In this paper is proposed a straightforward model selection approach that indicates the most suitable count regression model based on relevant data characteristics. The proposed selection approach includes four of the most popular count regression models (i.e. Poisson, negative binomial, and respective zero-inflated frameworks). Moreover, it addresses two of the most relevant problems commonly found in real-world count datasets, namely overdispersion and zero-inflation. The entire selection approach may be performed using the programme language R, being all commands used throughout the paper availabe for practical purposes. It is worth mentioning that counting regression models are still not widespread within the social sciences.
Fávero, Luiz Paulo; Souza, Rafael de Freitas; Belfiore, Patrícia; Corrêa, Hamilton Luiz; and Haddad, Michel F. C.
"Count Data Regression Analysis: Concepts, Overdispersion Detection, Zero-inflation Identification, and Applications with R,"
Practical Assessment, Research, and Evaluation: Vol. 26
, Article 13.
Available at: https://scholarworks.umass.edu/pare/vol26/iss1/13