A BAYESIAN APROACH FOR ZERO-INFLATED COUNT DATA IN PSYCHOLOGICAL RESEARCH

Abstract

Count data in psychological research are commonly modelled using zero-inflated Poisson regression. This model can be viewed as a latent mixture of an “always-zero” component and a Poisson component. In this study we introduce a Bayesian approach for zero inflated Poisson model, and discuss model comparisons and the interpretation of their parameters. As illustrated with two real-world examples psychological research, both Bayesian and classic approach of models can easily be fitted with a great gain.