Poisson-Based Regression Analysis of Aggregate Crime Rates
- D. Wayne Osgood
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This article introduces the use of regression models based on the Poissondistribution as a tool for resolving common problems in analyzing aggregatecrime rates. When the population size of an aggregate unit is small relativeto the offense rate, crime rates must be computed from a small number ofoffenses. Such data are ill-suited to least-squares analysis. Poisson-basedregression models of counts of offenses are preferable because they arebuilt on assumptions about error distributions that are consistent withthe nature of event counts. A simple elaboration transforms the Poissonmodel of offense counts to a model of per capita offense rates. Todemonstrate the use and advantages of this method, this article presentsanalyses of juvenile arrest rates for robbery in 264 nonmetropolitancounties in four states. The negative binomial variant of Poisson regressioneffectively resolved difficulties that arise in ordinary least-squaresanalyses.
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- Poisson-Based Regression Analysis of Aggregate Crime Rates
Journal of Quantitative Criminology
Volume 16, Issue 1 , pp 21-43
- Cover Date
- Print ISSN
- Online ISSN
- Kluwer Academic Publishers-Plenum Publishers
- Additional Links
- negative binomial
- crime rates
- aggregate analysis
- D. Wayne Osgood (1)
- Author Affiliations
- 1. Crime, Law and Justice Program, Department of Sociology, Pennsylvania State University, 201 Oswald Tower, University Park, Pennsylvania, 16802