An empirical approach to determine a threshold for assessing overdispersion in Poisson and negative binomial models for count data. Commun Stat Simul Comput 2018 Jul 05;47(6):1722-1738
Date
12/18/2018Pubmed ID
30555205Pubmed Central ID
PMC6290908DOI
10.1080/03610918.2017.1323223Scopus ID
2-s2.0-85021808446 (requires institutional sign-in at Scopus site) 75 CitationsAbstract
Overdispersion is a problem encountered in the analysis of count data that can lead to invalid inference if unaddressed. Decision about whether data are overdispersed is often reached by checking whether the ratio of the Pearson chi-square statistic to its degrees of freedom is greater than one; however, there is currently no fixed threshold for declaring the need for statistical intervention. We consider simulated cross-sectional and longitudinal datasets containing varying magnitudes of overdispersion caused by outliers or zero inflation, as well as real datasets, to determine an appropriate threshold value of this statistic which indicates when overdispersion should be addressed.
