TY  - JOUR
PY  - 2017//
TI  - Utilizing alternate models for analyzing count outcomes
JO  - Crime and delinquency
A1  - Rydberg, Jason
A1  - Carkin, Danielle Marie
SP  - 61
EP  - 76
VL  - 63
IS  - 1
N2  - 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>
LA  - en
SN  - 0011-1287
UR  - http://dx.doi.org/10.1177/0011128716678848
ID  - ref1
ER  -