Bias crime incidence in united states counties, 2000-2009: an application of social disorganization theory
نام عام مواد
[Thesis]
نام عام مواد
[Thesis]
نام عام مواد
[Thesis]
نام عام مواد
[Thesis]
نام نخستين پديدآور
Ryan Brevin Martz
نام ساير پديدآوران
Chermak, Steven M.
وضعیت نشر و پخش و غیره
نام ناشر، پخش کننده و غيره
Michigan State University
تاریخ نشرو بخش و غیره
2014
مشخصات ظاهری
نام خاص و کميت اثر
244
يادداشت کلی
متن يادداشت
Committee members: Bynum, Timothy S.; Gold, Steven J.; McGarrell, Edmund F.
یادداشتهای مربوط به نشر، بخش و غیره
متن يادداشت
Place of publication: United States, Ann Arbor; ISBN=978-1-321-13458-2
یادداشتهای مربوط به پایان نامه ها
جزئيات پايان نامه و نوع درجه آن
Ph.D.
نظم درجات
Criminal Justice - Doctor of Philosophy
کسي که مدرک را اعطا کرده
Michigan State University
امتياز متن
2014
یادداشتهای مربوط به خلاصه یا چکیده
متن يادداشت
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.
موضوع (اسم عام یاعبارت اسمی عام)
موضوع مستند نشده
Sociology; Criminology
اصطلاحهای موضوعی کنترل نشده
اصطلاح موضوعی
Social sciences;Bias crime;County;Hate crime;Social disorganization theory;Ucr
نام شخص به منزله سر شناسه - (مسئولیت معنوی درجه اول )