@article{ref1,
title="Utilizing alternate models for analyzing count outcomes",
journal="Crime and delinquency",
year="2017",
author="Rydberg, Jason and Carkin, Danielle Marie",
volume="63",
number="1",
pages="61-76",
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<p /> <p>Language: en</p>",
language="en",
issn="0011-1287",
doi="10.1177/0011128716678848",
url="http://dx.doi.org/10.1177/0011128716678848"
}