We perform data analysis and build models. Say, for example, I built a regression model that has more than one predictor (multiple regression). We then check many things: normality, multicollinearity, etc. Specifically, we check for multicollinearity, for a numeric/continuous variable, VIF (Variance Inflation Factors) etc. If we find that there is multicollinearity, we then drop one of the highly correlated features.
My question is: what can be done with categorical variables? I mean if two categorical variables are correlated/associated does it mean I have to drop it? I am not clear how to handle categorical variables like we handle correlations between continuous variables.
What do we mean by two factor variables are correlated or dependent or independent? What if there is a collinearity? How do you identify that collinearity? How do you deal with it?