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:

Computing interaction effects and standard errors in logit and probit models

For a general overview about the logit model, I suggest to quickly read the following:

important special cases of the logistic model

Inappropriate Interpretation of the Odds Ratio

More specifically, for the correct interpretation of interaction effects, I warmly suggest to read the following.

Interpretation of interactions in non-linear models – m b

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)