Hi, I have fitted quasibinomial GLM [glm(y ~ ..., family = quasibinomial)] to a binary response variable; quasibinomial, because there were clear signs of underdispersion in a 'simple' binomial GLM, and so the dispersion is a free parameter in the model. My question is now: In a quasi-binomial model with a binary-only response variable, what are the most appropriate tests to compare different models? I have studied Faraway's book (Extending the linear Model with R) and concluded a likelihood-ratio test seems to be inappropriate, as seems to be the Wald-test. In chapter 7 an F-test is suggested, but this refers to an example with a beta-distributed response. Can I conclude that the following code example will be fine in my case: anova(model1, model2, test= "F") ? Moreover, the summary of the GLM, including parameters of the predictors, shall be presented. The summary method however does not conduct an F test; so in sync with my ideas above, shall I also use F-tests for the individual predictors (personally, I would, but as I am not sure I ask here...)? Thanks a lot, Thomas