Penologists recognize that both inmate- and prison-level characteristics are relevant to understanding 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 multi-level modeling for related research, this study examined 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. The study examined three secondary data sets, using information common to each set. It compared results from hierarchical logistic models with those from stepwise pooled logistic regression models to see whether results differed significantly by method of estimation. The pooled models revealed 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 revealed much more consistency in prediction (or lack thereof) at either level across all three models, as well as significant aggregate-level main and interaction effects. Notes, tables, references