# If VIF value is greater than 5,

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?

• The usual variance-inflation factor does not even describe the variance inflation in a logistic regression. Generalized variance-inflation factor is what you would want to calculate for your generalized linear model. // How are you using the logistic regression model? What do you want to learn by fitting it? If you just want to make predictions, variance inflation is not so important.
– Dave
Jun 2 at 1:17
• The model is being used for conducting significance testing to test the association between outcome and explanatory variable of interest, and not for predictions, so represents a potential issue with precision of estimates. Is there a rule of thumb recommended for a generalized variance inflation factor value to assess severe multicollinearity?
– sili
Jun 2 at 1:30
• Interactions induce collinearity. You probably shouldn't worry about it. Or at least, center your predictors, create the interaction from the centered variables, then use all three of those variables in your model instead of the original ones. Jun 2 at 2:48

Consequently, there is much more to the story than just a VIF threshold of, say, $$5$$ or $$10$$ like sometimes get recommended in introductory courses.