The Relative Predictive Validity of the Static and Dynamic Domain Scores in Risk-Need Assessment of Juvenile Offenders

• Author: Andrew McGrath,Anthony P. Thompson
• DOI: 10.1177/0093854811431917
• Date: 01 March 2012
• Published date: 01 March 2012

THE RELATIVE PREDICTIVE VALIDITY OF
THE STATIC AND DYNAMIC DOMAIN SCORES
IN RISK-NEED ASSESSMENT OF JUVENILE
OFFENDERS
ANDREW MCGRATH
ANTHONY P. THOMPSON
Charles Sturt University
This study examined the predictive validity of the Australian Adaptation of the Youth Level of Service/Case Management
Inventory (YLS/CMI-AA). The focus was on the subcomponents of the inventory, which represent one static and seven
dynamic risk-need domains. Reoffending outcomes within 1 year of the inventory were obtained for a large sample (N =
3,568) of young people under juvenile justice supervision in the community. Logistic regression analyses investigated the
relative contribution of YLS domain scores. The results showed that the static and four dynamic domain scores independently
predicted recidivism and that the combination of those domain scores yielded a small improvement in prediction. A similar
pattern of results was obtained from analyses of the simple additive scores for the YLS domains. The findings support the
YLS/CMI-AA total score as a sufficiently useful predictor of risk, and they clarify the contribution of static and dynamic risk
components.
Keywords: juvenile offending; risk assessment; recidivism; Youth Level of Service/Case Management Inventory
It is widely accepted that risk factors and treatment needs should be considered jointly
when working with juvenile offenders (Day, Howells, & Rickwood, 2004; Hoge &
Andrews, 1996). This dual approach applies broadly within correctional and forensic psy-
chology, but the framework is especially relevant for young offenders. Juvenile justice
legislation typically emphasizes protection of the public and guidance and assistance to
young people as dual principles underpinning society’s response to juvenile crime (Thompson,
2001). Within psychology, it is well recognized that risk factors and treatment needs can be
credibly and usefully assessed with structured psychometric inventories (Clements, 1996;
Hollin, 2002; Lally, 2003). This approach has been broadly adopted in community juvenile
justice systems, and numerous inventories exist in Canada (Hoge & Andrews, 2006, 2011),
the United States (Gavazzi et al., 2003; Howell, 1995; Schwalbe, Fraser, Day, & Arnold,
2004), England and Wales (Baker, Jones, Roberts, & Merrington, 2003), and Australia
(Thompson & Pope, 2005; Thompson & Putniņš, 2003).
Conceptually, the distinction between static and dynamic risks turns on whether a factor
that is associated with offending can be modified. Historical precursors (e.g., age at first
offense, number of previous convictions) are useful for predicting risk of reoffending but
AUTHORS’ NOTE: The authors thank the New South Wales (NSW) Department of Attorney General and
Justice (Juvenile Justice) and the NSW Bureau of Crime Statistics and Research for assistance in undertaking
the research. The opinions here do not necessarily reflect the views of these organizations, or any of their
officers. Please address correspondence to Andrew McGrath, School of Psychology, Charles Sturt University,
Building C6, Panorama Avenue, Bathurst, NSW 2795, Australia; email: amcgrath@csu.edu.au.
CRIMINAL JUSTICE AND BEHAVIOR, Vol. XX, No. X, Month 2007 250-XXX
ol. 39 No. 3, March 2012 250-263
DOI:
DOI: 10.1177/0093854811431917
© 2007 American Association for Correctional and Forensic Psychology
© 2012 International Association for Correctional and Forensic Psychology
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McGrath, Thompson / RELATIVE PREDICTIVE VALIDITY 251
are not amenable to change to reduce this risk. By contrast, dynamic risk factors concern
conditions that are currently related to offending and are potentially modifiable. The inclu-
sion of static and dynamic risk factors in assessment inventories makes logical sense, but
it is also justified on other grounds. Foremost is the long-standing and substantive literature
on factors that are associated with juvenile offending. This literature is both empirical and
theoretical. In essence, the literature shows: (a) some unalterable aspects of demography
and antisocial history are associated with propensity to crime and reoffending (Cottle, Lee,
& Heilbrun, 2001; Nagin & Paternoster, 2000; Piquero, Farrington, & Blumstein, 2003),
(b) a variety of modifiable psychosocial factors are associated with crime and risk is pro-
portional to number and severity (Durlak, 1998; Farrington, 2002; Hubbard & Pratt, 2002),
(c) the interplay of psychosocial risk factors can be theoretically constructed into coherent
explanations of offending patterns (Moffitt, 1993; Paternoster & Brame, 1997; Sampson &
Laub, 2003), and (d) psychosocial interventions that are appropriately targeted and deliv-
ered can reduce juvenile offending (Borduin et al., 1995; Loeber & Farrington, 1998). A
second pillar on which structured risk-need assessment rests is the compelling correctional
strategy (risk-need-responsivity) promoted by Andrews and colleagues for the past 20 years
(Andrews, Bonta, & Wormith, 2006; Andrews & Dowden, 2006; Andrews et al., 1990). In
summary, there is a solid rationale and sound evidence for attention to static and dynamic
risk factors when working with juvenile offenders. However, more attention needs to be
given to how dynamic risk factors are represented in risk-need inventories and their psy-
chometric integrity. In the research reported here, we investigated static and dynamic fac-
tors in one particular risk-need inventory for youth called the Australian Adaptation of the
Youth Level of Service /Case Management Inventory (YLS/CMI-AA; Hoge & Andrews,
1995; Thompson & Pope, 2005).
Like the Youth Level of Service/Case Management Inventory (YLS/CMI; Hoge &
Andrews, 2006, 2011) and similar youth adaptations of the adult inventory (Andrews,
Bonta, & Wormith, 2004), the YLS/CMI-AA incorporates a collection of checklist items
that represent static and dynamic domains. A healthy research literature supports the Youth
Level of Service family of inventories (YLS). For example, recent meta-analytic studies
have found that the predictive validity for general recidivism is good. Schwalbe (2007)
found a mean weighted effect size area under curve (AUC) of .641 based on 11 YLS stud-
ies. Olver, Stockdale, and Wormith (2009) found a mean-weighted correlation of .32 based
on an overlapping but larger sample of 19 studies. These predictive validity indices were
between YLS inventory total score and recidivism. Less attention has been paid to the
relative contribution of static versus dynamic component scores to prediction. Olver et al.
considered pursuing this in their meta-analysis but were dissuaded because such informa-
tion was typically not provided in the studies reviewed. The relation of static and dynamic
risk factors to juvenile recidivism was examined in a meta-analysis conducted by Cottle
et al. (2001). In the 22 studies that were examined, two static offense history variables were
most strongly associated with recidivism, and a number of dynamic family and social
variables were also significant predictors. This meta-analysis provides only general support
for the inclusion of such components in risk-need assessment, however. The studies
reviewed varied considerably on key variables such as age of juveniles and criterion for
recidivism. Moreover, only 6 of the studies incorporated variables into a formal risk assess-
ment approach. A similar pattern of findings was observed in a meta-analysis of the adult

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252 CRIMINAL JUSTICE AND BEHAVIOR
recidivism literature examining 131 studies (Gendreau, Little, & Goggin, 1996). Moderate
effect sizes were observed for a number of static and dynamic predictors, with some com-
posite evidence supporting the incremental validity of dynamic predictors.
Although few YLS studies address the predictive contribution of static and dynamic
components, some relevant information is available. At the bivariate level, domain scores
are typically found to correlate significantly with recidivism. Small to moderate coefficients
are reported in a number of published (Thompson & Pope, 2005; Upperton & Thompson,
2007) and unpublished (Flores, Travis, & Latessa, 2003; Hoge & Andrews, 2006) studies.
In several of these (Flores et al., 2009; Thompson & Pope, 2005; Upperton & Thompson,
2007), the domain dealing with prior and current offenses was the highest bivariate correla-
tion, although not necessarily significantly stronger than some dynamic domains. Holsinger,
Lowenkamp, and Latessa (2006) found that the personality domain had the highest bivari-
ate correlation with institutional misconducts.
The relative contribution of domain scores to predictive validity is approached in some
studies, but limited statistical analysis and methodological shortcomings qualify conclu-
sions. For example, Marshall, Egan, English, and Jones (2006) found that AUC indices for
several file-based measures of charges and convictions were virtually the same for the total
YLS score as for the total score calculated from the dynamic domains. However, the crite-
rion measures were retrospective in a relatively small sample of 94 male and female youth.
Marczyk, Heilbrun, Lander, and DeMatteo (2003, 2005) investigated the relation between
the YLS domain scores and decisions to certify youthful offenders to adult or juvenile
court, as well as to subsequent arrests. The results were a mixed picture with scores from
the YLS static and some dynamic domains showing predictive validity for certification
decisions but not...