Psychological Assessment 2015, Vol. 27, No. 3, 1013–1021

© 2015 American Psychological Association 1040-3590/15/$12.00 http://dx.doi.org/10.1037/a0038670

Assessing Youth Offenders in a Non-Western Context: The Predictive Validity of the YLS/CMI Ratings Chi Meng Chu, Yirong Lee, Gerald Zeng, Grace Yim, and Chen Yeh Tan

Yaming Ang Singapore

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Ministry of Social and Family Development, Singapore

Shannon Chin

Kala Ruby

Singapore Anglican Community Services, Singapore

Ministry of Social and Family Development, Singapore

Empirical support for the usage of the Youth Level of Service measures has been reported in studies conducted in the North America, United Kingdom, and Australia. Recent meta-analytic studies on the Youth Level of Service/Case Management Inventory (YLS/CMI) have revealed that the measure has modest to moderate predictive validity for general recidivism, but there are very few studies on the predictive validity of the YLS/CMI ratings for recidivism in non-Western contexts. This study examined the predictive validity of the YLS/CMI 2.0 ratings for general recidivism in a sample of 3,264 youth offenders within a Singaporean context (Mfollow-up ⫽ 1,764.5 days; SDfollow-up ⫽ 521.5). Results showed that the YLS/CMI 2.0 overall risk ratings and total scores significantly predicted general recidivism for both male and female youth offenders. Overall, the results suggest that the YLS/CMI 2.0 is suited for assessing youth offenders in terms of their risk for general recidivism within a non-Western context. Keywords: predictive validity, protective factors, risk assessment, risk-needs-responsivity, strengths

With increasing demand for resources as well as the need to provide evidence and to ensure accountability for the efficacy of offender rehabilitation, using offender risk assessment measures to inform classification and management decisions is a critical com-

ponent of any offender rehabilitation practice. As such, empirically reliable, valid, and culturally sensitive risk assessment measures that systemically assess the risk and needs of offenders are paramount to the success of offender rehabilitation including those of the youth offenders.

This article was published Online First January 19, 2015. Chi Meng Chu, Clinical and Forensic Psychology Branch and Centre for Research on Rehabilitation and Protection, Ministry of Social and Family Development, Singapore; Yirong Lee, Clinical and Forensic Psychology Branch, Ministry of Social and Family Development; Gerald Zeng, Centre for Research on Rehabilitation and Protection, Ministry of Social and Family Development; Grace Yim and Chen Yeh Tan, Probation Services Branch, Ministry of Social and Family Development; Yaming Ang, Freelance Researcher, Singapore; Shannon Chin, Community Rehabilitation and Support Service, Singapore Anglican Community Services, Singapore; Kala Ruby, Probation Services Branch, Ministry of Social and Family Development The views expressed are those of the authors and do not represent the official position or policies of the Ministry of Social and Family Development. We thank the staff of Clinical and Forensic Psychology Branch and Probation Services Branch of the Ministry of Social and Family Development for their support. In addition, we would like to express gratitude to Ms. Jennifer Teoh, Ms. Bernadette Alexander, Ms. Aileen Tan, Mr. James Loh, Mr. Alvin Koh, Ms. Amelia Wong, Mr. Han Siang Lim, Ms. Cheryl Tan, Ms. Amanda Tan, and Ms. Melissa Yeo for their facilitation at different stages of the project. Correspondence concerning this article should be addressed to Chi Meng Chu, Centre for Research on Rehabilitation and Protection, Ministry of Social and Family Development, 512 Thomson Road, MSF Building, 12th Floor, Singapore 298136. E-mail: [email protected]

Risk–Needs–Responsivity Framework and Risk Assessment Measures According to the risk–needs–responsivity (RNR) framework (Andrews & Bonta, 2010), effective offender rehabilitation requires the accurate classification of the offender’s level of risk and needs. With accurate identification and classification of risk and needs, clinicians can make informed decisions about the levels of supervision, as well as the type and intensity of the interventions that should be provided. The framework also states that intervention should target those criminogenic needs that are functionally related to criminal behavior. Moreover, the RNR framework also posits that the style and mode of intervention should match the offender’s abilities and learning style. RNR principles have been shown to be important in both offender assessment and intervention domains, and the level of service risk assessment measures (including the Youth Level of Service/Case Management Inventory [YLS/CMI]; Hoge & Andrews, 2002, 2011) are the most widely used products of the RNR (Andrews, Bonta, & Wormith, 2010). In the past, assessments of risk and needs were often based on unstructured clinical judgments, and such a decision-making approach was criticized as being inaccurate (Ægisdóttir et al., 2006; Grove, Zald, Lebow, Snitz, & Nelson, 2000; Monahan, 1981). However, risk assessment practices have advanced over the 1013

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past three decades, and there is a greater reliance on risk assessment measures that are structured and empirically based. These structured risk assessment measures have been found to provide valid and consistent assessments of risk for future offending behavior and potential intervention needs (Hoge, 2002), and the YLS/CMI represents one of the most widely used, structured risk assessment measures for assessing the risk of general recidivism and criminogenic needs in youth offenders. The YLS/CMI is comprised of static and dynamic risk factors that are associated with reoffending; in particular, static risk factors are variables that are not amenable to change through planned intervention over time. Hence, these static factors are unlikely to be ameliorated for purpose of managing or reducing the risk of future offending over time (Douglas & Skeem, 2005). In contrast, dynamic risk factors for reoffending (also known as criminogenic needs) fluctuate with time and circumstances. The dynamic risk factors can be changed as a result of deliberate intervention (Webster, Douglas, Belfrage, & Link, 2000), and hence reducing the overall level of risk for future offending. The YLS/CMI assists the practitioners to reliably assess risk factors and criminogenic needs and provides much needed information to manage risk and target the relevant areas for intervention. In addition to these risk factors, the YLS/CMI has an accompanying case management component to guide the practitioners to make recommendations and also account for other factors that may affect the successful management of the youth offender. Furthermore, the YLS/CMI also allows for the assessment of strengths in the youth offenders, and such an assessment can provide essential information to risk assessment and intervention planning. Recent studies have emphasized on the utility of protective factors for mitigating the level of risk that an individual poses, and also possibly reducing the likelihood of recidivism (e.g., Hartman, Turner, Daigle, Exum, & Cullen, 2008; Lodewijks, de Ruiter, & Doreleijers, 2010). However, there are few studies on the predictive utility of strengths or protective factors in youth risk assessment measures, namely the Structured Assessment of Violence Risk in Youth (SAVRY; e.g., Lodewijks, de Ruiter, & Doreleijers, 2010; Rennie & Dolan, 2010), and no published study has systematically examined the YLS/CMI strength ratings yet.

Predictive Validity of the Ratings for YLS Measures Across Various Countries Empirical support for the usage of the YLS measures have been reported in studies conducted in the United States (e.g., Onifade et al., 2008), United Kingdom (e.g., Marshall, Egan, English, & Jones, 2006), Canada (e.g., Schmidt, Campbell, & Houlding, 2011), Australia (e.g., McGrath & Thompson, 2012), Japan (Takahashi, Mori, & Kroner, 2013), and Singapore (Chu, Ng, Fong, & Teoh, 2012). Recent meta-analytic studies on the YLS measures have revealed that its ratings have modest to moderate predictive validity for general recidivism. In particular, Schwalbe (2007) found a mean weighted area under curve (AUC) of .641 based on a review of 11 YLS studies. As a general rule for practice, AUCs greater than .54, .63, and .71, as well as correlation coefficients (r) that are greater than .10, .24, and .37, are regarded as small, moderate, and large effects, respectively (Rice & Harris, 2005). In an overlapping but larger sample of 19 studies, the mean-weighted correlation between YLS total scores and general recidivism was

.32 (Olver, Stockdale, & Wormith, 2009). In their meta-analysis, Olver et al. also showed that the ratings of the YLS measures had lower predictive validity for general recidivism when they were used in other western contexts outside of Canada (mean-weighted correlation of .26 vs. .35). Olver and colleagues suggested that “‘international’ differences contributed to the variability across studies” (p. 348). Further examination of such variability across contexts with regard to the predictive validity of the ratings for the level of service risk assessment measures revealed that the location effect is a function of the authors’ allegiance (i.e., being a Canadian, which reflects the integrity of the assessment), and the reliability of the outcome measure(s) (Andrews et al., 2011). This finding is also reflected in other risk assessment measures that are developed in Canada (see Harris, Rice, & Quinsey, 2010; Olver et al., 2009; Yang, Wong, & Coid, 2010). Moreover, the generalizability of the original development sample to the samples used in subsequent studies does affect the predictive validity. In other words, there may be a decrease in true predictive validity of the ratings for a risk assessment measure as “it transverses national, hence legal, boundaries” (Andrews et al., 2011, p. 426). Differences in legislation, definitions of outcomes, interpretation of the criteria for rating of the risk assessment measure will affect its predictive validity when used in different contexts. To the best of the authors’ knowledge, there are only two published studies on the predictive validity of the YLS/CMI ratings for various recidivistic outcomes within non-Western contexts (Chu et al., 2012; Takahashi et al., 2013). It was noted that Chu et al.’s study (N ⫽ 104) was restricted to youth who sexually offended in a Singaporean context; notably, the YLS/CMI (total score) was useful in predicting nonsexual recidivism for youth who sexually offended (AUC ⫽ .65) but was limited in predicting sexual recidivism (AUC ⫽ .29). On the other hand, Takahashi et al. (N ⫽ 389) found the YLS/CMI ratings had wide-ranging predictive validity for nonviolent, violent, and general recidivism for both community-based and institutionalized juvenile offenders (AUCs ⫽ .50 to .87); in particular, shorter follow-up periods and assessments for community-based juvenile offenders resulted to higher predictive validity for recidivistic outcomes, especially in terms of nonviolent and general recidivism. Notwithstanding these two studies, there is generally a dearth of published studies on the predictive validity of the ratings for the YLS/CMI subscales within non-Western contexts.

Application of the RNR Framework and YLS/CMI in Singapore Singapore is an independent island-state in South East Asia with a total population of 5.4 million (Singapore Department of Statistics, 2013). Pertaining to crime statistics, youth arrests accounted for about 10% of all arrests in Singapore (Singapore Police Force, 2013). Many statutes in Singapore are based on English common law (e.g., the Criminal Procedure Code, 2012), but there are some statutes that are based on legislation from other jurisdictions; for example, the Penal Code (2008) is based on the Indian Penal Code, which was (nonetheless) first formulated by the English in 1800s. As such, there are similarities in the way that offenses are defined in Singapore when compared with the abovementioned countries, but the exact language of the laws might vary somewhat. In

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ASSESSING YOUTH OFFENDERS

particular, cultures and societies often define what attitudes and behaviors are considered “normal” and “deviant.” Although there is some agreement across cultures about what constitutes offending behavior, the development of deviant attitudes and behaviors can differ due to cultural norms, gender roles, morals, religion, taboos, and expectations (e.g., Bhugra, Popelyuk, & McMullen, 2010; Lahlah, Van der Knaap, Bogaerts, & Lens, 2013). It is possible that the motivation, risk factors, and pathways for offending may differ cross-culturally due to cross-cultural differences as to how individuals cope, self-regulate, or even report crime. Therefore, it may be necessary to examine the empirical evidence whenever there is any adaptation of assessment and intervention frameworks (that are developed for the Western contexts) into non-Western contexts, as well as the accompanying measures (e.g., measures might need new norms or cut-offs that would suit the new context due to the aforementioned factors). In the early 2000s, there was a collective move by the youth and adult correctional services toward using structured and empirically informed approach with regard to assessing youth offenders’ risk and needs. The RNR framework was introduced in Singapore to provide a theoretical and empirical-based approach to conduct offender assessment and rehabilitation. Importantly, the YLS/CMI, and subsequently the YLS/CMI 2.0, was chosen as the primary risk assessment measure to assess the risk and needs of youth offenders (Chua, Chu, Yim, Chong, & Teoh, 2014). Within the Singaporean context, YLS/CMI 2.0’s coding criteria and cut-offs for risk categories were modified and developed following its introduction in 2011, respectively, for assessing the local youth offenders and in accordance with the local legislation and procedures.

Present Study Considering that there is currently limited empirical knowledge pertaining to the YLS/CMI 2.0’s predictive validity for recidivistic outcomes within non-Western contexts, the present study sought to examine the predictive validity of the YLS/CMI 2.0 total scores and overall risk ratings for general recidivism in Singapore using a large sample of youth offenders. In addition, the present study sought to examine the predictive validity of the YLS/CMI 2.0 subscale ratings for general recidivism, as well as the association between general recidivism and (a) the strength ratings, and (b) other needs/special considerations.

Method Source Sample The sample consisted of 3,264 youth (age 12–18 years) who were convicted of criminal offenses. They were referred to the Probation Services Branch of the Ministry of Social and Family Development (Singapore) between January 2004 and December 2008 and were placed on community supervision following their court sentencing. The mean age of these youth at referral to the Probation Services Branch was 15.42 years (Mdn ⫽ 15.00; SD ⫽ 1.19), and the large majority of the youth were males (90.4%, 2,951/3,264). Slightly more than half of the youth were Chinese (53.6%, 1,749/3,264); 31.9% were Malay (1,042/3,264), 9.3% (303/3,264) were Indian; and 5.2% (170/3,264) were of other

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ethnicity. The current sample included 96.9% (3,264/3,370) of the youth offenders who were placed on community supervision during this period; the remaining could not be coded as a result of missing information or file retrieval difficulties. In terms of offense characteristics, the mean number of index offenses1 committed was 2.61 (SD ⫽ 2.82, range ⫽ 1– 40); 31.6% (1,030/3,264) had committed index violent offense(s), 2.1% (69/3,264) had committed index sexual offense(s), and 78.6% (2,564/3,264) had committed index nonviolent nonsexual offense(s). A small minority of the sample (1.9%, 63/3,264) had a prior offense history2 as indicated on criminal records.

Measures YLS/CMI 2.0 (Hoge & Andrews, 2011). The YLS/CMI 2.0 is a structured assessment instrument designed to facilitate the effective intervention and rehabilitation of youth who have committed criminal offenses (age 12–18 years) by assessing their risk level, criminogenic needs, and strengths. It consists of 42 items (scored as either present or absent) that are divided into eight subscales (Prior or Current Offenses/Dispositions, Family Circumstances/Parenting, Education/Employment, Peer Relations, Substance Abuse, Leisure/Recreation, Personality/Behavior, and Attitudes/Orientation). The item scores (i.e., the number of indicated risk factors/needs) can be aggregated to obtain a total risk/needs score. In addition to the eight subscales, the YLS/CMI 2.0 also consists of items that pertain to noncriminogenic needs and responsivity factors, which can be rated as present or absent. It should be noted that the YLS/CMI 2.0 coding descriptions for some items were adapted, with consultation from Professor Robert Hoge, to suit the Singaporean context. For example, for the items in Prior or Current Offenses/Dispositions subscale, the term convictions was localized to follow legal terminology in Singapore. Similarly, for the items in Family Circumstances/Parenting, Education/Employment subscale, school and work contexts were localized to include the learning centers in youth correctional institutions and also compulsory military service for male youth. Localized examples were also included in the item descriptions for the various subscales to assist with the ratings. With regard to the determination of the cut-off scores, the distributions of scores from the original normative samples were considered (as advised by Professor Hoge). In addition, the probability of future recidivism for individuals with particular scores, as well as the specificity and sensitivity of the scores were also taken into account to fine-tune the cut-offs for the Singaporean male and female youth offender samples. The cut-off scores of the risk bins for the male youth offenders under community supervision in Singapore are: 0 to 10 (low), 11 to 19 (moderate), 20 to 26 (high), and 27 to 42 (very high). On the other hand, the cut-off scores of the risk bins for the female youth offenders on community supervision in Singapore are: 0 to 12 (low), 13 to 19 (moderate), and 20 to 42 (high). There is no very high risk bin for the 1 Index offense(s) refers to the offense(s) that the youth was charged with and convicted of, when they first came into contact with the juvenile justice system during this period (i.e., 2004 to 2008). 2 Prior offense history refers to the youth’s history of convictions, as indicated in official records. The prior offense history did not include those offenses that the youth were not charged with as a result of diversionary procedures.

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female youth offenders on community supervision. As the proportion of the youth offenders who were assessed as high and very high risk were small in this sample (4.0%), we have decided to group these two categories together for purpose of analyses. Although the predictive validity of the overall risk rating was examined in this study, it should be noted that the raters had not used any professional override to change the overall risk rating.

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Procedure The approval for the current research study was obtained from the Ministry of Social and Family Development before the commencement of the study. For the purpose of this study, two psychologists, one probation officer, as well as five research assistants had conducted clinical file reviews between January 2011 and September 2012. They had completed coding for the YLS/CMI 2.0 based on file information available at the time of the initial assessment at the presentencing stage; information available subsequent to the presentencing stage were not considered for coding purposes to minimize criterion contamination. As such, this is a prospective study (i.e., using data from the time of the index offense to predict events that occurred after the index offense) with the data coded retrospectively. These raters had attended a 3-day training program conducted by accredited trainers; the training program involved lectures, discussions, case studies and scoring practices, as well as a test. The clinical files that were obtained from Probation Services Branch contained (a) psychological reports prepared by psychologists at CFPB; (b) presentence reports prepared by probation officers; (c) charge sheets; (d) statement of facts; (e) any previous assessment and treatment reports; as well as (f) school reports. Psychological and presentence reports contain specific information pertaining to several key areas of assessment (i.e., personal, family, psychiatric, and criminal offending histories, as well as the current offending behaviors and risk management issues). Specifically, these areas yielded valuable information about the youth’s upbringing, interaction with peers and authorities, general and academic functioning, values, family and school environments, as well as information relating to the youth’s offending, treatment, management/supervision, and responsivity issues. To examine the interrater reliability for the ratings pertaining to the YLS/CMI 2.0, the raters had separately coded a randomly selected sample of 31 files, and the intraclass correlation coefficients for single rater (using absolute agreement definition; ICCs) were .63 (good) for the YLS/CMI 2.0 total score (see Cicchetti, 1994, for a classification of ICCs). The ICCs (single rater, absolute agreement definition) for the eight subscales were .43 (fair) for Prior or Current Offenses/Dispositions, .50 (fair) for Family Circumstances/Parenting, .60 (good) for Education/Employment, .50 (fair) for Peer Relations, .49 (fair) for Substance Abuse, .45 (fair) for Leisure/ Recreation, .55 (fair) for Personality/Behavior, and .48 (fair) for Attitudes/Orientation. In terms of the definition of the recidivistic outcome, general recidivism refers to any conviction of sexual (e.g., indecent exposure, molestation, peeping, rape, and sodomy), violent (e.g., physical assault, rioting, murder, and robbery), nonviolent nonsexual (e.g., theft, fraud, burglary, drug use, and drug trafficking) offenses that were committed following the initial court order, breaches of court orders, or any combination of the aforementioned outcomes.

Official recidivism data were only obtained following the completion of coding of all other variables and the cut-off date for the recidivism data was April 20, 2011.

Statistical Analyses The sample was characterized using descriptive statistics, with categorical data reported as numbers and percentages, and continuous data presented in relation to the mean and standard deviation. Histograms of the continuous data were plotted to check for skewed distributions. Chi-square tests of association were computed for categorical data, and correlational analyses were also conducted to examine the relationship between continuous data and the recidivistic outcome (dichotomous data). Receiver operating characteristics (ROC) analyses were conducted to examine the predictive validity of the YLS/CMI 2.0 total scores, and Cox regression analyses were also conducted to examine whether the YLS/CMI 2.0 overall risk ratings were predictive of recidivistic outcomes while accounting for differences in follow-up period. Benjamini and Hochberg false discovery rate (FDR) corrections were conducted to control for Type I error that may arise from computing multiple comparisons; specifically, it is a less conservative but more powerful statistical approach than Bonferroni-type adjustments (Benjamini & Hochberg, 1995). Effect sizes were also computed to demonstrate the strength of the associations between variables. Analyses were conducted using SPSS version 19.

Results Recidivism Data The mean follow-up period was 1,764.5 days (Mdn ⫽ 1,762.5, SD ⫽ 521.5, range ⫽ 840 –2,666). With regard to the recidivism rates, 37.6% (1,228/3,264) of the current sample was convicted of new offense(s) during the follow-up period; 33.5% (1,095/3,264) of the sample were convicted of new nonviolent nonsexual offenses, 10.3% (336/3,264) violent offenses, and 0.5% (17/3,264) sexual offenses.

YLS/CMI 2.0 Total Score and Subscales Table 1 shows the means and standard deviations, as well as the correlation to general recidivism (i.e., any type of reoffense and/or breach of court orders) for the subscale scores of the YLS/CMI 2.0. The mean total score of the YLS/CMI 2.0 for the overall sample was 11.78 (SD ⫽ 4.10, range ⫽ 1–26), and the correlation between the YLS/CMI 2.0 total score and general recidivism was .24, p ⬍ .001. All the YLS/CMI 2.0 subscale scores were also significantly correlated to general recidivism even after FDR corrections. Table 2 shows the breakdown of risk categories for each subscale. In terms of predicting general recidivism in the overall sample, as well as the male and female subsamples, the AUCs for the YLS/CMI 2.0 total score were .64 (95% confidence interval [95% CI] [.62, .66], SE ⫽ 0.01, p ⬍ .001), .65 (95% CI [.62, .66], SE ⫽ 0.01, p ⬍ .001), and .67 (95% CI [.62, .66], SE ⫽ 0.03, p ⬍ .001), respectively. Furthermore, Cox regression analyses revealed that the YLS/ CMI 2.0 overall risk ratings of low, moderate, and high were significantly different from each other in the overall sample

ASSESSING YOUTH OFFENDERS

Table 1 Means, Standard Deviations, and Correlations to General Recidivism for the Youth Level of Service/Case Management Inventory (YLS/CMI) 2.0 Total and Subscale Scores

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YLS/CMI Overall sample (N ⫽ 3,264) YLS/CMI Total Score Prior or Current Offenses/Dispositions Family Circumstances/Parenting Education/Employment Peer Relations Substance Abuse Leisure/Recreation Personality/Behavior Attitudes/Orientation Male subsample (n ⫽ 2,951) YLS/CMI Total Score Prior or Current Offenses/Dispositions Family Circumstances/Parenting Education/Employment Peer Relations Substance Abuse Leisure/Recreation Personality/Behavior Attitudes/Orientation Female subsample (n ⫽ 313) YLS/CMI Total Score Prior or Current Offenses/Dispositions Family Circumstances/Parenting Education/Employment Peer Relations Substance Abuse Leisure/Recreation Personality/Behavior Attitudes/Orientation

M

SD

rrecidivism

p

11.78

4.10

.24

⬍.001a

0.18 2.11 2.02 3.06 0.18 2.21 0.85 1.18

0.41 1.17 1.54 1.12 0.53 0.96 0.98 0.86

.14 .21 .18 .05 .07 .10 .11 .15

⬍.001a ⬍.001a ⬍.001a .006a ⬍.001a ⬍.001a ⬍.001a ⬍.001a

11.63

4.05

.25

⬍.001a

0.19 2.07 2.00 3.01 0.16 2.20 0.84 1.17

0.41 1.16 1.56 1.12 0.49 0.94 0.98 0.84

.14 .21 .19 .06 .07 .10 .11 .14

⬍.001a ⬍.001a ⬍.001a .002a ⬍.001a ⬍.001a ⬍.001a ⬍.001a

13.21

4.29

.26

⬍.001a

0.12 2.46 2.27 3.57 0.36 2.25 0.92 1.27

0.33 1.27 1.32 0.97 0.83 1.14 0.97 1.01

.19 .24 .14 .06 .10 .10 .11 .23

.001a ⬍.001a .016a ns ns ns .047 ⬍.001a

Denotes that the difference was statistically significant (p ⬍ .05) after Benjamini and Hochberg False Discovery Rate correction.

a

(hazard ratio [HR]Moderate-Low ⫽ 2.09, 95% CI [1.84, 2.38], p ⬍ .001; HRHigh-Low ⫽ 3.61, 95% CI [2.82, 4.60], p ⬍ .001; HRHigh-Moderate ⫽ 1.72, 95% CI [1.37, 2.17], p ⬍ .001), as well as male (HRModerate-Low ⫽ 2.09, 95% CI [1.83, 2.38], p ⬍ .001; HRHigh-Low ⫽ 3.62, 95% CI [2.79, 4.71], p ⬍ .001; HRHigh-Moderate ⫽ 1.74, 95% CI [1.36, 2.22], p ⬍ .001) and female subsamples (HRModerate-Low ⫽ 2.17, 95% CI [1.35, 3.39], p ⫽ .001; HRHigh-Low ⫽ 4.18, 95% CI [2.04, 8.54], p ⬍ .001; HRHigh-Moderate ⫽ 1.92, 95% CI [1.01, 3.66], p ⬍ .05).

YLS/CMI Strength Ratings and Other Needs/Special Considerations The mean number of strengths was 0.29 (Mdn ⫽ 0; SD ⫽ 0.62; range ⫽ 0 to 4); the majority of the sample was not rated as having strengths (78.9%, 2,576/3,264), 15% (489/3,264) had one strength, 4.7% (152/3,264) had two, 1.4% (45/3,264) had three, and 0.2% (2/3,264) had four. There was also a significant correlation between the number of strengths and general recidivism, roverall ⫽ ⫺.14, p ⬍ .001. Examining gender differences, the correlation between the number of strengths and general recidivism was significant for the male subsample (rmale ⫽ ⫺.15, p ⬍ .001) but not for the female

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subsample (rfemale ⫽ ⫺.02, ns). Table 3 shows the other needs/ special considerations that are significantly associated with recidivism. For the male subsample, 11 variables were significantly associated with general recidivism after FDR correction. In contrast, none were significantly associated with general recidivism for the female subsample after FDR correction.

Discussion Predictive Validity of the YLS/CMI 2.0 Ratings in a Non-Western Context Apropos of overall predictive validity of the YLS/CMI 2.0 ratings, we noted that ROC and Cox regression analyses revealed that the YLS/CMI 2.0 total scores and overall risk ratings were predictive of general recidivism for male and female youth offenders. The YLS/CMI 2.0 risk categories (using Singapore norms) had also differentiated the low-, moderate-, and high-risk groups, with each group having significant differences in terms of time to reoffense—this suggests that the local norms have sufficient validity. In addition, ROC and correlational analyses, with a mean follow-up of almost 5 years, showed that the YLS/CMI 2.0 total scores were moderately predictive of general recidivism (AUCoverall ⫽ .64, roverall ⫽ .24; AUCmale ⫽ .65, rmale ⫽ .24; AUCfemale ⫽ .67, rfemale ⫽ .26). These indices were consistent and comparable with the results from recent meta-analyses examining the predictive validity of the YLS/CMI 2.0 ratings from non-Canadian jurisdictions (Olver et al., 2009; Schwalbe, 2007) but were significantly lower than those predictive validity indices for the Japanese community subsample in Takahashi et al.’s (2013) study (AUC ⫽ .76). Despite the significant predictive utility of the YLS/CMI 2.0 ratings, there is still a fair amount of the variance in the recidivism rate between the offenders that is unexplained. The unexplained variance may be, in part, explained by differences in intraindividual-, environmental-, and system-level variables (e.g., developmental changes across the follow-up period, differences between actual and reported crimes as well as neighborhood crime rates and socioeconomic factors; Olver et al., 2009; Onifade, Petersen, Bynum, & Davidson, 2011). Nevertheless, this study has yielded a set of variables that are significantly predictive of general recidivism in the Singaporean context over a substantial follow-up period. The mean total score of this sample was similar to the mean from the Japanese study (Takahashi et al., 2013), but it was generally lower than those figures reported in published studies from Western contexts (e.g., Marshall et al., 2006; McGrath & Thompson, 2012; Olver, Stockdale, & Wong, 2012; Welsh et al., 2008). Apart from the domains of Education/Employment as well as Leisure/Recreation, the means of the subscales were generally lower than those taken from the published Western studies. However, the means of the subscales were higher than those from the Japanese study except for the domains of Prior or Current Offense/ Disposition and Personality/Behavior. Thus, it appears that the observed differences are likely functions of the characteristics of the comparison groups (e.g., some included high-risk, institutionalized youth offenders, and those with mental health issues), cross-boundary differences (e.g., a relatively less serious youth crime situation in Singapore as compared to other Western contexts, and tough laws to combat substance abuse), and differences

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Table 2 The Breakdown of Risk Categories for Youth Level of Service/Case Management Inventory (YLS/ CMI) 2.0 Overall Risk Rating and Subscales Risk categories Low

Moderate

Higha

1,325 (40.6%) 2,688 (82.4%) 2,102 (64.4%) 580 (17.8%) 162 (5.0%) 2,858 (87.6%) 250 (7.7%) 1,528 (46.8%) 574 (17.6%)

1,809 (55.4%) 573 (17.6%) 1,086 (33.3%) 2,104 (64.5%) 1,378 (42.2%) 370 (11.3%) 472 (14.5%) 1,730 (53.0%) 2,640 (80.9%)

130 (4.0%) 3 (⬍0.1%) 76 (2.3%) 580 (17.8%) 1,724 (52.8%) 36 (1.1%) 2,542 (77.9%) 6 (0.2%) 50 (1.5%)

1,199 (40.6%) 2,414 (81.8%) 1,941 (65.8%) 551 (18.7%) 148 (5.0%) 2,613 (88.5%) 198 (6.7%) 1,397 (47.3%) 501 (17.0%)

1,642 (55.6%) 534 (18.1%) 951 (32.2%) 1,872 (63.4%) 1,334 (45.2%) 317 (10.7%) 456 (15.5%) 1,548 (52.5%) 2,406 (81.5%)

110 (3.7%) 3 (0.1%) 59 (2.0%) 528 (17.9%) 1,469 (49.8%) 21 (0.7%) 2,297 (77.8%) 6 (0.2%) 44 (1.5%)

126 (40.3%) 274 (87.5%) 161 (51.4%) 29 (9.3%) 14 (4.5%) 245 (78.3%) 52 (16.6%) 131 (41.9%) 73 (23.3%)

167 (53.4%) 39 (12.5%) 135 (63.4%) 232 (74.1%) 44 (14.1%) 53 (16.9%) 16 (5.1%) 182 (58.1%) 234 (74.8%)

20 (6.4%) 0 (0%) 17 (5.4%) 52 (16.6%) 255 (81.5%) 15 (4.8%) 245 (78.3%) 0 (0%) 6 (1.9%)

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YLS/CMI Overall sample (N ⫽ 3,264) Overall Risk Rating Prior or Current Offenses/Dispositions Family Circumstances/Parenting Education/Employment Peer Relations Substance Abuse Leisure/Recreation Personality/Behavior Attitudes/Orientation Male subsample (n ⫽ 2,951) Overall Risk Rating Prior or Current Offenses/Dispositions Family Circumstances/Parenting Education/Employment Peer Relations Substance Abuse Leisure/Recreation Personality/Behavior Attitudes/Orientation Female subsample (n ⫽ 313) Overall Risk Rating Prior or Current Offenses/Dispositions Family Circumstances/Parenting Education/Employment Peer Relations Substance Abuse Leisure/Recreation Personality/Behavior Attitudes/Orientation a

Denotes that the high and very high risk groups were combined for analyses purposes due to small proportions in relation to other subgroups.

in coding criteria of the YLS/CMI (which could be a reflection of the differences in the legal environments). However, the scores for the Prior or Current Offense/Disposition appeared very low even when compared to the mean from the Japanese study—the low prior offense rate might have impacted this, and that range restriction of scores might have affected the validity findings. Overall, this suggests that a reexamination of the coding criteria may be necessary to obtain more variability in the subscale score, which may in turn increase the utility of this subscale for predicting further offenses. Pertaining to gender differences, the present study showed that the female youth offenders were rated higher on the YLS/CMI 2.0 total score, as well as most of the other subscales (apart from Prior or Current Offenses/Dispositions) as compared to their male counterparts. This pattern of results is somewhat similar to several Western studies on the YLS/CMI (e.g., Olver et al., 2012; Schmidt et al., 2011; McGrath & Thompson, 2012) but is different from others (e.g., Jung & Rawana, 1999; Marshall et al., 2006). With regard to the YLS/CMI 2.0 subscales, the results of this study showed that eight and four subscales were univariately associated with general recidivism for the male and female subsamples, respectively. It is clear that this finding whereby different criminogenic needs are predictive of recidivism relates the need principle in the RNR framework, and that targeting these needs will

reduce the propensity for criminal reoffending. On the other hand, the findings on gender differences relate to the responsivity principle. Compared to their male counterparts, it seems that the female youth offenders in Singapore have a higher level of criminogenic needs (as measured on the YLS/CMI 2.0) when they enter the juvenile justice system. Possibilities for such an observation could be (a) there are different pathways for offending across gender, which would be linked to differences in risk factors for offending behavior; and/or (b) many “gender-neutral” assessment measures that were developed for males had limited relevance for females, thus some risk factors and needs that are most relevant to female offenders might have been omitted or ignored (e.g., Taylor & Blanchette, 2009; Salisbury & Van Voorhis, 2009; Van Voorhis, Wright, Salisbury, & Bauman, 2010). Such possibilities could be areas of future empirical inquiry within the Singaporean context, and there appears to be a need to feature gender-responsive risk factors in risk assessment measures too. In addition, there are some grounds to explore gender-responsive interventions for youth offenders considering the findings on criminogenic needs in the present study (see Table 3). Notwithstanding the gender differences, we hypothesized that the other domains exert their influence on the perpetuation of criminal conduct through the abovementioned significant risk fac-

ASSESSING YOUTH OFFENDERS

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Table 3 Significant Associations Between Other Needs/Special Considerations and General Recidivism (Split by Gender; Chi Square Analyses) Recidivist n (%)

Nonrecidivist n (%)

p

␸

275/1,133 (24.3%) 99/1,133 (8.7%) 258/1,133 (22.8%) 52/1,133 (4.6%) 55/1,133 (4.9%) 14/1,133 (1.2%) 31/1,133 (2.7%) 388/1,133 (34.2%) 15/1,133 (1.3%) 149/1,133 (13.2%) 148/1,133 (13.1%) 318/1,133 (23.4%) 166/1,133 (14.7%) 393/1,133 (34.7%) 18/1,133 (1.6%) 20/1,133 (1.8%)

269/1,818 (14.8%) 116/1,818 (6.4%) 286/1,818 (15.7%) 38/1,818 (2.1%) 59/1,818 (3.2%) 8/1,818 (0.4%) 30/1,818 (1.7%) 462/1,818 (25.4%) 6/1,818 (0.3%) 120/1,818 (6.6%) 96/1,818 (5.3%) 373/1,818 (20.5%) 204/1,818 (11.2%) 518/1,818 (28.5%) 14/1,818 (0.8%) 12/1,818 (0.7%)

.001a .017 ⬍.001a ⬍.001a .027 .015 .044 ⬍.001a .002a ⬍.001a ⬍.001a ⬍.001a .006a ⬍.001a .037 .005a

.12 .04 .09 .07 .04 .05 .04 .10 .06 .11 .14 .09 .05 .07 .04 .05

30/95 (31.6%) 10/95 (10.5%)

38/218 (17.4%) 9/218 (4.1%)

.005 .029

.16 .12

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Other needs/special considerations Male subsample (n ⫽ 2,951) Chronic history of offenses (family) Marital conflict (family) Financial/accommodation problems (family) Abusive father (family) Other problems (family) Diagnosis of conduct disorder/oppositional defiant disorder Financial/accommodation problems Gang involvement (ever been involved) History of assault on authority figures History of bullying History of running away History of sexual/physical assault Peers outside age range Poor problem-solving skills Victim of physical/sexual abuse Witness of domestic violence Female subsample (n ⫽ 313) History of running away Victim of physical/sexual abuse a

Denotes that the difference was statistically significant (p ⬍ .05) after Benjamini and Hochberg False Discovery Rate correction.

tors within our local context, but this is an empirical question that needs to be further explored using structural equation modeling. Although the static domain (i.e., Prior or Current Offenses/Dispositions) remained as a significant predictor of general recidivism in this study (over the long term), it is clear that other dynamic variables have a part to play in the risk assessment of youth offenders. In terms of YLS/CMI strength ratings and other needs/special considerations (i.e., responsivity factors and other needs), we believe that this is the only published study on the YLS/CMI (to the best of the authors’ knowledge) that has examined the utility of these components in addition to the YLS/CMI subscales. The number of strengths in the male subsample had an inverse univariate relationship with general recidivism suggesting that protective factors that buffered the youth offenders against further criminal offending behavior albeit a relatively small effect size. However, this relationship was not found in the female subsample—suggesting that there could be different protective factors across gender. Perhaps strengths also may interact with risk factors when used to predict (non)recidivism, but the actual processes that may not have been systematically examined here. For example, protective and risk factors were found to predict desistance within 3 years, but desistance over a longer period was negatively predicted by only risk factors (Stouthamer-Loeber et al., 2008), suggesting that protective factors have a shorter “shelf life” in terms of predicting (non)recidivism. More research is clearly needed in this area. In the extant literature on the YLS/CMI, very little empirical work has been published on the other needs/special considerations. One recent study examined whether risk and need assessment is linked to the case management of youth offenders as well as whether adherence to RNR principles in case management is related to recidivism (Luong & Wormith, 2011), but this study did not examine the utility of the other needs/special considerations. The present study suggests that other needs/special considerations

(e.g., chronic history of family criminality, and family financial/ accommodation problems, diagnosis of conduct disorder/oppositional defiance disorder, gang involvement, and history of running away) variables were found to be associated with general recidivism in youth offenders. Some of these variables have already been shown to be associated with delinquent or offending behavior within the Singaporean context (Ang & Huan, 2008; Chu, Daffern, Thomas, & Lim, 2012); therefore, the CMI component allows the practitioners to consider the relevant these variables that may otherwise affect the assessment of risk, as well as the offender rehabilitation process (as responsivity issues). Similar to the YLS/ CMI strength ratings, more research needs to be conducted to understand the utility of these variables better.

Limitations and Future Directions First, we relied on the electronic data and archival file data for coding of the risk assessment measures and recidivism follow-up, hence there would inevitably be an underestimate of the reoffending due to the further offenses not having been detected. Likewise, the retrospective methodology used in the present study would have also underestimated the presence of risk factors, criminogenic needs, responsivity factors, as well as strengths. Second, some of the dynamic risk factors and protective factors might have lower predictive validity over the longer term, so it is possible that the predictive accuracy of the YLS/CMI 2.0 ratings for general recidivism might have been affected given the developmental changes that the youth offenders undergo and long-term follow-up in this study. Third, the predictive validity might also have been affected by the interrater reliability of ratings for the subscales; although they were not classified as poor, predictive accuracy would most likely be improved with more reliable ratings. This is especially important in high-stakes situations, such as those relating to making decisions regarding the level of supervision or incarceration.

CHU ET AL.

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Fourth, it is unclear whether some of the other needs/special considerations are related to the various subscales and whether they should be equally weighted when we consider these variables in determining risk (which is probably made more difficult with dichotomous responses [yes/no]); these issues need to be explored further. Finally, like much risk assessment research, the predictive validity of the measure might be artificially lowered by the probation officers’ or psychologists’ identification and diffusion of potential criminal offending behavior that might be exhibited during the individuals’ court orders via psychological (e.g., counseling or relaxation), increased supervision, and/or social (e.g., social or sporting activities) interventions. As such, it is possible that the predictive accuracy of the risk assessment instrument might be attenuated. Future research on youth risk assessment measures should use prospective and repeated measures designs, in which the risk assessments are based on interviews as well as information that is available in archival records. Moreover, it is beneficial to examine the short- and long-term validity of the measure and its components (see, e.g., Chu, Thomas, Ogloff, & Daffern, 2013), and to further map the relationships between risk factors, strengths, as well as other needs/special considerations. Furthermore, it will be useful to examine how the usage of the YLS/CMI 2.0 in case management of youth offenders can contribute to improved outcomes.

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Received August 16, 2013 Revision received October 28, 2014 Accepted December 4, 2014 䡲