The Relationship Between Patterns of Change in Dynamic Risk and Strength Scores and Reoffending for Men on Community Supervision

Published date01 September 2021
DOI10.1177/0093854821993512
Date01 September 2021
Subject MatterArticles
CRIMINAL JUSTICE AND BEHAVIOR, 2021, Vol. 48, No. 9, September 2021, 1208 –1228.
DOI: https://doi.org/10.1177/0093854821993512
Article reuse guidelines: sagepub.com/journals-permissions
© 2021 International Association for Correctional and Forensic Psychology
1208
THE RELATIONSHIP BETWEEN PATTERNS OF
CHANGE IN DYNAMIC RISK AND STRENGTH
SCORES AND REOFFENDING FOR MEN ON
COMMUNITY SUPERVISION
KAYLA A. WANAMAKER
SHELLEY L. BROWN
Carleton University
Research is needed focusing on the predictive nature of dynamic risk and strength score changes. The current study includes
11,953 Canadian men under community supervision with Service Planning Instrument re-assessment data. Using a retrospec-
tive, multi-wave longitudinal design, hierarchical linear modeling (HLM) was conducted to assess patterns of change in total
dynamic risk and strength scores across three to five timepoints over 30 months. Change parameters from the HLM were
incorporated into regression models, linking change to three reoffending outcomes: technical violations, new charges, and
new violent charges. Results indicated that total dynamic risk scores decreased over time and total dynamic strength scores
increased over time, although the rate of change for both was gradual. Change in total dynamic risk scores was predictive of
all outcomes, whereas change in total dynamic strength scores only predicted technical violations. Results demonstrated the
utility of re-assessing dynamic risk and strength scores over time.
Keywords: risk assessment; dynamic risk; strength; community supervision; reoffending
Assessing an individual’s risk to reoffend while on community supervision is a funda-
mental task of correctional organizations. Similarly, it is important to be able to detect
changes in risk to reoffend to improve case management practices and assist with prevent-
ing failure while on community supervision. To accurately assess an individual’s risk to
reoffend, risk assessment tools and protocols are utilized to assess various factors that are
predictive of criminal behavior. These risk factors are classified into one of two categories:
static or dynamic risk factors. Static risk factors are historical factors that are unchangeable
as a function of intervention. That is, static risk factors either do not change, such as “age at
first arrest,” or if they do change, it is not as a function of intervention, such as “number of
AUTHORS’ NOTE: The authors thank Orbis Partners Inc. and Alberta Justice and Solicitor General for
providing access to SPIn and reoffense datasets. This article is based on Kayla Wanamaker’s doctoral dis-
sertation (2020) entitled A Multi-Wave Longitudinal Examination of How Strengths and Risks Inform Risk
Assessment and Treatment Profiles for Justice-Involved Men and Women Using the Service Planning
Instrument (SPIn). Correspondence concerning this article should be addressed to Kayla A. Wanamaker,
Department of Psychology, Carleton University, 1125 Colonel By Drive, Ottawa, Ontario, Canada K1S 5B6;
e-mail: kaylawanamaker@cmail.carleton.ca
993512CJBXXX10.1177/0093854821993512Criminal Justice and BehaviorWanamaker, Brown / DYNAMIC RISK AND STRENGTH
research-article2021
Wanamaker, Brown / DYNAMIC RISK AND STRENGTH 1209
prior convictions.” These factors, therefore, cannot act as treatment targets but are useful for
classifying an individual’s degree of risk for reoffending (Mann et al., 2010). In contrast,
dynamic risk factors are changeable factors that can be targeted through correctional inter-
vention, as they are modifiable via treatment (Bonta & Andrews, 2017). As such, dynamic
risk factors, often used interchangeably with the term “criminogenic needs,” measure an
individual’s propensity to commit an offense within a proximal time frame and can help
inform rehabilitation efforts (Bonta & Andrews, 2017). Some examples of dynamic risk
factors include engaging with antisocial peers, criminogenic attitudes, or poor use of leisure
time. While static and dynamic risk factors aid in determining who will recidivate and when
they are most likely to recidivate, empirical findings also illustrate that strengths can influ-
ence an individual’s risk to reoffend and can aid in informing rehabilitation efforts (Jones
et al., 2015). In brief, strengths can be classified as protective factors if they moderate the
relationship between risk and recidivism among those who are high-risk, reducing the like-
lihood of reoffending (Jones et al., 2015), or classified as promotive factors if they nega-
tively correlate with recidivism regardless of one’s overall risk level.
Although research has highlighted the importance of dynamic risk factors (e.g., Bonta &
Andrews, 2017), less is known about how these dynamic factors fluctuate over time (Lloyd,
2015). This information is essential for providing successful and timely intervention meth-
ods. Furthermore, when discussing the various categories of risk assessments, the inclusion
of positive factors, or strengths, is often missing. Thus, the purpose of this study is to exam-
ine how dynamic strength and dynamic risk factors change over time and how they may
change in conjunction, which is essential to further advance not only the correctional field
of risk assessment, but also how we approach treatment and rehabilitative efforts.
MULTI-WAVE ASSESSMENT OF DYNAMIC RISK
Although many studies have employed multi-wave designs to assess dynamic risk fac-
tors (e.g., Labrecque et al., 2014; Olver et al., 2007), most of these studies have used small
samples or a two-way design. While research employing two-way designs can meaning-
fully examine change prior to and after an intervention takes place, when assessing change
over a longer time period without examining the effects of an intervention, it has been sug-
gested that studies include at least three timepoints to increase the accuracy of detecting
change over time (Brown et al., 2009), and this is particularly true when the nature and/or
frequency of intervention(s) is unknown. Recently, studies have been conducted examining
the predictability of dynamic re-assessments using three or more timepoints among samples
of youth (e.g., Clarke et al., 2019), forensic inpatients (e.g., Quinsey et al., 2004), and sex
offenders (e.g., Babchishin, 2013). To date, seven studies have used a three-point (or more)
multi-wave approach to assess dynamic risk scores among justice-involved men in general
(e.g., Brown et al., 2009), with only three studies examining change in strengths over time
(Davies, 2019; Hanby, 2013; Lloyd, 2015).
THREE-POINT STUDIES ASSESSING DYNAMIC RISK
Several multi-wave studies have examined change in dynamic risk factors among jus-
tice-involved men. Studies conducted by Brown et al. (2009), Howard and Dixon (2013),
and Jones et al. (2010) will be discussed in detail. Brown et al. (2009) examined the extent

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