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

Orientador: Goran Neshich / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Biologia / Made available in DSpace on 2018-08-20T22:35:51Z (GMT). No. of bitstreams: 1 Moraes_FabioRogeriode_D.pdf: 15399723 bytes, checksum: 4f1315f86b2c74d078c5105b299a9750 (MD5) Previous issue date: 2012 / Resumo: Dentro do ambiente celular, há uma variedade de moléculas e a interação entre si regulam praticamente todos os processos necessários e essenciais para a manutenção da vida. Interações entre proteínas estão envolvidas no controle de vários processos intra e intercelulares, como regulação metabólica e da expressão gênica, reconhecimento antígeno-anticorpo etc. que definem as características biológicas do funcionamento da vida entre os diversos organismos. Ao conhecer a interface de interação de uma proteína chave para desenvolvimento de casos patológicos, é possível desenhar drogas com alta especificidade com o sítio de ligação. Para avançar nessa frente, o conhecimento da estrutura proteica é fundamental, porém não suficiente. É necessário conhecermos o sítio de ligação alvo para cada parceiro de interação. Este estudo visa entender as características do nano-ambiente das interfaces proteicas - área através da qual as macromoléculas se comunicam e exercem sua funcionalidade. Propomos utilizar uma abordagem de estudo das características físico-químicas e estruturais dos resíduos formadores de interfaces de complexos conhecidos e com estrutura quaternária resolvida experimentalmente, utilizando um conjunto de dados sem redundância sequencial, extraindo os parâmetros/descritores que descrevem de forma objetiva as diferentes classes de complexos, revelando as características principais sobre interações proteína-proteína. A finalidade deste trabalho é de conhecer os detalhes que definem uma área como interface e aplicá-lo em uma ferramenta preditiva para todas as proteínas com arranjo estrutural conhecido e/ou modelado. Propomos de forma pioneira, o uso de classificadores específicos para cada tipo de aminoácido e independente do uso de descritores sobre conservação de aminoácidos. Resultados obtidos com classificador linear e por ensemble de redes neurais destacam a nossa abordagem, desenhada e aplicada nesta tese, como uma com os melhores indicadores de desempenho na predição precisa dos resíduos de aminoácido na interface entre as abordagens descritas recentemente na literatura. Ainda, enquanto os outros métodos dependem de descritores sobre conservação de aminoácidos, é mostrado aqui que nenhum ganho de desempenho é obtido com a incorporação de tais descritores em nosso modelo classificador. Esse resultado indica que o uso de descritores puramente físico-químicos e estruturais é suficiente para explicar o grau de conservação dos aminoácidos / Abstract: Inside cells, there is a variety of molecules and their interactions regulate virtually all necessary and essential processes to the maintenance of life. Interactions among proteins are involved in the control of several processes within and out of the cell, such as, metabolic and gene expression regulation, anti-body and antigen recognition, etc. that defines biological characteristics of life among many organisms. If the protein interface amino acids of a key protein related to a given pathologic phenomenon are known, it is possible to rationally design drugs with high specificity for a specific binding site. To gain insight in this field, the knowledge of the protein three-dimensional structure is mandatory, but not sufficient. It is also necessary to know the interface between the target protein and its partners. This study focuses in understanding the characteristics of the area through which the macromolecules communicate to each other and exercise their function. Here, it is proposed an approach to study the physicochemical and structural characteristics of the interface forming residues with known quaternary structure (experimentally solved). It was selected a sequence non-redundant dataset and by extracting parameters/descriptors, that objectively describe different complex classes, it was possible to unravel the basic characteristics of protein-protein binding. The goal of this study is to unravel the details that outline a specific area as interface and apply it in a form of a predictive tool for all proteins with known atomic structure. It is proposed by the first time, the use of amino acid specific classifiers regarding amino acid type and free of amino acid conservation attributes. The results obtained here by employing linear and ensemble of neural network classifiers show that, based on purely physicochemical and structural descriptors, it is possible to get precise predictions about interface forming residues in protein-protein assemblies. Comparatively, the method described here retains better performance indicators than the ones recently described in the literature. In addition, we showed that, for our method, adding "conservation" attributes does not induce any performance gain, which is a major difference if compared to other described methods. This result indicates the purely physicochemical and structural descriptors are sufficient to explain how conserved amino acids are / Doutorado / Bioinformatica / Doutor em Genetica e Biologia Molecular