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• W2118839145 abstract "Penologists recognize that both inmate- and prison-level characteristics are relevant to an understanding of individual inmates' behaviors; yet extant studies have focused only on unilevel models with either individual- or aggregate-level predictors and outcomes. To explore the potential of multilevel modeling for related research, we examine empirical relationships predicting the likelihood of inmate misconduct with individual-level (inmate) variables and aggregate levels of prison population crowding. The framework for the model borrows from both individual- and aggregate-level theories of informal social control. We examine three secondary data sets, using information common to each set. We compare results from hierarchical logistic models with those from stepwise pooled logistic regression models to see whether results differ significantly by method of estimation. The pooled models reveal inconsistency in the significance of inmate predictors (social demographics and criminal histories) across the three samples, and non-significant relationships involving prison crowding and an interaction between crowding and an inmate's age for all samples. By contrast, the hierarchical models reveal much more consistency in prediction (or a lack thereof) at either level across all three models, as well as significant aggregate-level main and interaction effects. The theoretical and methodological implications of these results are discussed." @default.
• W2118839145 created "2016-06-24" @default.
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• W2118839145 date "2001-03-01" @default.
• W2118839145 modified "2023-10-18" @default.
• W2118839145 title "Considering hierarchical models for research on inmate behavior: Predicting misconduct with multilevel data" @default.
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• W2118839145 doi "https://doi.org/10.1080/07418820100094871" @default.
• W2118839145 hasPublicationYear "2001" @default.
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