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" Bias crime incidence in united states counties, 2000-2009: an application of social disorganization theory "
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Latin Dissertation
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| Language of Document
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English
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| Record Number
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802720
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| Doc. No
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TL47891
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| Call number
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1612357600; 3633612
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| Main Entry
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Karimu, Olusola O.
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| Title & Author
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Bias crime incidence in united states counties, 2000-2009: an application of social disorganization theory
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\ Ryan Brevin Martz
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Chermak, Steven M.
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| College
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Michigan State University
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| Date
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2014
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| Degree
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Ph.D.
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| student score
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2014
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| field of study
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Criminal Justice - Doctor of Philosophy
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| Page No
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244
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| Note
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Committee members: Bynum, Timothy S.; Gold, Steven J.; McGarrell, Edmund F.
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| Note
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Place of publication: United States, Ann Arbor; ISBN=978-1-321-13458-2
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| Abstract
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This goal of this dissertation is to identify predictors of bias criminality in the United States at the county level from 2000 - 2009. There is relatively little known about bias crime occurrence in the United States. In addition, increased public attention to bias criminality requires additional social science research examining the predictors of bias crime in American communities. By examining traditional indicators of social disorganization theory, this dissertation seeks to explore the likelihood of bias crime occurrence at the macro-level. As such, the unit of analysis is United States counties. The N is 3,141. The data upon which this dissertation is based come from the Federal Bureau of Investigation (FBI), the United States Census Bureau (USCB), the Association of Religious Data Archives (ARDA), and Congressional Quarterly's Voting and Elections Collection. From the data, measures of economic deprivation, social heterogeneity (diversity), social cohesion, and residential mobility were created. These measures represent traditional indicators of social disorganization theory. Four models are introduced in this dissertation in order to answer several research questions that explore the differences between how these predictors affect various types of bias crime. Negative binomial regression and OLS regression are used to analyze the data and address the research questions. Specifically, anti-race motivated bias crime, anti-sexual orientation motivated bias crime, and anti-religion motivated bias crime types are considered.
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| Subject
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Sociology; Criminology
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| Descriptor
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Social sciences;Bias crime;County;Hate crime;Social disorganization theory;Ucr
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| Added Entry
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Chermak, Steven M.
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Michigan State University
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Criminal Justice - Doctor of Philosophy
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