Understanding Changes in Youth Offenders’ Risk Profiles: A Latent Transition Analysis

• Date: 01 July 2020
• Author: Grace S. Chng,Xuexin Xu,Chi Meng Chu,Dongdong Li,Kala Ruby
• DOI: 10.1177/1541204019883922
• Published date: 01 July 2020
• Subject Matter: Articles

Article

Understanding Changes

in Youth Offenders’ Risk

Profiles: A Latent

Transition Analysis

Xuexin Xu

1

, Dongdong Li

1

, Chi Meng Chu

1

,

Grace S. Chng

1

, and Kala Ruby

1

Abstract

This study examined youth probationers’ risk profiles at the start and the end of probation and the

types of transition in risk profiles over time. It further identified the association between the

transition types, their adverse family background as well as their probation completion status. Using

a sample of 935 youth probationers in Singapore, a latent transition analysis was conducted based on

seven dynamic domains captured in the Youth Level of Service/Case Management Inventory 2.0.

Based on the risk profiles, three subgroups of youths were identified: (1) the “De-escalators” had

reduced risk in one or multiple domains; (2) the “Persistors” continued to have moderate risk in

most domains; and (3) the “Escalators” showed an increase in risk levels in one or multiple domains.

Compared to the De-escalators, the Persistors and Escalators were less likely to complete their

probation orders. Further analysis revealed that youths from nonintact families or families with

conviction history showed higher relative risk in being Persistors. These findings contribute to our

understanding on the changes in probationers’ risk profiles over time and provide information for

early and more targeted intervention efforts.

Keywords

youth probation, latent transition analysis, YLS/CMI, risk profile

Youth crime is a costly social issue, which receives great attention from scholars and policy makers.

The Risk–Needs–Responsivity (RNR) framework has been developed for risk assessment and

treatment of offenders and is widely adopted by youth rehabilitation serv ices around the globe

(Bonta & Andrews, 2016). Following this framework, risk assessment tools (such as the Youth

Level of Service/Case Management Inventory [YLS/CMI]) have been developed to identify the risk

1

Ministry of Social and Family Development, Government of Singapore, Singapore

Corresponding Author:

Xuexin Xu, Ministry of Social and Family Development, Government of Singapore, 12 Thomson Road, #12-00, MSF Building,

Singapore, 298136, Singapore.

Email: XU_Xuexin@msf.gov.sg

Youth Violence and JuvenileJustice

2020, Vol. 18(3) 294-312

ªThe Author(s) 2019

Article reuse guidelines:

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DOI: 10.1177/1541204019883922

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and needs of youth offenders (Hoge & Andrews, 2002, 2011). A significant body of research has

provided empirical evidence on the validity of these tools by examining the relationship between

risk scores and youth recidivism (Chu & Zeng, 2017). Majority of these studies assessed risk levels

at a single time point, and empirical research on changes in dynamic risk factors remains to be sparse

(Mulvey et al., 2016).

In addition to the literature on test validity using traditional linear methods, recent research has

started to use a person-centered approach to examine the profiles of youth offenders (Campbell et al.,

2018). Such approach has been used to classify youth offenders into latent subgroups based on their

characteristics of risk and needs in multiple domains and therefore improve the planning of inter-

ventions and the accuracy of risk assessments (Chng, Chu, Zeng, Li, & Ting, 2016). However, little

research has adopted the person-centered approach to examine the types of transition in risk profiles

over time.

To fill this research gap, this study aimed to identify subgroups of youth probationers based on

their dynamic risk factors at the start and end of probation orders and the patterns of transition over

time. The relationships between the types of transition, the youth offenders’ adverse family back-

ground, and their probation completion outcome were also examined to provide further insights on

the potential utility of such classifications.

Youth Offenders’ Risk Profiles

Offender risk assessment has a long history of classifying cases into different subgroups in order to

match interventions to the individual risk and need levels. Two different approaches have been used

to create risk profiles: (a) a variable-centered perspective that ranks the importance of various risk

factors in predicting recidivism and (b) a person-centered approach to classify youth offenders into

subgroups with homogeneous needs.

The variable-centered approach. As indicated by its name, the traditional variable-centered approach

aims to explain relationships between variables of interests (e.g., different risk domains). Numerous

studies have documented the risk and needs of youth offenders and examined the effects of certain

risk factors on recidivism, which is commonly used as a measure for the outcome of youth rehabi-

litation and reintegration services. A meta-analysis of 23 published studies with 15,265 juveniles

was conducted to identify risk factors predicti ng juvenile recidivism (Cottle, Lee, & Heilbru n,

2001). It suggested that offense history, nonsevere pathology, family problems, conduct problems,

use of leisure time, and delinquent peers were strong predictors of youth reoffending.

The YLS/CMI, and subsequently the YLS/CMI 2.0, has been used across the globe to evaluate

the risk and needs of youth offenders and to predict their reoffending behavior (Hoge & Andrews,

2002, 2011). The association between YLS/CMI scores (total or domain) and the recidivism of youth

offenders has been well-established in both the Western (McGrath & Thompson, 2012; Onifade

et al., 2008; Rennie & Dolan, 2010; Schmidt, Campbell, & Houlding, 2011) and the Asian contexts

(Chu et al., 2015; Chu, Yu, Lee, & Zeng, 2014; Li, Chu, Goh, Ng, & Zeng, 2015). For example,

Rennie and Dolan (2010) examined a custody sample of 135 youths in England and found prior

and current offenses, education/employment, family circumstances/parenting, peer relations, sub-

stance abuse, and attitudes/orientation to be significantly associated with higher rates of recidivism.

Similarly, McGrath and Thompson (2012) found that in the Australian Adaptation of the YLS/CMI,

prior and current offenses, education/employment, peer relations, substance abuse, and attitudes/

Beliefs were significant predictors of recidivism. Using a sample of 3,264 youth offenders in

Singapore, Chu et al. (2015) also demonstrated that YLS/CMI total and domain scores were signif-

icant predictors for general recidivism and that the family circumstances/parenting domain was one

of the strongest predictors. The results were replicated in another study with 701 youth probationers

Xu et al. 295