The Contribution of Static and Dynamic Factors to Recidivism Prediction for Black and White Youth Offenders

• Author: William T. Miller,Christina A. Campbell,Jordan Papp,Ebony Ruhland
• DOI: http://doi.org/10.1177/0306624X211022673
• Published date: 01 December 2022
• Date: 01 December 2022
• Subject Matter: Articles

https://doi.org/10.1177/0306624X211022673

International Journal of

Offender Therapy and

Comparative Criminology

2022, Vol. 66(16) 1779 –1795

© The Author(s) 2021

Article reuse guidelines:

sagepub.com/journals-permissions

DOI: 10.1177/0306624X211022673

journals.sagepub.com/home/ijo

Article

The Contribution of Static

and Dynamic Factors to

Recidivism Prediction for

Black and White Youth

Offenders

William T. Miller1, Christina A. Campbell1,

Jordan Papp1, and Ebony Ruhland2

Abstract

Scholars have presented concerns about potential for racial bias in risk assessments

as a result of the inclusion of static factors, such as criminal history in risk

assessments. The purpose of this study was to examine the extent to which

static factors add incremental validity to the dynamic factors in criminogenic risk

assessments. This study examined the Youth Level of Service/Case Management

Inventory (YLS/CMI) in a sample of 1,270 youth offenders from a medium-sized

Midwestern county between June 2004 and November 2013. Logistic regression

was used to determine the predictive validity of the YLS/CMI and the individual

contribution of static and dynamic domains of the assessment. Results indicated

that the static domain differentially predicted recidivism for Black and White youth.

In particular, the static domain was a significant predictor of recidivism for White

youth, but this was not the case for Black youth. The dynamic domain significantly

predicted recidivism for both Black and White offenders, and static risk factors

improved prediction of recidivism for White youth, but not for Black youth.

Keywords

risk assessment, juvenile justice, recidivism, criminal history, racial bias

1University of Cincinnati, OH, USA

2University of Michigan, Ann Arbor, USA

Corresponding Author:

William T. Miller, University of Cincinnati, 2160 McMicken Circle, Cincinnati, OH 45221, USA.

Email: millewt@ucmail.uc.edu

1022673IJOXXX10.1177/0306624X211022673International Journal of Offender Therapy and Comparative CriminologyMiller et al.

research-article2021

1780 International Journal of Offender Therapy and Comparative Criminology 66(16)

As a strategy to reduce both recidivism and delinquency caseload sizes, policymakers

and administrators have turned to empirically-supported interventions and assessments

to promote the healthy development of youth (Mears, 2010). In the field of corrections,

researchers established the Risk-Need-Responsivity (RNR) model as a leading frame-

work for guiding offender treatment and assessments (Bonta & Andrews, 2016). The

RNR model consists of three principles: risk, need, and responsivity. The risk principle

guides who should be treated, the need principle informs what should be addressed in

treatment, and the responsivity principle addresses how to administer an intervention

(Bonta & Andrews, 2007). Due to the efficacy of the RNR model, it has led to the devel-

opment of risk assessment tools necessary for informing treatment-based decisions

(Smith et al., 2009). To date, risk assessment tools based on the RNR model have played

an essential role in helping practitioners accurately predict recidivism and increasing

equity in treatment at all stages of the juvenile court process (Latessa & Lovins, 2010).

Most modern risk assessment tools estimate recidivism risk by measuring factors

that cannot be changed, known as static factors (e.g., criminal history) and change-

able factors, known as dynamic factors (e.g., antisocial thoughts, antisocial personal-

ity, antisocial peers, work/school problems, a lack of prosocial leisure activities, and

substance misuse) (Brown & Singh, 2014). These criminogenic risk factors were

chosen based on evidence indicating that they are significant predictors of future

delinquency (Bonta & Andrews, 2016). Despite this, much dissent remains regarding

which risk factors are most important to understand the risks and needs of diverse

populations involved in corrections. In particular, scholars debate the extent to which

static risk factors, such as criminal history, differentially predict recidivism and affect

justice system outcomes for minority youth. The purpose of this study is to examine

the unique contributions of static and dynamic factors used to inform recidivism risk

for Black and White adjudicated youth.

History of Risk Assessment Tools

Before the implementation of risk assessment within juvenile courts, recidivism risk

was decided based on the professional judgment of someone deemed qualified to

make this decision (i.e., a judge, a seasoned probation officer, or a psychiatrist)

(Andrews et al., 2006; McCafferty, 2017). These officials had minimal guidance when

estimating crimingenic risk for youth involved in delinquency, which led to consider-

able variation in treatment (Andrews et al., 2006; Brogan et al., 2015). The use of

professional discretion was eventually replaced with second-generation risk assess-

ments, which relied solely on empirically validated static factors to predict future

offenses (Andrews et al., 2006; Singh, 2012). As risk assessments evolved in purpose

(i.e., informing treatment decisions rather than solely predicting recidivism), relevant

factors that are subject to change became a core focus of prediction. Modern third and

fourth generation risk assessments incorporate both static and dynamic risk and need

items (Andrews et al., 2006). Although static risk factors have remained throughout

this evolution of risk assessment, still, little is known about their utility for diverse

populations of youth offenders.