title: The analysis of zero-inflated count data: beyond zero-inflated Poisson regression.
abstract: Infrequent 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. Hurdle models are an alternative class of two-component models that are seldom used in psychological research, but clearly separate the zero counts and the non-zero counts by using a left-truncated count model for the latter. In this tutorial we revisit both classes of models, and discuss model comparisons and the interpretation of their parameters. As illustrated with an example from relational psychology, both types of models can easily be fitted using the R-package pscl.
authors.affiliation: Department of Data Analysis, Ghent University, Belgium. tom.loeys@ugent.be
authors.forename: Tom
authors.initials: T
authors.lastname: Loeys
authors.forename: Beatrijs
authors.initials: B
authors.lastname: Moerkerke
authors.forename: Olivia
authors.initials: O
authors.lastname: De Smet
authors.forename: Ann
authors.initials: A
authors.lastname: Buysse
mesh_terms: D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D015233:Models, Statistical; D016012:Poisson Distribution; D011584:Psychology / Q000706:statistics & numerical data*