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NCJ Number: 221034 Find in a Library
Title: Using Poisson Class Regression to Analyze Count Data in Correctional and Forensic Psychology: A Relatively Old Solution to a Relatively New Problem
Journal: Criminal Justice and Behavior  Volume:34  Issue:12  Dated:December 2007  Pages:1659-1674
Author(s): Glenn D. Walters
Date Published: December 2007
Page Count: 16
Type: Measurement/Evaluation Device
Format: Article
Language: English
Country: United States of America
Annotation: This study reanalyzed data from two previously published studies in an attempt to illustrate how count data could be analyzed with Poisson class procedures and compared the results to those obtained with binomial and linear regression.
Abstract: Count data present a formidable challenge to researchers in the correctional and forensic psychology fields. The benchmark model for count data is the Poisson distribution, and the standard statistical procedure for analyzing count data is Poisson regression. However, highly restrictive assumptions lead to frequent misspecification of the Poisson model. Alternate approaches, such as negative binomial regression, zero modified procedures, and truncated and censored models are consequently required to handle count data in many social science contexts. Two empirical examples from correctional and forensic psychology are provided to illustrate the importance of replacing ordinary least squares regression with Poisson class procedures in situations when count data are analyzed. Figures, tables, references
Main Term(s): Data analysis
Index Term(s): Data integrity; Forensic psychology; Regression analysis; Statistical analysis; Statistics
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