[R] which test-statistic to use for quasibinomial GLMs?

[Thomas Mang]

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