Journal
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
Volume 47, Issue 6, Pages 1722-1738Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/03610918.2017.1323223
Keywords
Count data; outliers; overdispersion; Pearson chi-square; zero inflation
Categories
Funding
- VHAHealth Services Research and Development (HSRD) program [IIR-06-219]
- Veterans Affairs [I50HX001229] Funding Source: NIH RePORTER
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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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