Logistic regression models (generalised linear models with a binary response) are odd things. I am in the habitat of using randomised quantile residuals (R package statmod) for GLM diagnostics. I have read elsewhere on this site that:
if the model contains only categorical [predictor] variables and interactions among the variables are not needed, the model must fit the data and no calibration assessment is needed
This statement seems correct because the response variable can take on only two values (overdispersion can't happen) and there are no continuous predictors. Can someone please provide a citeable reference in support of this statement? The closest I can get is p. 595 of Crawley 2007 (The R Book), where it was noted that overdispersion is impossible for a binary response.