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Reducing Prison Misconduct with Evidence-Based Administrative Segregation Policies

Award Information

Award #
2014-IJ-CX-0003
Location
Congressional District
Status
Closed
Funding First Awarded
2014
Total funding (to date)
$32,000

Description of original award (Fiscal Year 2014, $32,000)

Strategies for the effective management of prisons have long drawn polarizing views amongst penologists. Recently, the use of administrative segregation (AS)-also referred to as solitary confinement-has been the subject of great debate. There is a widely held belief among policy makers and corrections officials that the use of AS is an effective strategy for increasing safety and promoting order throughout the prison system. However, among the many controversial issues that the practice raises is the contention that AS increases, rather than decreases, the likelihood of subsequent criminal behavior. Ironically, despite the fact that AS has been used widely in U.S. jail and prison systems for more than 200 years, it has remained an elusive subject of extensive empirical investigation. In response, this dissertation seeks to fill a critical gap in the literature by examining the understudied area of the experience AS has on inmate behavior. The purpose of this dissertation is to develop a set of evidence-based AS policy recommendations that will guide policy makers in reducing the inmate misconduct that occurs in prison. To accomplish this objective, this study will pursue the following two specific aims: (1) Determine if the length of time spent in AS has an influence on subsequent criminal behavior; and (2) Identify key factors that increase the iatrogenic effects of AS. Using a quasi-experimental, matched comparison group design, the institutional misconduct histories of inmates who have experienced AS in the Ohio prison system during a five-year period will be compared to a sample of propensity score matched inmates who did not spend any time in AS. The samples will be matched on a number of theoretically relevant variables, including age, gender, race, risk level, and mental health status. Data will be analyzed using a series of bivariate and multivariate analyses, including binary logistic regression, multivariate least-squares regression, and survival analysis using Cox proportional hazard models. This study has both theoretical and practical value and its intention is to provide prison officials with the information they need to make better, more informed evidence-based decisions regarding the use of AS.
ca/ncf

Date Created: September 14, 2014