The analysis of zero-inflated count data: Beyond zero-inflated Poisson regression.
Article first published online: 23 SEP 2011
© 2011 The British Psychological Society
British Journal of Mathematical and Statistical Psychology
Volume 65, Issue 1, pages 163–180, February 2012
How to Cite
Loeys, T., Moerkerke, B., De Smet, O. and Buysse, A. (2012), The analysis of zero-inflated count data: Beyond zero-inflated Poisson regression. British Journal of Mathematical and Statistical Psychology, 65: 163–180. doi: 10.1111/j.2044-8317.2011.02031.x
- Issue published online: 10 JAN 2012
- Article first published online: 23 SEP 2011
- Received 18 October 2010; revised version received 5 July 2011
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.