In this period I am working on the second paper for my PhD thesis.
Because of my strategy and what I am studying, I am using two logit models with an interaction term (dummy-by-dummy and dummy-by-continuous). It seems like the logit regression can give an interpretational advantage when you include interaction terms in your binary model.
Indeed, in case of binary models, the interaction effects are often misinterpreted:
For a general overview about the logit model, I suggest to quickly read the following:
More specifically, for the correct interpretation of interaction effects, I warmly suggest to read the following.
To me, this fourth article has been mind-blowing. However, the interpretation of my model is somehow complicated by the presence of a third variable, i.e., I want to compare logit y x1 x2 x1x2 with logit y x1 x2 x1x2 x3.
(The material I have uploaded is freely downloadable on the internet, if someone has some claim then I will change this post)