I am running a binomial logistic regression with a dependent variable is 0/1 and the explanatory variables are categorical - binary and multiple categories. When I add an interaction term, and run a VIF test for presence of multicollinearity, several of the coefficients of predictor variables take a VIF value of 5 or greater. I have read that as a rule of thumb 5 indicates issues with multicollinearity and that the model should be respecified. Thoughts on whether that threshold is appropriate. Is there a rule of thumb recommended for a generalized variance inflation factor value to assess severe multicollinearity?