The Development and Validation of a Classification System Predicting Severe and Frequent Prison Misconduct

Published date01 March 2020
Date01 March 2020
DOI10.1177/0032885519894587
Subject MatterArticles
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Article
The Prison Journal
2020, Vol. 100(2) 173 –200
The Development
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Classification System
Predicting Severe
and Frequent Prison
Misconduct
Grant Duwe1
Abstract
This study presents the results from the development and validation of a
fully automated, gender-specific risk assessment system designed to predict
severe and frequent prison misconduct on a recurring, semiannual basis.
K-fold and split-population methods were applied to train and test the
predictive models. Regularized logistic regression was the classifier used on
the training and test sets that contained 35,506 males and 3,849 females
who were released from Minnesota prisons between 2006 and 2011. Using
multiple metrics, the results showed the models achieved a relatively high
level of predictive performance. For example, the average area under the
curve (AUC) was 0.832 for the female prisoner models and 0.836 for the
male prisoner models. The findings provide support for the notion that
better predictive performance can be obtained by developing assessments
that are customized to the population on which they will be used.
Keywords
prison, misconduct, classification, risk assessment, prediction
1Minnesota Department of Corrections, St. Paul, USA
Corresponding Author:
Grant Duwe, Research & Evaluation, Minnesota Department of Corrections, 1450 Energy
Park Drive, Suite 200, St. Paul, MN 55108, USA.
Email: grant.duwe@state.mn.us

174
The Prison Journal 100(2)
Introduction
Over time, correctional authorities have increasingly relied on risk assess-
ment instruments in an effort to optimize limited resources and foster greater
safety in both prison and the community. These instruments have been uti-
lized to help determine institutional custody levels, the type of community
supervision, and whether individuals should be paroled or released to the
community prior to the adjudication of their criminal cases. Because institu-
tional and community programming resources are often scarce, they have
also been used to identify which offenders to prioritize for programming.
As the application of risk assessment instruments within corrections has
grown, so has the body of research on the development and validation of
these tools. Much of this research, however, has focused on the prediction of
recidivism—the most widely used outcome measure for correctional popula-
tions. Conversely, the existing literature has paid relatively little attention to
risk assessment instruments that predict institutional misconduct. Defined as
the failure by inmates to follow institutional rules and regulations (Camp
et al., 2003), prison misconduct encompasses behavior that ranges from dis-
obeying orders and possession of “contraband” (i.e., alcohol, drugs, etc.) to
assaults against staff and other inmates.
When individuals (re)enter a jail or prison, correctional systems typically
make classification decisions regarding the security or custody levels at
which inmates should be confined. To promote better safety and security for
both inmates and staff, individuals thought to be at a higher risk of institu-
tional misconduct are often placed at more restrictive custody levels such as
close or maximum. In contrast, lower risk inmates are more likely to be
placed at minimum or medium custody levels in which they have fewer
restrictions and greater freedom of movement within the facility.
The determination of risk is often based on an assessment of whether
inmates will engage in any misconduct. As shown later, however, more than
one quarter of male prisoners and 43% of female inmates in Minnesota’s
prison system had at least one discipline conviction during their confinement.
If maintaining institutional safety through effective risk management is a key
objective of prison classification systems, then simply predicting who will
have any misconduct is not especially meaningful. Instead, what is more
important from a risk management perspective is identifying who will have
serious, violent misconduct and/or a lot of discipline convictions. In other
words, which inmates are most likely to compromise facility safety and con-
sume a great deal of staff time?
The career criminal literature has long documented that a relatively small
proportion of offenders account for a disproportionate share of crime (Chaiken
& Chaiken, 1984; Wolfgang et al., 1972; Wright & Rossi, 1986). Similarly, a

Duwe
175
relatively small segment of the inmate population is responsible for much of
the misconduct in prison. As shown later, approximately 10% of the male
prisoners in Minnesota accounted for 70% of all discipline convictions, 79%
of all misconduct resulting in a segregation or restrictive housing penalty (i.e.,
more serious misconduct), and 100% of assaults against other inmates and
staff (i.e., violent misconduct). Likewise, about 10% of the female inmate
population in Minnesota was responsible for 62% of all discipline convictions,
71% of misconduct resulting in segregation, and 100% of all violent miscon-
duct. If the highest risk inmates—the top 10%—can be accurately identified,
then prison classifications systems can be used to further improve institutional
safety. For example, in addition to custody level, prison systems can apply
other measures, such as the delivery of programming, to reduce the likelihood
that higher risk inmates will engage in misconduct.
Present Study
This study develops and validates a fully automated, gender-specific risk assess-
ment for inmates in Minnesota that is designed to predict serious and/or frequent
misconduct (SFM) in 6-month intervals. Recent research has shown that, com-
pared with a manual scoring method, a fully automated assessment is more reli-
able, efficient, and cost-effective (Duwe & Rocque, 2017). In particular, a fully
automated assessment eliminates interrater disagreement, which leads to better
predictive performance. Moreover, Duwe and Rocque (2017) reported that auto-
mation of the Minnesota Screening Tool Assessing Recidivism Risk (MnSTARR)
2.0 would yield a return on investment (ROI) of more than US$20 after 5 years,
generating close to US$5 million in staff time saved.
In addition to the fact that male and female prisoners are housed in sepa-
rate facilities in Minnesota, the factors that increase and decrease the risk of
SFM may vary by gender. It was important, therefore, to create separate
assessments for male and female prisoners. It was also important to design an
assessment that predicts SFM in 6-month intervals. As discussed later in
more detail, the Minnesota Department of Corrections (MnDOC) reassesses
inmates every 6 months they are in prison. Rather than developing an intake
assessment that predicts misconduct over the entirety of an inmate’s confine-
ment, it was necessary to design an instrument that predicts SFM for each
individual offender every 6 months they are in prison.
Prior Research on the Predictors of Prison
Misconduct
The two main perspectives that have been utilized to explain prison miscon-
duct are importation and deprivation. Pitched largely at the individual level,

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The Prison Journal 100(2)
importation argues that misconduct occurs as a result of the characteristics
and experiences that offenders bring with them into prison. Deprivation, situ-
ated more at the institution level, holds that situational factors within the
prison environment influence offender misconduct (Tewksbury et al., 2014).
Even though prison misconduct is not synonymous with criminal offend-
ing, both represent rule-violating behavior. Moreover, prison misconduct has
been found to be a significant predictor of recidivism (Duwe, 2014; Gendreau
et al., 1996). Furthermore, existing research suggests that prison misconduct
and recidivism share many of the same risk and protective factors. Indeed, as
with recidivism, the strongest predictors of misconduct tend to be static fac-
tors such as criminal history, age, and race (Caudy et al., 2013; Gendreau
et al., 1997).
Reflecting the findings reported by Gendreau et al. (1997) that antisocial
companions increase the likelihood of misconduct, several studies have indi-
cated that gang membership (i.e., identification as a member of a security threat
group [STG]) is positively associated with rule violations (Gaes et al., 2002;
Griffin & Hepburn, 2006; Tewksbury et al., 2014). Gendreau et al. (1997) also
noted that social achievement (e.g., education, employment, marital status) and
early family factors had modest associations with disciplinary infractions.
Consistent with the deprivation perspective, Gendreau et al. (1997) also
noted that institutional factors have an effect on misconduct. Existing research
has demonstrated that prisons vary in their effect on individual prisoners’
likelihood of engaging in misconduct (Camp et al., 2003). Indeed, previous
studies suggest misconduct is affected by institution-level factors such as
size, location, and security level (Huebner, 2003; Steiner & Wooldredge,
2014). Other research indicates disciplinary infractions are influenced by the
overall characteristics of the inmates as well as the staff (Camp et al., 2003).
Although earlier work has established that custody levels...

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