skip navigation

PUBLICATIONS

Register for Latest Research

Stay Informed
Register with NCJRS to receive NCJRS's biweekly e-newsletter JUSTINFO and additional periodic emails from NCJRS and the NCJRS federal sponsors that highlight the latest research published or sponsored by the Office of Justice Programs.

NCJRS Abstract

The document referenced below is part of the NCJRS Virtual Library collection. To conduct further searches of the collection, visit the Virtual Library. See the Obtain Documents page for direction on how to access resources online, via mail, through interlibrary loans, or in a local library.

 

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
Publisher: http://www.sagepub.com/ 
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
To cite this abstract, use the following link:
http://www.ncjrs.gov/App/publications/abstract.aspx?ID=242879

*A link to the full-text document is provided whenever possible. For documents not available online, a link to the publisher's website is provided. Tell us how you use the NCJRS Library and Abstracts Database - send us your feedback.