Cross-classified random effects models (CCREMs) are used in the analyses of cross-sectional and longitudinal multilevel data that are not strictly hierarchical. Because of the complexity of this technique, many researchers simply ignore the cross-classified structures of their data and use hierarchical linear models. The study simulated crosssectional and longitudinal multilevel data with cross-classified structures and examined the impact of misspecifying CCREMs on parameter and standard error estimates in these data. The dissertation consists of two studies. Study One examines cross-sectional multilevel data and Study Two examines longitudinal multilevel data. In Study One, three-level cross-classified data were generated. Two random factors were crossed at either the top level or the intermediate level. It was found that ignoring a crossed random factor causes the variance of the remaining crossed factor and the adjacent levels to be overestimated. The fixed effects themselves are unbiased; however, the standard errors associated with the fixed effects are biased. When the ignored crossed factor is at the top level, the standard error of the intercept is underestimated whereas the standard error of the regression coefficients associated with the covariate of the intermediate level and the remaining crossed factor are overestimated. When the ignored crossed factor is at the intermediate level, only the standard error of the regression coefficients associated with the covariate of the bottom level is overestimated. In Study Two, longitudinal multilevel data were generated mirroring studies in which students are measured repeatedly and change schools over time. It was found that when the school level is modeled hierarchically above the student level rather than as a crossed factor, part of the variance at the school level is added to the student level, causing underestimation of the school-level variance and overestimation of the studentlevel variance and covariance. The standard errors of the intercept and the regression coefficients associated with the school-level predictors are underestimated, which may cause spurious significance for results. The findings of the dissertation enhanced our understanding of the functioning of CCREMs in both cross-sectional and longitudinal multilevel data. The findings can help researchers to determine when CCREMs should be used and to interpret their results with caution when they misspecify CCREMs.

abstract: Discretion is central to policing. The way officers use their discretion is influenced by situational, officer, and neighborhood-level factors. Concerns that discretion could be used differentially across neighborhoods have resulted in calls for increased police transparency and accountability. Body-worn cameras (BWCs) have been promoted to further these goals through increasing oversight of police-citizen encounters. The implication is that BWCs will increase officer self-awareness and result in more equitable outcomes. Prior researchers have largely evaluated the direct impact of BWCs. Researchers have yet to examine the potential for BWCs to moderate the influence of neighborhood context in individual incidents. To address this gap, I use Phoenix Police Department data collected as part of a three-year randomized-controlled trial of BWCs to examine variation in police discretion. These data include over 1.5 million police-citizen contacts nested within 826 officers and 388 neighborhoods. I examine two research questions. First, how do proactivity, arrests, and use of force vary depending on situational, officer, and neighborhood contexts? This provides a baseline for my next research question. Second, examining the same contexts and outcomes, do BWCs moderate the influence of neighborhood factors on police behavior? As such, I examine the untested, though heavily promoted, argument that BWCs will reduce the influence of extralegal factors on officer behavior. Using cross-classified logistic regression models, I found that situational, officer, and neighborhood factors all influenced proactivity, arrest, and use of force. BWCs were associated with a lower likelihood of proactivity, but an increased likelihood of arrest and use of force. Officers were more proactive and were more likely to conduct arrests in immigrant and Hispanic neighborhoods. The moderating effects suggest that officers were even more likely to proactively initiate contacts and conduct arrests in immigrant and Hispanic neighborhoods when BWCs were activated. However, after BWCs were deployed, use of force was significantly less likely to occur in black neighborhoods. Given that high-profile police use of force incidents involving black suspects are often cited as a major impetus for the adoption of BWCs in American police agencies, this finding is a key contribution to the literature. / Dissertation/Thesis / Doctoral Dissertation Criminology and Criminal Justice 2020