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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.
Chapin Hall at the University of Chicago 3 Axelrod et al. | Profiles of Youth
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.
Chapin Hall at the University of Chicago 4 Axelrod et al. | Profiles of Youth
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
Chapin Hall at the University of Chicago 5 Axelrod et al. | Profiles of Youth
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
Chapin Hall at the University of Chicago 6 Axelrod et al. | Profiles of Youth
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
Chapin Hall at the University of Chicago 7 Axelrod et al. | Profiles of Youth
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
Chapin Hall at the University of Chicago 8 Axelrod et al. | Profiles of Youth
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
Chapin Hall at the University of Chicago 9 Axelrod et al. | Profiles of Youth
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
Chapin Hall at the University of Chicago 10 Axelrod et al. | Profiles of Youth
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.
Chapin Hall at the University of Chicago 11 Axelrod et al. | Profiles of Youth
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
Chapin Hall at the University of Chicago 12 Axelrod et al. | Profiles of Youth
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
Chapin Hall at the University of Chicago 13 Axelrod et al. | Profiles of Youth
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
additional supportive services?
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.
Chapin Hall at the University of Chicago 14 Axelrod et al. | Profiles of Youth
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.
Chapin Hall at the University of Chicago 15 Axelrod et al. | Profiles of Youth
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
Chapin Hall at the University of Chicago 16 Axelrod et al. | Profiles of Youth
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.
Chapin Hall at the University of Chicago 17 Axelrod et al. | Profiles of Youth
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.
Chapin Hall at the University of Chicago 18 Axelrod et al. | Profiles of Youth
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.
Chapin Hall at the University of Chicago 19 Axelrod et al. | Profiles of Youth
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.
Chapin Hall at the University of Chicago 20 Axelrod et al. | Profiles of Youth
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
Chapin Hall at the University of Chicago 21 Axelrod et al. | Profiles of Youth
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
Chapin Hall at the University of Chicago 22 Axelrod et al. | Profiles of Youth
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.
Chapin Hall at the University of Chicago 23 Axelrod et al. | Profiles of Youth
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
Chapin Hall at the University of Chicago 24 Axelrod et al. | Profiles of Youth
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
Chapin Hall at the University of Chicago 25 Axelrod et al. | Profiles of Youth
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
Chapin Hall at the University of Chicago 26 Axelrod et al. | Profiles of Youth
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
Chapin Hall at the University of Chicago 27 Axelrod et al. | Profiles of Youth
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
Chapin Hall at the University of Chicago 28 Axelrod et al. | Profiles of Youth
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.
Chapin Hall at the University of Chicago 29 Axelrod et al. | Profiles of Youth
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.
Chapin Hall at the University of Chicago 30 Axelrod et al. | Profiles of Youth
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,
Non-violent Offenses
Chronic,
Violent Offenses
Chapin Hall at the University of Chicago 31 Axelrod et al. | Profiles of Youth
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,
Non-violent Offenses
Chronic,
Violent Offenses
Per
cen
t o
f Y
ou
th
Chapin Hall at the University of Chicago 32 Axelrod et al. | Profiles of Youth
Table 11. Percent of Youth in Each Pre-Probation LCA Class, by Characteristic
LCA Class
Characteristic First time,
Violent Offenses
Chronic,
Non-violent Offenses
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%
Absent for >20% of School Days 31% 38% 37%
Absent for >30% of School Days 20% 28% 27%
Absent for >40% of School Days 13% 17% 17%
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 15 at First Probation 26% 25% 23%
Age 16 at First Probation 34% 39% 38%
Age 17 at First Probation 16% 19% 23%
Age 18+ at First Probation 1% 1% 2%
First Probation in 2010 32% 26% 36%
First Probation in 2011 35% 37% 33%
First Probation in 2012 33% 37% 31%
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.
Chapin Hall at the University of Chicago 33 Axelrod et al. | Profiles of Youth
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,
Non-violent Offenses
Chronic,
Violent Offenses
Chapin Hall at the University of Chicago 34 Axelrod et al. | Profiles of Youth
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
Chapin Hall at the University of Chicago 35 Axelrod et al. | Profiles of Youth
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.
Chapin Hall at the University of Chicago 36 Axelrod et al. | Profiles of Youth
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.
Chapin Hall at the University of Chicago 37 Axelrod et al. | Profiles of Youth
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.
Chapin Hall at the University of Chicago 38 Axelrod et al. | Profiles of Youth
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
additional supportive services?
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.
Chapin Hall at the University of Chicago 39 Axelrod et al. | Profiles of Youth
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.
Chapin Hall at the University of Chicago 40 Axelrod et al. | Profiles of Youth
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 >20% of School Days vs Not 1.18 1.27 1.08
Absent for >30% of School Days vs Not 1.74** 1.53** 0.88
Absent for >40% of School Days vs Not 0.88 0.91 1.04
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
Chapin Hall at the University of Chicago 41 Axelrod et al. | Profiles of Youth
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
Chapin Hall at the University of Chicago 42 Axelrod et al. | Profiles of Youth
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
Chapin Hall at the University of Chicago 43 Axelrod et al. | Profiles of Youth
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
Chapin Hall at the University of Chicago 44 Axelrod et al. | Profiles of Youth
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.
Chapin Hall at the University of Chicago 45 Axelrod et al. | Profiles of Youth
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.
Chapin Hall at the University of Chicago 46 Axelrod et al. | Profiles of Youth
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)
Age 15 at First Probation
Age <14 at First Probation
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)
Age 16 at First Probation
Age <14 at First Probation
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)
Age 17 at First Probation
Age <14 at First Probation
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
Age <14 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)
Absent for >20% of School Days
Not Chronically Absent
- 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)
Absent for >30% of School Days
Not Chronically Absent
- 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)
Chapin Hall at the University of Chicago 47 Axelrod et al. | Profiles of Youth
Absent for >40% of School Days
Not Chronically Absent
- 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
Does Not Have IEP - 1.08
(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
Does Not Have IEP - 1.06
(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
1-4 Arrests Pre-Probation
- - 1.16
(0.22) - - -
1.22* (0.07)
- - - 1.40*** (0.00)
- - - 3.17*** (0.00)
-
10+ Arrests Pre-Probation
1-4 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
No Prior Involvement - - 1.04
(0.82) - - -
0.89 (0.44)
- - - 1.06
(0.64) - - -
0.73 (0.14)
-
Previous Property Index Crime Involvement
No Prior Involvement - - 1.20
(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 = *.