Utilizing alternate models for analyzing count outcomes

Although ordinary least squares (OLS) regression was once a common tool for modeling discrete count outcomes in criminology and criminal justice, the past several decades have seen an increasing reliance on regression techniques specifically designed for such purposes. Utilizing a practical example...

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Bibliographic Details
Main Author: Rydberg, Jason (Author)
Contributors: Carkin, Danielle Marie (Other)
Format: Electronic Article
Language:English
Published: 2017
In: Crime & delinquency
Year: 2017, Volume: 63, Issue: 1, Pages: 61-76
Online Access: Volltext (Resolving-System)
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Keywords:
Count regression models
Hurdle models
Quantitative
Zero-inflated models
Description
Summary:Although ordinary least squares (OLS) regression was once a common tool for modeling discrete count outcomes in criminology and criminal justice, the past several decades have seen an increasing reliance on regression techniques specifically designed for such purposes. Utilizing a practical example from the 1958 Philadelphia Birth Cohort, this article describes and compares various estimation strategies for modeling such outcome variables, including a discussion of the inappropriateness of OLS for such purposes and specific features of discrete count distributions that complicate statistical inference—overdispersion, non-independence, and excess zeros. Practical advice for selecting an appropriate modeling strategy is offered.
ISSN:1552-387X
DOI:10.1177/0011128716678848