Jumping in late here, generally agreeing with the pther ecologists (who say that the data are often overdispersed even after accounting for known grouping factors via random effects). I also agree that negative binomial models would be a great addition to lmer (? could perhaps do beta-binomial at the same time, without much additional cost?). [glmmADMB will also do these, it's not as widely used, but certainly worth further testing] Testing and strengthening lmer's ability to handle individual-level variation (as in lognormal-Poisson or logit-normal-binomial models) would provide another alternative to quasi() ... David Duffy's example (variation left over after random effects and negative binomial model) makes me kind of nervous, I would hope that such effects would be jumping out of exploratory graphics, or graphical analyses of residuals ... My advice to students is usually "do whatever is most feasible, you probably don't have enough data to distinguish between Var = phi*mu (quasi-) and Var = mu*(1+phi*mu) (neg binom) anyway" -- supported by Liang and McCullagh and Richards, to some extent ... although recent simulation studies with big data sets have told a somewhat different story ... Bottom line -- ecologists *will* often have overdispersion that can't be explained by known grouping factors, but they are pragmatists -- if you give them some way to handle that overdispersion (parametric models or individual-level variation) they probably won't complain about the lack of quasi- ... cheers Ben Bolker Liang, Kung-Yee, and Peter McCullagh. 1993. Case Studies in Binary Dispersion. Biometrics 49, no. 2 (June): 623-630. Richards, Shane A. 2008. Dealing with overdispersed count data in applied ecology. Journal of Applied Ecology 45: 218-227. doi:10.1111/j.1365-2664.2007.01377.x.
Ben Bolker Associate professor, Biology Dep't, Univ. of Florida bolker at ufl.edu / www.zoology.ufl.edu/bolker GPG key: www.zoology.ufl.edu/bolker/benbolker-publickey.asc