Understanding the Profiles of Youth Entering Juvenile ......informative regarding their outcomes;...

Understanding the Profiles of

Youth Entering Juvenile Probation

Jennifer Axelrod, Robert Goerge, Nick Mader, Kim Foley, and

Kaela Byers

August 2017

Chapin Hall at the University of Chicago 2 Axelrod et al. | Profiles of Youth

Axelrod, J., Goerge, R., Mader, N., Foley, K., & Byers, K. (2017). Understanding the profiles of youth

entering juvenile probation. Chicago: Chapin Hall at the University of Chicago.

© 2017 Chapin Hall at the University of Chicago

1313 East 60th Street

Chicago, IL 60637

ISSN: 1097-3125

The opinions expressed are solely those of Chapin Hall and do not necessarily reflect the official position of

any of its partners. Points of view or opinions contained within this document are those of the authors and

do not necessarily represent the official position or policies of the Cook County Probation and Court

Services Department or the Chicago Police Department. Data were provided by and belong to the Cook

County Juvenile Probation and Court Services Department and Chicago Police Department as well as the

agencies referenced herein. Any further use of this data must be approved by Cook County Probation and

Court Services, Chicago Police Department as well as the respective agencies named.


Acknowledgments

The authors would like to acknowledge the generous support of this project by Get In Chicago. We also

would like to express our appreciation to Avik Das and Melissa Parise for their partnership and for the

support of their team at Cook County Juvenile Probation and Court Services who worked closely with us to

ensure that the data used to inform this report were accurate. Finally, we would like to express our thanks

to the local and state agencies (Chicago Public Schools, Chicago Police Department, Circuit Court of Cook

County, Illinois Department of Juvenile Justice (DJJ), Illinois Department of Corrections (DOC), Illinois

Department of Child and Family Services (DCFS)) for their permission to use administrative data to

inform the analyses.


Table of Contents

Executive Summary ......................................................................................................................................... 7

Overview and Purpose of Study...................................................................................................................... 7

Research Questions ......................................................................................................................................... 7

Methods .......................................................................................................................................................... 7

Representative Findings .................................................................................................................................. 8

Implications..................................................................................................................................................... 9

Introduction ................................................................................................................................................... 11

Methods ........................................................................................................................................................ 13

Research Questions ................................................................................................................................... 13

Data........................................................................................................................................................... 13

Analysis .................................................................................................................................................... 14

Findings ........................................................................................................................................................ 16

Demographics and Descriptive Analysis .................................................................................................. 16

Description of Study Sample ................................................................................................................ 16

Firearm Offenses .................................................................................................................................. 20

Index Crimes ........................................................................................................................................ 22

Detention .............................................................................................................................................. 24

Other System Involvement ................................................................................................................... 24

Latent Class Analysis................................................................................................................................ 28

Youth Demographics by Class ............................................................................................................. 31

Logistic Regression .................................................................................................................................. 33

Multinomial Regression ............................................................................................................................ 38

Implications and Conclusions ........................................................................................................................ 41

Implications .............................................................................................................................................. 41

Conclusions .............................................................................................................................................. 42

References ..................................................................................................................................................... 45

Appendices .................................................................................................................................................... 46


List of Figures Figure 1. Age at Start of First Instance of Probation ....................................................................................... 17

Figure 2. CPD District by Home Address of Youth at Start of First Probation .............................................. 20

Figure 3. Type of Offenses Before and After Start of First Probation – by Violence and Firearm

Involvement .................................................................................................................................................... 21

Figure 4. Type of Offenses Before and After Start of First Probation – by Violent, Index, Property Crime

Types............................................................................................................................................................... 23

Figure 5. CPD Arrests Before and After Start of First Probation – by Violent, Index, Property Crime Types

........................................................................................................................................................................ 23

Figure 6. CPS Status at Start of First Instance of Probation ........................................................................... 25

Figure 7. Percent of Active Students Chronically Absent During School Year of Start of First Probation .... 26

Figure 8. Child Welfare and Incarceration Experiences ................................................................................. 27

Figure 9. LCA Class Characteristics, by Class ............................................................................................... 30

Figure 10. Proportion of Youth in Each Class ................................................................................................ 31

Figure 11. Youth in Each Pre-Probation LCA Class, by Characteristic ......................................................... 33

Figure 12 Logistic Regression - Relative Likelihood for Participation in Violent, Firearm-Related Crimes by

Youth Characteristic ....................................................................................................................................... 36

Figure 13. Logistic Regression - Relative Likelihood for Participation in Violent, Firearm-Related Crimes by

Prior Crime Involvement ................................................................................................................................ 37

Figure 14. Logistic Regression - Relative Likelihood for Participation in Violent, Firearm-Related Crimes by

Class Membership ........................................................................................................................................... 38


List of Tables

Table 1. Analytic Strategy by Research Question ........................................................................................... 14

Table 2. Study Sample Race/Ethnicity and Gender ........................................................................................ 16

Table 3. Number of Instances of Probation Per Youth (by distinct INTVIDs) ............................................... 18

Table 4. Number of Spells of Probation Per Youth (overlapping or adjacent instances collapsed) ................ 18

Table 5. Number of Days on Probation, Full Sample and by Year (2010-2012) ............................................ 18

Table 6. Violations of Probation ..................................................................................................................... 19

Table 7. Summary of Detention Experiences .................................................................................................. 24

Table 8. Number of Times Screened into Detention ....................................................................................... 24

Table 9. LCA Fit Indices ................................................................................................................................ 28

Table 10. LCA Class Definitions .................................................................................................................... 29

Table 11. Percent of Youth in Each Pre-Probation LCA Class, by Characteristic ......................................... 32

Table 12. Odds Ratios of Multinomial Prediction of LCA Class .................................................................... 40


Executive Summary

Overview and Purpose of Study This report highlights findings of Chapin Hall’s study Understanding the Profiles of Youth Entering

Juvenile Probation. The study provides a picture of youth who, while on probation, were involved in

additional offenses, includes aspects of their developmental history prior to entering probation, and

reports on their outcomes during adolescence and emerging adulthood. Chapin Hall completed this work

on behalf of the Cook County Juvenile Probation and Court Services with funding support from Get IN

Chicago.

Research Questions The study addressed three core questions intended to inform policy and practice:

What are the demographic characteristics of the youth who make up the overall sample (sample

descriptives)?

Is the population of youth entering probation made up of distinct populations (classes) characterized

by pre-probation involvement in the justice system (latent class analysis)?

o If so, do classes experience differential risk for future involvement in violent firearm-related

incidents?

o If so, what characteristics predict class membership in order to better inform provision of

additional supportive services?

When controlling for other known contributing factors, how do youth characteristics contribute to

risk for future firearm-related offenses (multinomial regression)?

Methods We used data on youth ages 10 to 20 years old who were on probation between 2010 and 2014. We

examined a subset of that group whose involvement in probation was of sufficient duration to be

informative regarding their outcomes; that is, if we examined youth whose juvenile probation experiences

were new or incomplete, we would be able to paint a less than complete picture of their trajectories to

date. Accordingly, we looked at a cohort of youth who were involved from 2010 through 2012. We

merged datasets that were provided by several sources: Circuit Court of Cook County, Chicago Police

Department (CPD), Illinois Department of Juvenile Justice (DJJ), Illinois Department of Corrections

(DOC), Chicago Public Schools (CPS), Illinois Department of Human Services (IDHS), Illinois

Department of Child and Family Services (DCFS).

Analytic Plan. The data analyses were conducted in phases. We first completed descriptive analyses that provided a

snapshot of youth characteristics, including demographic characteristics (age, ethnicity, etc.) and cross-

system involvement pre- and post-probation. The descriptive analysis indicates the number and

percentage of youth with various experiences and characteristics.

Next, we conducted latent class analysis (LCA). This statistical technique enables identification

of subgroups within a population that would otherwise be latent, or unseen. In essence, the

analysis identifies clusters of youth who look similarly across a few different categorical

characteristics. Categorical characteristics are those that have clearly defined subgroups such as

males/female, chronic school absence/not, enrolled/not, child welfare involvement/not. We used

LCA to identify clusters or classes of pre-probation characteristics of youth involvement with the

juvenile justice system, e.g., those that occurred prior to the first episode of probation. The

“classes” that emerge from LCA can then be examined with respect to outcomes, in essence


enabling us to examine whether “membership” in a class (e.g., possessing a certain mix of

characteristics) is significantly associated with later outcomes.

That brings us to the next set of analyses, a multinomial regression that examined whether class

membership was indeed associated with later firearms incidents. So, the LCA identifies

subgroups and the regression assesses whether those subgroups demonstrate different outcomes

that might array along a continuum of risk. The multinomial regression further informs the

identification of predictive or risk characteristics for each class.

Representative Findings As suggested above, the study describes a sample of youth on probation during 2010-2012. We obtained

information about: age; race; ethnicity; school enrollment; duration of time on probation; types and

number of violations of probation; placement in the Juvenile Temporary Detention Center; and

experience with child welfare, justice and public assistance systems.

Sample Descriptives. During the study time period, the majority of youth who had their first instance of

probation were non-Hispanic black males ages 15-19 who spent on average 583 days on probation. For

youth living in Chicago and with a history of attending CPS, three quarters of students were chronically

absent during the school year in which their first instance of probation occurred, suggesting that while

these youth were technically enrolled in school, they may have been disengaged. Alternatively, it may

also be that being arrested and placed on probation interfered with school attendance and that for at least

some, absenteeism may not have been a result of academic challenges or truancy per se; rather,

absenteeism may be a result of other types of challenges, including justice system involvement.

Latent Class Analysis.

Analyses revealed that the cohort of youth entering probation during this time period is comprised of

three latent subgroups of youth who:

Have defining characteristics according to pre-probation involvement in the justice system,

Experience differing risk for future involvement in firearm-related offenses; and therefore,

Likely have different or atypical service needs compared with their peers.

Based on their profile, we identified three subgroups (classes) of youth offenses: (1) Chronic,

Violent Offenses; (2) Chronic, Non-violent Offenses; and (3) First Time, Violent Offenses

And their relative proportions of the sample are depicted in the bar chart below.

36%

27%

37%

0%

10%

20%

30%

40%

First time,

Violent Offenses

Chronic,

Non-violent Offenses

Chronic,

Violent Offenses

Proportion of Youth in Each Subgroup


Multinomial Regression.

The purpose of this analytic strategy is to examine the statistical significance of differences among the

classes in order to determine what characteristics may be predictive of class membership. Results of the

analyses indicate that members of the Chronic, Violent Offenses class were significantly more likely –

more than three times more likely – to have an IEP for emotional/behavioral disorders. Youth belonging

to this class also had a significantly greater likelihood of being older, particularly in comparison to the

Chronic, Non-Violent Offenses class. These youth were more likely to not be actively enrolled in CPS

(e.g., dropped out, transferred, other, not in CPS) and, accordingly, are missing information about chronic

absenteeism. Another important characteristic of this group is their likelihood of violating probation.

Youth in the Chronic, Violent Offenses class were significantly more likely to have both technical and

non-technical violations of probation than either of the other two classes. While these two indicators are

unique in that they are not available at intake to be used for assessment, they may serve as an early

warning indicator for youth. If a youth begins to quickly amass probation violations, this should alert

Probation of increased level of risk for that youth.

Youth in the Chronic, Non-Violent Offenses population also were significantly less likely to be active in

CPS, have a high number of absences, and have more technical and non-technical violations of probation

than youth in the First Time, Violent Offenses class. Youth in the First Time, Violent Offenses group were

significantly more likely to be female than youth in either the Chronic, Violent Offenses, or the Chronic,

Non-Violent Offenses classes.

Implications Whereas these analyses provide helpful information about the backgrounds and offense histories of youth

who are involved in firearms incidents, it is critical to note that they may not generalize to other times and

contexts, nor do they permit prediction of the likelihood that an individual youth will experience a

particular outcome. Despite these cautions, and the need to replicate these findings across time, there are

important implications for screening, assessment, and intervention. Specifically, at the onset of probation,

a youth’s class profile – as determined by their previous involvement with the justice system – emerged

as the best predictor of future involvement in violent offenses involving the use of a firearm, with the

Chronic, Violent Offenses class clearly demonstrating the highest level of risk.

Backgrounds of youth with highest risk. Youth whose profiles cluster within the Chronic, Violent

Offenses class, which demonstrates the highest level of risk for future firearm-related violence,

are likely to exhibit the following backgrounds:

Many arrests prior to probation

Many prior screenings for detention prior to probation

Offenses for which probation was assigned are likely to include violent and/or property

offenses

Likely have an Individualized Education Plan (IEP) for emotional/behavioral disorders

Likely have a history of prior experiences of abuse and neglect

Are less likely to be active in CPS

Are likely to amass a high level of both technical and non-technical violations of probation

after onset of probation episode

Limitations and Cautions. As indicated above, this is a snapshot of one cohort of youth within a relatively

short time period (two years) and may not hold true in additional samples in Chicago or elsewhere. It is

important to use caution in applying any of these labels (classes) and essential that latent class

membership be translated into a set of assessment criteria that can be applied flexibly in practice. For

example, it may be possible to identify youth whose profiles suggest relatively high likelihood of

belonging to the Chronic, Violent Offenses class; if that case, Probation service arrays for those youth can


reflect the high level of need for specific supportive services to promote positive outcomes. In this way

risk profiles can be of general predictive value and can drive service provision rather than indicating a

certain trajectory.

Suggestions and Recommendations. Although these indicators should not be interpreted as

deterministic of youth outcomes, they can inform intervention and practice decisions to identify

and provide appropriate supportive services. A youth who exhibits a number of potentially

troubling factors is more likely to need intensive or specialized supports and services to promote

successful outcomes. These factors do not replace the need for normed and validated screening

and assessment tools, and more comprehensive assessments, to determine youth strengths, needs,

and risk for recidivism. They highlight the complexities of the experiences that many youth on

probation have experienced and the importance of having a holistic picture of an individual

youth. If these results hold across time and are interpreted appropriately, they may be of

assistance in informing caseloads, service planning, monitoring, and transitional supports for

youth. Finally, our work to date has not enabled an examination of factors associated with more

positive outcomes, e.g., potential sources of protection or resilience. Accordingly, more work is

needed to identify individual, family, school, intervention, and community sources that are

associated with fewer later offenses, fewer violent offenses, and lower recidivism.


Introduction

The goal of this research collaboration between Chapin Hall and Cook County Juvenile Probation and

Court Services (Probation) is to inform Probation’s understanding of the profiles of youth who are

perpetrators of incidents involving firearms while on probation. This project develops greater

understanding of subpopulations of children, youth, and young adults at-risk of firearm violence so that

ultimately the multiple risk factors that result in firearm violence can be better addressed. At the same

time, it informs a framework for an approach that would enable Probation to use data both to monitor and

to evaluate interventions that may be implemented.

To date, no programs have been shown to be effective at preventing firearm victimization.1 To develop

interventions to prevent victimization and more efficiently target youth at high risk of firearm incidents,

the public health approach offers a useful two-stage framework. First, data must be collected, and

second, risk factors associated with victimization need to be identified.22 The analysis described in this

report applies this approach to perpetration – as opposed to victimization – of violent firearm-related

offenses. The purpose of this analysis is to identify the risk factors and profiles of youth who have been

involved in perpetrating firearm-related incidents to inform implementation of Probation’s policies and

practices and the development of interventions designed to specifically address the needs of this

population of youth.

The goal of this project is to improve early identification of youth at high risk of future involvement prior

to engaging in violent gun-related incidents. The analyses and identification of potential risk indicators

should not be interpreted as deterministic or indicative of innate deficiency among youth, but rather as a

mechanism for early identification of youth in need of additional supportive intervention to promote

protective factors that mitigate the risk of future involvement in these types of incidents.

This effort is based on the premise that certain characteristics may place youth at greater or lesser risk of

such an event. Currently, those who are testing interventions have a very imprecise method of identifying

1 http://www.blueprintsprograms.com/ 2 Mercy, Rosenberg, Powell, Broome, & Roper, 1993

http://www.blueprintsprograms.com/


target populations – based on schools attended, race, age, and gender3 – which often leads to providing

intensive intervention to individuals that may have a low risk of being a perpetrator, thus diluting the

scarce resources that are available to service providers and law enforcement to more adequately serve

youth experiencing higher risk of involvement in firearm-related offenses. In the absence of universal

prevention programs to address the underlying structural inequity that fosters a climate of gun violence,

targeted intervention must be applied to direct resources to youth most in need of additional supportive

services.

3 Dahlberg, & Potter, 2001; Loeber et al., 2005; Lowry, Sleet, Duncan, Powell, & Kolbe, 1995; Logan, Vagi, & Gorman-

Smith, 2016; Resnick, Ireland, & Borowsky, 2004


Methods

Research Questions

To achieve the goals of this project to inform services provided to youth on probation, we examined the

following research questions:

1. What are the demographic characteristics of the youth who make up the population of youth on

probation?

2. Is the population of youth entering probation made up of distinct populations (classes)

characterized by pre-probation involvement in the justice system?

a. If so, do classes experience differential risk for future involvement in violent firearm-

related incidents?

b. If so, what characteristics predict class membership in order to better inform provision of


3. When controlling for other known contributing factors, how do individual youth characteristics

contribute to risk for future firearm-related offenses?

Data

To address these questions, we compiled data from multiple state-, county-, and city-level agencies to

learn more about youth experiences across systems both before and after their time on probation. Data for

this analysis included youth ages 10 to 20 years who were on probation between 2010 and 2014. The

analyses discussed in the following sections examine a cohort of youth from 2010 through 2012. This

cohort was selected for the purpose of maximizing the completeness of the data by reducing the

percentage of the study population still on probation at the end of the data timeframe. Data for this

analysis were derived from several sources:

Circuit Court of Cook County: We used the Juvenile Enterprise Management System (JEMS)

data from the Circuit Court of Cook County Juvenile Division to identify youth who were on

probation during the identified time frame, as well as their associated offenses and detention

experiences.

Chicago Police Department (CPD): We used CPD arrest data to understand the arrest history

of youth who had been arrested in Chicago.


Illinois Department of Juvenile Justice (DJJ): IDJJ data provided information on whether

and when youth were incarcerated as juveniles.

Illinois Department of Corrections (DOC): IDOC data provided insight into whether youth

were incarcerated as adults.

Chicago Public Schools (CPS): Student characteristic and enrollment data from CPS

provided additional understanding of youths’ educational trajectories and understanding of

possible academic challenges such as special education and chronic absenteeism.

Illinois Department of Child and Family Services (DCFS): We used DCFS data to analyze out-

of- home placement data and history of abuse and neglect investigations.

We linked data from these agencies using probabilistic record linkage to identify youth across systems.

Considerations of geography and time were important to consider, as the datasets covered different

jurisdictions and time periods. For example, the CPS and CPD datasets only contain information on youth

who attended school or were arrested in Chicago. As a result, we expect that there would be a number of

youth from the Circuit Court of Cook County cohort who do not appear in these datasets.

Analysis

Analyses were completed in four steps. The analytic strategy for examining each of the identified

research questions of interest is outlined in Table 1, and described in detail in this section.

Table 1. Analytic Strategy by Research Question

Research Question Analytic Strategy

What are the demographic characteristics of the youth who make up the overall sample?

Descriptive Analysis

Is the population of youth entering probation made up of distinct populations (classes) characterized by pre-probation involvement in the justice system?

Latent Class Analysis (LCA)

If so, do classes experience differential risk for future involvement in violent firearm-related incidents?

Logistic Regression

If so, what characteristics predict class membership in order to better inform provision of additional supportive services?

Multinomial Regression

When controlling for other known contributing factors, how do individual youth characteristics contribute to risk for future firearm-related offenses?

Logistic Regression

First, descriptive analyses were conducted to provide a snapshot of characteristics of the youth who made

up the overall sample. Descriptive analyses included examination of demographic characteristics as well

as a snapshot of cross-system involvement pre- and post-probation.


Next, a latent class analysis was conducted to examine the pre-probation characteristics of youth

involvement with the juvenile justice system prior to their first episode of probation in order to determine

if this population was made up of distinct subpopulations – or classes – who experience differential risk.

The body of literature on youth involvement in violence contains a number of studies examining

individual and community level characteristics in an effort to explain and predict youth participation in

violent incidents.4 These studies shed light on social and ecological factors that may contribute to

individual and gang-related violence perpetrated by youth. While these studies illuminate discussions of

structural poverty, oppression, and macro-systems reforms to promote social justice and violence

prevention, they contribute less to the micro-practice of evaluating individual services needs and risk

among a population of youth who have already come to the attention of Probation, particularly when the

probation population is largely homogeneous in terms of demographic characteristics and exposure to

environmental risk.

Additionally, individual characteristics that have long been associated with risk of involvement in violence

are also likely the same characteristics predictive of youth becoming involved with Probation in the first

place. While we also evaluated a constellation of individual characteristics of youth to examine and

confirm individual associations with future involvement with violence, this LCA analysis was selected to

augment this work by examining the universe of data characterizing youth contact with the juvenile justice

system prior to entering probation in order to parse the population according to these characteristics. The

benefit of exploring the population for latent classes is to define subpopulations according to factors

known at intake to probation, and to examine class associations with later involvement in firearm-related

incidents to determine whether the classes experience differential risk. If differential risk exists, these class

indicators and class characteristics could be used to assess youth needs according to class identifying

characteristics in order to inform service provision.

Once latent classes were established, logistic regression was conducted in order to examine the association

between class membership and future involvement in firearm-related incidents. Then, multinomial

regression was conducted to further inform the identification of predictive characteristics for each class.

We discuss results from all analyses in the Findings section.

4 Dahlberg, & Potter, 2001; Loeber et al., 2005; Lowry, Sleet, Duncan, Powell, & Kolbe, 1995; Logan, Vagi, & Gorman-Smith,

2016; Resnick, Ireland, & Borowsky, 2004


Findings

Demographics and Descriptive Analysis

In this section, we describe how we constructed the analytic sample, as well as demographic information

on these youth based on their Circuit Court of Cook County data and other system involvement from the

datasets previously described. We also discuss important temporal and geographical considerations for

the data used in the sample and analyses.

Description of Study Sample

The study sample is comprised of youth who began probation from 2010-2012.5 We excluded

interventions that originated from petitions that were transfer cases, adult transfers, courtesy supervision,

and interstate compact cases. This resulted in 4,290 youth in our study sample.

Table 2 shows race, ethnicity, and gender information for these youth.

Table 2. Study Sample Race/Ethnicity and Gender

Number Percent

Race/Ethnicity

Black Non-Hispanic

3,338

77.8

Asian 13 0.3

Hispanic* 693 16.2

White Non-Hispanic 231 5.4

Other 13 0.3

Unknown 2 0.0

Gender Male

3,884

90.5

Female 406 9.5

TOTAL

4,290

* Includes both White Hispanic and Black Hispanic Youth

More than three-quarters of the youth in our sample were Black Non-Hispanic, and approximately five

percent of sample youth are White Non-Hispanic. Another 16 percent reported Hispanic ethnicity, which

5 Youth could have had earlier instances of probation, but they were included in our sample as long as they had at least one

intervention of probation that began during 2010-2012.


Age

(Yea

rs)

includes both White Hispanic and Black Hispanic youth. The sample is also overwhelmingly male, at

over 90 percent. Figure 1 provides the distribution by age at the start of their first intervention of

probation, even if it occurred prior to 2010, based on youths’ birthdate in the Circuit Court of Cook

County data.

Figure 1. Age at Start of First Instance of Probation

20 0.0%

19 0.0%

18 1.5%

17 19.7%

16 35.5%

15 24.5%

14 12.8%

13 4.6%

12 1.1%

11 0.2%

10 0.0%

0% 5% 10% 15% 20% 25% 30% 35% 40%

Percent of Youth

The vast majority of the youth in our study were between 14 and 17 years of age when they started their

first intervention of probation. Over a third were 16 years old.

For the purposes of this analysis, we were interested in whether offenses occurred prior to or following

the start of the first intervention of probation. However, it is important to recognize that youth also

experienced varying levels of involvement with probation. Tables 3 and 4 show the number of instances

of probation per youth and the number of spells of probation per youth, with overlapping or adjacent

instances of probation collapsed.


Table 3. Number of Instances of Probation Per Youth (by distinct INTVIDs)

Number of Instances of

Probation

Number of Youth

Percent of Youth

1 3,612 84.2

2 596 13.9

3 73 1.7

4 9 0.2

TOTAL 4,290

Table 4. Number of Spells of Probation Per Youth (overlapping or adjacent instances collapsed)

Number of Instances of

Probation

Number of Youth

Percent of Youth

1 3,869 90.2

2 401 9.4

3 20 0.5

TOTAL 4,290

Approximately 90 percent of youth in this study had only a single spell of probation. In addition, it is

important to note that nearly 13 percent of youth (12.6%, or 542 youth) were still on probation at the end

of 2012.

We built on the number of instances of probation by looking at the total amount of time that youth spent

on probation during our time period. We added up days across instances if youth had more than one

instance. Table 5 below shows the summary statistics for the number of days that youth had on probation

for all youth as well as by the year they started their first instance of probation.

Table 5. Number of Days on Probation, Full Sample and by Year (2010-2012)

Start Year N Mean Median

All 4,290 583.52 525

2010 1,312 593.02 490

2011 1,481 576.04 505

2012 1,348 534.77 537.5

There are different factors that could contribute to the amount of time the youth spends on probation,

including the severity of the sentence, violations of probation (see Table 6), and timing of probation. As

Table 5 shows, the mean number of days on probation went down with every successive year, as youth

who started probation later in our timeframe had a shorter amount of time for their probation to be

observed within our analysis time period. As noted above, about 13 percent of the study sample was still

on probation at the end of 2014, so we know that we do not observe the total number of days that these

youth spent on probation overall.


We also examined whether youth had violations of probation. These could include technical violations, in

which a youth violates the terms of his or her probation (e.g., skips school or violates curfew, activities

which are not illegal but which the youth is not supposed to do based on the conditions of his or her

probation), or non-technical violations, in which a youth commits a new offense while on probation.

Table 6 shows the number and percent of youth that had either a technical or non-technical violation of

probation of either type, during the time period of this study.

Table 6. Violations of Probation

Characteristic Number of Youth Percent of Youth

Ever had a technical violation of probation 1,834 42.8 Ever had a non-technical violation of probation 1,976 46.1

Ever had a violation of probation of either type 2,824 65.8

More than 65 percent of youth had at least one violation, technical or non-technical, during the

analysis time period. Slightly more had a non-technical violation, which involves a new offense, than

had a technical violation of probation.

In addition to age and probation information, we also examined the police district in which youth lived at

the time they started their first instance of probation. Figure 2 below shows the distribution of youth by

district. The percentages reflect the number of youth living in each district out of all youth who had a

valid value for Chicago policy district. This figure excludes youth who lived outside of Chicago or did

not have a CPD district listed (n = 1,240). The largest percentages of youth lived in District 11, on

Chicago’s West Side, and District 7, in the Englewood neighborhood, at nearly 10 percent each.6

6 District 13, 21, and 23 merged with other districts in 2012.


Figure 2. CPD District by Home Address of Youth at Start of First Probation

Firearm Offenses

To learn more about the history of juvenile justice involvement of the youth in our sample, we examined

the types of offenses that youth had, with a particular emphasis on firearm-related offenses and violent

offenses. To identify and classify offenses, we used two sources: any offenses associated with a petition at

the Circuit Court of Cook County and offenses for which youth were arrested by CPD. We identified

firearm offenses by offense code statute, description, and type of offense (e.g., violent, property) by FBI

code. There are a few key notes to make about identifying offenses in these two datasets:

The CPD data only records the most serious offense associated with an arrest. Therefore, the data

does not capture all offenses associated with an arrest and less serious offenses may not be

identified.

CPD captures only youth who were arrested in Chicago, while the Circuit Court of Cook County

data would only reflect offenses for arrests for which a youth had been referred to court and for

which a petition had been filed. Therefore, arrests for youth which occurred outside of Chicago

and which did not result in a petition would not be captured in our data.

1.5%

4.7%

5.6%

6.4%

6.3%

7.2%

9.6%

7.6%

5.1%

6.4%

9.2%

2.7%

0.9%

1.5%

5.3%

0.9%

2.2%

1.0%

0.4%

1.4%

0.7%

5.0%

0.3%

3.0%

5.2%

0% 2% 4% 6% 8% 10% 12%

District 1

District 2

District 3

District 4

District 5

District 6

District 7

District 8

District 9

District 10

District 11

District 12

District 13

District 14

District 15

District 16

District 17

District 18

District 19

District 20

District 21

District 22

District 23

District 24

District 25

Percent of Youth

CP

D D

istr

ict


Based on this classification, we also flagged whether the offense occurred before or after the start date of

the youth’s earliest instance of probation. Below we show eight non-mutually exclusive categories, based

on the types of offense and the timing. We created four offense categories (violent firearm offense, violent

non-firearm offense, non-violent firearm offense, and non-violent non-firearm offense) and then further

divided each of these into whether they occurred prior to or after the start of the first instance of probation

and aggregated them at the youth level.7

Figure 3 below shows the percentage of youth in our sample who fell into each of these categories by

time period for the full study. The categories are not mutually exclusive; in theory, a single youth

could fall into all eight categories.

Figure 3. Type of Offenses Before and After Start of First Probation – by Violence and Firearm

Involvement

7 Note that violent offenses here could include both index and non-index crimes; for example, both simple and aggravated assault

would be included here.

11.1%

69.7%

21.1%

91.5%

12.0%

43.4%

17.7%

83.0%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Violent firearm offense Violent non-firearm offense Non-violent firearm offense Non-violent non-firearmoffense

Per

cent

of

Youth

Type of Offense

Before start of first probation After start of first probation


With the exception of violent firearm offenses, the percentage of youth with an offense in the other three

categories was lower for the period after the start of first probation than before. While the percentage of

youth who had a violent firearm offense before probation is slightly higher than the percentage who had

one after, it is important to note that these are primarily not the same youth in both groups; of the 914

youth who ever had a violent firearm offense, only 75 youth had a violent firearm offense in both time

periods.

The timing of the start of youths’ first probation is an important consideration for this analysis, as youth

who started probation earlier in our time period have more available time during the timeframe of

available data (ending in 2014) for further system involvement and offenses. For example, youth who

start on probation in 2012 will have a shorter period of time after the start of their first probation

compared to those who started probation in 2010 to observe post-probation criminal involvements. They

are also likely to be younger by the end of the timeframe at the end of calendar year 2014.

Index Crimes

To add context to our understanding of this cohort, we examined the types of serious offenses that youth

had before and after probation, drawn from both CPD arrests and petitions with the Circuit Court of Cook

County. Index crimes are those categorized as most serious by the FBI. Index violent crimes include

homicide, criminal sexual assault, robbery, aggravated assault, and aggravated battery. Index property

crimes include burglary, theft, motor vehicle theft, and arson.

Figure 4 and Figure 5 detail youth offenses and arrests by type before and after the start of their first

probation. Figure 4 illustrates petition offenses by type (e.g. index crime, violent index crime, property

index crime) and includes both arrests by CPD and petitions in JEMS. Figure 5 details arrests by type.

The percentages in Figure 5 represent youth who have ever had an arrest by CPD (n = 3,736) during the

time period. As noted earlier, some youth may have been arrested outside of Chicago and would not be

included here if they did not also have an arrest by CPD. In all cases for both figures, the percent of

youth arrested for or charged with index crimes went down after the start of their first probation.


Figure 4. Type of Offenses Before and After Start of First Probation – by Violent, Index, Property

Crime Types

Figure 5. CPD Arrests Before and After Start of First Probation – by Violent, Index, Property Crime

Types

87.4%

61.3%68.4%

56.7%

36.2%

44.2%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Index crime Violent index crime Property index crime

Per

cen

t o

f Y

ou

th

Type of Offense

Before start of probation After start of probation

73.7%

50.3%46.0%

57.2%

36.7% 38.9%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Index crime Violent index crime Property index crime

Per

cen

t o

f Y

ou

th

Type of Offense

Before start of probation After start of probation


Detention

We were also interested in the experiences of youth with the Juvenile Temporary Detention Center

(JTDC) and whether this plays a role in youths’ later experiences or trajectories within the criminal justice

system. We examined whether youth were ever in the JTDC – which could include being screened into

detention after arrest or being sent to the JTDC by a judge after a hearing – as well as how many youth

experienced each of those pathways. These groups are also not mutually exclusive, as it is likely that a

youth could be screened into detention after an arrest and also ordered to the JTDC by a judge. Table 7

and Table 8 detail youth experiences with detention.

Table 7. Summary of Detention Experiences

Characteristic Number of Youth Percent of Youth

Ever screened into detention 2,825 65.9 Ever ordered to detention by judge 2,819 65.7

Ever in JTDC 3,225 75.2

Over 65 percent of youth were screened into detention, and over 65 percent were ordered to detention by a

judge at least once during our time period. Additionally, over 75 percent were in the JTDC as a result of

either of these pathways at least once (Table 7). We also looked at the number of times youth were

screened into the JTDC after arrest over the entire time period (Table 8). Over a third of youth were never

screened into the JTDC and another quarter were only screened in once.

Table 8. Number of Times Screened into Detention

Number of times screened into JTDC Number of Youth Percent of Youth 0 1,465 34.2

1 1,008 23.5

2 595 13.9

3 397 9.3

4 291 6.8

5-7 392 9.1

8-10 116 2.7

11+ 26 0.6

Other System Involvement

In addition to criminal justice system involvement, we analyzed several other dimensions of system

involvement, including education, child welfare, public assistance, and incarceration.

Figure 6 shows the CPS enrollment status of youth at the time of their first instance of probation.

The status reflects whether the youth was active and, if not, what the leave reason for their inactive spell

was. Youth who had been transferred, dropped out, or left CPS due to incarceration are categorized as such.

Youth in an inactive category could have left CPS recently or many years before the start of their first


probation. Youth in the “Never in CPS” category have no records in CPS prior to the start of their first

instance of probation.

Figure 6. CPS Status at Start of First Instance of Probation

Over half of the youth in our cohort were active in CPS, while another approximately 28 percent had

either transferred or dropped out at some time prior to the start of first probation. Based on available data,

roughly 19 percent had never been enrolled in CPS prior to the point at which they started their first

probation.

To get a better sense of the academic trajectories of youth who were active at the time of the start of their

first instance of probation, we examined the attendance information for these youth during that school

year.8 Figure 7 shows the number of youth who were chronically absent for the school year they started

probation. Chronic absenteeism is defined as being absent without a valid excuse for at least 10 percent of

days.

8 Some youth who were active during the start of their first probation did not have attendance data for the school year during which

they were active. Some schools do not report attendance data (such as charter or some alternative schools). In other cases, If a youth

started probation in the summer (after June 30) and then disengaged from CPS soon after, they would not have attendance data

during the following academic year.

51.7%

21.9%

6.5%

1.0% 0.2%

18.6%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Active Inactive -

Transferred

Inactive - Dropped

out

Inactive -

Incarcerated

Inactive - Other Never in CPS

Per

cent

of

Youth

Status in CPS

At start of first probation


Figure 7. Percent of Active Students Chronically Absent During School Year of Start of First

Probation

Three quarters of students were chronically absent during the school year they started their first instance of

probation, suggesting that while these youth were technically enrolled in school, they may be facing

academic challenges and may not be fully engaged (Figure 7). Alternatively, it may also be that being

arrested and placed on probation interfered with school attendance and that for at least some youth chronic

absenteeism was not a result of academic challenges but rather a result of other types of challenges,

including justice system involvement.

We were also interested in other system involvement for the youth in our sample. Figure 8 shows

whether youth were ever involved in the child welfare system, and information on their

experiences in DJJ and DOC.

75.6%

24.4%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Chronically absent

Per

cen

t o

f Y

outh

Yes No


Figure 8. Child Welfare and Incarceration Experiences

In examining child welfare history, we find that nearly 22 percent of youth ever experienced a

substantiated investigation of abuse or neglect. In addition, 12 percent had an out-of-home placement, in

which they were removed from their homes and placed in substitute care.9 We were interested in how

these experiences may play a role in understanding juvenile justice system involvement and risk of future

involvement in shooting incidents.

Approximately 22 percent of the youth in our sample were ever in DJJ and nearly 23 percent were ever in

DOC as adults. Again, these categories are not mutually exclusive and youth could have been in one or

both of these categories. Youth could have been in DJJ prior to their time on probation, though

experiences in DOC would have been after the start of their first probation. Out of the entire sample, 38

percent were ever incarcerated in either DJJ, DOC, or both.

Now with an understanding of the demographic characteristics of the overall sample, we move in the next

section to an examination of the overall sample for subpopulations or latent classes that will help us better

characterize and understand differential risk among youth.

9 As compared to the less than one percent of youth under age 18 with substantiated investigation of abuse or neglect and the less

than one percent of youth under age 18 in out-of-home care statewide in 2014. (CWLA, 2016)

21.7%12.1%

22.1% 22.5%38.1%

78.3%88.0%

77.9% 77.5%61.9%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Ever had a

substantiated case of

abuse or neglect

Ever had an out-of-

home-placement

Ever in DJJ Ever in DOC Ever in either DOC or

DJJ

Per

cent

of

Youth

System Involvement

Child Welfare and Incarceration Experiences

Yes No


Latent Class Analysis

In this section, we report the findings of a Latent Class Analysis (LCA) conducted in order to answer our

second research question: Is the sample of youth entering probation made up of distinct classes

characterized by pre- probation involvement in the justice system? By identifying any existing

subpopulations, subsequent analyses can be conducted to identify differential risk among these subgroups

and any defining characteristics that may help identify youth class characteristics upon intake to probation

in order to better inform service provision and tailor services to their specific needs and level risk of risk.

Variables used for this analysis include number of previous arrests, number of prior detention screenings,

and prior crime type (e.g. property crime, violent crime, and crimes with firearms). Models with up to

four classes were considered. Lower values of Bayesian Information Criterion (BIC) indicate that a given

model more clearly distinguishes between groups than other models with a higher BIC, while also

balancing the number of groups used. Of the three models considered, the three class model resulted in

the lowest BIC value. Additionally, relative entropy is a measure of quality of classification. Values

closer to one are desirable and indicate higher level of classification certainty. The relative entropy value

for the three class model was .82, indicating low level of misclassification error for this model. Therefore

the three class model was ultimately selected as the model that best balanced fit, parsimony, and

conceptual interpretability according to these indices of fit and classification quality. The fit indices for

the 2, 3, and 4 class models considered are detailed in Table 9.

Table 9. LCA Fit Indices

Number of Classes Estimated

BIC

Sample-Adjusted BIC

Relative Entropy

2 25,146 24,804 0.65 3 23,134 22,508 0.82

4 23,800 22,889 NA

Next, item-response probabilities – or the proportion of individuals who for whom a particular response

applies for each item – were examined for the three class model in order to label and define the latent

classes according to what characterizes them as distinct subpopulations. Table 10 outlines class

definitions that resulted from examination of item-response probabilities.


Table 10. LCA Class Definitions

Class

Definitions

First Time, Violent Offense

Few prior arrests and detention screenings

High rates of pre-probation violent and firearm-related

offenses

Chronic, Non-Violent Offense

More pre-probation arrests than First Time, Violent Offense class

Offenses primarily lower-level and property offenses

Chronic, Violent Offense

Many arrests and detention screening prior to probation

episode

Offenses included both violent and property offenses

Figure 9 also provides an illustration of the three classes that visually demonstrates how each class is

distinct and how the individual class definitions outlined above were derived. The horizontal axis displays

each of the items used to classify youth, and the vertical axis shows the item-response probabilities on

those items that characterizes the corresponding class of youth. For example, this figure illustrates that

members of the First Time, Violent Offenses class and the Chronic, Non-Violent Offenses class

demonstrated similar response probability for most items. Exceptions to this pattern include the last two

indicators: violent index crime before probation, and firearm crime before probation. The Chronic, Non-

Violent Offenses group was characterized by very little probability associated with these items and the

First Time, Violent Offenses class was characterized by a very high probability associated with these two

items. Additionally, the two classes differed somewhat in the probability of pre-probation arrests and

screening, thus assignment of the chronic qualifier to the Chronic, Non-Violent Offenses class to account

for this difference. The Chronic, Violent Offenses class clearly differs substantially from the other two

classes in terms of the probability of having a high number of pre-probation arrests and screenings, and

high probability of involvement in all other types of offenses prior to probation.


Figure 9. LCA Class Characteristics, by Class

Class prevalence indicates the relative size of each of the latent classes that emerged. The latent classes are

mutually exclusive and exhaustive with each youth assigned to one class based on posterior class

probability values. Therefore the proportion of youth in all latent classes sums to one. Class prevalence is

illustrated in Figure 10.

The largest class, with 38 percent of the sample, is the Chronic, Violent Offenses class. This is followed by

the First Time, Violent Offenses class (35 percent), and the smallest class – the Chronic, Non-Violent

Offenses class (27 percent).

0%

20%

40%

60%

80%

100%

1-4 Arrests

Before Prob

5-9 Arrests

Before Prob

10+ Arrests

Before Prob

0 Scrns Before

Prob

1 Scrn Before

Prob

2 or More

Scrns Before

Prob

Property Index

Before Prob

Viol Index

Before Prob

Firearm Before

Prob

Est

imat

ed P

rob

abil

itie

s o

f C

lass

Mem

ber

ship

Class Characteristics

First time,

Violent Offenses

Chronic,


Chronic,

Violent Offenses


Figure 10. Proportion of Youth in Each Class

Youth Demographics by Class

Figures in the above section illustrate each class profile of youth, based on patterns of pre-probation

justice system involvement. Table 11 below details the average demographic characteristics of youth that

fall into each of the three identified classes.

35%

27%

38%

0%

5%

10%

15%

20%

25%

30%

35%

40%

First time,

Violent Offenses

Chronic,


Chronic,

Violent Offenses

Per

cen

t o

f Y

ou

th


Table 11. Percent of Youth in Each Pre-Probation LCA Class, by Characteristic

LCA Class

Characteristic First time,

Violent Offenses

Chronic,


Chronic,

Violent Offenses

Female 17% 6% 5% IEP - Cognitive Impairment 17% 17% 19%

IEP - Physical/Sense Impairment 4% 4% 3%

IEP - Emotional/Behavioral Disorder 7% 7% 18%

Prior Abuse/Neglect 21% 18% 24%

Out-of-Home at Probation 4% 2% 5%

CPS Status: Active 65% 63% 55%

CPS Status: Dropped Out 4% 9% 10%

CPS Status: Transferred 18% 18% 23%

CPS Status: Never was in CPS 12% 9% 10%

Absent for >10% of School Days 44% 47% 44%




No Chronic Absenteeism Info 37% 39% 47%

Days of Probation 640 520 519

Technical Violation of Probation 34% 43% 55%

Non-Technical Violation of Probation 38% 41% 63%

Age <14 at First Probation 8% 5% 4%

Age 14 at First Probation 15% 12% 10%




Age 18+ at First Probation 1% 1% 2%

First Probation in 2010 32% 26% 36%



N 1,218 875 1,325

Figure 11 illustrates these differences among classes on key characteristics. Of note, the First Time,

Violent Offenses class has is disproportionately made up of female youth and the highest percentage of

youth active in CPS. The Chronic, Non-Violent Offenses class has the highest percentage of youth absent

for more than 10%, 20%, and 30% of school days, respectively. The Chronic, Violent Offenses class has

the highest percentage of youth with an Individualized Education Plan (IEP) in special education for

cognitive impairment and emotional/behavioral disorders; history of experiencing abuse or neglect and

out-of-home placement; and the highest percentage of youth who have dropped out, transferred, or have

no chronic absenteeism information from CPS.


Figure 11. Youth in Each Pre-Probation LCA Class, by Characteristic

While these characterizations of the various classes help illustrate class membership, statistical significance

of these differences is examined in the Multinomial Regression section of this report, thus providing

additional information about characteristics that may be predictive of class membership.

Logistic Regression

Once the LCA was completed indicating the sample of youth in probation was made up of subpopulations

based on pre-probation system involvement, and all youth were assigned to classes based on posterior

class probabilities of belonging to a given class, we conducted a logistic regression analysis to examine

research question 2a and research question 3:

Do classes of youth experience differential risk for future involvement in violent firearm-related

incidents?

When controlling for other known contributing factors, how do individual youth characteristics

contribute to risk for future firearm-related offenses?

These two research questions sought to identify differential risk among the three identified latent classes,

as well as for any additional individual youth characteristics. While these questions focus specifically on

outcomes of firearm-related violent offenses – as this outcome was identified as the primary outcome of

interest – a table reporting outcomes for all types of offenses is included in Appendix 1.

0%

10%

20%

30%

40%

50%

60%

70%

Per

cen

t o

f Y

ou

th

First time,

Violent Offenses

Chronic,


Chronic,

Violent Offenses


Our key findings, discussed more below are that, when simultaneously taking into account all

characteristics of probation youth:

Females are about 90% less likely to be involved in violent, firearm-related crimes as compared

to males with equivalent backgrounds;

Youth with 10 or more arrests are about 50% more likely to be involved with a violent, firearm-

related crime compared to those with equivalent backgrounds but less than 5 prior arrests; and

Youth with prior involvement in violent crime are about 50% more likely to be involved with a

violent, firearm-related crime compared to those with equivalent backgrounds but no prior

violent crime involvement.

Other notable—but of borderline statistical significant—findings are a roughly 25% lower likelihood of

violent, firearm-related crime involvement for youth with an identified IEP for cognitive impairment,

and roughly 25% greater likelihood for youth with 5-9 arrests as compared to youth with fewer than 5

arrests.

Logistic regression analysis allowed us to examine the association between involvement in crimes after

the start of first probation and characteristics or experiences of youth prior to the start of probation in

order to further inform Probation’s efforts to appropriately align specialized services based on identified

needs of youth. Assessment of differential risk according to factors known upon entry into probation

services may help guide service planning to ensure youth with a specific set of needs receive the most

appropriate service array.

The initial logistic model included a range of predictive factors from the linked CPS, CPD, DCFS, and

Court records: the first year in which youth are assigned to probation (within the sample window), age,

gender, CPS enrollment status, record of chronic absenteeism (if available), IEP status, prior abuse/neglect

and out-of-home placement status at first probation, prior arrests, and previously having been screened into

detention. Contact with DJJ was not included in this analysis because of the extremely low rate of contact

for youth prior to first probation. Next, latent class membership was also added to estimate differential risk

according to class membership.

The results of this analysis are illustrated in Figure 12, Figure 13, and Figure 14. Results are reported by

odds ratios, which express how the relative likelihood of the outcome is associated with a change in the

predictive factor. Because each predictive factor is categorical, the odds ratio for each predictive factor’s

value is interpreted as relative to the “reference value” for that predictive factor. The reference values for

the predictive factors included in these figures are as follows:

Gender: male

CPS enrollment status: active


Record of chronic absenteeism: not chronically absent

Has IEP: does not have an IEP

Prior abuse/neglect: no prior history of abuse or neglect

Out-of-home at probation: not in out-of-home placement

Number of arrest pre-probation: 1-4 arrests pre-probation

Previous crime involvement (by type): no prior involvement

Previous JTDC screening pre-probation: no JTDC screenings before probation

Latent class membership: First Time, Violent Offense Class

Odds ratios that are above 1.0 indicate a positive association, whereas those below 1.0 indicate a negative

association. As illustrated in Figure 12, female youth are significantly less likely to be involved with

violent, firearm-related crimes: the odds ratio associated with being female is approximately 0.1, which is

interpreted as female youth having one-tenth the likelihood of involvement as compared to male youth.

Additionally, the odds ratio of 1.6 associated with youth whose CPS status is categorized as “Dropped

out” is interpreted as a 60% greater likelihood of involvement with firearm-related crimes as compared to

youth whose CPS status is “Active”. Note that error bars indicate the 95% confidence interval. Estimates

whose error bars are fully below (above) the 1.0 likelihood reference line are at statistically significantly

lower (greater) likelihood of participation in violent, firearm-related crimes.

Additionally, youth with an IEP for cognitive impairment were less likely to engage in future firearm-

related offenses. And contrary to expectations, we found that youth with prior history of experiencing

abuse or neglect, and those placed out-of-home at the time of first probation were also less likely to

engage in future firearm-related offenses, after controlling for all other factors.


Figure 12 Logistic Regression - Relative Likelihood for Participation in Violent, Firearm-Related

Crimes by Youth Characteristic10

Figure 13 illustrates relative likelihood of future participation in violent, firearm-related crimes

according to individual indicators of prior involvement in crime. Not surprisingly, findings indicate

that having ten or more arrests pre-probation or having prior involvement in violent crimes are

associated with the highest likelihood of future involvement in violent, firearm-related crimes.

10 Note: other predictors in the same logistic regression analysis include the indicators represented in Figure 13, and an

indicator of whether a youth has chronic absenteeism information.


Figure 13. Logistic Regression - Relative Likelihood for Participation in Violent, Firearm-Related

Crimes by Prior Crime Involvement11

While this analysis provides information on the individual contribution of an array of indicators to the risk of

future firearm offense involvement, while controlling for the effect of other indicators, the utility of this

information is limited by the complexity of the demographic and experiential profiles of youth in this

probation sample. This further supports the need to examine risk according to latent subpopulations so that

multiple aspects of a youth’s profile are taken into account when determining level of risk.

Therefore, we assessed differential risk according to class membership. Figure 14 shows the relative

likelihood of future involvement in violent, firearm-related crime according to latent class membership -

defined by pre-probation contact with the justice system – in reference to the First Time, Violent Offenses

class. This figure shows that the Chronic, Violent Offenses class clearly demonstrates the highest likelihood

relative to the other classes of future involvement in violent, firearm-related crime.

11 Note: other predictors in the same logistic regression analysis include the indicators represented in Figure 12, and an indicator of

whether a youth has chronic absenteeism information.


Figure 14. Logistic Regression - Relative Likelihood for Participation in Violent, Firearm-Related

Crimes by Class Membership12

Taken together, the results of this analysis indicate that consistent with previous literature examining

prediction of youth violence, there are a number of individual characteristics that are associated with

increased likelihood of future perpetration of firearm-related offenses. However, evaluation of the latent

classes previously established according to youth pre-probation justice system involvement also revealed

that the three classes do in fact experience differential risk of future firearm perpetration.13

In summary, these findings provide support for identification of the Chronic, Violent Offenses class as

youth with indicated risk who would benefit from additional supportive services in order to promote

positive outcomes as a result of involvement with Probation. As a result, it is necessary to further examine

characteristics of this as well as the other two classes in order to establish clear definitions that may help

inform assessment of youth at intake to Probation to determine service needs according to likely class

membership. The next section examines the predictive properties of individual youth characteristics to help

achieve this purpose.

Multinomial Regression

In this section, we report the findings of a Multinomial Regression analysis conducted to answer research

question 2b: what characteristics predict class membership in order to better inform provision of


12 Note: other predictors in the same logistic regression analysis include the indicators represented in Figure 12, and an indicator of

whether a youth has chronic absenteeism information. 13 One note of caution should be considered in interpreting the results of these analyses. The Coefficient of Determination – a

statistic that is interpreted as the proportion of the variance in the dependent variable that is predictable from the independent

variable – indicates that this model accounts for a small proportion of variance (.03). Though this proportion is quite small, large

values are uncommon in social sciences due to the complex nature of the variables that are studied. The small coefficient of

determination value does not indicate that our findings are unimportant or do not have practical utility – but rather indicate that these

findings are just one piece of a much larger landscape of characteristics, variables, and subpopulations that contribute to youth

involvement in firearm-related violence. While these analyses do not provide a comprehensive answer for predicting future firearm

perpetration, they do contribute additional information to guide and inform youth screening and service planning for probation

services.


We have previously established that the population of youth in probation is made up of three latent

classes or subpopulations defined by their pre-probation involvement in the justice system, and that these

classes experience differential risk of future involvement in firearm-related offenses. The ability to apply

these findings in practice dictates that we identify criteria to help inform assessment of youth that

indicates to which class they are likely to belong.

We also previously examined the distribution of various demographic characteristics according to class

which helped illustrate the class-specific populations. However, the purpose of this analytic strategy is to

go beyond description of the classes and examine the statistical significance of differences among the

classes in order to determine what characteristics may be predictive of class membership.

Table 12 reports the results of the Multinomial Regression analysis. Again, results are reported by odds

ratios, expressing how the relative likelihood of the outcome is impacted by a change in the predictive

factor. Because each predictive factor is categorical, the odds ratio for each predictive factor’s value is

interpreted as relative to the “reference value” for that predictive factor. As above, odds ratios that are

above 1.0 indicate a positive association, whereas those below 1.0 indicate a negative association.

These results indicate that members of the Chronic, Violent Offenses class were significantly more likely

- at more than three times the likelihood – of having an IEP for emotional/behavioral disorders. Youth

belonging to this class also had a significantly greater likelihood of being older, particularly in

comparison to the Chronic, Non-Violent Offenses class. These youth were more likely to have CPS status

other than “Active” and, accordingly, are missing information about chronic absenteeism. Another

important characteristic of this group is in relation to likelihood of violating their probation. Youth in the

Chronic, Violent Offenses class were significantly more likely to have both technical and non-technical

violations of probation than either of the other two classes. While these two indicators are unique in that

they are not available at intake to be used for assessment, they may serve as an early warning indicator

for youth. If a youth begins to quickly amass probation violations, this should alert Probation of increased

level of risk for that youth.

Youth in the Chronic, Non-Violent Offenses population were also significantly less likely to be active in

CPS and have a high number of absences, and have more technical and non-technical violations of

probation than youth in the First Time, Violent Offenses class. Youth in the First Time, Violent Offenses

group were significantly more likely to be female than youth in either the Chronic, Violent Offenses, or

the Chronic, Non-Violent Offenses classes.


In sum, while classes are defined by their pre-probation justice system involvement, there were a number

of other characteristics that were more or less likely to emerge in any given class. In addition to the class

definition, these criteria could be used to also inform decisions about the likelihood of a given youth

belonging to a given class population.

Table 12. Odds Ratios of Multinomial Prediction of LCA Class

Odds Ratio of Class Membership

Characteristic

Units

Chronic

Non-

Violent

Offenses vs

1st Time

Violent

Offenses

Chronic

Violent

Offenses vs

1st Time

Violent

Offenses

Chronic

Violent

Offenses vs

Chronic

Non-Violent

Offenses

Female vs Male 0.26*** 0.22*** 0.85 Black Non-Hispanic vs White 0.46*** 1.32*** 2.88***

Hispanic vs White 0.65** 1.24** 1.92***

Has IEP - Cognitive Impairment vs None 0.90 1.37 1.53*

Has IEP - Physical/Sensory Impairment vs None 0.84 0.85 1.01

Has IEP - Emotional/Behavioral Disorder

vs None 0.93 2.86 3.06***

CPS Status: Dropped Out vs Active 1.92*** 1.66*** 0.87

CPS Status: Transferred vs Active 0.81 0.80 0.99

CPS Status: Other vs Active 1.59*** 2.53*** 1.60***

CPS Status: Not in CPS vs Active 0.62*** 0.65*** 1.04

Absent for >10% of School Days vs Not 0.98 1.35 1.38


Absent for >30% of School Days vs Not 1.74** 1.53** 0.88


No Chronic Absenteeism Info vs Not 1.46*** 3.00** 2.05***

Prior Abuse/Neglect vs Not 0.93 1.07 1.15

Out-of-Home at Probation vs Not 0.58 1.07* 1.84

Days of Probation 1 day 0.999*** 0.999*** 1.000

Technical Violation of Probation vs Not 1.64*** 2.66*** 1.62**

Non-Technical Violation of Probation vs Not 1.27** 3.70* 2.92***

Age 14 at First Probation vs Age <14 0.86 1.02 1.18

Age 15 at First Probation vs Age <14 0.90 1.29 1.44

Age 16 at First Probation vs Age <14 0.99 1.79 1.82**

Age 17 at First Probation vs Age <14 1.01 3.16 3.12***

Age 18+ at First Probation vs Age <14 0.27*** 1.25*** 4.68***

First Probation in 2011 vs 2010 1.34** 0.86** 0.64*

First Probation in 2012 vs 2010 1.38** 0.91** 0.66*

*** = p < 0.01, ** = p < 0.05, * = p < 0.10


Implications and Conclusions

Implications

The purpose of this project was to inform Probation’s understanding of the profiles of youth who are

perpetrators of incidents involving firearms while on probation in order to inform service planning to

most effectively meet the needs of youth and promote positive youth outcomes as a result of involvement

with Probation. The results of these analyses indicate that the population of youth entering probation is

actually made up of three different latent subpopulations of youth who:

Have defining characteristics according to pre-probation involvement in the justice system,

Experience differing levels of risk for future involvement in firearm-related offenses; and

therefore,

May have different service needs.

While there are a number of individual characteristics that are associated with participation in violent,

firearm-related crimes, these indicators are associated with background experiences of youth when they

come into probation and are most meaningful as early warning signs and indicators of likely class

membership. As of onset of probation, a youth’s class profile, as determined by their previous

involvement with the justice system, emerged as the best predictor of future involvement in violent

offenses involving the use of a firearm, with the Chronic, Violent Offenses class clearly demonstrating

the highest level of risk.

Therefore it is essential that we translate latent class membership into a set of assessment criteria that can

be applied in practice to identify youth who may have the greatest likelihood of belonging to the Chronic,

Violent Offenses class so that Probation service arrays for those youth can reflect the high level of need for

supportive services to promote positive outcomes.

Youth who are likely members of the Chronic, Violent Offenses class, which demonstrates the highest level

of risk for future firearm-related violence, are likely to meet a number of the following criteria:

Many arrests prior to probation

Many prior screenings for detention prior to probation


Offenses for which probation was assigned are likely to include violent and/or property offenses

Have an IEP (cognitive impairment or emotional/behavioral disorders)

Likely have a history of prior experiences of abuse and neglect

Are less likely to be active in CPS

Are likely to amass a high level of both technical and non-technical violations of probation after

onset of probation episode

Whereas these analyses provide helpful information about the backgrounds and offense histories of

youth who are involved in firearms incidents, it is critical to note that they may not generalize to other

times and contexts, nor do they permit prediction of the likelihood than an individual youth will

experience a particular outcome. This is a snapshot of one cohort of youth within a relatively short time

period (two years) and may not hold true in additional samples in Chicago or elsewhere. It is important

to use caution in applying any of these labels (classes) and essential that latent class membership be

translated into a set of assessment criteria that can be applied flexibly in practice. For example, it may

be possible to identify youth whose profiles suggest relatively high likelihood of belonging to the

Chronic, Violent Offenses class; if that case, Probation service arrays for those youth can reflect the

high level of need for specific supportive services to promote positive outcomes. In this way risk

profiles can be of general predictive value and can drive service provision rather than indicating a

certain trajectory. In spite of these cautions, and the need to replicate these findings across time, there

are important implications for screening, assessment, and intervention. These indicators should not be

interpreted as deterministic of youths’ outcomes. However, these indicators should be assessed and

used to inform intervention and practice decisions to provide appropriate supportive services. A youth

who meets a number of these criteria is more likely to be in need of intensive or specialized support and

services to promote successful Probation outcomes. These factors do not replace the need for normed

and validated screening and assessment tools to determine youth’ strengths, needs, and risk for

recidivism. They highlight the complexities of the experiences that many youth on probation have

experienced and the importance of having a holistic picture of an individual youth as well as the cohort

of youth on probation to inform caseloads, service planning, monitoring and transitional supports for

youth in communities to provide resources and supports.

Conclusions

The goal for these analyses was to provide more in-depth understanding of the youth who spend time on

probation and the characteristics and risk factors that may influence their future criminal justice

involvement. It is our hope that the patterns and associations that emerged from this work provide insight


and opportunity for a more nuanced assessment of youth who enter Probation that allows for the tailoring

of services to youth who face diverse challenges and experiences. Although these indicators should not

be interpreted as deterministic of youth outcomes, they can inform intervention and practice

decisions to identify and provide appropriate supportive services. A youth who exhibits a number

of potentially troubling factors is more likely to need intensive or specialized supports and

services to promote successful outcomes. These factors do not replace the need for normed and

validated screening and assessment tools, and more comprehensive assessments, to determine

youth strengths, needs, and risk for recidivism. They highlight the complexities of the

experiences that many youth on probation have experienced and the importance of having a

holistic picture of an individual youth. If these results hold across time and are interpreted

appropriately, they may be of assistance in informing caseloads, service planning, monitoring,

and transitional supports for youth. Finally, our work to date has not enabled an examination of

factors associated with more positive outcomes, e.g., potential sources of protection or resilience.

Accordingly, more work is needed to identify individual, family, school, intervention, and

community sources that are associated with fewer later offenses, fewer violent offenses, and

lower recidivism.

We propose some steps to continue to build on this work:

Continued cooperation between researchers and criminal justice officials to (a) identify the

potential role of court interventions in influencing youth post-probation trajectories; and (b)

identify ways to either directly address or carefully interpret cases of data gaps on youth criminal

activity where jurisdictional reporting is not aligned;

Application of these analyses to help balance caseloads within the criminal justice system to

consider youth risk levels, in addition to head counts;

Align understanding of classes of youth with services and supports for youth on probation as they

access community-based services.

Continuation of these methods with additional years and types of data, to allow for (a) longer- term

perspective on trends for each given youth; (b) comparison of additional cohorts of youth; and (c)

additional depth and breadth of understanding of youth at-risk factors;

Development of specialized, easy-to-use predictive analysis models, such as those which Chapin

Hall has piloted with Get IN Chicago in other concurrent work, which could be used by courts or

correctional officers to identify youth who should be prioritized for referral to supportive


programming;

Examination of probation violation trajectories to determine at what point number of violations

become significant; and

Examination of progression of offenses, from potentially early ages and less serious criminal

activity to older/more serious activity, as well as how future risk changes in relation to this

progression.

The findings from this study are intended to be actionable and provide organizations with the information

necessary to provide services to youth who have been involved in the criminal justice system, with an eye

towards preventing further involvement and risk of involvement in crime, especially firearm-related

incidents.


References

CWLA. (2016). Illinois’s Children 2016. Retrieved from http://www.cwla.org/wp-

content/uploads/2016/03/Illinois.pdf.

Dahlberg, L. L., & Potter, L. B. (2001). Youth violence: Developmental pathways and prevention

challenges. American Journal of Preventative Medicine, 20(1S), 3–14.

Loeber, R., Pardini, D., Homish, D. L., Wei, E. H., Crawford, A. M., Farrington, D. P., Stouthamer-

Loeber, M., Creemers, J., Koehler, S. A., & Rosenfeld, R. (2005). The prediction of violence and

homicide in youth men. Journal of Consulting and Clinical Psychology, 73(6), 1074–1088.

Lowry, R., Sleet, D., Duncan, C., Powell, K., & Kolbe, L. (1995). Adolescents at risk for violence.

Educational Psychology Review, 7(1), 7–39.

Logan, J. E., Vagi, K. J., & Gorman-Smith, D. (2016). Characteristics of youth with combined histories of

violent behavior, suicidal ideation or behavior, and gun-carrying. The Journal of Crisis

Intervention and Suicide Prevention, 37(6), 402–414.

Mercy, J. A., Rosenberg, M. L., Powell, K. E., Broome, C. V., & Roper, W. L. (1993). Public health

policy for preventing violence. Health Affairs, 12(4), 7–29. doi:10.1377/hlthaff.12.4.7

Resnick, M. D., Ireland, M., & Borowsky, I. (2004). Youth violence perpetration: What protects? What

Predicts? Findings from the National Longitudinal Study of Adolescent Health. Society for

Adolescent Medicine, 35(5), 424e1-424e10.

http://www.cwla.org/wp-


Appendices

Appendix 1. Odds Ratios for Participation in Post-Probation Crime by Type, for Pre-Probation

Predictive Factors versus Reference Category Involvement with Crime After the Start of Probation Predictor Reference Violent, With Firearm Non-Violent - With Firearm Violent - No Firearm Non-Violent - No Firearm

First Probation in 2011 First Probation in 2010

0.80* (0.06)

0.79** (0.05)

0.82* (0.09)

0.82* (0.09)

0.83* (0.07)

0.83* (0.08)

0.86 (0.15)

0.86 (0.15)

0.77*** (0.00)

0.77*** (0.00)

0.81** (0.02)

0.81** (0.02)

0.60*** (0.00)

0.57*** (0.00)

0.59*** (0.00)

0.58*** (0.00)

First Probation in 2012 First Probation in 2010

0.55*** (0.00)

0.54*** (0.00)

0.55*** (0.00)

0.55*** (0.00)

0.63*** (0.00)

0.64*** (0.00)

0.65*** (0.00)

0.65*** (0.00)

0.62*** (0.00)

0.62*** (0.00)

0.65*** (0.00)

0.65*** (0.00)

0.46*** (0.00)

0.43*** (0.00)

0.44*** (0.00)

0.43*** (0.00)

Age 14 at First Probation

Age <14 at First Probation

1.04 (0.87)

1.01 (0.95)

0.99 (0.95)

1.01 (0.97)

0.80 (0.28)

0.77 (0.21)

0.74 (0.15)

0.76 (0.19)

1.15 (0.44)

1.15 (0.45)

1.12 (0.55)

1.15 (0.44)

2.16*** (0.00)

2.08*** (0.01)

1.86** (0.02)

2.03*** (0.01)



0.83 (0.37)

0.79 (0.30)

0.75 (0.20)

0.77 (0.26)

0.93 (0.72)

0.87 (0.46)

0.82 (0.30)

0.84 (0.39)

0.94 (0.68)

0.97 (0.86)

0.93 (0.68)

0.95 (0.76)

2.24*** (0.00)

2.02*** (0.00)

1.68** (0.04)

1.82** (0.02)



0.69* (0.08)

0.68* (0.09)

0.65* (0.05)

0.66* (0.06)

0.68** (0.04)

0.64** (0.02)

0.60*** (0.01)

0.61** (0.01)

0.57*** (0.00)

0.60*** (0.00)

0.56*** (0.00)

0.57*** (0.00)

1.53** (0.05)

1.43 (0.12)

1.07 (0.76)

1.21 (0.42)



0.64** (0.05)

0.65* (0.07)

0.60** (0.03)

0.61** (0.04)

0.53*** (0.00)

0.51*** (0.00)

0.47*** (0.00)

0.48*** (0.00)

0.43*** (0.00)

0.48*** (0.00)

0.42*** (0.00)

0.43*** (0.00)

1.22 (0.37)

1.23 (0.41)

0.83 (0.47)

0.95 (0.83)

Age 18+ at First Probation


0.31* (0.06)

0.37 (0.12)

0.34* (0.09)

0.34* (0.09)

0.51 (0.13)

0.63 (0.31)

0.58 (0.24)

0.58 (0.24)

0.50** (0.04)

0.59 (0.12)

0.52* (0.06)

0.54* (0.07)

0.73 (0.42)

0.95 (0.91)

0.60 (0.26)

0.74 (0.49)

Black Non-Hispanic White Non-Hispanic - 1.92

(0.13) 1.82

(0.17) 1.81

(0.17) -

1.18 (0.62)

1.11 (0.75)

1.11 (0.74)

- 1.35

(0.21) 1.18

(0.50) 1.18

(0.49) -

2.31*** (0.00)

2.08** (0.01)

2.15*** (0.01)

Hispanic White Non-Hispanic - 1.59

(0.31) 1.48

(0.39) 1.55

(0.33) -

1.12 (0.74)

1.03 (0.93)

1.10 (0.78)

- 1.04

(0.88) 0.99

(0.98) 0.98

(0.93) -

1.32 (0.36)

1.28 (0.43)

1.29 (0.42)

Female Male - 0.11*** (0.00)

0.13*** (0.00)

0.12*** (0.00)

- 0.08*** (0.00)

0.10*** (0.00)

0.09*** (0.00)

- 0.62*** (0.00)

0.68*** (0.00)

0.66*** (0.00)

- 0.21*** (0.00)

0.29*** (0.00)

0.30*** (0.00)

CPS Status: Dropped Out CPS Status: Active - 1.72

(0.22) 1.61

(0.29) 1.58

(0.31) -

0.98 (0.96)

0.92 (0.82)

0.88 (0.73)

- 0.92

(0.79) 0.82

(0.52) 0.87

(0.64) -

3.17*** (0.00)

2.35** (0.03)

2.64** (0.01)

CPS Status: Not in CPS CPS Status: Active - 1.32

(0.53) 1.32

(0.54) 1.27

(0.58) -

0.98 (0.96)

1.01 (0.98)

0.94 (0.88)

- 1.35

(0.29) 1.26

(0.43) 1.30

(0.37) -

2.30** (0.02)

2.06* (0.06)

2.22** (0.03)

CPS Status: Other CPS Status: Active - 1.34

(0.63) 1.14

(0.83) 1.18

(0.78) -

1.23 (0.67)

1.05 (0.93)

1.06 (0.90)

- 0.90

(0.80) 0.77

(0.52) 0.77

(0.52) -

5.28** (0.02)

3.78* (0.06)

3.68* (0.06)

CPS Status: Transferred CPS Status: Active - 1.29

(0.55) 1.22

(0.65) 1.24

(0.62) -

0.84 (0.61)

0.80 (0.54)

0.80 (0.51)

- 1.13

(0.67) 1.05

(0.86) 1.08

(0.78) -

2.56*** (0.01)

2.22** (0.03)

2.37** (0.01)

Absent for >10% of School Days

Not Chronically Absent

- 1.13

(0.56) 1.11

(0.61) 1.09

(0.68) -

1.30 (0.16)

1.29 (0.17)

1.25 (0.22)

- 1.06

(0.71) 1.00

(1.00) 1.02

(0.91) -

1.22 (0.34)

1.25 (0.31)

1.20 (0.39)



- 1.13

(0.58) 1.11

(0.64) 1.08

(0.72) -

0.98 (0.91)

0.94 (0.76)

0.93 (0.71)

- 1.11

(0.50) 1.08

(0.62) 1.07

(0.67) -

1.90** (0.01)

1.74** (0.04)

1.75** (0.03)



- 1.09

(0.70) 1.11

(0.64) 1.08

(0.74) -

1.10 (0.61)

1.14 (0.50)

1.08 (0.68)

- 1.34* (0.07)

1.39** (0.05)

1.36* (0.06)

- 1.46

(0.25) 1.40

(0.31) 1.32

(0.40)




- 0.81

(0.31) 0.81

(0.30) 0.82

(0.31) -

1.02 (0.90)

1.02 (0.90)

1.03 (0.87)

- 0.76* (0.06)

0.72** (0.03)

0.75* (0.05)

- 0.84

(0.57) 0.75

(0.36) 0.84

(0.58)

No Chronic Absenteeism Info

Has Chronic Absenteeism Info

- 0.81

(0.62) 0.79

(0.58) 0.76

(0.53) -

1.18 (0.64)

1.14 (0.71)

1.11 (0.76)

- 0.96

(0.88) 0.90

(0.73) 0.89

(0.69) -

0.51* (0.05)

0.48** (0.05)

0.45** (0.03)

Has IEP - Cognitive Impairment

Does Not Have IEP - 0.83

(0.20) 0.80

(0.11) 0.80

(0.12) -

0.88 (0.28)

0.84 (0.14)

0.84 (0.16)

- 1.07

(0.50) 1.01

(0.91) 1.01

(0.92) -

1.20 (0.25)

1.11 (0.52)

1.14 (0.43)

Has IEP - Physical/Sensory Impairment


(0.77) 1.16

(0.58) 1.08

(0.78) -

0.68 (0.14)

0.73 (0.22)

0.68 (0.14)

- 1.09

(0.67) 1.10

(0.64) 1.07

(0.72) -

0.54** (0.02)

0.52** (0.02)

0.54** (0.02)

Has IEP - Emotional/Behavioral Disorder


(0.72) 0.95

(0.76) 0.94

(0.71) -

0.89 (0.44)

0.81 (0.18)

0.78 (0.10)

- 2.10*** (0.00)

1.74*** (0.00)

1.78*** (0.00)

- 1.84*** (0.00)

1.19 (0.44)

1.29 (0.26)

Prior Abuse/Neglect No Prior Abuse/Neglect

- 0.93

(0.60) 0.91

(0.49) 0.92

(0.50) -

1.08 (0.48)

1.07 (0.57)

1.06 (0.61)

- 0.98

(0.80) 0.95

(0.57) 0.96

(0.64) -

1.07 (0.66)

1.04 (0.78)

1.08 (0.59)

Out-of-Home at Probation

Not in Out-of-Home Placement

- 0.86

(0.62) 0.83

(0.57) 0.83

(0.55) -

0.57* (0.07)

0.57* (0.07)

0.55* (0.06)

- 1.01

(0.98) 0.96

(0.83) 0.95

(0.79) -

0.77 (0.37)

0.79 (0.44)

0.71 (0.26)

5-9 Arrests Pre-Probation


- - 1.16

(0.22) - - -

1.22* (0.07)

- - - 1.40*** (0.00)

- - - 3.17*** (0.00)

-

10+ Arrests Pre-Probation


- - 1.59*** (0.00)

- - - 1.53*** (0.00)

- - - 1.91*** (0.00)

- - - 7.06*** (0.00)

-

Previous Firearm Involvement

No Prior Involvement - - 0.87

(0.47) - - -

0.74* (0.07)

- - - 0.62*** (0.00)

- - - 1.00

(0.99) -

Previous Violent Crime Involvement

No Prior Involvement - - 1.50*** (0.00)

- - - 1.56*** (0.00)

- - - 0.95

(0.54) - - -

0.88 (0.35)

-

Previous Violent Index Crime Involvement


(0.82) - - -

0.89 (0.44)

- - - 1.06

(0.64) - - -

0.73 (0.14)

-

Previous Property Index Crime Involvement


(0.13) - - -

1.18 (0.11)

- - - 1.29*** (0.00)

- - - 1.16

(0.22) -

1 JTDC Screening Pre-Probation

No JTDC Screenings Before Probation

- - 1.11

(0.39) - - -

1.23* (0.05)

- - - 1.02

(0.83) - - -

1.19 (0.21)

-

2 or More JTDC Screenings Pre-Probation

No JTDC Screenings Before Probation

- - 1.02

(0.88) - - -

0.95 (0.68)

- - - 1.07

(0.56) - - -

1.54** (0.04)

-

Chronic, Non-violent Offenses

First time, Violent Offenses

- - - 1.00

(0.97) - - -

1.10 (0.46)

- - - 0.69*** (0.00)

- - - 1.73*** (0.00)

Chronic, Violent Offenses

First time, Violent Offenses

- - - 1.59*** (0.00)

- - - 1.72*** (0.00)

- - - 1.66*** (0.00)

- - - 4.45*** (0.00)

N 3,418 3,418 3,418 3,418 3,418 3,418 3,418 3,418 3,418 3,418 3,418 3,418 3,418 3,418 3,418 3,418

Coefficient of Determination 0.01 0.03 0.04 0.03 0.01 0.04 0.05 0.05 0.04 0.06 0.09 0.08 0.02 0.09 0.13 0.12

Notes: Each cell represents the adjusted odds ratio of a youth being involved with the corresponding type of crime, in the case that they have the corresponding predictive characteristics, relative to having

the reference characteristic. The p-value for each estimated odds ratio is presented in parentheses below the odds ratio. Significance stars correspond to: p<0.01 = ***, p<0.05 = **, p<0.10 = *.

Understanding the Profiles of Youth Entering Juvenile ......informative regarding their outcomes;...

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