Changes in Risk Profiles: Latent Transition Analysis of Youth on Probation

• Published date: 01 December 2023
• DOI: http://doi.org/10.1177/00938548231206537
• Author: JoAnn S. Lee,Carl E. Appleton,Olivia K. Stuart
• Date: 01 December 2023
• Subject Matter: Articles

CRIMINAL JUSTICE AND BEHAVIOR, 2023, Vol. 50, No. 12, December 2023, 1783 –1804.

DOI: https://doi.org/10.1177/00938548231206537

Article reuse guidelines: sagepub.com/journals-permissions

© 2023 International Association for Correctional and Forensic Psychology

1783

CHANGES IN RISK PROFILES

Latent Transition Analysis of Youth on Probation

JOANN S. LEE

University at Buffalo

CARL E. APPLETON

OLIVIA K. STUART

George Mason University

We use a person-centered approach to examine how patterns of risk and protective factors can change among youth on proba-

tion (N = 7,024). Data were collected using the Youth Assessment and Screening Instrument. We used latent transition

analysis to identify distinct subgroups at intake and exit; estimated probabilities of moving between groups; and examined

recidivism rates. We selected the model with five groups and found that the groups at intake and exit were similar. We char-

acterized the groups in order of declining risk: Highest Risk, Social Drug Risk, Individual Risk, Drug Risk, and Low Risk.

Youth were most likely to move out of the Highest Risk group and most likely to stay in the two lowest risk groups. Common

transitions yielded improved recidivism rates. This knowledge can inform case plans that will increase young people’s likeli-

hood of moving into a lower risk group, thereby improving recidivism rates.

Keywords: youth; risk assessment; probation; juvenile justice; dynamic risk

Probation is the most common outcome for adjudicated cases, with 65% of petitioned

juvenile delinquency cases resulting in over 246,000 youth in a probation program

(Hockenberry & Puzzanchera, 2021). Probation is an opportunity to divert these youth

away from a trajectory toward prison onto a more positive trajectory before they reach

adulthood. Yet, only a small fraction report positive long-term outcomes after their involve-

ment with the juvenile justice system (Gilman et al., 2015; Mendel, 2007).

The risk-need-responsivity (RNR) framework presents an evidence-based approach that

evaluates an individual’s overall risk for recidivism, identifies the individual’s criminogenic

AUTHORS’ NOTE: The authors thank Dr. Faye Taxman for her general support of the overall project; Luke

Brogan for his enthusiasm and affirmative insights; the staff that collected this administrative data, especially

those who took the time to help us understand the data; and especially the youth participants. There is no fund-

ing to report. We have no known conflict of interest to disclose. Correspondence concerning this article should

be addressed to JoAnn S. Lee, School of Social Work, University at Buffalo, 653 Baldy Hall, Amherst, NY

14260; e-mail: joannlar@buffalo.edu.

1206537CJBXXX10.1177/00938548231206537Criminal Justice and BehaviorLee et al. / Latent Transition Analysis of Youth on Probation

research-article2023

1784 CRIMINAL JUSTICE AND BEHAVIOR

needs, and evaluates their responsivity to treatment (Andrews & Bonta, 2010). Using the

RNR framework guides probation officers and case workers on how to use risk assessments

to determine service plans for youth, identifying youth who will benefit most from interven-

tion. The assessment of dynamic need also directs interventions. A robust body of literature

supports the RNR principle’s ability to guide effective correctional intervention (Andrews

& Bonta, 2010; Bonta et al., 2011). This research emphasizes the importance of assessing

individuals’ risk and need factors at the outset of their probation. Probation officers then use

this information to guide their development of supervision plans specifically tailored to

address their clients’ criminogenic needs, lowering their risk level, and reducing their likeli-

hood of recidivating (Latessa et al., 2013; Smith et al., 2012). However, while much atten-

tion has been paid to identifying risk at the outset of probation, less attention has been paid

to understanding how it changes despite advances in risk assessments that differentiate

between dynamic (or changeable) and static factors. Indeed, the RNR developers recom-

mend that probationers be re-assessed throughout their time on supervision, but studies

often do not include assessment data from multiple time points, which limits our ability to

identify the exact nature of the changes that occur (Andrews et al., 2004). As such, research

is needed to explore how scores on risk assessments may change while youth are on proba-

tion and determine how those changes are related to recidivism.

CHANGES IN RISK ASSESSMENT SCORES

Many local agencies administer standardized risk assessments to youth at the start of

probation. These assessments, like the Youth Assessment and Screening Instrument (YASI;

Orbis Partners, 2001), are multi-factor instruments designed to predict future delinquency

by examining varying levels of static and dynamic risk and protective factors (Baird et al.,

2013). Risk factors increase the likelihood of future delinquency while protective factors

buffer the impact of a negative outcome when risk is present (Farrington & Ttofi, 2012;

Serin et al., 2016). Static factors are typically historical in nature and thus unchanging (i.e.,

criminal history). Dynamic risk factors, also known as criminogenic needs, are changeable

characteristics that research has shown relate to criminal behavior (i.e., criminal thought

patterns, criminal peers, and family problems; Serin et al., 2016).

While scholars have focused on identifying youth most at risk for recidivism through

standardized risk assessments, much less attention has been paid to how scores on risk

assessments change (Serin et al., 2016; Skeem et al., 2014). However, studies of a range of

juvenile justice populations (e.g., initial offense, serious offenses, residential placement)

indicate that, when multiple assessments are administered, the most recent assessment is the

strongest predictor of outcome (Baglivio, Wolff, Jackowski, & Greenwald, 2017; Barnes et

al., 2016; Mulvey et al., 2016). In addition, change in risk score is related to recidivism

(Baglivio, Wolff, Jackowski, & Greenwald, 2017; Baglivio, Wolff, Piquero, et al., 2017;

Barnes et al., 2016). This suggests that developing our understanding of how scores on risk

assessment change over time may be an essential missing link to better utilizing risk assess-

ment data.

In addition, risk factors in different domains are probably linked. Previous research tak-

ing a person-centered approach to analyzing risk assessments have identified distinct sub-

groups of youth using standardized risk assessments administered at intake (Brown et al.,

2021; Lee & Taxman, 2020; Onifade et al., 2008; Schwalbe et al., 2008; Walker et al.,