# Overdispersion in poisson glm

When calculating the dispersion deviance/degrees freedom I get the value 1.8. Is it absolutly necessary to carry out the glm using quasipoisson? What is deemed 'significantly overdispersed' ?

The Poisson model assumes equal mean and variance. If that doesn't hold, then the Poisson model isn't correct. Quasi-poisson is one possibility when there is overdispersion. Others include: Negative binomial regression (NBR) - similar to Poisson model, but using the negative binomial distribution instead, which has a dispersion parameter. Available in the MASS package in R, also integrated into Stata. Hurdle regression - for circumstances with more 0s than would be expected from the Poisson/NB model. It combines a logit/probit with Poisson/NB, where the logit/probit is used to estimate y=0 vs y>0, and a truncated Poisson/NB is used to estimate the cases where y>0. Available in the pscl package. Also available as a separate Stata add-on I cannot remember. Zero-inflated - zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZNB) models are similar to the hurdle model, but assume two mechanisms at work to generate 0s: never-takers, and potential takers who didn't take in this instance. Available in the pscl package, and available in Stata.
• If you want to carry out a formal test for overdispersion you can use the LR test of Poisson vs. NB but there are also many other tests (e.g. dispersiontest in AER). However, overdispersion can be relevant before it is significant. So in case of doubt I recommend to use NB or quasipoisson etc. Jan 9 '15 at 20:26