An empirical approach to determine a threshold for assessing overdispersion in Poisson and negative binomial models for count data |
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| Authors: | Elizabeth H Payne Mulugeta Gebregziabher James W Hardin Viswanathan Ramakrishnan Leonard E Egede |
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| Institution: | 1. Department of Public Health Sciences—Biostatistics, Medical University of South Carolina, Charleston, SC, USA;2. Health Equity and Rural Outreach Innovation Center (HEROIC), Ralph H. Johnson Department of Veterans Affairs Medical Center, Charleston, SC, USA;3. The EMMES Corporation, Rockville, MD, USA;4. Division of Biostatistics, Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, USA |
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| Abstract: | 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. |
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| Keywords: | Count data outliers overdispersion Pearson chi-square zero inflation |
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