Abstract
Objectives
To present and test an opportunity perspective on prison inmate victimization.
Methods
Stratified random samples of inmates (n 1 = 5,640) were selected from Ohio and Kentucky prisons (n 2 = 46). Bi-level models of the prevalence of assaults and thefts were estimated. Predictors included indicators of inmate routines/guardianship, target antagonism, and target vulnerability at the individual level, and several indicators of guardianship at the facility level.
Results
Assaults were more common among inmates with certain routines and characteristics that might have increased their odds of being victimized (e.g., less time spent in recreation; committed violence themselves during incarceration), and higher levels of assaults characterized environments with lower levels of guardianship (e.g., architectural designs with more “blind spots”, larger populations, and less rigorous rule enforcement as perceived by correctional officers). Similar findings emerged for thefts in addition to stronger individual level effects in prisons with weaker guardianship (e.g., ethnic group differences in the risk of theft were greater in facilities with larger populations and less rigorous rule enforcement).
Conclusions
The study produced evidence favoring a bi-level opportunity perspective of inmate victimization, with some unique differences in the relevance of particular concepts between prison and non-prison contexts.
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Notes
The incidence models are available upon request from the first author.
Kentucky also has three privately operated facilities for adult offenders. Those facilities were not included in the study per the wishes of the KDOC. With two exceptions, inmates housed in correctional camps, mental health units, reception units, or youthful offender units were excluded due to practical constraints and unmeasured structural and managerial differences that exist between those units and the primary facilities in which these units exist. Inmates housed in the correctional camp at the Ohio State Penitentiary (Ohio’s supermax facility) were included for theoretical reasons dictated by the larger project. Inmates housed in the correctional camp for females at the Trumbull Correctional Institution were also included. Ohio has three other facilities for women, but two of those facilities are pre-release centers, which typically do not house inmates longer than one year. The camp for females at Trumbull Correctional Institution is the most similar institution to the Ohio Reformatory for Women, which is the primary facility for women in Ohio. The camp for females at Trumbull Correctional Institution, which is physically separate from the main facility, was treated as a separate facility in all of the analyses.
Non-English speaking inmates were excluded from the sampling frames. The numbers of these inmates were very small and, for the vast majority of prisons, fell below ten inmates per facility in each state.
Tri-level modeling, with facilities nested within states, was deemed unnecessary based on estimates of all empirical relationships derived from state-specific bi-level models. That is, all level-1 estimates for Kentucky fell within 95 % confidence intervals for the respective estimates for Ohio. When pooling the data across states and statistically controlling for state location at level-2, all facility estimates were virtually identical with and without the control variable included. Finally, although state location maintained significant zero-order relationships with both outcome measures, where the prevalence of both assaults and thefts were lower in Kentucky, these relationships were rendered nonsignificant when grand mean-centering the level-1 predictors. In other words, it appears that between-state differences in inmate populations accounted for the observed differences in victimization rates across Ohio and Kentucky. For these reasons, data were pooled across states in order to generate the most efficient estimates of level-2 effects.
An argument could be made to use group mean-centering with these types of models because some compositional effects could be spurious with unmeasured facility effects. The estimates were very similar using both group mean- and grand mean-centering, so we chose the latter in order to provide more rigorous tests of the level-2 effects examined.
Models were derived with Laplace estimation using HLM 7.0, which produces coefficients very similar to maximum likelihood estimation while also enabling Chi-square tests of model fit (Raudenbush et al. 2011). The value of Chi-square for model fit is the difference in the Deviance statistic between two models. We compared the fit of each level-1 model relative to the corresponding null (unconditional) model, and each full model (with both level-1 and level-2 predictors) relative to the corresponding level-1 model.
A table of the level-2 zero-order effects is available upon request from the first author.
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Acknowledgments
This study was supported, in part, by grants from the National Institute of Justice (Award #2007-IJ-CX-0010) and the National Science Foundation (Award #SES-07155515). The opinions, findings, and conclusions expressed in this study are those of the authors and do not necessarily reflect those of the Department of Justice or the National Science Foundation. The authors wish to thank Guy Harris, along with Brian Martin and Gayle Bickle with the Ohio Department of Rehabilitation and Correction, and Ruth Edwards and Tammy Morgan with the Kentucky Department of Correction for their assistance with the collection of the data for this study.
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Appendix: Operationalization of Independent Variables
Appendix: Operationalization of Independent Variables
Indicators of inmate activities and guardianship included average number of hours per week in recreation, weekly hours in education classes and/or vocational training, weekly hours working at a job, number of visits an inmate received during the preceding month, custody level, and residence in the general inmate population. The metric scales of weekly hours in various activities, each capped at 40 h, and months served were logged (natural log) due to over-dispersion on the original scales.
Indicators of target antagonism included an inmate’s sex, primary offense incarcerated for (violent or property, with drug and public order crimes as the reference), prior imprisonment for a separate offense, whether an inmate was officially charged with committing violence during the previous 6 months, and whether s/he was charged with committing theft during same period. Measures of target vulnerability included other inmate demographics (age, African American, Latino), social backgrounds at arrest (high school degree or greater, excluding GED, employment status, whether the inmate lived with dependent children), known gang affiliation(s), time served in the current facility, and perceptions of correctional officers.
Inmate perceptions of officers were operationalized as two orthogonal factors from a principal components analysis of five survey questions asking the extent to which inmates agreed that (a) Overall, the correctional officers here do a good job; (b) The correctional officers are generally fair to inmates; (c) Officers treat me the same as any other inmate here; (d) Inmates often complain about being treated unfairly here; and (e) Officers treat some inmates better than others (KMO = .69). Items (d) and (e) were reverse coded. Inverse relationships between these factors and victimization reflect lower victimization odds corresponding with greater officer legitimacy.
Regarding facility measures explored for the multivariate models, aside from those reflecting Morris and Worrall’s (in press) recommendations (architecture, proportion inmates housed in dorms, and age of facility), we examined a few other indicators of guardianship over inmate populations. These measures included whether a facility was in Ohio or Kentucky (Ohio prisons are often crowded while all Kentucky prisons operate at capacity), design capacity, population size at time of study, ratio of officers to inmates, and a facility’s average score on a factor capturing officers’ perceptions of rule enforcement, with higher values reflecting more agreement that rules are typically under-enforced in the facility. This measure was derived from a principle components analysis of the officer data and four items asking the extent to which they agreed that (a) The rules for inmates are under-enforced in this facility; (b) It is impossible to issue disciplinary tickets to inmates for all rule violations we are aware of; (c) The warden usually supports my decisions regarding when to issue disciplinary tickets (reverse coded); and (d) When I question inmates about a rule violation they may have committed they often verbally attack me. A single factor emerged from the analysis (KMO = .63). The small number of facilities (n = 46) restricted the number of facility measures included in the multivariate model.
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Wooldredge, J., Steiner, B. A Bi-level Framework for Understanding Prisoner Victimization. J Quant Criminol 30, 141–162 (2014). https://doi.org/10.1007/s10940-013-9197-y
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DOI: https://doi.org/10.1007/s10940-013-9197-y