Citation

Rydberg J, Carkin DM. Crime Delinq. 2017; 63(1): 61-76.

Copyright

(Copyright © 2017, SAGE Publishing)

DOI

10.1177/0011128716678848

PMID

unavailable

Abstract

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.

Keywords quantitative, count regression models, zero-inflated models, hurdle models

Language: en