late adolescence” (p. 75) and that this emotional desensitization 

can occur with repeated exposure to community violence. Further, Gaylord-Harden and 

colleagues (2017) found in their study of adolescent boys that depressive symptoms increased as 

exposure to violence increased, but only to a point; then emotional desensitization occurs and 

depressive symptoms reduce. They go on to conclude that this reduction in depressive symptoms 

is not indicative of mental health but rather provides evidence to suggest the complexity of 

exposure to violence; they also suggest that depressive symptoms found more in boys/men 

should be considered, such as aggression, anger, and risk-taking behaviors (also see Martin, 

Neighbors, & Griffin, 2013). One other explanation may be that these youth change their 

behaviors to reduce worry (i.e., joining a gang or carrying a gun). Indeed, Reid and colleagues 

(2017) found that with repeated exposure to violence, there was an increase in the likelihood of 

youth carrying a gun. 

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Finally, and again unexpectedly, we found that worry was positively related to academic 

progress; perhaps worry might create an increased vigilance that allows an adolescent to 

anticipate and avoid negative outcomes related to academic progress. Because worry was 

reported by the participants, it is possible that this unexpected association is a reflection of their 

experiential avoidance (i.e., psychological avoidance of negative emotions), a known sequelae of 

trauma (e.g., Kashdan, Morina, & Priebe, 2009). It is also possible that youth with a history of 

violent victimization may represent the most chronically victimized sub-group of our sample. As 

a chronically victimized sub-group, they may be experiencing a blunting of their stress response, 

including lowered free-floating anxiety (McLaughlin et al., 2015). 

We also found that boys worry more than girls, but that witnessing violence had a bigger 

impact on worry for girls than it did for boys. And we found that witnessing violence was 

associated with higher levels of worry for younger adolescents than for older adolescents. 

Social Support and Family Control Variables. Social support and family control variables 

also affect the psychological adjustment variables. Maternal warmth has been described in terms 

of involvement and acceptance, typically related to better psychological adjustment (Lamborn et 

al., 1991). Maternal warmth is positively associated with both behavioral and global self-worth, 

which is intuitive (see Lockhart et al., 2017). Maternal warmth is also positively associated with 

traumatic stress, which is counterintuitive, especially in light of the abundance of research that 

indicates that high levels of warmth and support are associated with positive psychological 

adjustment (e.g., Eisman, Stoddard, Heinze, Caldwell, & Zimmerman, 2015; Washington et al., 

2017; though see Williamson et al., 2017 for inconsistent results). There are a couple of 

possibilities to explain these counterintuitive findings. First, though many participants in this 

sample live in a single parent household, it could be the case that while maternal warmth levels 

are high, there is low paternal warmth. Wagner and colleagues (1996) found that adolescents 

have more benefit from having two parents who they consider having warm relations with rather 

than just one, especially during stressful times (also see del Barrio, Holgado-Tello, & Carrasco, 

2016). 

Positive neighborhood connectedness is positively associated with hopelessness, street code, 

behavioral and global self-worth, worry, and traumatic stress. With the exception self-worth, all 

of these results are counterintuitive. By comparison, Foster and colleagues (2017) found that 

community connectedness was associated with positive outcomes for youth, specifically with 

lower levels of anxiety. Similarly, Li and colleagues (2007) found that positive neighborhood 

perceptions were significant in buffering exposure to violence on externalizing symptoms; 

however, these perceptions were measured by the parents of participants rather than the youth 

participants themselves. Moreover, Chen and colleagues (2016) who found that youths exposed 

to community violence had lower levels of neighborhood cohesion than those not exposed to 

violence. 

A possible explanation for the counterintuitive results with respect to psychological 

adjustment and positive neighborhood connectedness is related to youth gang membership. Gang 

membership reached high levels in the neighborhoods where the MYS was conducted, and MYS 

data show that gang involvement is associated with greater feelings of neighborhood belonging 

(b = 0.017, se = 0.005, t = 3.55, p < .001, based on a linear mixed model using a Granger 

causality framework and controlling for age and gender). Not surprisingly, the MYS data also 

show that gang involvement is positively associated with street code (b = 0.202, se = 0.014, t = 

14.37, p < .001) and hopelessness (b = 0.042, se = 0.010, t = 4.14, p < .001); these analyses are 

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also based on a linear mixed model using a Granger causal framework and controlling for age 

and gender. 

In contrast, negative neighborhood connectedness (i.e., a belief that negative conditions and 

characteristics are largely absent from the neighborhood) are negatively associated with 

hopelessness, street code, worry, and traumatic stress, and positively associated with self-worth. 

Additionally, the slope coefficients for negative neighborhood connectedness are larger than 

those for positive neighborhood connectedness, suggesting that the absence of negative 

neighborhood factors has a stronger impact on psychological adjustment than the presence of 

positive neighborhood factors (particularly when those positive factors may be related to gangs 

and thus have negative implications). As Graves and Shapiro (2016) describe, those who feel as 

if they live in a safer environment (i.e., not exposed to violence thus not traumatized by it) might 

have higher levels of self-efficacy and feelings that they control their environment rather than the 

environment controlling them, thus also feelings of control over their lives are present. One way 

of exploring negative neighborhood connectedness is with respect to social disorder. Chung and 

Steinberg (2006) studied how neighborhood disorder was related to youth offending behaviors 

and found a complex system where community connectedness is related to both prosocial and 

antisocial outcomes related to adolescents. Ross and Mirowsky (2009) also studied neighborhood 

disorder, including danger in the neighborhood, and found that when prevalent in the 

neighborhood, results in more constant distress among residents, which can then lead to feelings 

of mistrust, powerlessness, and social isolation. It is not unreasonable, then, that individuals who 

experience these feelings look to groups where they might feel acceptance to alleviate these 

feelings, such as gangs. 

Consistent with prior research (e.g., Voisin et al., 2016, also see Salzinger et al., 2002), 

results from this study suggest that both witnessing violence and violent victimization are 

negatively related to academic progress. So are hopelessness and street code, with these two 

components of psychological adjustment partially mediating the effects of exposure to violence 

on academic progress. Behavioral and global self-worth, worry, and traumatic stress all were 

positively associated with academic progress, with self-worth and worry partially mediating the 

effect of exposure to violence. The positive association between self-worth and academic 

progress is intuitive; and while not completely intuitive, the positive relationship between worry 

and academic progress can perhaps be explained by hypervigilance created by worry and 

possibly expectations of academic achievement creating worry and consequent greater attention 

to academic risk, especially when there is also exposure to violence (Overstreet & Braun, 1999). 

We are at a loss to explain the positive relationship between traumatic stress and academic 

progress. However, traumatic stress caused by exposure to violence may serve as a wake-up call, 

leading those who experience it to believe that education may be the only way out of poverty. In 

addition to the psychological adjustment variables, maternal warmth is (unexpectedly) negatively 

related to academic progress, while parental monitoring and curfew are (expectedly) positively 

related to academic progress (see Pinquart, 2016). 

Court Outcome Severity. Finally, and most important, are results showing determinants of 

court outcome severity. These results are especially revealing for three reasons. First, and most 

obvious, they address the study’s focus: how and why exposure to violence affects juvenile court 

involvement for a population of adolescents characterized by extremely high rates of poverty. 

Second, this is the only component of the analysis that includes all variables in the model. Third, 

this analysis comes closest to providing a causal explanation for obtained results: it both assesses 

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how variables and scales measured at time t affect outcomes measured at time t+1, specifically 

how events and conditions at t affect change in outcomes between t and t+1. 

As was demonstrated in the initial analysis that lacked potential mediators, the two exposure 

to violence variables both positively influence court outcome severity. Thus, a logical 

assumption is supported: impoverished adolescents who witness violence or who are victimized 

by violence are more likely to have subsequent contact with the court, and their court outcomes 

are more severe, than impoverished adolescents who are not exposed to violence. Additionally, a 

second logical assumption, that academic progress is negatively associated with court outcome 

severity, is also supported; further, academic progress partially mediates the relationship between 

exposure to violence and court outcome severity. An additional secondary assumption holds that 

psychological adjustment associated with exposure to violence affects the severity of court 

outcomes. Among the psychological adjustment scales, hopelessness follows the clearest pattern: 

it is positively associated with court outcome severity, and it partially mediates the effects of 

exposure to violence on court outcome severity both directly and indirectly through academic 

progress. Hopelessness, then, is particularly worthwhile to consider with respect to programming 

and interventions with this population. In addition to hopelessness, behavioral self-worth has a 

negative impact on court outcome severity, and it partially mediates the effect of exposure to 

violence on court outcome severity. Although they are not statistically significant predictors of 

court outcome severity in their own right, two other psychological adjustment scales, global selfworth and traumatic stress, partially mediated the effect of exposure to violence on court 

outcome severity; however, as was the case in so many other analyses, the mediating effect of 

traumatic stress was largely uninterpretable. Overall, among the psychological adjustment scales, 

hopelessness had the largest impact on court outcome severity, both directly and indirectly 

(through academic progress). The final component of RQ3 was in relation to how social support 

and family control scales impact the path of exposure to violence to juvenile court involvement. 

Among the social support and family control scales, only parental monitoring had an impact 

(negative) on court outcome severity. Spano and colleagues (2012) found in a sample of MYS 

youth that stable and high levels of parental monitoring can shield exposure to violence. 

However, another analysis of MYS data showed that for those youths who have been exposed to 

violence, parental discipline is (a) less effective (Spano, Vazsonyi, & Bolland, 2009) and (b) 

reduces the effectiveness of their parental monitoring (Spano, Rivera, Vazsonyi, & Bolland, 

2008). Although demographic variables and their interactions with other predictor variables were 

included in analyses to address the first objective, they are more useful for addressing the second 

objective. 

Summary of Results that Suggest Promising Intervention Points 

Our second objective was to identify conditions where interventions can most effectively 

reduce the risk of juvenile justice involvement for vulnerable adolescents. One of the most 

important conditions is age; another is gender; and a third is where moderating protective factors 

may be strongest and where moderating risk factors are weakest. 

Exposure to violence. Most generally, age and gender distributions for the different 

variables are important. Witnessing violence is nearly flat with respect to age, which is intuitive: 

anyone who lives in violent neighborhoods and spends time outdoors is vulnerable to witnessing 

neighborhood violence (and even those who remain indoors can see neighborhood violence 

through the window). Richards and colleagues (2015) assessed exposure to violence daily for a 

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week in 169 urban African American adolescents and found that there was nearly one incident 

per day recorded by the participants. Youth of different ages may be exposed to different levels 

of violence (Bell & Jenkins, 1993; Selner-O’Hagan, Kindlon, Buka, Raudenbush, & Earles, 

1998), but they may also process and react to that exposure differently (see Bell & Jenkins, 

1997). For example, Schwab-Stone and colleagues (1999) found that younger adolescents were 

more affected by exposure to violence with respect to internalizing symptoms than older 

adolescents. Not surprisingly, in our study, violent victimization varies largely as a function of 

age, with older adolescents more prone to victimization than younger adolescents. Although, 

research has also found that exposure to violence at any age has both short-term and long-term 

negative effects (see Carey & Richards, 2014). In fact, Farrell and Zimmerman (2018) found that 

these effects can have effects over 12-13 years. 

Gender, by itself, shows little ability to modify the relationship between relationship between 

exposure to violence and psychological adjustment. It is important, still, to acknowledge that not 

only could boys and girls be exposed to different levels of violence (see Bell & Jenkins, 1997; 

Buka et al., 2001), but they may also process and react to that exposure differently (see Chen, 

2010; Jenkins & Bell, 1994). In our study, boys and girls report witnessing violence at similar 

rates (M =0.343 versus M = 0.283, respectively), but boys are more susceptible to violent 

victimization than girls (M = .579 versus M = 0.241, respectively). Others have found that girls’ 

psychological adjustment, though, is more negatively affected by exposure to violence than boys 

(Jenkins & Bell, 1994; White et al., 1998; Fitzpatrick & Boldizar, 1993). Additionally, gender 

does not interact with exposure to violence to predict academic progress. However, we do find 

significant street code × gender and traumatic stress × gender effects for academic progress, with 

the effects of these two psychological adjustment variables on academic progress stronger for 

boys than for girls. 

Our findings that no youth (younger or older, male or female) is immune to exposure to 

violence or the effects of it, coupled with others’ results might initially suggest that interventions 

to address the negative effects of violent victimization be universal. More careful analysis shows 

that these initial suggestions do not necessarily hold, however. 

Crimes against a person. With respect to change in crimes against a person as a function of 

violent victimization, the effect was strongest (and dramatic) for the oldest group of girls, 

although the effect was also positive for all other age by gender groups except the youngest girls 

who exhibited essentially no change. With respect to changes in court outcome severity as a 

function of violent victimization, the change from time t to time t+1 was greater than it was for 

crimes against a person. The relationship between violent victimization and court outcome 

severity is positive for all six groups. The middle age group of boys and girls had relatively 

similar levels of change in court outcome severity as a function of violent victimization, and 

higher levels than other age groups. The older age group of boys and girls also were similar with 

respect to court outcome severity as a function of violent victimization, while the oldest boys 

saw a fairly low increase in court outcome severity as recency/frequency of violent victimization 

increased. The youngest age group of girls had the lowest overall levels of court outcome 

severity, though they did experience modest increases in court outcome severity as a function of 

violent victimization and the younger boys with a fairly high change in court outcome severity as 

recency/frequency of violent victimization increased. 

In our results, boys almost always had higher levels of crimes against a person (except for the 

oldest girls at the highest levels of exposure to violence) and higher levels of court outcome 

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severity than girls; however, there was no clear pattern with respect to age. Evangelist and 

colleagues (2017) found that boys were slightly more disadvantaged than girls with respect to 

adjudication. They also found that the youngest group of youth were the least likely to receive a 

formal adjudication, followed by the oldest youth and that the middle age group of adolescents 

was most at risk for formal adjudication. Mears and colleagues (2014) found higher levels of 

informal adjudication at younger ages that decreased with age until age 15 when it increased 

slightly and found levels of formal processing to be at the lowest levels for the youngest 

adolescents (age 10) and increased with age until age 15 when it decreased slightly. While we 

did not categorize court outcome severity as informal or formal, we did categorize formal 

processing as more a more severe court outcome than informal processing. 

Court outcome severity. Court outcome severity as a function of age shows that the most 

contact, and the most severe outcomes, occur for adolescents 12-14 years of age. This finding, in 

some ways, appears to be counterintuitive: we might expect that older adolescents would be 

engaging in more severe crimes; further, we might expect that court sentencing is affected by 

prior violations, and that older adolescents would have accumulated more prior violations. 

Indeed, these expectations are consistent with findings by Rodriguez (2010). On the other hand, 

older adolescents may have come to realize that their bad behavior will be detected and 

punished, so they avoid it. Or perhaps, they have just become more skilled at avoiding detection 

of their bad behavior. Whatever the case, it would initially appear that the most effective 

expenditure of resources would target adolescents during their middle years. 

In terms of gender, boys receive more severe punishment than girls—perhaps because they 

commit more severe offenses, or because they have a history of more offenses, or because they 

are viewed as a greater threat to society than girls. Consistent with this hypothesis, Tam and 

colleagues (2016) found that boys were more likely to have more severe court outcomes than 

girls in their study of adolescents involved in the juvenile court system in California. We found 

no significant gender interactions with either exposure to violence or psychological adjustment in 

predicting court outcome severity. Nor did gender, by itself, interact with any of the social 

support or family control variables to predict court outcome severity. 

We can obtain the best understanding of the risk posed for each age group by examining how 

age interacts with exposure to violence, psychological adjustment, and academic progress to 

predict court outcome severity. While age does interact with exposure to violence to predict 

psychological adjustment (e.g., witnessing violence × agelin is negatively associated with 

hopelessness; witnessing violence × agelin is positively associated with street code), the results 

are not consistent across the psychological adjustment variables. Thus, these results do not 

necessarily provide us with much general guidance about whether and how to target age groups 

to reduce the negative effects of exposure to violence on psychological adjustment. Because age 

is structurally associated with academic progress, we did not examine how any age interactions 

affect academic progress. When we look at how age interactions affect court outcome severity, 

we find more helpful information. Violent victimization × agequad is related to court outcome 

severity, such that the greatest negative court outcomes as a function of violent victimization 

occur for the youngest adolescents. Similarly, the greatest reduction in court outcome severity as 

a function of behavioral self-worth occurs for the youngest adolescents; a similar effect occurs 

for global self-worth. For all of these findings, the oldest group of adolescents experiences a 

similar amount of absolute change, but their relative change is lesser than for the younger 

adolescents. This suggests the possibility that interventions targeted at the youngest age group 

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(e.g., 9-11 year olds) might pay the greatest dividend. Several papers in a 2012 issue of 

Criminology & Public Policy discuss the merits of early intervention (e.g., Farrington, 2012; 

Wright, McMahon, Daly, & Haney, 2012). 

Neighborhood connectedness. One area where age and gender do show some consistency, 

however, is negative neighborhood connectedness, or connectedness because of the absence of 

negative neighborhood characteristics. For example, hopelessness declines more rapidly as a 

function of negative neighborhood connectedness for boys than for girls, suggesting that this 

component of neighborhood connectedness is a stronger protective factor for boys than for girls. 

Results also show that hopelessness declines most rapidly as a function of negative 

neighborhood connectedness for the youngest age group, and particularly for the youngest age 

group of boys. Street code also declines more rapidly as a function of negative neighborhood 

connectedness for boys than for girls and while our results suggest this relationship, others have 

found little difference with respect to gender in code of the street (e.g., Stewart & Simons, 2006). 

Thus, the most effective intervention point for promoting negative neighborhood connectedness 

to offset the risk associated with hopelessness and street code may be younger boys. 

Unfortunately, the interactive effects of age, gender, and negative neighborhood connectedness 

only partially fit this pattern: the strongest relationship between neighborhood connectedness and 

behavioral self-worth occurs for the youngest girls. 

We previously identified that, unlike negative neighborhood connectedness, positive 

neighborhood connectedness may be a risk factor and enhance the negative effects of exposure to 

violence. Similarly, positive neighborhood connectedness is a stronger predictor of street code 

for boys than for girls, but the effect was strongest for the oldest age group (14-15 year olds). 

Using MYS Data, Drummond and colleagues (2011) found that indeed having a positive sense of 

community was protective against hopelessness, however, it was also found to be positively 

related to weapon brandishing. To complicate matters, we found positive neighborhood 

connectedness to be a protective factor for self-worth, and results show that its strongest effect is 

for boys and for the youngest age group. In an analysis of MYS data, Bolland and colleagues 

(2016) found that neighborhood connectedness (not separated into positive and negative 

connectedness) increased with age for boys, but decreased with age for girls. They also found 

that delinquent behavior was negatively associated with community connectedness for boys. 

Conclusion. The lack of clear pattern for age or gender with respect to psychological 

adjustment or family control variables on court involvement indicates that perhaps we need to 

consider the substance of intervention at different points rather than target intervention only at a 

certain age or grade level or only for boys or girls. Our inconsistent findings with respect to age 

and gender are consistent with others’ investigations of youth within the juvenile justice system

(e.g., Evangelist et al., 2017). 

Addressing Exposure to Violence: Approaches to Intervention 

Our third objective was to identify classes (i.e., types) of intervention that might be most 

effective for these adolescents. The results reported in this study suggest that programs and 

policies can be implemented at five different levels to potentially reduce the effects of exposure 

to violence on court involvement for adolescents living in extreme poverty: (a) municipal and 

county government, (b) schools, (c) the neighborhood, (d) family, and (e) the individual. We 

recognize that there is considerable overlap between these systems (see Bronfenbrenner, 1992) 

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and provide some discussion with respect to this. There has been limited research on the overlap 

of these systems with respect to how exposure to violence affects youth (although see Yule, 

Houston, & Grych, 2019 for a meta-analysis). We have identified, but not tested any of the 

promising programs that are identified in the following sections. The very complexity of the 

model, coupled with the high levels of poverty that characterize the population studied, means 

that definitive programs have yet to be developed to address many of the needs of these youths. 

In the following sections, we do explore how different approaches could be implemented, and 

provide our thoughts on the potential for success of each. 

Municipal and County Government. In this category, we include not just governmental 

units, but also departments of these units (e.g., Police Department) and quasi-governmental units 

that function at the city and county level (e.g., Juvenile Court, Health Department, Child 

Protective Services). For the most part, interventions at this level constitute policies rather than 

programs. Within this category, we explore (a) databases, (b) rapid response and research, and 

(c) coordination of systems. 

Databases. One possible policy change would encompass multiple governmental units. 

Currently in place is a mandated requirement for any professional-level service provider to report 

suspected cases of child abuse or neglect to the local Child Protective Services Office. Arguably, 

however, exposure to violence takes a similar, if not necessarily as dramatic, toll on children and 

adolescents. A central database documenting children’s exposure to violence, and additional 

resources to help families that need those resources to cope with the harm (e.g., physical, 

psychological, developmental, legal) that arises from exposure to violence, would be a 

potentially useful policy. We do, however, acknowledge that there may be regulatory challenges 

associated with collecting, compiling, storing, and maintaining these data. 

Similarly way, a database documenting locations where violence occurs to create a 

geographical information system (GIS), would potentially helpful. The Police Department 

probably has the best information for this, and it documents cases where a crime is reported or a 

service is rendered. But police may be aware of violence, and possibly called in to prevent 

violence, even in the absence of a complaint or an arrest. A GIS that identifies violent areas 

could be useful in (a) allowing police to be more effective in their patrolling activities, and (b) 

identifying areas where additional programs and resources could be allocated to prevent violence 

(see Neighborhood section that follows). If exposure to violence can be identified at the 

individual level, it might be used as a marker and a step toward primary prevention. This 

identification and then early intervention might be best undertaken as a joint effort between 

multiple quasi-governmental agencies. Additionally, descriptive records documenting violence in 

the community could be integrated into the database, providing additional support for anyone 

who reports exposure to violence. Based on self-reports and supportive documentation, 

individuals might be eligible for diversion programs if they enter the juvenile court system. 

Rapid Response and Research. An adjunct to this approach would be a rapid response 

approach wherein anyone added to the database would be visited by service providers from the 

Police Department (i.e., members of the Family Intervention Team, who are non-sworn officers 

tasked with working with youth to prevent juvenile justice involvement), the Health Department, 

Child Protective Services, or one of the mental health organizations serving the area to determine 

whether and which follow-up services would be warranted. While this may seem intrusive to 

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some (many) of the youth involved, it could alternatively be framed as research, wherein a 

researcher (e.g., a psychology, sociology, or social work faculty member or student at a local 

university) contacts the youth and recruits him or her into a study of community violence; 

caregivers and other adults living in the household should also be recruited into the study and 

interviewed. Given the success of the Mobile Youth Survey and spin-off research studies in 

recruiting participants, this would seem to be an effective means for initial contact. After asking 

an initial set of questions, the interviewer would be able to make an initial assessment of the 

youth’s situation, and if it seems problematic, the interviewer could talk with the youth, then 

with his or her adult caregiver, about their interest in follow-up services, which would potentially 

target the entire household rather than just the adolescent. Besides potentially serving as a 

method of case-finding, results from the initial (and potentially follow-up interviews) would 

provide a better understanding about the prevalence of trauma related to violence in the 

community, and how to better support adolescents who are exposed to violence in their 

neighborhoods. Results could be shared not only with service providers who design and 

implement programs, but also with community leaders who may be able to use the data in 

thinking about neighborhood-based solutions to violence. 

Coordination of Systems. A third policy approach would be more comprehensive; but given 

the overall costs of addressing poverty and its associated problems, and a general lack of success 

in trying to do so, such a comprehensive and coordinated approach may be justified. Services 

provided by quasi-governmental units, such as school districts, police departments, courts, health 

departments, and child protective services, are designed to meet the statutory goals and 

objectives of each organization. Thus, schools educate students; police departments prevent and 

control crime; etc. Often goals overlap, and interorganizational programs are developed to meet 

the needs of client groups. But joint programs must always take the backseat to programs 

designed to meet each organization’s primary goal. This leads to a fundamental law of 

organizational behavior: “organizations strive to maintain their autonomy” (see Gouldner, 1959; 

Van de Ven & Ferry, 1984), perhaps because a loss of autonomy threatens their ability to meet 

their primary goal. Even so, and in support of the coordination of systems, a non-siloed 

distribution of funding could help overcome siloed agency solutions to these complex problems. 

For example, Vermont’s Integrated Family Services (https://ifs.vermont.gov/) program 

coordinates family services across agencies, including child protective services, developmental 

services, and adult and child mental health services. The funding for the Integrated Family 

Services program is disbursed across agencies, encouraging cross-agency collaboration. 

We cite this organizational literature because we realize how difficult the suggestion may be. 

But we want to stress our findings that exposure to violence interferes with academic progress 

and affects court outcome severity, and the possibility that exposure to violence my affect more 

children than child abuse and neglect. Thus, at least for an important segment of the population, 

the primary goal of the school district, or the juvenile court system, or child protective services, 

may not be achievable without important modification of the historic approach to achieving that 

goal. For example, youth who have been exposed to violence may not be able to learn in the 

same ways as youth who have not, and perhaps the curriculum will need to be reshaped in 

schools that serve these youth. 

Judicial punishment may not affect youth who have been exposed to violence in the same 

way that it affects youth who have not been so affected; moreover, if likelihood of engaging in 

delinquent behavior increases as a function of physiological changes caused by exposure to 

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violence (e.g., increased cortisol production), then punishment for these delinquent acts might be 

seen as punishing the victim, or at least punishing a person for something he or she may not be 

able to control. In fact, Ford and colleagues (2006) suggest that trauma history should be 

evaluated at the time of intake into the criminal justice system. The courts could make use of 

such a database then, considering exposure to violence in its disposition of cases. In the case of 

abuse and neglect, abuse that occurs proximal to other violent exposure may have a more severe 

effect than abuse that occurs in isolation of community violence. Finally, health outcomes (e.g., 

diseases connected to stress) may be more prevalent in neighborhoods where violence is 

normative, or at least occurs frequently, and any public health initiative designed to prevent these 

diseases must at least consider how exposure to violence affects them. 

Even more significant, the effects of exposure to violence has an impact on every one of 

these governmental or quasi-governmental units. Thus, independent efforts to reduce the 

negative impact of exposure to violence are unlikely to be as effective as a coordinated effort 

among organizations. A potentially effective approach to this problem would be to treat violence 

as a public health emergency, much as the federal government has declared the opioid epidemic 

as a public health emergency. This approach has been suggested by Finkelhor and colleagues 

(2013), and others (e.g., CDC 2009) echo the sentiment that exposure to community violence is 

indeed a significant public health issue for youth, particularly racial and ethnic minority youth 

(Chen et al., 2016). One critical aspect of public health is surveillance, which reinforces the 

earlier suggestions about (a) developing a database of youth who have been exposed to violence 

so that they might potentially receive multidisciplinary services, and (b) developing a GIS 

indicating where incidents of violence are most common so that they can potentially be targeted 

by neighborhood approaches to violence prevention. 

School. Next, it is important to address practice implications with respect to academic 

progress. Several studies found results similar to those reported in our study, showing that 

students who have been exposed to violence have lower school performance than those who have 

not (e.g., Henrich et al., 2004; Patton, Woolley, & Hong, 2012). Not surprisingly, involvement in 

the juvenile justice system, particularly when there is an out-of-home court outcome, resulted in 

lower academic progress or achievement (Aizer & Doyle, 2015; Mendel, 2011). Prior research 

has shown that risk, particularly cumulative risk, is associated with decreased academic 

performance (e.g., Gutman, Sameroff, & Cole, 2003), as well as outcomes such as problematic 

internalizing and externalizing behavior (Appleyard, Egeland, van Dulmen & Sroufe, 2005). 

These results suggest that schools have a vested interest in preventing violence, particularly in 

catchment areas where violence is most common. We suggest attention within the schools 

regarding (a) school engagement and (b) school policy, and we suggest (c) trauma-informed 

programs and (d) exploring gifted education. 

School Engagement. Not surprisingly, feelings of connectedness to school are associated 

with positive outcomes such as academic achievement (Skinner & Pitzer, 2012) and health and 

well-being (Jose, Ryan, & Pryor, 2012). Wang and Eccles (2012a) found that school engagement 

generally declines in middle and high school, which is negatively related to academic outcomes 

such as grade point average. Additionally, when students do not feel a connection to school or 

are not particularly engaged in school, poor outcomes such as substance use (Wormington, 

Anderson, Schneider, Tomlinson, & Brown, 2016), depression (Shochet et al., 2006), 

delinquency (Chen et al., 2016), exposure to community violence (Elsaesser, Gorman-Smith, 

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Henry, & Schoeny, 2017), and association with deviant peers (Rudasill, Niehaus, Crockett, & 

Rakes, 2014) can occur. In the MYS sample, Bolland and colleagues (2016) found that 

delinquency was negatively correlated with school connectedness. This suggests the importance 

of interventions to promote school engagement, especially in school servicing academically 

vulnerable students and particularly in middle and high school. 

School Policy. Umlauf and colleagues (2015) found that exposure to violence can affect 

sleep, which then could affect academic achievement—and as our results show, also juvenile 

court involvement. One approach to solving the problem of sleep would be to implement a later 

start time in the schools. Dunster and colleagues (2018) studied a school in Seattle where the 

start time for high school students was delayed from 7:50 to 8:45 a.m. and found multiple 

benefits to the students, including more sleep and higher grades. Most of the high schools and 

middle schools within the MCPSS begin before 7:30 a.m. Most of the elementary schools begin 

between 8:00 a.m. and 8:15 a.m., although at least one has a start time before 8:00 a.m. While 

staggering the start time of elementary, middle, and high schools can alleviate traffic congestion, 

the early start time for many of these schools might be revisited. 

Trauma-Informed Programs. Another approach to addressing the effects of exposure to 

violence within the schools is to use trauma-informed approaches or to create trauma-responsive 

schools (e.g., Dorado, Martinez, McArthur, & Leibovitz, 2016; Katoka et al., 2018; Plumb, 

Bush, & Kersevich, 2016). However first, it is also important to note that better screening for 

trauma exposure could be useful. That is, it is important for teachers, counselors, and other 

school personnel to be aware of the symptoms of trauma exposure. However, identification of 

trauma exposure in youth may prove challenging (see Cummings, Addante, Swindell, & 

Meadan, 2017), suggesting that the awareness of symptoms of trauma exposure is even more 

important for school employees. Additionally, Wang and Eccles (2012b) found that teacher 

support was influential in student engagement for middle and high school students, reinforcing 

the notion that teachers be aware of the circumstances that their students may find themselves in 

(such as being exposed to community violence) so they are able to provide appropriate support to 

their students. Woodbridge and colleagues (2016) advocate for routine school-based screenings 

and for culturally competent support and interventions. West and colleagues (2014) (see also Ko 

et al., 2008) advocate for trauma-informed systems of care, including within the education 

system, that would be available to students who have been exposed to violence. Because youth 

must attend school (in Alabama, youth are mandated to attend school until the age of 16), the 

integration of trauma-informed practices within the schools might be especially effective, with 

the schools also coordinating, communicating, and referring students to other services as needed. 

Trauma-responsive and trauma-informed programs are gaining attention, enough to warrant a 

special issue of School Mental Health (see Overstreet & Chafouleas, 2016). 

One trauma-informed program that has been effective in schools is Cognitive Behavioral 

Intervention for Trauma in Schools (CBITS, Jaycox, Langley, & Hoover, 2018). However, it is 

also important to note that some of the youngest students seem to be most at risk (at least in 

some areas) for exposure to violence, and that cognitive behavioral therapy (CBT) may not work 

particularly well for young students, particularly those with cognitive and/or developmental 

delays (e.g., it is not atypical for MYS participants to have low standardized test scores and low 

IQ test scores). It is well recognized that the efficacy of CBT depends on the cognitive capacity 

of the child being treated (Kendall, Lerner, & Craighead, 1984; Shirk, 1999; Weisz & Weersing, 

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1999), although demonstrated that it can be effective for developmentally delayed children if 

appropriate modifications are made (Grosso, 2012; Suveg, Comer, Furr, & Kendall, 2006). It is 

important to determine how effectively this can be accomplished by practitioners without a firm 

theoretical understanding of the principles of CBT. 

Another trauma-responsive approach that might guide schools is through the mnemonic 

CAPPD (Calm; Attuned; Present; Predictable; Don’t let children’s emotions escalate your own; 

see Walkley & Cox, 2013). Walkley and Cox, however, caution that the change in school climate 

is often needed for a school to become more trauma-responsive and that this does not happen 

automatically. School social workers might be particularly instrumental in school climate 

change. 

It is important to note that trauma-informed care does not just respond to traumatic stress, but 

to trauma created by, for example, exposure to violence that might result in many different 

psychological adjustment outcomes, including perhaps feelings of hopelessness. This is 

something that should be explored in much greater depth, however, to determine exactly what 

types of psychological adjustment respond to trauma-informed programs. 

Gifted Education. In thinking about these trauma-informed approaches, however, it is 

important to better understand whether exposure to violence does, indeed, result in trauma. For 

students growing up in abject poverty where exposure to violence is extensive, exposure to 

violence may be normative (e.g., Gaylord-Harden, Dickson, & Pierre, 2016; Gorman-Smith & 

Tolan, 1998; Guerra, Huesmann, & Spindler, 2003), and therefore not necessarily traumatic so 

much as expected. If this is the case, typical trauma-informed programming may not necessarily 

be effective. Rather, programs that attempt to show students ways to counter expectations might 

be more effective, but also very difficult to design for students who have already given up on 

education as a way out of poverty. One potentially promising approach involves gifted 

education. Using these same data, Bolland and colleagues (2018) found that weapon carrying 

was lower for gifted boys during middle adolescence than it was for their non-gifted (i.e., not 

identified as gifted) peers. Impulsivity was also, in early and middle adolescence, found to be 

higher for boys not identified as gifted than boys identified as gifted. Gifted programming, while 

initially focused on engaging and developing intellectual skills, now includes programming 

focused on creativity, problem solving, leadership, and civic skills (Betts, Kapushion, & Carey, 

2016). In the two studies of giftedness using these data (see Bolland et al., 2018; Bolland et al., 

2019), gifted status seemed to be particularly protective for boys. Thus, another effective 

approach within the school system might be to implement some aspects of gifted programming 

more throughout the curriculum rather than reserve that programming for students only identified 

as gifted. 

Neighborhood. Perhaps the most distal factor that affects court outcome severity in this study 

is exposure to violence, and reducing exposure to violence would be an effective way to reduce 

youth involvement in the court system. Youth mainly experience violence in the three contexts: 

inter-parental relationships, parent-child relationships, and community (Margolin et al., 2009); 

given the focus of this study, we will focus mainly on exposure to community violence, although 

as suggested previously, domestic violence could also occur. Our findings persuasively 

demonstrate that protecting youth from exposure to violence is in and of itself a powerful defense 

against their involvement with the juvenile justice system. We found that the two exposure to 

violence variables had important effects on the main outcome of court outcome severity, which 

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can best be targeted at the neighborhood level, where we considered both positive (positive 

feelings towards neighborhood) and negative (the absence of negative feelings towards 

neighborhood) neighborhood affiliation. It is important to first discuss the code of the street that 

exists within MYS neighborhoods and its general origins before discussing two points of 

intervention: (a) neighborhood organizing and (b) community belonging. 

Code of the Street. We begin by reporting several statistics from the Mobile Youth Survey 

(MYS) sample between 1998 and 2011: 

1. 12.29% carried a gun during the past week; 

2. 11.81% pulled a knife or gun on someone else during the past month; 

3. 14.14% cut, stabbed, or shot a gun at someone lese during the past week; 

4. 14.23% had a knife or gun pulled on them during the past week; 

5. 16.40% had a knife or gun pulled on them during the past week; 

6. 25.67% felt unsafe in their neighborhood most or all of the time. 

Clearly, the neighborhoods in this study are violent places. Perhaps even more important are 

adolescents’ perceptions of violence in their neighborhoods. Again, data from the 1998-2011 

MYS sample suggest expectations about the inevitability of violence in their neighborhoods: 

7. 49.12% believed that it was impossible to avoid fights in their neighborhood; 

8. 28.93% believed that if you didn’t carry a knife or gun, something bad might

happen to you; 

9. 39.22% believed that carrying a weapon let others know that they shouldn’t mess 

with you. 

10. 49.21% believe that hitting someone knocks some sense into them; 

11. 57.18% believe that if you are in an argument, you should stand your ground and 

get what you want. 

Notably, these questions were taken from the Street Code scale. 

In such situations, informal social control, or informal rules and norms of behavior suffer, 

and in some cases are non-existent. Thus, adolescents who have largely abandoned traditional 

avenues to success (e.g. education) turn to a more visceral, and risk-laden (both for themselves 

and the larger neighborhood) strategy of establishing their worth through their toughness and 

street-smarts (Agnew, 2001; Anderson, 1999). The risk to self occurs because there is always 

someone who is tougher, or smarter, or both, and someone (most often the less tough or less 

smart) will be injured. The risk to neighborhood occurs (a) due to collateral damage created by 

these confrontations, and (b) the increased danger and resulting emphasis on street code leads 

adolescents to become more aggressive as a way of avoiding victimization. Thus, street code 

becomes self-perpetuating: greater centrality of street code in the belief systems of neighborhood 

adolescents leads to greater violence, which in turn leads to increased importance of street code 

(Stewart & Simons, 2010). In essence, street code also becomes a coping mechanism for stress 

exposure within a neighborhood, such as exposure to violence (Agnew, 2013). In fact, Sanchez 

and colleagues (2013) found that exposure to violence resulted in externalizing symptoms with 

for boys, even when there were attempts of avoidance. That might suggest that both exposure to 

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violence and code of the street are inherent to a neighborhood and unavoidable, especially for 

boys. 

In light of the MYS results just presented about violence and street code, it is important to 

consider two important questions. First, is it possible to change the aggressive and violent 

behavior of individual adolescents when they encounter high levels of violence and violent 

beliefs in their neighborhood? Second, is it ethical to attempt to change the aggressive and 

violent behavior of individual adolescents when (a) they are living in the midst of violence, and 

(b) they believe that they will be victimized if they are not aggressive and violent themselves? 

We believe that encouraging individual adolescents to abandon their street code beliefs in the 

presence of high degrees of neighborhood violence, or to commit to non-aggression and nonviolence in a neighborhood where the code of the street is pervasive, simply will not work. 

General Origins. Thus, in thinking about how this situation can be changed, it is worthwhile 

to consider how it came about. Between the 1950s and the 1960s, there was a well-documented 

exodus of middle-class and affluent residents from African American communities in the United 

States. We use the term “communities” rather than neighborhoods here because, before this 

exodus they were bound together by a common culture and destiny, and social interaction was 

vibrant. Socioeconomically, these communities were very heterogeneous; families were legally 

and politically bound to neighborhoods by restrictive housing laws, resulting in de facto 

segregation. But with the easing of housing restrictions, mainly by the courts, de facto (and to a 

lesser extent, de jure) segregation began to erode, and more affluent African American families 

moved to wealthier neighborhoods in pursuit of the American dream. As a result, African 

American neighborhoods became increasingly impoverished, to the extent that poverty became 

hyperconcentrated (Wilson, 2012) and where prior community norms that were strong became 

weakened and often disappeared (McBride Murry, Berkel, Gaylord-Harden, Copeland-Linder, & 

Nation, 2011). 

Consider, for a moment, how poverty, and particularly hyperconcentrated poverty, affects 

people’s lives. First, depression is more prevalent among people living in poverty than among 

those who are more affluent (e.g., James, Hart, Banay, Laden, & Signorello, 2017; also see 

Lorant et al., 2003). In the MYS neighborhoods, the rates of clinical depression among adults 

reached nearly 50% (Mugoya et al., 2017). Thus, not only is any individual just as likely as not 

to be depressed, but half of his or her neighbors are also like to depressed. Second, the social 

networks of people living in poverty tend to be attenuated compared with those of more affluent 

individuals. Third, residential turnover is much higher in impoverished neighborhoods than in 

more affluent neighborhoods. Within the MYS sample, the mean 3-year neighborhood change 

(e.g., Wave 1 – Wave 3) rate was 32.5%. This is likely an underestimate, because adolescents 

who moved between neighborhoods were more difficult to find and re-interview than those who 

remained in the same neighborhood. Thus, neighborhoods characterized by hyperconcentrated 

poverty are also neighborhoods of strangers (Merry, 1981), where residents do not know one 

another and where trust, a key element of social capital, is missing. All of these conditions are 

interrelated (e.g., high residential mobility leads to attenuated social networks; isolation created 

by attenuated social networks is associated with depression). Put succinctly, many residents 

experience feelings of isolation, alienation, loneliness, and disempowerment, and the 

development and enforcement of informal rules and behavioral norms suffers or disappears 

altogether (see Sampson, Raudenbush, & Earles, 1997; Riger, 1993). 

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One resident of a Huntsville, AL, public housing neighborhood, where we conducted a study 

that served as precursor to the MYS, explained to us the reason why she lived in public housing: 

her husband left, her car broke, and she had no way to afford rent in the private sector. “But,” she

said as she looked around the neighborhood, “everyone else is living here because they are lazy.” 

The United States is one of the few industrialized nations where poverty is viewed as a character 

flaw; but it is astonishing that people living in poverty would embrace this view of their 

impoverished neighbors. This emphasis on differences and divisions (it is likely that one of the 

“lazy” neighbors the woman scorned would have a similar view of her neighbors) leads to a 

generalized distrust, which makes collective action difficult; but the enforcement of community 

norms requires collective action. As another resident explained to us, “Good neighbors mind 

their own business.” – but as noted, with a cost, because informal rules and norms, and the social 

control they create, can potentially provide a useful way to contain bad behavior, including 

aggression and violence, among adolescents. 

The challenge, then, is to transform impoverished neighborhoods into social communities. 

“Community” derives from the Latin communitas, whose root is commūnis – meaning 

“common” or “public.” To achieve community, it is therefore necessary to provide means for 

residents to understand their commonalities, their similarities. Such an understanding is essential 

to trust, to social networking, and to the development of social capital. Given the previously 

identified arguments, the best way accomplish this is to provide a safe environment for 

neighborhood residents to get to know one another and discover their commonalities. This can be 

accomplished most effectively by working simultaneously to (a) strengthen social networks 

within the neighborhoods, through which innovations can diffuse, social support can be 

provided, and residents can mobilize to act; and (b) empower residents of the neighborhoods to 

exercise more control over their lives, both individually and collectively (Hawkins & Catalano, 

1992). These goals are the essence of a socially competent community (Iscoe, 1974) of 

neighborhood residents who can work together to establish and enforce neighborhood norms, 

develop hope for a collective future, and otherwise solve the problems in their midst (Cohen & 

Phillips, 1997). This is also the essence of community development, defined as “a process that 

stimulates opportunities for membership, for influence, for mutual needs to be met, and for 

shared emotional ties and support” (Chavis & Newbrough, 1986, p. 337).

A sense of community, and the commitment to the neighborhood it creates, leads to greater 

contact among neighbors, allows for better acquaintance and interaction among residents, 

provides a space to discuss shared problems (Unger & Wandersman, 1983), and it motivates 

them to take action to alleviate perceived threats to their neighborhood. Chavis and Wandersman 

(1990) argue, "When people share a strong sense of community they are motivated and 

empowered to change problems they face, and are better able to mediate the negative effects of 

things over which they have no control" (p. 73). In addition to enhancing social interaction, 

participation in neighborhood activities is also empowering, for involvement in neighborhood 

organizations helps individuals feel more competent and less alienated (Zimmerman & 

Rappaport, 1988). Feelings of empowerment and efficacy, in turn, lead people to respond to 

perceived threats; and it builds community by creating a sense of pride in the neighborhood 

(Chavis & Newbrough, 1986). The neighborhood community, in fact, becomes a source of group 

identity for these residents where there is a reduced apprehension about participating in 

neighborhood activities (Chavis & Wandersman, 1990) and through a collective experience, 

individuals may gain a greater sense of control over their lives and may attempt things they 

otherwise would have felt beyond their capacity to accomplish (Minkler, 1985). 

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Neighborhood Organizing. Perhaps the best way to achieve these goals is to provide 

residents with opportunities to interact in a safe environment and get to know one another. One 

way to accomplish this is through neighborhood organizing, but with a twist. Community 

organizing is generally undertaken in pursuit of some social action goal, for example union 

membership, voter registration, boycott of some unfair business or governmental practice), and 

as such it is adversarial. In contrast, we propose community organization as a way of advancing 

social contact and interaction, with no specific action as the ultimate goal. As such, it is best 

achieved by sponsoring safe events in neutral and safe environments, preferably on a recurring 

schedule. These could include a sewing circle, a quilting bee, exercise classes, or lunches. To be 

effective, though, these can’t be developed and conducted by outsiders. As noted previously, 

distrust of other neighborhood residents is strong, and it likely extends to outsiders and 

institutions. Therefore, it is critical for a community organizer to spend time in, and literally 

become part of the neighborhood – introducing herself to residents and explaining the purpose of 

her presence (e.g., helping neighborhood residents gain a sense of empowerment). Given the 

importance of religion to people, and particularly minority people living in poverty, perhaps a 

local church would be an ideas sponsor of such a program. 

In designing activities, the community organizer should talk with residents, getting their 

input about events that would be enjoyable and helpful, and about how best to organize and 

publicize these activities and events. Over time, residents would likely become increasingly 

involved in coordinating the activities, and even designing new ways to help neighborhood 

residents. As an outsider, the organizer has no potential alliance with neighborhood factions, and 

she can likely referee between conflicts if they emerge. It is important to understand that this 

must be a long-term commitment. The organizer cannot work in a neighborhood for a few 

months and then leave: such an approach would have little opportunity of success; in fact, it 

would likely be harmful, leading residents to feel built up, only to be abandoned. 

The importance of the built environment should not be overlooked in this process. Kondo 

and colleagues (2018) reviewed studies of neighborhood-focused violence reduction 

interventions that targeted the built environment, including “housing, land use and zoning, 

alcohol outlets, blight remediation, transportation and mobility, greening, and schools” (p. 256). 

Demolition of high-rises and provision of affordable scattered site housing was associated with 

reduced rates of homicide, assault, and other violent crime in the surrounding areas. 

Enhancement of deteriorated structures, including fixing broken doors and windows and cleaning 

and greening of vacant spaces was associated with decreased violence, including gun violence. 

Reducing alcohol availability and street connectivity was also associated with lower violent 

crimes. These are typically initiated by local governments; but as residents get to know one 

another and learn to trust one another, they can become effective advocates for changes that will 

benefit their neighborhood, and perhaps even collaborate on the design of projects to create these 

changes. One relatively straightforward built environment project that can be achieved with 

relatively little expenditure is the development of community gardens. They not only produce 

food, but they also provide a venue for residents to work together and watch their efforts grow 

and bear fruit. 

Community Belonging. The OJJDP maintains a searchable database of evidence-based, 

model treatment programs for youth at risk for juvenile justice involvement 

(https://www.ojjdp.gov/MPG/). It describes the programs, documents the evidence available for 

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each program, and presents implementation information (i.e., cost, training, timeframe). The 

OJJDP Model Programs Guide recommends several community-based, empirically-supported 

prevention and intervention programs for enhancing community belonging and reducing youth 

gang membership. These programs are diverse in their approach, varying in types of facilitator 

(e.g., community member, social services professional, policeperson), intervention targets (e.g., 

criminal behavior, youth mental health symptoms, parent-child and parent-teacher relationships, 

gang membership), and setting (e.g., school, home, community). Such methodological diversity 

allows a wide range of options when selecting an evidence-based program for implementation. A 

neighborhood development program designed to reduce the conditions that lead to violence 

should, first of all, facilitate the development of stronger social network ties among neighbors. 

As we noted previously, strong network connectedness can lead to increased gang activity, but 

this may occur primarily when positive connections are not available; it may also be a problem 

particularly for youth and young adults. We believe it makes sense for neighborhood 

development programs to target adults who have largely grown beyond the lure of gangs, 

including the parents of adolescents. Finally, the significant associations between neighborhood 

connectedness (both negative and positive) and youth psychological adjustment indicated the 

psychological salience of social connectedness for adolescent youth. Youth desire social 

belonging and actively monitor their social status. Interventions for youth at risk for juvenile 

justice involvement should leverage the developmental salience of social connectedness for this 

population by (a) accounting for youth social networks in implementing the interventions, (b) 

prioritizing highly influential peers in the efforts to affect community youth culture, and (c) 

offering youth membership in alternative high-status, prosocial peer groups. 

Neighborhood connectedness is a consistent modifier of the scales that most affect court 

outcome severity. Although it is both a risk factor (positive neighborhood connectedness) and a 

protective factor (negative neighborhood connectedness), it reflects the importance of the 

environment, and the need to change the environment in which poverty exists to reduce the risk 

of court involvement for the most marginalized (economically, racially, and culturally) 

adolescents. We do not expect to be able to eliminate poverty, but it may be possible to make 

changes in the environment that will make poverty less strongly associated with crime and 

violence. Interestingly, neighborhood connectedness is an important predictor of four of the 

psychological adjustment variables (hopelessness, street code, behavioral self-worth, global selfworth), three of which significantly mediate the relationship between exposure to violence and 

juvenile court outcomes. Thus, it seems critical to include neighborhood development programs 

as a, if not the, major component of programs to reduce the negative impact of exposure to 

violence. Such an approach would likely lead to less neighborhood violence (and hence less 

exposure to violence), but it would also reduce its negative effects when it does occur. 

Family3

. Consistent with Maslow’s hierarchy of needs and the universally established SESand-health-gradient (e.g., Evans, Wolfe, & Adler, 2012), the most direct way to enhance parental 

functioning for socio-economically disadvantaged parents is to provide them with pathways to 

economic security. This should include both the economic community development (see 

previous Neighborhood section) and the educational/vocational training for the parents. These all 

lead to increased perceptions of support and decreased stress (see Lee, Halpern, Hertz-Picciotto, 

3 While within the family context, youth are primarily victimized by their parents, either by witnessing interparental violence or being victimized by the parents (Margolin et al., 2009), that is not the focus of this 

project. However, we do not discount that this can happen. 

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Martin, & Suchindran, 2006), which then can lead to more positive parenting (McConnell, 

Breitkreuz, & Savage, 2011). Concerning family-level variables, we suggest interventions that 

focus on (a) parental monitoring, (b) the relationship between the mother or mother-figure and 

the child, (c) the use of trauma-sensitive therapeutic approaches, and (d) increasing stability. 

Parental Monitoring. As youth age, parental involvement may naturally begin to decrease 

(e.g., Hayes, Hudson, & Matthews, 2003), however, Matjasko and colleagues (2013) found that 

in populations where exposure to violence is likely, this decrease in parental involvement may be 

detrimental. Similarly, Bacchini and colleagues (2011) found that parental monitoring was 

protective with respect to the relationship between exposure to violence and externalizing 

symptoms for boys and internalizing symptoms for girls. Other research has been inconsistent 

with respect to parental involvement and its protective nature against youth risky behaviors 

including delinquency (e.g., Davidson & Cardemil, 2009; Ozer, Lavi, Douglas, & Wolf, 2015), 

and it promotes positive outcomes such as academic achievement (e.g., Jeynes, 2003). Turanovic 

and Pratt (2015) concluded that attachment to family is a protective factor with respect to violent 

victimization and future negative outcomes such as risky behaviors in adulthood. Thus, we 

advocate for the inclusion of family as a point of intervention for youth who have been exposed 

to violence (see Duncan, 1996). Morris and colleagues (2017) discuss interventions that include 

parents specifically for children who live in impoverished communities, including Triple P 

(Positive Parenting Program, see Nowak & Heinrichs, 2008; Sanders et al., 2008) and NurseFamily Partnership (see Olds et al., 2014). 

Mother-Child Relationship. We generally found that maternal warmth was associated with 

better psychological adjustment for youths in this study, including higher self-worth, lower 

hopelessness, and lower street code. As youth’s perceptions of maternal warmth are likely 

indicative of the quality of their relationship with the mother (or maternal figure), the quality of 

this relationship is a protective factor against juvenile justice involvement following youth 

exposure to violence. The OJJDP Model Programs Guide recommends a number of empiricallybased prevention and intervention programs that target the child-mother relationship across 

development, starting in the infant period. They address different aspects and correlates of the 

relationship, including maternal mental health, caregiving in the infancy period, and effective 

parenting strategies across development. Indeed, especially important in this context is parental 

psychological adjustment as it is related to many other outcomes, including child attachment, 

behavior, and even child psychological adjustment (e.g., Downey & Coyne, 1990; Lee et al., 

2006; Luoma et al., 2001). Therefore, prevention efforts for youth juvenile justice involvement 

should include a focus on the functioning of the parents. 

The Annie E. Casey Foundation recommends the Strong African American Families program 

and rates it as a promising program designed for children (ages 5-11) and their parents that 

includes family therapy and parent training with outcome goals including closer relationships 

with parents and reduced delinquency and criminal behavior (see Brody et al., 2004); this 

program is currently implemented in Mobile as part of the Casey Evidence2Success grant. 

Although we did not directly test them, youth relationships with other parental figures or adult 

mentors are also very important for successful youth development and should also serve as 

prevention and intervention targets (e.g., Hardaway, Sterrett-Hong, Larkby, & Cornelius, 2016; 

Miranda-Chan, Fruiht, Dubon, & Wray-Lake, 2016); in communities characterized by poverty, 

there is often less access to these mentors (e.g., Raposa, Erickson, Hagler, & Rhodes, 2018). 

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Trauma-Sensitive Interventions. Our finding that maternal warmth was associated with 

higher traumatic stress for youths is unexpected and inconsistent with previous studies (see Chen 

et al., 2016). Extending the blunting of the stress response hypothesis, it is possible that 

victimized youth who have a supportive maternal figure do not experience the blunting of the 

stress response system and experience more traumatic stress symptoms; this may be exacerbated 

if the maternal figure supported their more adaptive processing of the trauma, or because 

maternal warmth is a proxy for a higher functioning maternal figure, which would be associated 

with lower youth victimization. The Substance Abuse and Mental Health Services 

Administration (2014) suggests that service providers who work with traumatized individuals 

understand the (a) impact of trauma and (b) the signs of trauma, (c) integrate knowledge about 

trauma within the environment, and (d) take care to not re-traumatize individuals who have a 

trauma history. 

While these are appropriate guidelines for service providers, given the amount of time that 

youth spend in the care of a parent, it is also important that family members understand these 

guidelines as well and how to care for their children in a trauma-informed manner, if needed. 

Indeed, perhaps strong maternal support allows adolescents to openly react and discuss their 

trauma, which might be re-traumatizing. As trauma-sensitive schools provide their students with 

trauma-informed practices, perhaps trauma-sensitive parenting would also be beneficial to these 

adolescents. 

As previously discussed, involving parents in interventions might be effective in promoting 

positive parenting, related to positive psychological adjustment in youth. Included in these 

interventions should also be a focus on communication between parents and their children. 

Eisman and colleagues (2015) suggest that positive communication between parents and children 

is necessary to alleviate outcomes related to trauma; however, Buffington and colleagues (2010) 

advise that parents may need education about trauma so that they can be appropriately supportive 

of their children. Finnegan and colleagues (1998) studied the mother-child relationship and 

found that when mothers were perceived as over-protective, there was increased peer 

victimization for boys, whereas when there was perceived rejection from mothers, girls were at a 

greater risk for peer victimization. 

Berkowitz and colleagues (2011) suggest Child and Family Traumatic Stress Intervention 

(CFTSI) that includes psychoeducation for children and caregivers about symptoms related to 

trauma, understanding reactions related to trauma, and assessing risk. CFTSI also includes a 

multidisciplinary supportive therapy intervention and has shown promise in youth who have 

been exposed to violence, where communication between caregivers and children is promoted, 

especially about feelings, symptoms, and behaviors associated with trauma as well as behavioral 

skills taught to both caregivers and youth to aid in coping with that trauma. It is worth noting that 

this pilot test was conducted with youth recruited from a pediatric emergency room department 

and there is not information about their socioeconomic status. 

Additionally, exposure to violence does not only affect adolescents in these communities. 

Parents who are exposed to violence are likely also to experience the same maladjustment that 

adolescents do, which then can affect parenting. For example, maternal stress and poor 

psychological adjustment might play a role in prohibiting the full benefits of maternal warmth. 

Grant and colleagues (2000) found that sources of support, such as moms, may not be able to 

provide the high levels of support or warmth that a child might need in response to their own 

trauma. Similarly, shared trauma (not necessarily the same exposure) may provide a closeness 

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for parent and child as a shared experience, but the parenting still might be affected. Kerns and 

colleagues (2014) found that when parents were distressed by a traumatic experience (shared 

with a child), their child/ren had more negative outcomes such as emotional problems, 

posttraumatic stress, and lower prosocial behaviors. Thus, changing the neighborhood 

environment to reduce exposure to violence may be a necessary prerequisite for any of these 

family interventions to be successful. 

Stability. Finally, with respect to the family, we consider interventions focused on stability. 

Neighborhood stability is one form of stability that may be important and has been found to be 

related to delinquency within a neighborhood (e.g., Cantillon, 2006). Residential mobility can 

and does affect the lives of people, particularly people living in poverty who may move from one 

gang territory to another. Such moves can have unintended consequences for education and law 

enforcement; for instance, if gang conflicts are intensified, neighborhoods and schools become a 

staging ground for this conflict. Fowler and colleagues (2015) found that housing instability in 

adolescence predicted later psychological maladjustment and crime. Even “upward mobility” 

within a family resulting in moving to a more advantaged neighborhood might result in negative 

outcomes such as increases in street code and increased chance of dropout of school (e.g., 

Metzger, Fowler, Anderson, & Lindsay, 2015). 

Additionally, household stability might be quite important to focus on with respect to 

interventions. Household stability has been found to be related to hopelessness (e.g., Bolland, 

Lian, & Formichella, 2005), behavioral problems (e.g., Cavanagh & Huston, 2006), and 

exposure to violence (e.g., Cavanagh, Stritzel, Smith, & Crosnoe, 2018). In adolescence, when 

developmental change is rapid, developmental instability often occurs (e.g., Vannucci et al., 

2018), and stability in other areas of life may become increasingly important. Lower-income 

families are more likely to experience household instability due to movement of the entire family 

between residences and/or neighborhoods (i.e., residential mobility) as well as household 

member instability (Raver, Blair, Garrett-Peters, & Family Life Project Key Investigators, 2015; 

Raver, Roy, & Pressler, 2015). This instability has been found to negatively influence child 

development (Fomby & Cherlin, 2007; Roy, McCoy, & Raver, 2014; Sandstrom & Huerta, 

2013). Additionally, results from several studies suggest that in single-parent households and/or 

in homes where youth are socially isolated from their families, there are higher risks of exposure 

to violence (Bell & Jenkins, 1993; Esbensen & Huizinga, 1991; Ebensen, Huizinga, & Menard, 

1999). Instability has also been found to be related to trust and parenting ability (Conger et al., 

2002; McLoyd, 1990). 

In the MYS sample, there is considerable household instability. Each year, we asked 

respondents to indicate the person most like a mother and most like a father to them, providing 

response choices for each (e.g., mother/father, aunt/uncle, older sister/brother). The median 

wave-to-wave change in the person identified as most like a father was 44.5%, and the median 

wave-to-wave change in the person identified as most like a mother was 25.8%. We also asked 

respondents to indicate how much of the time they lived with the person who was most like a 

mother and with the person who was most like a father to them, and we used these responses to 

identify family structure. Across waves of data (and ignoring cases where the respondent was 19 

and therefore liberated), we found that the median rate of true two-parent families (i.e., motherfather, mother-stepfather, stepmother-father, stepmother-stepfather) was 33.1%; when we 

considered a more inclusive definition of mother- and father-roles (i.e., including 

aunt/grandmother/older sister as potential mother figures and uncle/grandfather/older brother as 

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father figures), the median rate of two-parent-equivalent families increases to 40.5% across 

waves. 

We considered how these measures of instability affect maternal warmth, parental 

monitoring, and curfew using restricted maximum likelihood in a linear mixed model, with a 

first-order autoregressive covariance structure and a Kenward-Rogers estimate of degrees of 

freedom; in each case, we used a Granger causality framework with age and gender as 

covariates. In the first set of analyses, we estimated the effects of change in mother figure and 

change in father figure together in each model. Results show that change in mother figure was 

negatively associated with change in maternal warmth (b = -0.066 se = 0.022, t = -2.97, p < .01), 

but change in father figure was not. Results also show that both change in mother figure (b = -

0.384, se = 0.047, t = -8.15, p < .001) and change in father figure (b = -0.145, se = 0.040, t = -

3.62, p < .001) were negatively associated with change in parental monitoring. Finally, change in 

father figure was negatively associated with change in curfew (b = -0.052, se = 0.020, t = -2.61, 

p < .01). 

The analysis of family structure used the same approach as previously identified. Results for 

true two-parent families showed significant positive effects for maternal warmth (b = 0.129, se = 

0.019, t = 6.68, p < .001), parental monitoring (b = 0.252, se = 0.041, t = 6.09, p < .001), and 

curfew (b = 0.062, se = 0.021, t = 2.91, p < .01). Results are similar for analyses using twoparent equivalent families. 

These findings suggest why household instability contribute to poor juvenile court outcomes 

for the adolescents in the MYS sample. Change in mother figure is negatively associated with 

parental monitoring and curfew; change in father figure is negatively associated with maternal 

warmth, parental monitoring, and curfew. And having two parents or parent figures in the home 

is positively associated with maternal warmth, parental monitoring, and curfew. All three of 

these social support and family control variables have direct or moderating effect on court 

outcome severity, and an indirect effect on court outcome severity through academic progress. 

Of course, it is difficult to engineer family structure and stability. But several approaches 

could prove to be helpful. First, the court should consider carefully how alternative residential 

placement of the child may affect the child. That is, removing juveniles from their homes (e.g., 

residential treatment, juvenile detention) may present additional sources of instability and result 

in additional negative outcomes (Zelechoski et al., 2013). While residential treatment might be 

ordered or suggested for those adolescents with more severe emotional or behavioral problems, 

this treatment plan may not be best, in part, because displacement from the home might be 

sudden. Although the courts do certainly take this into consideration in their placement 

decisions, perhaps a more thorough assessment of how such placement may affect the overall 

psychological adjustment of the child would be in order (e.g., change in mother figure is a 

positive predictor of hopelessness; Bolland et al., 2005). Similarly, schools should consider 

whether and how disciplinary actions may affect the living arrangements of a student before 

taking such actions. Third, more thorough assessments of the child’s living arrangement may be 

useful in child protective services decisions, and during investigations it could commit resources 

that would potentially strengthen these living arrangements. Finally, it may be the case that even 

seemingly minor interventions could help promote stability. For example, the establishment of 

routines (e.g., consistent meal times, bed times, time to do homework) is associated with better 

child adjustment (Kliewer & Kung, 1998), and it can moderate (Ivanova & Israel, 2005; Ivanova 

& Israel, 2006; Kliewer & Kung, 1998) and mediate (Brody & Flor, 1997) the effects of negative 

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events or conditions on child outcomes. Thus, programs to establish household routines can 

potentially be effective even if the structural aspects of instability cannot be eliminated. 

Individual. Finally, we generally found that exposure to violence was associated with 

worsening psychological adjustment for youths, such as increased hopelessness, traumatic stress, 

and street code, and decreased self-worth and academic progress. Further, our finding that more 

complex (i.e., multiple) exposure to violence did not predict more intensive involvement with the 

juvenile justice system than simple exposure speaks to the critical importance of addressing any 

violence exposure. Even a single exposure serves as a powerful risk factor for juvenile justice 

involvement, which in turn likely alters the youth’s developmental trajectory.

Given the inconsistent findings with respect to age and gender, that is, the lack of pattern, 

suggesting when or for whom interventions should take place, we suggest that the results be 

taken into consideration as they have been presented. That is, the indirect effects and interaction 

terms in the model suggest what kinds of interventions may be most effective for any given 

individual. We consider interventions that focus on (a) trauma and traumatic stress, (b) 

hopelessness, and (c) self-worth. 

Trauma and Traumatic Stress. Exposure to violence, whether witnessing violence or violent 

victimization, is a traumatic event (see Fowler, Tompsett, Braciszewski, Jacques-Tiura, & 

Baltes, 2009), and many studies of community violence have indeed found that exposure 

produces post-traumatic stress disorder (e.g., McGill et al., 2014). Yet, our results show that 

traumatic stress is among the least important mediators linking exposure to violence to court 

outcome severity; these results further show a significant mediation effect only for witnessing 

violence but not violent victimization (although this may be an artifact of the way in which we 

measured traumatic stress), and the only significant mediation path is witnessing violence 

traumatic stress  academic progress  court outcome severity. The direction of this finding is 

not as expected, however: increased stress is associated with improved academic progress, 

particularly for boys. Moreover, neither of the strongest mediators in the relationship between 

exposure to violence and court outcome severity, hopelessness and global self-worth, are 

particularly strongly related to traumatic stress (r = .082 and r = -.097 respectively). 

This all leads us to conclude that, while evidence-based programs to reduce the trauma that 

occurs as a function of exposure to violence help reduce traumatic stress for youth in general, 

they may not have much of an effect on future involvement with the juvenile justice system for 

the population under study here. This may be because trauma associated with exposure to 

violence among extremely impoverished youth manifests and resolves itself in different ways 

than among less-impoverished populations of youth. As previously discussed, this might be 

because youth who live in neighborhoods view violence as inevitable, and as they are exposed to 

violence these preconceptions are reinforced. Thus, they may feel increasingly trapped in a 

difficult environment, with no sense of a future, and they develop increased feelings of 

hopelessness. 

While we believe that interventions targeting traumatic stress may not be the most effective 

interventions when it comes to exposure to violence and psychological adjustment and/or court 

involvement, we still advocate for trauma-informed and trauma-sensitive interventions, even for 

this population. Buffington and colleagues (2010) identify components of trauma-focused, 

evidence-based treatments: (a) “psycho-education, (b) caregiver involvement and support, (c) 

emotion regulation skills, (d) anxiety management, (e) cognitive processing, (f) construction of a 

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trauma narrative, and (g) personal empowerment training” (p. 18). And, our results support the 

use of evidence-based cognitive behavioral treatment for traumatic stress and emotional and 

behavioral problems (e.g., anxiety, depression, mood dysregulation) for violence-exposed youth. 

In the category of Children Exposed to Violence and Victimization, the OJJDP Model Programs 

Guide lists several evidence-based cognitive behavioral treatments, such as the Cognitive 

Behavioral Intervention for Trauma in the Schools (CBITS) and the Trauma-Focused Cognitive 

Behavioral Therapy (TF-CBT, see Voisin and Berringer, 2014). CBT helps youth to understand 

their constellation of symptoms and to develop a set of effective coping strategies to effectively 

manage these symptoms while leading an enjoyable and meaningful life. As previously noted, 

care must be taken when employing CBT with individuals or groups who may have cognitive 

delays, to modify the approach to acknowledge disabilities but without distorting the basic 

principles of CBT; while this may be readily accomplished by professionals with graduate 

training in psychology, it may be a challenge for practitioners without such training. 

Hopelessness. Our second thought more explicitly involves hopelessness. Feelings of 

hopelessness in the study population far exceed those for middle-class and affluent youth. A 

comparison between results published elsewhere (Reifman & Windle, 1995; Spirito, Williams, 

Stark, & Hart, 1988) and the MYS results shows that hopelessness among MYS respondents was 

4.48 times higher than in the general adolescent population, 1.83 time higher than for adolescents 

in the general population who experienced significant emotional problems and were in therapy, 

and 0.74 times as high as for adolescents in the general population who had been 

institutionalized because they had attempted suicide (Bolland et al., 2007). 

This should not be surprising, given a growing literature on adolescent hopelessness. One 

important aspect of hopelessness is difficulty envisioning a future (Lorion & Saltzman, 1993; 

Mac Giollabhui et al., 2018; MacLeod et al., 2005), such that adolescents who are hopeless tend 

to live in the moment and not consider the future consequences of their actions (Bolland, 2003). 

Consistent with this, Burnside and Gaylord-Harden (2019) found that among court-involved 

youth, hopelessness predicted future exposure to violence, likely because they found themselves 

in places where violence was more likely to occur. So and colleagues (2015) found that future 

orientation (e.g., hope for the future) serves as a protective factor against delinquency in 

adolescence (see also Jackman & MacPhee, 2017; Stoddard, Zimmerman, & Bauermeister, 

2011). Similarly, Daigle and Hoffman (2018) found that when adolescents have been victimized, 

their future orientation suffers. Monahan and colleagues (2015) found that in a sample of 

juvenile offenders, greater exposure to violence was related to lower future orientation through 

young adulthood, as well as to lesser impulse control in the short-term. The hopelessness found 

in many of the youth who live in these communities is most likely not depression-associated 

hopelessness, but rather a cognitive style that is adaptive in their environment. That is, 

hopelessness is adaptive in many of the MYS youths’ developmental context and may not be a 

sign of psychopathology. It is, in essence, a sensible cognitive adaptation to their environment. 

One major problem, though, is that hopelessness (unlike, for example, depression) is seldom 

addressed directly in established youth interventions, and never directly addressed in evidencebased interventions. Although hopelessness is in some ways similar to depression, and in fact is 

directly linked to depression in one theory (Abramson, Metalsky, & Alloy, 1989), they are not 

the same thing. In simplest terms, depression is a mood disorder that may be caused by 

biological or environmental factors. Typically, a major depressive episode lasts for 

approximately 20 weeks; however, depressive episodes caused by environmental factors (e.g., 

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trauma) are less severe and shorter lived. Symptoms of major depression include sleep 

disturbance, psychomotor disturbance, loss of energy, loss of interest, feelings of worthlessness, 

difficulty thinking, and thoughts of death (Buchwald & Rudick-Davis, 1993). Individuals who 

are depressed often feel isolated and/or hopeless (Gilbert, 1988), find it difficult to plan ahead 

(Breier-Williford & Bramlett, 1995), and lack the energy to engage in purposive behavior 

(Wilson, Sandoz, Kitchens, & Roberts, 2010). Feelings of lack of control, which contribute to 

depression, become a self-fulfilling prophesy as people react to events rather than anticipate 

them. Although depression can result in feelings of hopelessness (Gilbert, 1988), hopelessness as 

a malady typically stems strictly from environmental factors, particularly for impoverished 

youth. McLaughlin, Miller, and Warwick (1996) defined hopelessness as a system of negative 

expectations concerning self and future life. Joiner and Wagner (1995) saw it as “an expectation 

that highly desired outcomes will not occur or that negative ones will occur..., and that nothing is 

going to change things for the better....” (p. 778). Thus, it appears to be rooted in cognition rather 

than mood. Unlike situational depression, which also has environmental causes but tends to be 

short-term (diminishing as the environment changes), hopelessness tends to be a long-term 

condition, particularly if the impoverished environment does not change: MYS results show that 

the year-to-year-to-year correlation for hopelessness remained statistically significant through 84 

months (median r = .126, p < .05). All of this suggests that successful approaches for treating 

depression will not necessarily work for hopelessness in this population. This may be particularly 

true for CBT, which has had a successful history in treating depression. However, hopelessness 

is at its peak during early adolescence (Figure 5), when cognitive ability is just beginning to 

develop, and particularly for impoverished adolescents whose cognitive development is often 

delayed. 

How, then, can hopelessness best be addressed at the individual level? Attempting to 

“correct” the cognitive style of hopelessness to a rosier one ignores the realities of the 

environments which these youth live in and ignores the adaptive properties of this style. That is, 

hopelessness resulting from poverty-related stress (Wadsworth & Berger, 2006), must to be 

acknowledged as valid with these youth. Although no definitive conclusions have emerged, 

suggestions have been advanced. Marotta and Voisin (2017) focused on the lack of future 

orientation that stems from hopelessness, and they advocate for the strengthening of resilience 

(i.e., “positive adaptation in individuals who have been exposed to significant adversity,” Yule et 

al., 2019, p. 2) in youth, particularly African American youth living in communities 

characterized by poverty. DuRant and colleagues (1994) found that resiliency factored into 

whether impoverished youth who were exposed to violence also engaged in violence; 

unfortunately, they measured resiliency in terms of hopelessness and future orientation, which 

does not advance us much toward a goal of reducing hopelessness (i.e., the way to reduce 

hopelessness is to reduce hopelessness). It also points out the difficulty of promoting resilience 

as a therapeutic approach: it is an amorphous concept that is difficult to measure. Perhaps as a 

result, no evidence-based programs have been developed to increase resilience, though recently, 

Wadsworth and colleagues (2018) have proposed their program Building a Strong Identity and 

Coping Skills (BaSICS) which does address resiliency in youth who experience poverty-related 

stress. 

In the absence of evidence-based programs to directly address hopelessness, two approaches 

seem possible. First, evidence-based programs that effectively address some of the behavioral 

consequences of hopelessness (see Bolland, 2003) could be implemented. One such program, 

Positive Action, has been recommended by the Annie E. Casey Foundation on its Blueprints 

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website. It is a universal school-based program (for elementary- and middle-school grades) 

prevention program that has reduced delinquency and violence, increased emotional regulation 

and positive social/prosocial behavior, and increased school attendance, and it has shown 

promise with low income students (see Lewis et al., 2016). Second, programs to reduce 

hopelessness could be designed based on research results presented here (also see Bolland et al., 

2005). These results suggest that hopelessness decreases when (a) feelings of connectedness 

increase, and (b) disruption decreases. Thus, such a program could attempt to increase feelings of 

connectedness with schools (particularly for youth who change schools because of residential 

mobility) by conducting individualized orientations for students; promote better feelings of 

connectedness with the neighborhood by reducing negative neighborhood characteristics (e.g., 

blight, litter); and promote better caregiver-child relationships. They could also attempt to reduce 

disruption, for example by decreasing changes in caregiver (i.e., mother and father figure) and 

helping establish routines in the home. 

Self-Worth. Our third thought involves self-worth, which significantly relationship between 

exposure to violence and court outcome severity (see Table 24). Much of the existing research 

about effects related to exposure to violence focus on internalizing and externalizing symptoms, 

neglecting psychological well-being. However, there is scant research suggesting that poverty 

has a negative effect on the socioemotional wellbeing (including self-worth) of children (Evans 

& English, 2002; Li et al., 2007). Additionally, Buckner and colleagues (2004) found that selfesteem mediated the relationship between exposure to violence and problematic mental health 

symptoms (e.g., depressive symptoms) in children who lived in communities characterized by 

extreme poverty (although see Youngstrom, Weist, & Albus, 2003 for inconsistent results). One 

factor that has been shown to effect self-esteem is family and neighborhood cohesion (see 

DiClemente et al., 2018). In fact, DiClemente and colleagues found that for boys who had been 

exposed to high levels of violence, high levels of neighborhood cohesion resulted in increased 

self-esteem (although possible high levels of neighborhood cohesion in the presence of high 

levels of gang activity raise caution flags about how these results should be interpreted). Thus, 

approaching self-worth through either neighborhood or family interventions might be 

appropriate. Given the negative relationship between self-worth and depression, CBT and other 

therapies used to treat depression could be effective (the previous caution about using CBT with 

this population is less pronounced for self-worth than it was for hopelessness, because self-worth 

is less affected by age than is hopelessness; compare Figure 5 with Figures 19 and 25). 

Strengths and Limitations 

The first limitation of this study is that it is largely based on self-report data. Specifically, 

measures of exposure to violence, psychological adjustment, social support, and family control 

are all derived from responses to survey questions. These self-reports are notoriously subject to 

measurement error (see Gnambs & Kaspar, 2015), due to misunderstanding of questions or 

purposeful misrepresentation in responses. In terms of misunderstanding, Mobile Youth Survey 

participants may have been particularly susceptible to measurement error (in a sample of 457 

MYS youths, composite scores on the Kaufman Brief Intelligence Test (K-BIT II; Kaufman, 

Lichtenberger, Fletcher-Janzen, & Kaufman, 2005) ranged from 52 to 124, M = 85.11, SD = 

13.36, translating to percentile ranks ranging from 0.1 to 95.0, M = 22.93, SD = 21.67). We were 

aware of this challenge, however, and we attempted to limit the negative effects of cognitive 

limitations, in two ways. First, we read questions, and often response options, aloud to 

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respondents. Second, we invited respondents who appeared to have difficulty following along to 

be interviewed in a one-on-one format, allowing as much time as necessary to answer each 

question and more clarification and explanation than would be possible in group settings. In 

terms of purposeful misrepresentation, we were able to detect inconsistency in responses, which 

might occur if a respondent were attempting to overstate or understate a particular behavior, and 

we eliminated the data of respondents who exceeded a fairly strict inconsistency rate (i.e., who 

were inconsistent in their reporting of 15% or more of the behaviors that were assessed). This, of 

course, does not completely eliminate purposeful misrepresentation, but it should reduce its 

effects. 

A related strength of the study is that two of the measures, those having to do with the 

schools and the courts, used objectively measured outcomes obtained from the Mobile County 

Public School System and the Mobile County Juvenile Court. In developing measures of 

academic progress, crimes against a person, and court outcome severity, we made decisions 

about how to sort, distinguish, and combine data (see Appendix D and Appendix E), and there is 

plenty of room for disagreement about these decisions. Even so, the statistical models that used 

self-report data along with the objective measures produced a number of statistically significant 

results. Arguably, if the measurement error produced by the self-report measures had been 

eliminated, the results would have been stronger. Similarly, any error introduced by coding 

decisions for the objective data should have attenuated results, and arguably if this error were 

eliminated the results would have been stronger. Put differently, any error in the study, either 

introduced by respondents or by investigator decisions, would introduce Type II error into the 

results; the obtained results should therefore be viewed as conservative. 

Our decision to use a Granger Causation framework in the analysis of court outcome severity 

introduces a further conservative bias into the results, but it allow a stronger causal interpretation 

of the results than would otherwise be possible. This is not to suggest that the results definitively 

demonstrate causation; they do not. But any enhanced ability to draw causal inference, however 

tentatively, strengthens the usefulness of the conclusions. 

Another limitation/strength of the study involves the homogeneity of the population. The 

MYS sample is remarkably homogeneous in terms of socioeconomic status (SES), race, and 

geographical location. This obviously limits severely the generalizability of the results to Mobile 

and other similar urban areas where extreme poverty is rampant in racially-segregated areas of 

the metropolitan area, and it may explain why the results reported here are sometimes 

inconsistent with those found in other studies. Surely, however, a number of similar urban areas 

exist in the United States. On the other hand, however, the extreme homogeneity of the 

population studied is also a strength of the study. Consider, for example, how heterogeneity in 

SES can distort the validity of findings. Arguably, a number of factors considered in this study 

are associated with SES: these include exposure to violence (e.g., Evans, 2004; Friedson & 

Sharkey, 2015), feelings of hopelessness (e.g., Bolland et al., 2005; Landis et al., 2007), 

academic progress (e.g., Blair & Raver, 2016; Hair, Hanson, Wolfe, & Pollak, 2015; Lacour & 

Tissington, 2011), and juvenile court contact (e.g., Rodriguez, 2013; Voisin et al., 2017). If SES 

is left uncontrolled, correlations among these variables will be artificially increase, distorting the 

true magnitude of the relationship. The alternative, most often used in studies, is to measure SES 

and statistically control for its effect. This approach, however, is limited by any error in 

measuring SES. Unfortunately, a substantial literature has developed questioning the accuracy of 

measurement of SES (e.g., Oakes & Rossi, 2003). As the magnitude of the error increases, the 

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magnitude of relationships among variables and the statistical significance of results, becomes 

exaggerated and therefore less accurate. 

Future Research 

The results suggest several areas where additional research may be fruitful and help address 

some unresolved issues identified in this report. First, youth exposure to violence in communities 

of concentrated poverty is an inherently complex problem that occurs at multiple levels of 

society, has both local and universal aspects, is dynamic, and is determined by current and 

historical factors. Our results demonstrated some of these factors by illuminating the interplay of 

individual, social, and institutional variables over time. Given these formidable complexities, 

successful solutions to this problem also must be complex. Future research should utilize 

complex, multidisciplinary frameworks that incorporate multiple levels of analysis and temporal 

processes. For example, Wadsworth and colleagues (2018) proposed an elegant research 

framework that integrates the literatures on stress and coping, psychophysiology, cultural 

identity development, and empowerment theory. 

Related, parents create the developmental environments in which their children grow up, 

such as physical, intellectual, emotional, and interpersonal environments. All primary aspects of 

human development are rooted in the family environment. Therefore, to assist youth growing up 

in environments of concentrated poverty, it is crucial to assist their parents who are creating their 

developmental environments. Given this, future research should pursue comprehensive 

approaches to assisting the parents. Because parental economic viability is essential for the 

provision of the family environment, interventions to support parental employment and financial 

self-sufficiency (e.g., educational remediation, vocational and professional training) should be 

included as core components of these approaches. Interventions to address parental mental health 

problems and substance abuse should also be included. Further, stresses associated with 

concentrated poverty may disrupt the intergenerational and community transmission of healthy 

parenting practices, such as preparation of healthy meals, creation of family routines, and 

positive parenting. Therefore, parents in environments of concentrated poverty may also benefit 

from skills-based family wellness coaching that could facilitate the transmission of healthy 

parenting practices between family and community members (Hudziak & Ivanova, 2018; 

Ivanova et al., 2019). 

Second, future research should investigate how witnessing violence impacts behavior as well 

as juvenile court outcomes. In other words, an exploration into the mechanism that is triggering 

more severe juvenile court outcomes for youth who have been exposed to violence would be 

appropriate. Such an investigation could focus on whether youth who are exposed to violence 

engage in more serious forms of, or higher rates of, violent behavior, and whether this violent 

behavior is what leads to negative educational and juvenile court outcomes. Similarly, it may be 

useful to explore how gang involvement affects both psychological adjustment and these 

outcomes. This would add considerable complexity to the structural equations model tested in 

this study; but it could be undertaken with the Mobile Youth and Poverty Study data. 

Related, better research on resilience in this population would be worthwhile. Such research 

would explore what factors enable some youth to thrive in the face of hyperconcentrated poverty. 

As suggested previously, this requires considerable attention to the meaning and measurement of 

resilience. It would be important to determine, for example, whether in this population resilience 

is simply the absence of negative outcomes, or whether it also involves positive outcomes; and, 

if it requires positive outcomes, it is important to theoretically determine what those outcomes 

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necessarily reflect the official position or policies of the U.S. Department of Justice.
 

 

 

 

 

 

 

are and how to measure them. This would also allow a rigorous examination of whether 

resilience can truly prevent juvenile court involvement, or alternatively if the institutional racism 

(and equally important, structural bias against those living in poverty) makes resilience largely 

irrelevant. If theoretically justifiable measures of resilience can be developed, it would provide a 

fertile ground for developing interventions to reduce violence in the community. 

Third, a more thorough examination of violent behavior would be useful. There are some 

possibilities with respect to future research that might be undertaken with the population of youth 

who have been exposed to violence. For example, investigation into the fight, flight, freeze 

system (FFFS) might be warranted and yield interesting results. Tache, Lambert, Ganiban, & 

Ialongo (2018) found that “community violence exposure was positively associated with 

aggressive behavior for adolescents low on the FFFS, but not associated with aggressive 

behavior for adolescents high on FFFS. Youth low on FFFS may be less perceptive of cues 

indicating imminent threat, less sensitive to emotions involving fear or panic, and more willing 

to engage with violent situations requiring aggression.... Certain aspects of fearfulness (e.g., 

caution) may help to protect against aggression for African American adolescents” (p. 714).

This naturally leads to a need for better understanding of the biology of FFFS, and how the 

physical environment may affect violence for youth living in hyperconcentrated poverty. Poverty 

is associated with substandard housing, often poorly insulated against the elements, and as a 

result, often damp. This leads to mold, and to cockroach and dustmite infestations, all of which 

are produce allergens (Arbes, Gergen, Elliott, & Zeldin, 2005; Sly, 1999). Many youth growing 

up in these homes develop atopy (sensitivity to allergens) during their childhood; for atopic 

individuals, exposure to allergens stimulates the production of immunoglobulin E (IgE) 

(Eggleston et al., 1998; Gergen, Arbes, Calatroni, Mitchell, & Zeldin, 2009), which in turn 

triggers a cascade of immunomodulatory and proinflammatory cytokines; the latter bind to 

neurons in the preoptic nucleus of the brain and triggering the release corticotropin releasing 

hormone (CRH) in the brain (Black, 1994; Turnbull & Rivier, 1999). Thus, exposure to allergens 

may create a high level of ambient cortisol in atopic individuals. 

As argued by Tache and colleagues (2018), fear and threat are at the heart of the FFFS. When 

a youth encounters a threatening situation, the amygdala triggers the production of CRH by the 

hypothalamic-pituitary-adrenocortical (HPA) axis (Sajdyk, Shekhar, & Gehlert, 2004). This, in 

turn, induces expression of proopiomelanocortin in the anterior pituitary gland and results in the 

(a) synthesis of adrenocorticotropin and -endorphin, and (b) production of adrenal glucocorticoids (notably cortisol) (Black, 1994). This, coupled with high ambient levels of cortisol 

produced by the immunological response, may result in hypercortisolism, wherein the 

sympathetic nervous system is activated, with increased blood pressure and heart rate, diversion 

of stored energy to muscle, and inhibition of digestion (Goldstein, 2004; McCarty & Gold, 1996; 

Rodrigues, LeDoux, & Sapolsky, 2009)—all of which are central to the FFFS. However, in an 

environment where flight or freeze may not be feasible (e.g., because of the code of the street), 

fighting and violence often results. However too much cortisol my result in another, very 

different physiological response. When too much CRH is released too often over a prolonged 

period of time, in can lead to atrophy of neurons in the hippocampus and the amygdala 

(Roozendaal, McEwen, &, Chattarji, 2009; Vyas, Pillai, & Chattarji, 2004). This results in a 

hypocortisolism and a blunted stress response (Buske-Kirschbaum et al., 2003), which has been 

associated with psychopathy (O’Neal et al., 2010), sociopathy (van Goozen & Fairchild, 2008), 

aggression (Gunnar & Vazquez, 2001), and an inability to recognize fear (Korte, 2001). Indeed, 

the effects of hypocortisolism are similar to those found in people with lesions in the inferior 

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frontal region of the brain (Korte, 2001); this latter condition has been associated with marked 

increases in violent behavior (Hoptman, 2003). 

A better understanding the prevalence of hypercortisolism and hypocortisolism among 

impoverished adolescents living in neighborhoods where poverty itself is hyperconcentrated is 

critical: although these two conditions may both result in violence, the etiology of the violence is 

very different and potentially subject to different interventions. These interventions may be quite 

sophisticated and involve medical treatment; they may be very simple and involve the 

replacement of substandard housing; or they may be in an area that we can only identify once 

more study is conducted. 

Fourth, at a more sociological level, understanding how violence spreads through 

neighborhoods is important. Violence typically does not occur in isolation: there are witnesses, 

and victims share their experiences. Again, both street code and gang involvement are important 

elements of this, such that any act of violence may require retaliation either by the victim or by 

his or her mates. Burnside and colleagues (2018) found these network effects with respect to 

exposure to violence. Perhaps just as important is an understanding of the meaning of violence in 

an environment of concentrated poverty. That is, it would be useful to determine whether 

violence is interpreted as a purposeful assault on an individual by a bad or evil person, or as 

environmentally driven acts where individuals are victimized because they were in the wrong 

place at the wrong time. 

Fifth, methodological research would help clarify some of the issues presented in this report. 

For example, a number of the measures used in this study are not normally distributed. It is 

important to determine whether this results in a non-normal error distribution, and how this may 

bias results. If the error distributions do violate the normality assumption, it would be important 

to explore how accurately different error distribution models capture the data, and to reanalyze 

the data using the appropriate distribution. This assumes, however, that existing statistical 

analysis software is capable of estimating these models. They also found that those who fit a 

profile of high exposure to violence are also lower in their future orientation levels. 

Another methodological issue would be to examine how polyvictimization differs from 

multiple exposures, and how it might influence court involvement could be undertaken. 

Polyvictimization typically refers to exposure to different kinds of exposure to violence, rather 

than multiple exposures of the same kind of violence (Finkelhor, Turner, Omrod, Hamby, & 

Kracke, 2009). Indeed, high levels of polyvictimization are more common for youth of low 

socioeconomic status (Finkelhor et al., Turner, 2007). Moreover, polyvictimization produced 

higher levels of psychological symptoms than single-victimization (Finkelhor et al., 2007; 

Gustafsson, Nilsson, & Svedin, 2009; Turner, Shattuck, Finkelhor, & Hamby, 2016). 

Still another methodological issue involves the measurement of traumatic stress: our scale 

more closely measures secondary traumatic stress rather than direct traumatic stress. The 

consequences of the scale we used (e.g., I have trouble sleeping) are very similar to those used in 

a traditional traumatic stress scale; however, the antecedent (when something bad happens to a 

friend or family member ...) is different. One might argue that the consequents are more 

important than the antecedent, but that is an empirical question that should be tested. If the two 

approaches yield similar results, then the conclusions reported here are valid for both witnessing 

violence and violent victimization; if not, they are only valid for witnessing violence. 

Finally, future research should evaluate programs that might be implemented to respond to 

the problems associated with exposure to violence. For example, given the limitations of CBT 

for youth with moderate-to-serious cognitive delays, and the need to modify programs using 

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CBT for these youth, it is important to determine the effectiveness of group treatments, such as 

CBITS, when developmental delays or disabilities may be present or even common. 

Additionally, it would be useful to further evaluate the effectiveness of interventions that target 

traumatic stress to determine their effects on hopelessness and self-worth, given our findings that 

hopelessness was the most important mediator between exposure to violence and court 

involvement. 

Conclusions 

The uncertainty associated with poverty creates existential challenges for day-to-day survival 

of children growing up poor. One area where this is particularly the case is exposure to violence 

and the psychological and behavioral adjustment that it causes. One especially important 

outcome potentially related to psychological and behavioral maladjustment is involvement in the 

juvenile court system, where African Americans are overrepresented at every stage or level 

(Kempf-Leonard, 2007; Leiber, 2016; Stahl, Finnegan, & Kang, 2006). In this study, we 

explored direct and indirect pathways from exposure to violence to juvenile court involvement 

using data from the Mobile Youth and Poverty Study (MYPS), a community-based longitudinal 

cohort study of adolescents who live in communities characterized by extreme and concentrated 

poverty. Specifically, this study has three objectives: (a) better understand how traumatic events 

increase the risk of juvenile justice involvement for vulnerable adolescents; (b) identify 

developmental points where interventions can most effectively reduce this risk; and (c) identify 

classes (i.e., types) of intervention that might be most effective for these adolescents. To meet 

these objectives, this study links data from multiple sources (i.e., longitudinal survey responses, 

juvenile court and school records) of over 8,000 adolescents living in impoverished 

neighborhoods in Mobile, AL and surrounding suburbs between 1998 and 2011. The MYPS 

sample consists almost exclusively of African American and racially mixed adolescents. Given 

the relationship between socioeconomic status and racial/ethnic minority status, our sample of 

youth is appropriate to address these objectives. 

Results from this study indicate that exposure to violence does influence juvenile court 

involvement with respect to type of offense (i.e., crimes against a person) as well as the severity 

of court outcomes. That is, exposure to violence does increase the likelihood of committing 

crimes against a person. While the effects we found are not large, the analysis approach we use is 

conservative and the effects are non-trivial. With respect to court outcome severity, which 

considers all categories of offenses, exposure to violence has a larger impact. We also found that 

multiple sources and/or instances of victimization did not result in an increased likelihood of 

crimes against a person or in court outcome severity. That is to say, any exposure to violence, 

whether witnessing violence or being victimized by violence, is meaningful and should be 

considered important with respect to later outcomes, including psychological adjustment, 

academic progress, and juvenile court outcomes. 

Given that our results do suggest a path, both direct and indirect, from exposure to violence 

to both violent behavior and juvenile court involvement in high poverty neighborhoods, it is 

important to identify ways to mitigate the problem of exposure to violence and its path toward 

juvenile justice involvement. Our results suggest a need to broaden initiatives to prevent violence 

in these neighborhoods, one that goes beyond strict law enforcement to the community. A broad 

public health approach to prevent violence in these at-risk neighborhoods is one promising 

approach. Finkelhor and colleagues (2013) suggest that exposure to community violence is 

indeed a significant public health issue for youth, particularly among racial and ethnic minority 

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youth (Chen et al., 2016). Such an approach must focus not only on the individual, but more 

broadly on the neighborhood. Specifically, these results suggest that reducing the negative 

characteristics of a neighborhood is an approach that would lead to positive outcomes. To 

accomplish this, it is necessary to work with neighborhood residents and community leaders as a 

whole; this replaces the traditional concept of individual therapy with a broader concept of 

community therapy. 

We also found that witnessing violence has an impact on youth’s psychological adjustment, 

academic progress, and eventual juvenile court involvement. It is important to acknowledge this 

impact and attend to those who have witnessed violence not only those who have been 

victimized by it. Thus, ameliorating the effect of exposure to violence on psychological 

adjustment is an approach that could be undertaken to interrupt the path of exposure to violence 

to juvenile court involvement. As discussed, there are many existing programs aimed at targeting 

traumatic stress, self-esteem, and more recently, even worry. However, there have been virtually 

no trials, let alone evidence-based programs, developed to reduce hopelessness; yet, hopelessness 

is the most important mediating factor in the current study. We found that both age and gender 

moderate the path from exposure to violence to juvenile justice contact, but not always in a 

consistent manner. Thus, we conclude that interventions should be considered with respect to the 

substance for particular age groups and genders rather than more of a universal prevention 

approach. 

A final approach to this problem is to ameliorate the effect of exposure to violence on 

academic progress. We found that exposure to violence indeed does negatively affect normative 

academic progress, and that academic progress partially mediates the relationship between 

exposure to violence and court outcome severity. We also found that psychological adjustment 

also partially mediates the effect of exposure to violence on academic progress. Perhaps traumainformed training for teachers, school counselors, and school social workers should be promoted. 

Additionally, programs targeting trauma-informed coping skills could be beneficial. 

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Appendix A 

Mobile Youth and Poverty Study Archive 

The Mobile Youth and Poverty Study (MYPS) consists of data derived from several sources, 

as noted in the following sections. Data from these different sources have been linked to 

permanent identification numbers. 

Mobile Youth Survey (MYS, 1998-2011) 

The Mobile Youth Survey (MYS) is a multiple cohort, longitudinal study (14 annual waves 

of data) of adolescents (aged 9.75 years – 19.25 years) conducted in the most impoverished 

neighborhoods in the Mobile, Alabama Metropolitan Statistical Area (MSA). In 1998, we 

enrolled a cohort of 1,771 youths from 13 neighborhoods (median poverty rate = 73%). We 

attempted to recruit all appropriately-aged adolescents in these neighborhoods through a 

combination of active (i.e., knocking on doors) and passive (e.g., posting flyers) recruitment 

procedures; while our final response rate is difficult to determine (we do not have a definitive 

sampling frame), we estimate that we obtained a response rate of between 50% and 60%. During 

each subsequent year, we attempted to re-survey each enrolled participant until he or she aged 

out of the study; we also enrolled a new cohort each year. As enrollees moved to new 

neighborhoods, we followed them to the extent possible; as a sufficient number of previous 

enrollees moved to a new neighborhood, we begin recruiting new participants in that 

neighborhood. By 2011, we were conducting surveys in over 50 neighborhoods in the Mobile 

MSA. The data collected through the MYS was funded in part by NICHD, NIDA, CDC, 

SAMHSA, the cities of Mobile, Prichard, the Mobile Housing Board, the Southern Nursing 

Research Society, and The University of Alabama. 

The first seven waves of the MYS consisted of 294 questions about risk behaviors and 

attitudes associated with violence, substance use, and sex; family structure and function; feelings 

about self, neighborhood, and peers; and experiences in school. In 2005, we added a number of 

questions related to identity style, ego strengths; intimate relationships; and connectedness to 

school and to friends; we also increased the number of questions about the nature of the 

respondent’s relationship with his or her mother or mother figure. In 2010, two additional items 

were added to the MYS. Version 3 of the MYS (used in 2010-2011, with 408 items) typically 

required about an hour and a half to complete. Respondents were guaranteed confidentiality in 

their responses (no questions dealt with child abuse). MYS questions and scales are described in 

the MYPS Archive. For each, the original source for the questions is specified (when one exists), 

although it should be noted that in most cases questions were adapted to address the situations 

and reading levels of the respondents. Unlisted questions were either taken from generallyavailable sources (e.g., How old are you?) or were developed specifically for the purpose of this 

study. 

Between 1998 and 2011, we enrolled a total of 12,448 MYS participants, who collectively 

contributed 36,171 annual data points (an average of 2.9 annual data points per enrollee). Of 

these enrollees, 2,594 contributed five or more data points. We examined the annual earned 

income of a sample of MYS households in 2005, finding a mean annual earned household 

income of under $5,000. A recent comparison (see Bolland, 2012) of MYS enrollees and nonenrollees living in the MYS neighborhoods showed a very small difference in free/reduced cost 

lunch status (MYS enrollees were slightly more likely to receive free/reduced cost lunch) and 

race (MYS enrollees were slightly more likely to be African American). But once these 

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differences were controlled, we found no differences in standardized test scores, school 

violations, or school discipline outcomes between MYS enrollees and non-enrollees. 

The MYS sample serves as the basis for other data that were either collected during 

interviews or from archival records. Those other data sources are described in the following 

sections. 

Adult and Family Dynamics Questionnaire 

Between 2000 and 2011, we conducted over 1,200 interviews with adults living in MYS 

households. While not all respondents were asked all questions, a majority did provide answers 

to questions involving their romantic relationships; experiences with racial discrimination; 

exposure to violence; major life events; relationships with children in the household; 

neighborhood; employment and finances; social support; physical and mental health; religiosity; 

household environment; and risk behavior related to sex and substance use and abuse. Responses 

are connected, through identification numbers, with MYS enrollees living in the household. 

Notably, approximately 50 of the adult respondents were previously MYS enrollees. 

Substance Use Decision Making Survey 

In 2003, we selected a subsample of approximately 500 MYS enrollees to participate in a 

three-year longitudinal study of their cognitive processing abilities and styles and how those 

affect decisions. Enrollees in this substudy were between 10 and 12 at the time of the first annual 

interview, with two subsequent interviews at intervals of approximately 12 months. The initial 

interview assessed IQ, decision making heuristics, creativity, need for cognition, and other 

aspects of cognitive processing. During subsequent interviews, all of these measures except IQ 

were again collected; during subsequent interviews, we also examined attributional styles and 

sensation-seeking behavior. 

Sleep Survey 

In 2010-2011, we interviewed a subsample of approximately 250 MYS enrollees about their 

sleep habits and hygiene, sleep disorders, and household conditions that might affect sleep 

quality (e.g., overcrowding, noise). 

Gene by Environment Neighborhood Interaction Study 

Between 2008 and 2011, we selected a subsample of approximately 600 MYS enrollees who 

had lived in public housing, and conducted interviews; we also collected DNA from these 

individuals. The interviews were conducted using the Diagnostic Interview Schedule for 

Children, the Child Behavior Check List, and the Youth Self Report. Additionally, respondents 

were asked about neighborhood ecology, collective efficacy, and exposure to violence. A 

caregiver for each respondent was also interviewed. 

Mobile County Public School System Student Records 

In 1998, the Mobile County Public School System (MCPSS) began storing student records 

electronically. We have been able to match approximately 90% of MYS enrollees to their student 

records. Records include standardized test scores (Stanford Achievement Test, SAT, OtisLennon School Ability Test, OLSAT), school violations and disciplinary action, emergency 

contacts, special education status, absenteeism, grades, and household financial information 

provided by free/reduced cost lunch applications. 

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Mobile County Juvenile Court Records 

In 1999, the Mobile County Juvenile Court (MCJC) began storing court records 

electronically. We have matched approximately 40% of MYS enrollees to these juvenile court 

records. Records include each incident that resulted in a court appearance, the date of the 

incident, the charge code, and the disposition. 

Mobile Housing Board Records 

In 1998, the Mobile Housing Board (MHB) began storing public housing and Section 8 

Housing records electronically. We have been able to match over 50% of MYS enrollees to these 

housing records. In addition to a census of household members, these records also provide results 

of an audit of household income and financial resources. 

Mobile Police Department Crime and Service Reports 

In 2005, the Mobile Police Department (MPD) began geocoding its crime and service 

reports. We will aggregate these by MYS neighborhood each year to provide a measure of crime, 

by type, in each of these neighborhoods. 

Mobile Youth and Poverty Study Geodatabase 

Data from the MYS and studies of MYS subsamples, MCPSS, MCJC, and MHB provide a 

reasonably comprehensive set of addresses for MYS enrollees between 1998 and 2011. Using a 

geographical clustering approach, we have identified over 50 neighborhoods where MYS 

enrollees have lived during this period. We are in the process of developing a Geographical 

Information System that will link all of the MYS data (from both the full sample and 

subsamples), as well as MCPSS, MCJC, MHB, and MPD archival records to these 

neighborhoods. 

Uniqueness of the MYPS Archive 

Members of our research team have been engaged in research in Mobile for the past 21 years. 

During this time, we have spent considerable time in Mobile’s poorest neighborhoods, gotten to 

understand the culture of the neighborhoods and the needs of their residents, and worked with the 

residents to develop a framework for more effective programs. During this time we have also 

become quite involved with the public systems in Mobile. This involvement has included sharing 

study results with directors and senior staff; writing grant proposals in collaboration with them; 

and engaging in long conversations about the services they provide, the clientele they serve, and 

how to make their services more effective. 

Thus, we have been able to earn the trust of neighborhood residents and public officials, and 

this has enabled us to obtain good MYS response rates and develop the partnership agreements 

with public sector agencies, organizations, and systems described previously. Together, these 

have led to a unique and remarkable data archive, in which we have (a) obtained thousands of 

survey responses from adolescents over a 14 year period; (b) obtained access to individual court 

and school records of the individual survey respondents; (c) obtained geocoded police and crime 

records about the neighborhoods where these survey respondents live; and (d) created a database 

in which all of these data elements can be merged. It is fair to conclude that the MYPS is one of 

the largest, if not the largest, of its kind; the fact that it focuses on what has often been described 

as a “hard-to-study” population of impoverished adolescents makes it even more remarkable. We 

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have the ability to address a number of questions, including those specified in this study, in a 

way that has not been possible previously. 

Procedures to access MYPS Data 

Procedures are in place for researchers to access deidentified MYPS data at 

http://acbolland.people.ua.edu/data-use.html. Additionally, requests can be made to Anneliese 

Bolland, Ph.D. (acbolland@ua.edu) or John Bolland, Ph.D. (john.m.bolland@gmail.com). 

Included in these procedures are attention to data protection. In order to access MYPS data, 

researchers must first read through the guidelines in place for the MYPS archive. Then, 

researchers must identify research questions and the data they wish to use in their studies and use 

the MYPS Data Request Application, also filling in a Data Protection Plan, to request 

deidentified data. The application will then be reviewed by MYPS staff and a decision will be 

made and researchers will be notified. Before any MYPS data are made available to researchers, 

IRB approval or a waiver of IRB approval must be submitted to MYPS staff from the 

researchers’ home institutions. This approval lasts one year, however, researchers are able to 

request annual renewal. Researchers are also responsible for reviewing existing published 

manuscripts that use MYPS data to ensure that they are not proposing to answer research 

questions that have already been answered. Additionally, MYPS staff keep track of what studies 

are in progress to ensure that new requests do not overlap with existing studies. 

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Appendix B 

Mobile Youth Survey Data Collection Protocols 

Mobile Youth Survey (MYS) Questionnaire 

The first seven waves of the MYS consisted of 294 questions about risk behaviors and 

attitudes associated with violence, substance use, and sex; family structure and function; feelings 

about self, neighborhood, and peers; and experiences in school. In 2005, we added a number of 

questions related to identity style, ego strengths; intimate relationships; and connectedness to 

school and to friends; we also increased the number of questions about the nature of the 

respondent’s relationship with his or her mother or mother figure. In 2010, two additional items 

were added to the MYS. Version 3 of the MYS (used in 2010-2011, with 408 items) typically 

required about an hour and a half to complete. Respondents were guaranteed confidentiality in 

their responses (no questions dealt with child abuse). MYS questions and scales are described in 

the Mobile Youth and Poverty Study (MYPS) Archive. For each, the original source for the 

questions is specified (when one exists), although it should be noted that in most cases questions 

were adapted to address the situations and reading levels of the respondents. Unlisted questions 

were either taken from generally-available sources (e.g., How old are you?) or were developed 

specifically for the purpose of this study. 

IRB Protocols 

All data collected and/or received as part of the MYPS, including the MYS, were approved 

by an Institutional Review Board (IRB, i.e., The University of Alabama or University of 

Alabama at Birmingham). All protocols are up to date and continue to be approved by The 

University of Alabama’s IRB. 

Data Collection Procedures 

When participants came to a scheduled survey administration (in 1998 and during every 

subsequent year), they were checked in (to ensure that (a) we had had previous contact with them 

and that they were able to correctly provide us their address and birthdate, as obtained during the 

recruitment process; (b) they had not already been surveyed during the current year; and (c) that 

they had parental consent) and placed in a room with other MYS participants (typically 10 to 

20). We read the assent statement on the cover page out loud and ask respondents to print their 

name at the bottom of the page, along with their address, their birth date, and the date the survey 

is administered; they then tear this page off of the MYS questionnaire, and we collected these 

cover pages. We usually read the questions aloud, and ask each respondent to mark the 

appropriate answer in his or her survey booklet. If an individual had difficulty keeping up with 

the group, or circumstances require individual attention, a member of the survey team works 

one-on-one with that respondent. When respondents completed the survey, we paid them $10 

($15 starting in 2005). 

If adolescents were scheduled to attend a group administration and did not come, we 

attempted to recontact them and schedule another time for the survey. If adolescents did not 

participate in the group survey administration in their neighborhood (either because they did not 

come to their scheduled survey administration time or because we were unable to make contact 

with them during the time we were in their neighborhood), we attempted to contact them later in 

the summer to schedule a time to administer the survey to them in their home. During the first 

seven waves of data collection, virtually all respondents who moved to untargeted 

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neighborhoods were surveyed in their homes. If others were in the room during the survey, we 

typically allowed respondents to read and answer the questions themselves; if others are not in 

the room, we read the questions aloud while the respondents recorded their answers in the 

response booklet. In some special circumstances, we initially scheduled an in-home survey rather 

than a group administration. During 1998, approximately 10% of all the surveys were 

administered in the respondent’s home; by 2000, this had increased to over 20% (because of the 

annual increase in the number of respondents living in untargeted neighborhoods). 

Response and retention rates. In 1998 (Wave 1), we surveyed a total of 1,774 respondents. 

It is difficult to estimate the response rate, because we did not have a definitive sampling frame 

of adolescents who actually lived in the targeted neighborhoods (even in public housing, housing 

authority records only include youths formally listed on leases, and not all of those are actually 

living in the residence at any given time). Although the total number of refusals in 1998 was only 

9.1% of the 1,526 actively recruited respondents, another 11.5% were never contacted, and 

20.4% agreed to participate but did not come to their survey appointment and were not available 

during our subsequent attempts to survey them. A conservative estimate of the cooperation rate 

in public housing neighborhoods is between 59% and 67% for 1998, and between 64% and 72% 

in nonpublic housing neighborhoods. 

Response rates are more difficult to calculate. We conducted an extensive analysis of one 

public housing neighborhood (Orange Grove). Based on Housing Authority lease information, 

adjusted for information we obtained from leaseholders, we estimate the 1998 response rate 

(combined for actively and passively recruited households) to be between .482 and .517. 

However, if we also consider delayed enrollment in the MYS (i.e., enrollment between 1999 and 

2004), we obtain a response rate of between 72.7% and 78.0% for eligible youths living in 

Orange Grove during the summer of 1998. Using the same logic for Alabama Village, we 

estimate a total response rate (current + delayed enrollment) of 72.3% for youths living in the 

neighborhood during the summer of 1998. Fortunately, there were no major drug busts in any 

neighborhood immediately after we conducted the survey, and by 1999 the cooperation rate had 

increased to approximately 88%, where it remained throughout the study. When we compare 

respondents from active recruitment households and passive recruitment households, we do not 

find statistically significant differences. 

Table 2 shows the initial size of each cohort, and the number of respondents from each 

cohort who contributed one, two, three, ..., twelve data points. Overall, 3,561 participants

(30.7%) contributed a single data point; 2,434 (21.0%) participants contributed two data points; 

1,731 (14.9%) contributed three data points; 885 (7.6%) participants contributed four waves of 

data; and 2,971 (25.7%) contributed five-or-more data points. 

Follow-up rates are higher than Table 2 might initially suggest, because during any given 

year the number of respondents who age out of the sample each year averages 11% (i.e., 100% of 

respondents age out over a nine-year period). Follow-up rates for the Year 1 cohort, discounting 

those who have aged out of the study and those who have not been verified, are .733 for Wave 2; 

.614 for Wave 3; .558 for Wave 4; .439 for Wave 5; .433 for wave 6; and .442 for Wave 7. Not 

surprisingly, the largest loss to follow-up occurs between Waves 1 and 2, with virtually no loss 

to follow-up between Waves 5 and 7. However, as the number of eligible prior respondents has 

increased each year, and as they have moved to new neighborhoods, the response rate has 

decreased. Non-respondents do not differ significantly from respondents. 

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Table B1 provides a detailed analysis of the first two waves of data, which will help clarify 

this. In 1998, 1,775 adolescents were interviewed, 1,673 of whom could be confirmed as 

residents of Mobile County. Fifty-eight (3.3%) aged out of the study, leaving a total of 1,615 

Cohort 1 respondents eligible to participate in wave 2. 1,249 were surveyed, for a response rate 

(and a Year 1-Year 2 follow-up rate) of .733. An analysis of those who attritted in 1999 proves 

interesting, however. We were able to confirm (based on Mobile County Public School System 

records) that 222 of respondents lost to follow-up were living at a different address than they 

provided us in 1998. Eleven of these apparently moved out of the county, and 211 were living at 

different addresses in Mobile County. We were also able to confirm from school records that 127 

additional non-respondents were living at their 1998 address; 15.7% of these were scheduled to 

be surveyed but did not participate; 29.1% refused; 14.2% were gone for the summer; and 40.9% 

were not contacted. We were unable to verify whether 17 additional non-respondents (who were 

never contacted) lived at their 1998 address or had moved. 

One additional set of statistics about attrition, provided in the recapture column, is revealing. 

Even though they dropped out of the study between 1998 and 1999, 34.2% dropped back into the 

study during subsequent years! Thus, the concept of loss to follow-up is at least partially a 

misnomer: a substantial number of the dropouts were not lost to follow-up, they were 

temporarily misplaced to follow-up, only to be found later. This occurred, albeit at different 

rates, for every category of attritters. This is consistent with earlier findings about response rates 

for current and delayed enrollees in the MYS, and it provides support for our assumption, to be 

developed in the Missing Data and Sample Representativeness section, that data in the study can 

be treated as missing at random (MAR). This is useful information, because it allows 

considerable flexibility in data analysis. 

Data Storage. Data were collected using scannable questionnaires; they have a cover page, 

on which identifiable information was written, and 24 (1998-2005) or 36 (2006-2011) data pages 

which have no identifiers other than questionnaire number (which can be traced back to the 

number on the cover sheet). Questionnaires have now all been shredded and recycled. Cover 

sheets were electronically scanned and are now stored on a secure University of Alabama 

network. Only one person has the password to the folder where these files are stored. The 

scanned MYS data are stored in two files, which can be linked by permanent identification 

number (PID). The data file has only de-identified data, including (a) PID; (b) neighborhood; (c) 

response booklet number for each year the respondent participated; (d) neighborhood where the 

respondent lived each year; (e) exact age of the respondent each year (i.e., the age of the 

respondent on the date that the survey was completed each year); and (f) responses to questions 

on the survey (across multiple years; i.e., a single row of data has all responses for a case for all 

of the years that the respondent participated in the MYS). Note that this file does not include 

name, birthdate, address, or any other identifiers. The names file contains (a) PID; (b) name of 

the respondent each year that he or she completed the survey; (c) address of the respondent each 

year that he or she completed the survey; (d) birthdate of the respondent; (e) date the survey was 

completed each year; and (f) booklet number each year that the respondent completed the 

survey. These files are stored on password-protected, whole-disk encrypted computers, making 

it extremely difficult for the data to be compromised if a computer is lost or stolen. The UA 

Office of Information Technology has agreed to conduct an annual review of our data security 

procedures. All analyses are done using the data file. 

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Current State of the Research. All data have been collected, so no new recruitment of 

participants of data collection will occur. The compiled data is being used to answer a 

fundamental research question: What are the challenges for youth growing up in poverty, and 

how do protective and risk factors affect the behaviors associated with those challenges? To 

effectively address this question, we can use all of the variables in the MYS, either as outcome 

measures, predictor measures, covariates, or ways to describe the sample. The analyses use a 

variety of statistical measures, mostly involving either linear mixed models or structural 

equation models. 

Connection to Other Research. In the consent form, we specified that the data that were 

collected may be used in conjunction with other data that we collect, and that we may re-contact 

the participant at some point in the future to conduct additional research based on answers he or 

she provides in response to the MYS questions. We have, in fact, conducted two federally 

funded studies in which we conducted additional interviews with samples of MYS participants 

(one involving decision making styles and how they affect substance use, the other involving 

how the interaction between genes and environment affects risk behavior, see Appendix A). 

Additionally, we have obtained school records, juvenile court records, and housing authority 

records for the county, and have been able to connect those records to MYS data. We have also 

obtained geocoded police reports for neighborhoods where MYS participants live. All of these 

additional sources of data have been reviewed and approved by the UA IRB. 

Table B1 

Detailed Analysis of Retention, Attrition, and Recapture for 1998 MYS Sample 

Disposition N Percent (source) Recapture Percent 

of N 

(a) 1998 sample 1,775 

(a.1) Not confirmed 102 .057 (a.1/a) 

(a.2) Aged out 58 .033 (a.2/a) 

(b) Eligible for the 1999 MYS 1,615 .910 (b/a) 

(b.1) Interviewed in 1999 1,249 .773 (b.1/b) 

(b.2) Not interviewed 366 .227 (b.2/b) 125 .342 

(b.2.1) Moved to new address outside 11 .030 (b.2.1/b.2) 2 .182 

Mobile County 

(b.2.2) Could not be located at 1998 211 .576 (b.2.2/b.2) 88 .417 

address 

(b.2.3) Remained at same address 127 .347 (b.2.3/b.2) 33 .260 

(b.2.3.1) Scheduled but not 20 .157 (b.2.3.1/b.2.3) 6 .300 

interviewed 

(b.2.3.2) Refused 37 .291 (b.2.3.2/b.2.3) 9 .243 

(b.2.3.3) Gone for the summer 18 .142 (b.2.3.3/b.2.3) 4 .222 

(b.2.3.4) No contact 52 .409 (b.2.3.4/b.2.3) 14 .269 

(b.2.4) No contact; unverified address 17 .046 (b.2.4/b.2) 2 .117 

Missing Data and Sample Representativeness 

In any community-based longitudinal study, missing data are an issue. Therefore, steps were 

taken to determine whether missing data in the MYS were informative (i.e., missingness predicts 

143 

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the value that would have been obtained if the data would have been collected), which leads to 

sampling bias and undermines results and conclusions. If missing data are not informative, they 

are either MAR or missing completely at random (MCAR); sampling bias is eliminated in both 

of these cases. In a similar way, we considered whether and how the representativeness of the 

sample (whether missing data due to people in the population not being sampled) is informative 

and contributes to sampling bias. 

In the MYS, the sampling frame was not well defined. In 1998, we received lists of public 

housing residences where eligible adolescents lived from the Mobile and Prichard Public 

Housing Authorities, and we randomly selected half of these residences to contact and select 

potential participants. In private residential neighborhoods, however, we did not know in which 

buildings eligible adolescents lived, so we randomly selected half of the residences in these 

neighborhoods. In addition to these targeted participants, we also encouraged anyone between 

the ages of 10 and 18 who lived in these neighborhoods (both public and private) to participate. 

Hence, the 1998 sampling frame was essentially all eligible adolescents who lived in the 13 

targeted neighborhoods. But because we did not have a definitive list of those individuals, the 

sampling frame was, strictly speaking, unknown and unknowable. 

We were able to estimate the sampling frame, however, based on MCPSS school records. We 

identified neighborhood boundaries, using ARCGIS, and assigned MYS participants to these 

neighborhoods based on geocoded addresses given on the MYS cover sheets. We then identified 

schools where ten-or-more MYS participants were enrolled, and we geocoded addresses of 

students enrolled in those schools; geocoded addresses that fell within the target MYS 

neighborhoods were retained, and students living at those addresses were assumed to constitute 

the MYS sampling frame—but with two important limitations. First, not all students were 

enrolled in the MCPSS. In particular, students could attend private or parochial schools, and they 

could be home schooled. However, we believe that this did not seriously limit our conclusions: 

we know definitively of only one student who was enrolled in a parochial school and one student 

who was home schooled in the MYS sample. There may well have been others, but we were able 

to match nearly 96% of MYS participants to MCPSS records, so the overall effect could not have 

been large. Second, and a larger issue, students were able to drop out of school at age 16 during 

most years of the MYS study. As a result, the sampling frame could not be estimated for 

adolescents older than 15. Given these limitations, we estimate that approximately 33% of 

eligible students younger than age 16 and living in the MYS target neighborhoods were 1998 

MYS participants. More complete information can be found in Bolland’s (2012) study of

representativeness and missing data in the MYS. 

MCPSS records not only provided names of students living in MYS target neighborhoods; 

they also provided race, age, gender, grade in school, free/reduced cost lunch status, Stanford 

Achievement Test verbal and math scores, and school violations. Thus, we were able to compare 

MYS participants versus MYS dropouts each year, and we were able to compare MYS enrollees 

with non-enrollees each year on these measures. The first set of comparisons provides 

information about potential sampling bias due to dropout, while the second provides information 

about potential sampling bias due to non-representativeness of the sample. 

Dropout. To assess whether missingness in the MYS dataset due to dropout was informative, 

we estimated a statistical model, using a generalized estimating equations (GEE) framework, in 

which we compared dropouts and non-dropouts each year in terms of information from their 

school records (Bolland, Tomek, & Bolland, 2017) for the first ten waves of data (1998-2007). 

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Specifically, if person i participated in the survey at both time t and time t+1, dropouti,t+1 = 0; if 

person i participated in the survey at time t but not at time t+1, dropouti,t+1 = 1; and if person i did 

not participate in the survey at time t, dropouti,t+1 is undefined and therefore missing. Results 

showed that dropout was statistically associated with race (p < .05; African American < White), 

but with a small-to-moderate effect size. Free lunch status was also statistically significant (p < 

.001; free < not free), but the effect size was small. Grade was associated with dropout (p < .001; 

lower grades < higher grades), but the effect size is miniscule. Stanford Achievement Test (SAT) 

reading test scores achieved statistical significance (p < .05; lower score < higher score), but the 

effect size was miniscule. Gender, SAT math scores, and school violations were all unassociated 

with dropout (all ps > .05). 

Representativeness. Here, we considered MYS enrollment, such that enrollmenti = 1 if 

person i ever participated in the MYS, and enrollmenti = 0 if i never participated in the MYS. To 

assess whether missingness in the MYS dataset due to representativeness was informative, we 

estimated a statistical model, using a generalized estimating equations (GEE) framework, in 

which we compared enrollees versus non-enrollees each year in terms of information from their 

school records (Bolland et al., 2017) for the first ten waves of data (1998-2007). Race is 

statistically associated with enrollment (p < .001; White < African American), with a small-tomoderate effect size. Free Lunch status (p < .001; not free < free) and grade (p < .01; lower grade 

< higher grade) are both associated with enrollment, but both effect sizes are miniscule. SAT 

reading scores (p < .01; higher score < lower score), SAT math scores (p < .05, higher score < 

lower score), and school violations (p < .01; fewer violations < more violations) are all 

associated with enrollment status, but again all of the effect sizes are miniscule. 

The general conclusion is that, while missing data do appear to be informative, the level of 

information conveyed is mostly very, very small. Moreover, in almost all cases, the direction of 

missingness is opposite of what we would expect. Typically we would expect that vulnerable, 

hard-to-reach populations are least likely to participate in studies (see Bolland et al., 2017). So, 

we might expect that African Americans would be less likely to participate in the MYS and to 

drop out of the study; that students receiving free lunches would be less likely to participate than 

those not receiving free lunches; that students scoring higher on standardized tests would be 

more likely to participate than those scoring lower; that students who have fewer school 

violations would be more likely to participate than those who have more violations. Yet, none of 

these conditions hold. Given that the goal of the MYS was to sample the most vulnerable 

adolescents in Mobile, reflected in our choice to target neighborhoods with the lowest income 

levels, we may have succeeded better than we expected, with the most vulnerable individuals in 

the most vulnerable neighborhoods enrolling in the study and not dropping out. Of course, the 

effect sizes are so small that bias is highly unlikely. But whatever bias may have occurred as a 

function of data missingness, it is likely to have contributed to, rather than detracted from, our 

overall intent in designing and conducting the MYS study. 

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Appendix C 

MYS Measures 

In this appendix, information about the constructs and scales used in the analyses, derived 

from Mobile Youth Survey (MYS) items, is presented. 

Construct: DEMOGRAPHICS 

Variable: Race 

Table C1 

Race Items on the MYS 

Item number Question Response options 

m24 Are you black/African American? 1 No 2 Yes 

m25 Are you white? 1 No 2 Yes 

m26 Are you Hispanic/Latino? 1 No 2 Yes 

m27 Are you mixed race and/or Creole? 1 No 2 Yes 

Construct: DEMOGRAPHICS 

Variable: Age 

Table C2 

Age Item on the MYS 

Item number Question Response options 

m1 How old are you now? 1 9 years old 

2 10 years old 

3 11 years old 

4 12 years old 

5 13 years old 

6 14 years old 

7 15 years old 

8 16 years old 

9 17 years old 

10 18 years old 

11 19 years old 

146 

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Construct: DEMOGRAPHICS 

Variable: Gender 

Table C3 

Gender Item on the MYS 

Item number Question Response options 

m2 Are you male or female (a boy or a 1 Male 2 Female 

girl)? (boy) (girl) 

Construct: EXPOSURE TO VIOLENCE4 

Scale: Witnessed violence 

Table C4 

Witnessing Violence Item on the MYS 

Item 

number Question Response options 

m167 During the past 3 months (90 days), did 

you see someone being cut, stabbed, or 

shot? 

1 No 2 Yes, just 

once 

3 Yes, more 

than once 

Construct: EXPOSURE TO VIOLENCE 

Scale: Violent victimization: Weapon brandished against you 

Table C5 

Violent Victimization (Weapon Brandishment) Item on the MYS 

Item 

number Question Response options 

m169 In the past 3 months (90 days), did 

someone pull a knife or a gun on you? 

1 No 2 Yes, just 

once 

3 Yes, more 

than once 

4 Short-term reliability for witnessing violence is .384, which is reasonable but not high; the short-term 

reliability for being shot at is .127 (ns). The behavior variables is that they are heavily skewed, so that even a 

single case of moving from no-to-yes or yes-to-no can heavily influence the correlation. Moreover, the other 

measures are mostly attitudes or beliefs, which wouldn’t be expected to change over five weeks, so any 

deviation from r = 1 is mostly measurement error. On the other hand, events can occur over a five-week 

period. Thus, we are not reporting short-term reliability for the exposure to violence measures. 

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Construct: EXPOSURE TO VIOLENCE 

Scale: Violent victimization: You were cut or shot at 

Table C6 

Violent Victimization (Cut or Shot at) Items on the MYS 

Item 

number Question Response options 

m171 

In the past year (12 months), did someone 

cut or stab you bad enough that you had to 

see a doctor? 

1 No 2 Yes, 

just 

once 

3 Yes, more 

than once 

m173 

In the past year (12 months), did someone 

shoot a gun at you? 

1 No 2 Yes, 

just 

once 

3 Yes, more 

than once 

Construct: EXPOSURE TO VIOLENCE 

Scale: Violent victimization: Brandishment, cut, or shot at 

This violent victimization variable is a combination of the two previous violent victimization 

variables. Initially, it would seem that if a person is cut or shot at, a gun or knife would also have 

been pulled on him or her. Results from the MYS are equivocal on this point. First, 2,111 (out of 

a total of 32,787; 6.44%) observations reported that the respondent had been cut or shot at but 

that a weapon had not been brandished against them. This would suggest that the error rate is 

small; however, only 4,004 observations reported that the respondent had been cut or shot at, and 

the error rate for these respondents is 52.72%, which suggests that the issue is not negligible. 

However, two factors help rationalize this error rate. First, the non-aligned time periods mean 

that a respondent may truly have had a weapon brandished against him or her and cut or shot at, 

but correctly reported no weapon brandishment (e.g., if the event happened six months prior to 

the survey date). We can partially estimate the impact of this misalignment, because the MYS 

also asks whether a knife or gun has ever been pulled against the respondent (which would 

include, but not be limited to, the nine month period between three months and 12 months). If we 

assume that all of these “ever” events happened during this nine-month period, the number of 

inconsistent cases drops to 1,189, for an error rate of 3.63% (as a function of all observations) 

and 29.7% (as a function of observations that reported being cut or shot at). 

Second, weapon brandishment implies a close encounter, as would absolutely be necessary if 

a respondent were cut or stabbed. But a gun could be shot from some distance, which would not 

necessarily be viewed as brandishment. In addition, a shot could be fired in the direction of a 

respondent without intent, as in a shot fired into a crowd or a stray shot fired at another person; 

again, neither would be accompanied by a report of brandishment. Despite this limitation, 

combining these two variables seems useful, in that it simplifies the analysis, and weapon use is 

a more extreme extension of weapon brandishment. 

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Construct: PSYCHOLOGICAL ADJUSTMENT 

Scale: Hopelessness 

Table C7 

Hopelessness Items on the MYS 

Item 

number 

Item Response options 

m84 All I see ahead of me are bad things, not good things. 1 Agree 2 Disagree 

m85 There’s no use in really trying to get something I want 

because I probably won’t get it.

1 Agree 2 Disagree 

m86 I might as well give up because I can’t make things 

better for myself. 

1 Agree 2 Disagree 

m87 I don’t have good luck now and there’s no reason to 

think I will when I get older. 

1 Agree 2 Disagree 

m88 I never get what I want, so it’s dumb to want 

anything. 

1 Agree 2 Disagree 

m89 I don’t expect to live a very long life. 1 Agree 2 Disagree 

Source 

Kazdin, A.E., French, N.H., Unis, A.S., Esveldt-Dawson, K., & Sherick, R.B. (1983). 

Hopelessness, depression, and suicidal intent among psychiatrically disturbed inpatient 

children. Journal of Consulting and Clinical Psychology, 51, 504-510. 

Note. M85-M89 are adapted from Kazdin, et al.; these five questions have the highest itemtotal correlations with the Kazdin’s Hopelessness Scale for Children. M89 was added to address 

beliefs about early mortality (see DuRant, et al., 1994). 

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Reliability 

Cronbach’s alpha = .773 across all 14 waves of data. Care should be exercised in interpreting 

this statistic because observations are not independent, and some respondents may have as many 

as 10 data points while may have a single data point. Table C9 shows additional information, again 

across all waves of data. Five-week test-retest reliability for a sample of 49 adolescent public 

housing residents in Huntsville, AL (1998) was high (r = .622). 

Table C8 

Reliability: Hopelessness Scale 

Item Item-total correlation α if item deleted 

m84 .503 .742 

m85 .477 .750 

m86 .552 .729 

m87 .566 .725 

m88 .545 .731 

m89 .461 .752 

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Construct: PSYCHOLOGICAL ADJUSTMENT 

Scale: Worry 

Table C9 

Worry Items on the MYS 

Item 

number 

Question Response options 

m12 How much do you worry about 

getting good grades? 

m71 How much do you worry about 

being pressured into doing 

something dangerous by your 

friends? 

m72 How much do you worry about 

not fitting in with other kids in 

the neighborhood or at school? 

m73 How much do you worry that 

your family has enough money 

to get by? 

m74 How much do you worry that 

you might not get a good job 

when you get older? 

m75 How much do you worry about 

getting along with people of 

other races? 

m194 How much do you worry about 

gangs in your neighborhood? 

m227 How much do you worry about 

whether you are ‘straight’ or

‘gay’?

m228 How much do you worry that 

you might get AIDS? 

Source 

1 I am not 

in 

school 

2 Not at 

all 

3 Some 4 Very 

much 

1 Not at 

all 

2 Some 3 Very 

much 

Some 3 Very 

much 

1 Not at 

all 

2 

Some 3 Very 

much 

1 Not at 

all 

2 

Some 3 Very 

much 

1 Not at 

all 

2 

Some 3 Very 

much 

1 Not at 

all 

2 

Some 3 Very 

much 

1 Not at 

all 

2 

Some 3 Very 

much 

1 Not at 

all 

2 

Some 3 Very 

much 

1 Not at 

all 

2 

Small, S. & Rodgers, K.B. (1995). Teen assessment project survey question bank. Madison, WI: 

Center for Action, University of Wisconsin. 

Note. Question format, but not question content, was taken from Small & Rogers (1995). 

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Reliability 

Cronbach’s alpha = .739 across all 14 waves of data. Care should be exercised in interpreting 

this statistic because observations are not independent, and some respondents may have as many 

as 10 data points while may have a single data point. Table C12 shows additional information, 

again across all waves of data. Five-week test-retest reliability for a sample of 49 adolescent 

public housing residents in Huntsville, AL (1998) was high (r = .591). 

Table C10 

Reliability: Worry Scale 

Item Item-total correlation α if item deleted 

m12 .282 .735 

m71 .491 .702 

m72 .442 .711 

m73 .496 .700 

m74 .493 .701 

m75 .502 .700 

m195 .341 .727 

m227 .368 .722 

m228 .322 .734 

152 

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Construct: PSYCHOLOGICAL ADJUSTMENT 

Scale: Self-Worth 

Table C11 

Self-Worth Items on the MYS 

Instructions: For each of the following questions, please indicate which of the two statements 

that are listed is most like you. 

Item number Response options 

m116 1 I am usually unhappy with myself. 

2 I am usually happy with myself. 

m117 1 I sometimes do things I know I shouldn’t do.

2 I hardly ever do things I know I shouldn’t do.

m118 1 I usually don’t like the way I behave.

2 I usually like the way I behave. 

m119 1 I like the kind of person I am. 

2 I don’t like the kind of person I am.

m120 1 I usually get into trouble because of the things I do. 

2 I usually don’t do things that get me into trouble.

m121 1 I usually make good decisions. 

2 I usually don’t make good decisions.

m122 1 I usually behave myself very well. 

2 I often find it hard to behave myself. 

m123 1 I am not happy with the way I do a lot of things. 

2 The way I do things is fine. 

m124 1 I don’t like the way I am leading my life.

2 I like the way I am leading my life. 

Source 

Harter, S. (1982). The perceived competence scale for children. Child Development, 53, 87-97. 

Note. Question format is different from that used by Harter (1982). 

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Reliability 

Cronbach’s alpha = .655 across all 14 waves of data. Care should be exercised in interpreting

this statistic because observations are not independent, and some respondents may have as many 

as 10 data points while may have a single data point. Table C13 shows additional information, 

again across all waves of data. Five-week test-retest reliability for a sample of 48 adolescent public 

housing residents in Huntsville, AL (1998) was acceptable (r = .468). 

Table C12 

Reliability: Self-Worth Scale 

Item Item-total correlation α if item deleted 

m116 .283 .639 

m117 .226 .656 

m118 .390 .614 

m119 .304 .638 

m120 .300 .638 

m121 .369 .620 

m122 .316 .632 

m123 .414 .609 

m124 .415 .610 

154 

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Construct: PSYCHOLOGICAL ADJUSTMENT 

Scale: Street Code 

Table C13 

Street Code Items on the MYS 

Item 

number 

Item Response options 

m187 It is not possible to avoid fights in my neighborhood. 1 Agree 2 Disagree 

m188 If you don’t carry a knife or gun in my neighborhood, 

something bad might happen to you. 

1 Agree 2 Disagree 

m189 Kids who are in a gang get respect from other kids in 

my neighborhood. 

1 Agree 2 Disagree 

m190 When I get mad, I usually don’t care who gets hurt. 1 Agree 2 Disagree 

m191 Carrying a weapon lets other kids know that they 

shouldn’t mess with you.

1 Agree 2 Disagree 

m192 If someone else starts a fight with me, I am going to 

finish it. 

1 Agree 2 Disagree 

m193 Hitting someone really knocks some sense into them. 1 Agree 2 Disagree 

m194 When you are in an argument, you should stand your 

ground to get what you want. 

1 Agree 2 Disagree 

Sources. 

Anderson, E. (1999). Code of the street: Decency, violence, and the moral life of the inner city. 

New York, NY: Norton. 

Bandura, A. (1973). Aggression: A social learning analysis. Englewood Cliffs, NJ: Prentice Hall. 

Note. The concept of street code was developed by Elijah Anderson. However, the questions 

we used to measure street code were developed by Albert Bandura. 

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Reliability 

Cronbach’s alpha = .735 across all 14 waves of data. Care should be exercised in interpreting 

this statistic because observations are not independent, and some respondents may have as many 

as 10 data points while may have a single data point. Table C15 shows additional information, 

again across all waves of data. Five-week test-retest reliability for a sample of 48 adolescent 

public housing residents in Huntsville, AL (1998) was good (r = .596). 

Table C14 

Reliability: Street Code Scale 

Item Item-total correlation α if item deleted 

m187 .305 .733 

m188 .375 .718 

m189 .354 .723 

m190 .394 .715 

m191 .471 .700 

m192 .574 .678 

m193 .591 .674 

m194 .368 .721 

156 

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Construct: PSYCHOLOGICAL ADJUSTMENT 

Scale: Traumatic Stress 

Table C15 

Traumatic Stress Items on the MYS 

Item 

number 

Item Response options 

m106 I have bad dreams about the bad things 

that have happened to a family member 

or friend. 

1 Almost 

never 

2 Sometimes 3 Very 

often 

m106 I have trouble sleeping at night when 

bad things happen to a family member 

or friend. 

1 Almost 

never 

2 Sometimes 3 Very 

often 

m108 I think I would feel better if I could talk 

to someone about the bad things that 

happen to a family member or friend. 

1 Almost 

never 

2 Sometimes 3 Very 

often 

m109 When bad things happen to a family 

member or friend, it feels like they are 

happening to me. 

1 Almost 

never 

2 Sometimes 3 Very 

often 

m110 I think about bad things that have 

happened to a family member or friend, 

even when I don’t want to.

1 Almost 

never 

2 Sometimes 3 Very 

often 

m111 After bad things happen to a family 

member or friend, I feel uncomfortable 

being with them because it reminds me 

of the bad things that happened. 

1 Almost 

never 

2 Sometimes 3 Very 

often 

m113 I worry that bad things might happen to 

a family member or friend. 

1 Almost 

never 

2 Sometimes 3 Very 

often 

Source 

Developed for the MYPS 

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Reliability 

Cronbach’s alpha = .774 across all 14 waves of data. Care should be exercised in interpreting 

this statistic because observations are not independent, and some respondents may have as many 

as 10 data points while may have a single data point. Table C17 shows additional information, 

again across all waves of data. No five-week test-retest reliability is available for traumatic 

stress. 

Table C16 

Reliability: Traumatic Stress Scale 

Item Item-total correlation α if item deleted 

m106 .510 .743 

m107 .545 .735 

m108 .461 .753 

m109 .511 .742 

m110 .541 .736 

m111 .448 .755 

m113 .451 .754 

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Construct: SOCIAL SUPPORT 

Scale: Maternal Warmth 

Table C17 

Maternal Warmth Items on the MYS 

Instructions: Please tell us about [the] person who is most like a mother to you. 

Item 

number 

Item Response options 

m30 I can usually count on her 1 I don’t have 2 Agree 

to help me out if I have anyone who is like 

some kind of problem. a mother to me 

3 Disagree 

m31 She usually keeps pushing 1 I don’t have 2 Agree 

me to do my best in anyone who is like 

whatever I do. a mother to me 

3 Disagree 

m32 We do fun things together. 1 I don’t have 2 Agree 

anyone who is like 

a mother to me 

3 Disagree 

m33 She usually helps me if 1 I don’t have 2 Agree 

there is something I don’t anyone who is like 

understand. a mother to me 

3 Disagree 

m34 When she wants me to do 1 I don’t have 2 Agree 

something, she usually anyone who is like 

explains the reasons why. a mother to me 

3 Disagree 

m35 She spends time just 1 I don’t have 2 Agree 

talking with me. anyone who is like 

a mother to me 

3 Disagree 

Source 

Lamborn, S.D., Mounts, N.S., Steinberg, L., & Dornbusch, S. (1991). Patterns of competence 

and adjustment among adolescents from authoritative, authoritarian, indulgent, and neglectful 

families. Child Development, 62, 1049-1065. 

159 

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Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
 

 

 

Reliability 

Cronbach’s alpha = .704 across all 14 waves of data. Care should be exercised in interpreting

this statistic because observations are not independent, and some respondents may have as many 

as 10 data points while may have a single data point. Table C19 shows additional information, 

again across all waves of data. Five-week test-retest reliability for a sample of 48 adolescent public 

housing residents in Huntsville, AL (1998) was modest (r = .297). 

Table C18 

Reliability: Maternal Warmth Scale 

Item Item-total correlation α if item deleted 

m30 .424 .669 

m31 .387 .679 

m32 .461 .666 

m33 .476 .655 

m34 .424 .671 

m35 .463 .657 

160 

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Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
 

 

Construct: SOCIAL SUPPORT 

Scale: Neighborhood Connectedness 

Table C19 

Neighborhood Connectedness Items on the MYS 

Item 

number 

Item Response options 

Positive 

m249 I feel I am an important part of my neighborhood. 1 Agree 2 Disagree 

m250 If I moved away from my neighborhood, I would be 

sorry to leave. 

1 Agree 2 Disagree 

m252 I have friends in my neighborhood who know they can 

depend on me. 

1 Agree 2 Disagree 

m254 There are people in my neighborhood, other than my 

family, who really care about me. 

1 Agree 2 Disagree 

m255 I have friends in my neighborhood I can depend on. 1 Agree 2 Disagree 

m259 If I am upset about a personal problem, there are 

people in my neighborhood I can turn to. 

1 Agree 2 Disagree 

Negative 

m251 Very few of my neighbors know me. 1 Agree 2 Disagree 

m253 I do not like living in my neighborhood. 1 Agree 2 Disagree 

m256 If you don’t look out for yourself in my neighborhood, 

no one else will. 

1 Agree 2 Disagree 

m257 No one in my neighborhood takes any interest in what 

their neighbors are doing. 

1 Agree 2 Disagree 

m258 It is hard to make good friends in my neighborhood. 1 Agree 2 Disagree 

Sources 

Glynn, T.J. (1981). Psychological sense of community: measurement and application. Human 

Resources, 34, 789-818. 

Perkins, D.D., Florin, P., Rich, R.C., Wandersman, A., & Chavis, D.M. (1990). Participation and 

the social and physical environment of residential blocks: Crime and community context. 

American Journal of Community Psychology, 18, 83-115. 

Note. These questions were originally developed to address sense of community. Because 

some of the questions were worded positively and some were worded negatively, we chose to 

create two neighborhood connected scales, one consisting of positively worded questions 

(Positive) and one consisting of negatively worded questions (Negative). 

161 

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necessarily reflect the official position or policies of the U.S. Department of Justice.
Reliability 

For Positive Neighborhood Connectedness, Cronbach’s alpha = .734 across all 14 waves of 

data; for Negative Neighborhood Connectedness, Cronbach’s alpha = .608 across all 14 waves of

data. Care should be exercised in interpreting this statistic because observations are not 

independent, and some respondents may have as many as 10 data points while may have a single 

data point. Table C21 shows additional information, again across all waves of data. Five-week 

test-retest reliability for a sample of 48 adolescent public housing residents in Huntsville, AL 

(1998) was good (r = .474 for Positive; r = .427 for Negative). 

Table C20 

Reliability: Neighborhood Connectedness Scale 

Positive 

Item Item-total correlation α if item deleted 

m249 .411 .714 

m250 .396 .720 

m252 .539 .678 

m254 .487 .693 

m255 .535 .678 

m259 .462 .700 

Negative 

Item Item-total correlation α if item deleted 

m251 .320 .577 

m253 .369 .551 

m256 .364 .553 

m257 .350 .561 

m258 .413 .528 

162 

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necessarily reflect the official position or policies of the U.S. Department of Justice.
 

 

Construct: FAMILY CONTROL 

Scale: Curfew 

Table C21 

Curfew Items on the MYS 

Item number Question Response options 

m55 Are you allowed to stay out as late as you want 

on school nights? 

1 No 2 Yes 

m56 Are you allowed to stay out after dark on 

school nights? 

1 No 2 Yes 

m57 Are you allowed to stay out as late as you want 

on weekend nights? 

1 No 2 Yes 

m58 Are you allowed to stay out after dark on 

weekend nights? 

1 No 2 Yes 

Source 

Lamborn, S.D., Mounts, N.S., Steinberg, L., & Dornbusch, S. (1991). Patterns of competence 

and adjustment among adolescents from authoritative, authoritarian, indulgent, and neglectful 

families. Child Development, 62, 1049-1065. 

Reliability 

Cronbach’s alpha = .699 across all 14 waves of data. Care should be exercised in interpreting 

this statistic because observations are not independent, and some respondents may have as many 

as 10 data points while may have a single data point. Table C23 shows additional information, 

again across all waves of data. Five-week test-retest reliability for a sample of 47 adolescent 

public housing residents in Huntsville, AL (1998) was good (r = .513). 

Table C22 

Reliability: Curfew Scale 

Item Item-total correlation α if item deleted 

m55 .394 .686 

m56 .504 .623 

m57 .499 .627 

m58 .543 .597 

163 

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Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
Construct: FAMILY CONTROL 

Scale: Parental Monitoring 

Table C23 

Parental Monitoring Items on the MYS 

Item Question Response options 

number 

m59 Does your mother or father 

know who you hang out 

with? 

1 No 2 Yes 

m60 Does your mother or father 

know exactly where you are 

most afternoons (after 

school) and during the day on 

weekends and during the 

summer? 

1 No 2 Yes 

m61 How much does your mother 

or father really know about 

what you do most afternoons 

(after school) and during the 

day on weekends and during 

the summer? 

1 They 

don’t

know 

2 They 

know 

a little 

3 They 

know 

a lot 

m62 How much does your mother 

or father really know about 

where you go at night? 

1 I don’t

go out 

at 

night 

2 They 

don’t

know 

3 They 

know 

a little 

m63 Does your mother or father 

try to find out how you spend 

your time? 

1 They 

don’t

try 

2 They 

try a 

little 

3 They 

try a 

lot 

m64 How much does your mother 

or father really know about 

how you spend your time? 

1 They 

don’t

know 

2 The 

know 

a little 

3 They 

know 

a lot 

4 They 

know 

a lot 

Source 

Lamborn, S.D., Mounts, N.S., Steinberg, L., & Dornbusch, S. (1991). Patterns of competence 

and adjustment among adolescents from authoritative, authoritarian, indulgent, and neglectful 

families. Child Development, 62, 1049-1065. 

164 

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Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
 

 

Reliability 

Cronbach’s alpha = .636 across all 14 waves of data. Care should be exercised in interpreting 

this statistic because observations are not independent, and some respondents may have as many 

as 10 data points while may have a single data point. Table C25 shows additional information, 

again across all waves of data. Five-week test-retest reliability for a sample of 48 adolescent 

public housing residents in Huntsville, AL (1998) was very good (r = .624). 

Table C24 

Reliability: Parental Monitoring Scale 

Item Item-total correlation α if item deleted 

m59 .333 .619 

m60 .399 .601 

m61 .518 .540 

m62 .253 .709 

m63 .412 .571 

m64 .533 .519 

Table C25 

Ancillary MYS Questions Necessary to Create the Parental Monitoring Scale 

Item Question Response options 

m29 How often do you live 1 

with the person who is 

most like a mother to you? 

I don’t have 

any-one who is 

like a mother to 

me 

2 All of 

the 

time 

3 Most 

of the 

time 

4 Some 

of the 

time 

5 None 

of the 

time 

m37 How often do you live 

with the person who is 

most like a father to you? 

1 I don’t have 

any-one who is 

like a mother to 

me 

2 All of 

the 

time 

3 Most 

of the 

time 

4 Some 

of the 

time 

5 None 

of the 

time 

165 

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Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix D 

Mobile County Public School System Measures 

In 2011, a cooperative agreement was established between The University of Alabama and 

the Mobile County Public School System (MCPSS) for the purposes of research collaboration 

related to the improvement of achievement of students in the MCPSS. 

Scale Creation 

We used a single measure of academic outcome for this study: normative progress through 

the educational system. The MCPSS specifies age of kindergarten enrollment as five years old 

on or before September 1. Based on this, we determined that normative progress occurred if the 

following conditions held (see Table D1): 

Table D1 

Normative Academic Grade Progress 

Normative School Grade Progress 

Kindergarten ≥ 5 years old and < 6 years old on September 1 

1st Grade ≥ 6 years old and < 7 years old on September 1 

2nd Grade ≥ 7 years old and < 8 years old on September 1 

3rd Grade ≥ 8 years old and < 9 years old on September 1 

4th Grade ≥ 9 years old and < 10 years old on September 1 

5th Grade ≥ 10 years old and < 11 years old on September 1 

6th Grade ≥ 11 years old and < 12 years old on September 1 

7th Grade ≥ 12 years old and < 13 years old on September 1 

8th Grade ≥ 13 years old and < 14 years old on September 1 

9th Grade ≥ 14 years old and < 15 years old on September 1 

10th Grade ≥ 15 years old and < 16 years old on September 1 

11th Grade ≥ 16 years old and < 17 years old on September 1 

12th Grade ≥ 17 years old and < 18 years old on September 1 

From this, we developed an ordinal scale of normative school grade progress, assigned to 

each study participant during each of the 14 study years, such that normative progress > one year 

behind normative progress > two years behind normative progress, etc. However, three 

conditions complicate this scale: students who have graduated and students who have dropped 

out. We addressed these issues as follows. 

1. A student who has graduated is specified as having achieved normative progress each 

year post graduation. 

2. A student who has dropped out is specified as having achieved the lowest outcome rank 

for each year post dropout, unless the student re-enrolled in school. 

166 

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necessarily reflect the official position or policies of the U.S. Department of Justice.
 

Thus, we obtained the following six-point ordinal scale of academic progress (EDUC) each 

year (Table D2). 

Table D2 

Normative Academic Progress Scale 

0 Dropout 

1 4+ years behind normative advancement 

2 3 years behind normative advancement 

3 2 years behind normative advancement 

4 1 year behind normative advancement 

5 Normative advancement or HS graduate 

Several steps are required to develop this scale. 

Determine Date of Birth 

We were able to determine date of birth from several possible sources. 

1. Each year, the MYS participant provided his or her date of birth on the Mobile Youth 

Survey (MYS) cover sheet. 

2. School records provided date of birth each year of school enrollment. 

3. Juvenile Court records provided date of birth for each study participant with court 

contact. 

4. Housing Authority records provided date of birth for all study participants residing in 

public housing. 

5. Police Department records provided date of birth for all study participants enrolled in its 

Family Intervention Team program. 

In addition to these data sources, we were able to use two public records databases to provide 

additional confirmation when required. 

6. The Intelius database provided date of birth for some study participants we were able to 

match (birthdays were not all people in the Intelius database (we attempted to match 

5,909 participants; we were able to definitively match 59.1% with a high degree of 

certainty, of which Intelius listed date of birth for 40.9%. 

7. The LexisNexis database also provided date of birth for some study participants we 

were able to match (birthdays were not all people in the Intelius database). We 

attempted to match 12,368 participants; we were able to definitively match 78.7% with 

a high degree of certainty. This was aided in the LexisNexis database by the fact that 

one of the search fields was social security number, which we could match to school 

records; however, LexisNexis does not include in its database anyone who is under 18 

years of age, which excludes approximately 3% of the names that we searched for. Of 

those cases that were matched, LexisNexis listed date of birth for 91.79%; 

unfortunately, however, date of birth in the LexisNexis database only includes month 

and year. 

A total 751 MYS participants (6.06%) could not be confirmed; that is, they showed up in 

none of the other previously listed databases listed and they participated in the MYS only one 

167 

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time. This typically means that they used a bogus name (and received consent under that name) 

or registered under a legitimate and registered name but wrote a different name on their MYS 

cover sheet. They were excluded from all analyses. 

The mean number of dates of birth recorded from the previously listed sources for the 

remaining cases equals 20.16, with 93.08% having five-or-more recorded dates of birth. Of 

these, 93.1% were at least 85% consistent, and 88.1% were at least 90% consistent. Given all the 

ways that dates of birth can be erroneously recorded (e.g., numbers transposed; December 10 

being recorded as 10/10; MYS participants misrepresenting their age in order to fall within the 

age limits; poor memory; distraction; confusion, such as writing the current date instead of the 

birth date), this consistency rate seems reasonable. In cases of inconsistency, we used the date 

specified by the most sources, or as an alternative, the most consistent date. 

Determine Educational Status Each Year 

Each year because 1998-1999, the MCPSS has maintained a relational database that was 

changed every time the student’s status changed (e.g., change of school, change of address, 

change of homeroom). Between 1998-1999 and 2009-2010, this used the DAISI system, 

developed for the MCPSS; since 2009-2010, this has used the I-NOW system, developed for the 

State of Alabama. The relational database(s) yielded several flat files, which were used to 

construct EDUC; in these flat files, every change or update for a student is reflected in a new 

record for that student; thus, all of the situations for a student during a given year are maintained. 

All changes and updates were made at the school level rather than at the central school district 

administration level. 

The primary file used for this is the STU file, which lists demographic information (gender, 

address, race, date of birth) and school information (school, grade5

, special education status, 

telephone number, school bus number, etc.); the file also contains the date for each record, 

reflecting the date on which a change was made. From this information aggregated across school 

years, it should be possible to discern grade advancement or retention. Several factors introduce 

noise into these data, however. 

First, the MCPSS contracts with a number of alternative schools to provide educational 

services for its students, each of which also provides the same information as regular schools. 

Examples of these alternative schools are the Continuous Learning Center, the Phoenix Program, 

and the Drop Back In Academy. Unfortunately, these alternative schools do not all have rigorous 

standards for grade assignment, so a student may move from 8th grade in a MCPSS school to 

11th grade in an alternative school during the same school year. Additionally, a student may 

spend only a couple of weeks in an alternative school, for example because of an out-of-school 

suspension. For students who remained in an alternative school over an extended period of time, 

grade progression is often inconsistent as well. For example, a student may be listed in 10th and 

12th grade during the same year, then move back to 10th grade the following year. For alternative 

schools, the goal may be more to keep the student engaged in academic activities rather than to 

expose him or her to specific grade-level content. Because of all of this, grade-level information 

from alternative is noisy, and we ignored it when it was inconsistent with other data and did not 

fit grade progression patterns. 

5 During the 2010-2011 and the 2011-2012 school years, when the I-NOW database was in use, school grade was 

available only in the StudentAcadSession file. 

168 

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Second, inconsistent school grades for a given student within a particular year were partially 

a function of the date of any entry into the database. Students enrolled each year in early August, 

assigned to a grade at that time. In a number of cases, they were reassigned to a different grade in 

September. This makes sense, and could represent a determination that the work associated with 

the assigned grade was either too difficult or too easy for the student or could represent 

enrollment misinformation. In some cases, particularly for high-school students, grade was 

reassigned at mid-year, usually as an advancement; this probably reflects the accumulation of 

sufficient credits during the previous semester to warrant a grade-level advancement. The biggest 

challenge occurred when a grade-level advancement occurred after the academic year had ended, 

for example in May, June, or July. This likely either reflected an anticipated grade-level 

advancement for the following year or accumulation of credits during summer school. In some 

cases, however, it might have represented a delay in paperwork being filed for a change that took 

place earlier in the school year. 

We resolved these sources of inconsistency as follows: 

1. We ignored any changes made after June 1 of the academic year (e.g., if a change in 

grade was made on June 15, 1999, no change was made in the academic progression 

variable for the 1998-1999 academic school year. 

2. When records allowed annual grade progression (i.e. grade g at time t, followed by g+1 

at t+1, we selected that progression, even if there were alternative grades also specified 

at t and/or t+1. We followed that logic for sequences of three, four, five, etc. years. 

3. In three year sequences, where g = x at t, g = x and g = x+1 at t+1, and g = x+1 at t+2, it 

can be concluded that a grade was repeated; but we are not sure which grade was 

repeated. As a convention, we specified a normative grade progression 

between t and t+1, and a grade retention between t+1 and t+2, unless additional 

information was available that resolved the inconsistency in a different way. For 

example, if a middle school grade was specified in conjunction with an elementary 

school, we assumed a data entry error and that the grade associated with the specified 

school was correct. 

4. When we found inconsistent grades specified during a given school year, and grades 

were reported by both typical schools and alternative schools, we assumed that the 

regular school grade was correct. 

Determine Graduation and Dropout Status for Students 

Unfortunately, graduation was not specified in the MCPSS school records we obtained, and 

although dropout status was specified in the SWD file, this information was not always reliable. 

Thus, we had to make a number of arbitrary decisions concerning both graduation and drop out. 

We were helped in these decisions by a withdrawal date specified in the STU files. 

1. If (a) the student was listed in 12th grade during any year t; (b) there was no subsequent 

record of the student in the DAISI or I-NOW databases in years t+1, t+2, etc.; (c) there 

was no withdrawal date listed for the student during year t; and (d) there was no record 

of dropout for the student in the SWD file during year t, the student was assumed to 

have graduated in year t and EDUC was coded as 5 in year t and each subsequent year. 

2. If (a) the student was listed in the SWD file as having dropped out during year t and (b) 

there was no subsequent record of the student in the DAISI or I-NOW databases in 

169 

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years t+1, t+2, etc., the student was assumed to have dropped out in year t and EDUC 

was coded as 0 in year t and each subsequent year. 

3. The legal age for dropping out in Alabama was 16 during all but the last year of the 

study; however, under special circumstances the student was allowed to drop out earlier. 

4. If (a) the student was aged 14.4 years at the beginning of year t and the SWD file listed 

the student as having dropped out during year t, the student was assumed to have 

dropped out in year t and EDUC was coded as 0 in year t and each subsequent year. 

5. If (a) the student was aged 14.4 years at the beginning of year t and (b) there was no 

subsequent record of the student in the DAISI or I-NOW databases in years t+1, t+2, 

etc.; and (c) the student participated in the MYS in subsequent year(s), the student was 

assumed to have dropped out in year t and EDUC was coded as 0 in year t and each 

subsequent year. 

In addition to these conditions, 

6. If (a) the student was assumed to have dropped out in year t but reappears in the MCPSS 

records in some subsequent year t+k, EDUC was coded as 0 in year t and each 

year t+1, t+2, ..., t+k-1, but then EDUC was coded using the normative grade level 

criterion in year t+k and each subsequent year the student appears in the MCPSS 

records. This rule has two exceptions. First, if the student re-enrolls in either the Drop 

Back In Academy or the Evening Options Program, both of which focus on over-age 

youth and are oriented toward GEDs, they remain classified as dropouts. Second, if the 

student re-enrolls and subsequently graduates in year t (according to rule 1), they are 

coded as 5 in year t and subsequent years. These two exceptions are unlikely to have 

any impact on study participants, because they would typically have aged out of the 

MYS by the time the exception occurred. 

7. If (a) the student is younger than 14.4 years old at the beginning of year t; (b) withdraws 

during year t; and (c) is not enrolled in the MCPSS during year t+1, the student is 

assumed to have moved out of the MCPSS and EDUC was coded as missing 

during t and t+1. If the student fails to re-enroll subsequently in the MCPSS, all 

subsequent years were coded as missing. In this case, the student would be unlikely to 

participate in the MYS during the absent years, so this decision has little real impact. 

However, a student may withdraw from the school district to be home schooled or 

attend a private school, in which case he or she may have participated in the MYS. 

8. If the student was younger than 5 on September 1 of year t, EDUC was coded as 

missing for year t. This should have no impact, because youth did not become eligible to 

participate in the MYS until they were 9.75 years of age. 

Finally, a student may have dropped out of school before 1998, when electronic records 

became available, and therefore never show up in the records we obtained. If this 

occurred, EDUC was coded as missing even though its true value would be 0. 

170 

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Appendix E 

Juvenile Court Measures 

In 2010, a cooperative agreement was established between The University of Alabama and 

the Mobile County Juvenile Court (MCJC) the purposes of research to benefit the client 

population served by the MCJC. 

Scale Creation 

In the state of Alabama, the Juvenile Court has jurisdiction over offenses alleged to have 

been committed prior to an individual’s 18th birthday; at 18, the individual is charged in adult 

court. All states have transfer laws that allow younger offenders to be prosecuted as adults for 

serious offenses. In the state of Alabama, there is also a “once and adult, always an adult” law, 

where once an individual has been prosecuted as an adult, their “adult status” remains even for 

lesser offenses. Because the offenses of interest for this project were identified as “crimes against 

a person,” often more serious in nature, it was decided to exclude those who were older than age 

17 for analysis. 

The initial MCJC file included information about court involvement for juveniles between 

1999 and 2012 and was matched to participants in the Mobile Youth Survey (MYS) using 

procedures discussed previously. Cases that were not assigned to an MYS participant were 

removed from the dataset for analysis. 

Next, cases were assigned a referral wave based on the date in which the MYS was taken and 

the date of referral (Table E1). If the date of referral was between 0 and 365 days after the MYS 

administration, the referral wave was identified as the same wave. For example, if the MYS was 

taken on June 15, 1998 (wave 1) and a referral was made on March 1, 1999, the referral wave 

was also wave 1. Thus, MYS administration always preceded Juvenile Court referral. The court 

referral case was matched to the wave corresponding to the fewest positive days between MYS 

date taken and court referral date without going over 365 days. Two examples follow. For 

example, Participant A completed the MYS on August 13, 1998 and had contact with the court 

system on January 8, 1999 with a difference of 148 days between MYS survey and court contact. 

Participant A is identified as wave 1 with respect to court referral wave. 

Table E1 

Court Referral Date, MYS Date Wave Correspondence 

MYS Year MYS Wave Court Referral Wave 

1998 1 1 

1999 2 2 

2000 3 3 

2001 4 4 

. 

. 

. 

2011 14 14 

Cases were coded with respect to the type of referral reason and the amended to reason. In 

96.9% of the cases, the referral reason and the amended to reason were exactly consistent. In 

1.2% of the cases, there was no amended to reason identified, leaving an additional 1.9% cases 

where the referral reason and the amended to reason were not consistent. For 98% of those cases, 

171 

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the referral reason and amended to reason fell within the same category of offense. For 0.8% 

cases, the referral reason and amended to reason did not fall into the same category of offense. 

When the referral reason and amended to reason fell into different categories of offense, the 

amended to reason category was used. Largely, the cases were coded using guidance from the 

National Center for Juvenile Justice, specifically for the state of Alabama. Identified categories 

of court offenses include (a) crimes against a person, (b) property crimes, (c) public order 

offenses, (d) traffic offenses, (e) drugs and alcohol offenses, (f) procedural offenses, and (g) 

status offenses (e.g., Child in Need of Supervision (CHINS) Beyond Control) offenses (Table 

E2). CHINS offenses involving alcohol were coded as drugs/alcohol rather than status offenses. 

Table E2 

Categories of Offense 

Category of offense Example 

Crimes against a person Assault, murder, kidnapping, pistol carrying without permit, 

harassment, sexual abuse 

Property Arson, burglary, criminal mischief, robbery, theft of property 

Public order Attempt to elude law enforcement officer, disorderly conduct, 

hindering prosecution, loitering, violation of noise ordinance 

Traffic Improper turn, driving without license, speeding, reckless 

driving 

Drugs/alcohol Minor in possession of alcohol, distribution of controlled 

substance, marijuana possession 

Status CHINS beyond control, resisting arrest, truancy 

Procedural Probation revocation 

Removing Cases 

Cases were removed from the dataset for several reasons: 

1. The individual identified was over 20 years old and the category of offense was reserved 

for adults (e.g., violation of parent responsibility). 

2. The individual identified was under 8 years old and the category of offense was reserved 

for adults (e.g., violation of parent responsibility). 

3. The category of offense was an adult violation (e.g., violation of parent responsibility). 

4. The individual referred was an adult (i.e., 18 years of age or older). 

5. The individual referred was under 8 years old. 

6. The case was duplicated (i.e., based on PID, offense date, category of offense). 

a. In cases that were duplicated, if there was one case where there were no priors and 

one case where there were priors, the case with “priors” was removed.

b. In cases that were duplicated, when the outcome was different, the more severe 

outcome was retained. 

c. In cases that were duplicated, if one was petitioned and the other not, the one that 

was petitioned was retained. 

d. In cases that were duplicated, the one with the latest (most recent) court action date 

was retained. 

7. There was a grade/age discrepancy or category of offense could not be categorized (e.g., 

a 13 year old was identified as in 12th grade). 

8. The case could not be matched to the MYS wave. 

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Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
 

 

Once all exclusion criteria were applied, two variables were created for each category of 

offense. The first was a count of the number of referrals per referral wave for the category of 

offense. The second was a dichotomous variable (0 = no, 1 = yes) indicating whether a referral 

was made during each wave for the category of offense. For this project, only the Crimes Against 

a Person category of offense was used for analysis. 

A variable to measure court outcome severity was also created, taking into account all 

categories of offense within a wave for each participant. Largely, the National Center for 

Juvenile Justice and the Office for Juvenile Justice and Delinquency Prevention guided the 

coding of cases with respect to severity. The disposition description and disposition code 

variables were used to classify court outcome severity (Table E3). While there are additional 

variables provided by the juvenile court, the two disposition variables were the ones where the 

most information was provided with respect to severity. When a disposition description was 

missing, steps were taken to fill in the missing information from other variables (e.g., manner of 

handling6

, complaint action) when available. When “petitioned” was indicated in the manner of 

handling variable, but no other information was provided, the court outcome severity variable 

was classified as missing. The petitioned category also includes other, but less frequent, severe 

outcomes including transfer to criminal court, supervision or service provided, and judicial 

waiver. 

Table E3 

Court Outcome Severity 

Outcome Code 

No offense within wave 0 

Released without transfer or referral 1 

Nolle Prosse, dismissed with conditions 2 

Lecture and release 3 

Informal adjustment 4 

Released with transfer or referral 5 

Probation supervision or fined 6 

Residential placement 7 

6 The Manner of Handling is “a general classification of case processing within the court system” (OJJDP, 

2019). At this stage, cases can be petitioned (i.e., formally handled), or non-petitioned. It is individuals who 

are authorized by the court (e.g., judges, probation officers, other officers of the court) who determine whether 

a case should or should not be petitioned. 

173 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.
Appendix G 

Study Personnel Biographies 

Anneliese Bolland. Dr. A. Bolland is an associate research scientist at The University of 

Alabama in the Institute for Communication and Information Research within the College of 

Communication and Information Sciences. She earned her doctorate in educational research at 

The University of Alabama. Her research agenda includes studies of adolescents who grow up in 

neighborhoods characterized by economic disadvantage, including risk and protective factors for 

these adolescents. She has published manuscripts in the areas of school factors (e.g., 

connectedness, giftedness, alcohol initiation), family factors, psychological well-being, and 

methodology for community-based longitudinal research. She is also an evaluator. 

John Bolland. Dr. J. Bolland is a Professor Emeritus at the University of Alabama (UA). He 

retired in 2012 as a Professor and Research Chairholder in the College of Human Environmental 

Sciences at UA; prior to that, he held positions as Associate Professor in the School of Public 

Health at the University of Alabama at Birmingham, Senior Research Scientist in the College of 

Arts and Sciences at UA, and Assistant Professor in the Department of Political Science at the 

University of Kansas. During the past 30 years, his research has focused on poverty and how it 

affects risk for negative health and social outcomes. In 1998, he conceived and began directing 

the Mobile Youth Survey (which has morphed into the Mobile Youth and Poverty Study); active 

data collection for this study continued over a 14 year period. He has received funding for this 

research from the National Institute for Child Health and Human Development, the National 

Institute for Drug Abuse, the Centers for Disease Control and Prevention, and the Substance 

Abuse and Mental Health Services Administration. 

Masha Ivanova. Dr. Ivanova is Assistant Professor of Psychiatry and Psychology at the 

University of Vermont (UVM) Department of Psychiatry. She received her Ph.D. in Clinical 

Psychology at the University at Albany, State University of New York and completed a postdoctoral fellowship in developmental psychopathology at the UVM Research Center for 

Children, Youth, and Families. Dr. Ivanova has authored and co-authored over 60 publications 

and received funding from the National Institutes of Health, Health Resources and Services 

Administration, and several foundations. Dr. Ivanova’s research aims to advance the 

understanding of proximal (e.g., family) and distal (e.g., culture) environmental influences on 

child psychopathology and wellness. She is also a licensed Clinical Psychologist specializing in 

evidence-based approaches to enhancing the stability of the family environment for children 

enduring adversity. 

Richard Spano. Dr. Spano is a Research Assistant Professor in the Buehler Center for Health 

Policy and Economics at Northwestern University. He received his Ph.D. in Criminal Justice 

from the University at Albany, State University of New York. His research interest include 

firearm related injury, child abuse and neglect, and violence as a public health threat. More 

specifically, his recent research has examined the impact of early age of onset of gun carrying 

(for at-risk youth age 9-11) on violent behavior and gang membership. 

174 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. Opinions or points of view expressed are those of the author(s) and do not 

necessarily reflect the official position or policies of the U.S. Department of Justice.