When I run the logistic regression, two independent variables have VIF values greater than 10 like 13 or so. Logistic regression is the one I will use to measure the overall change in the dependent variable with one incremental changes in every each independent variable. However, when I run the linear regression model version on my same data, the VIF values I get from all the independent variables are either slightly greater than 1 or around 5 or 6. Besides, all the coefficients are highly significant all below 0.01.
If so, is it safe to use my logistic regression model to find out the incremental effect of every each independent variable without dropping out any independent variables?
If my purpose is to find an incremental effect of every each independent variable on the overall dependent value in a logistic regression model, is multicolinearity an important problem to consider? Can I ignore it?
If my p-values are all less than 0.01 which is the same indicator as t-value, will I still need to worry about colinearity issue even though my VIF scores for two variables are around 13-14? Based on what you said, if p-value is safe enough, will this be still a problem?
I am also referring to the following website comment: http://www.researchconsultation.com/multicollinearity-multiple-regression.asp
So, in sum, my ultimate goal is to use the final output from the logistic regression model generated from the independent variables and one binary dependent variable. If so, do you think I can ignore the multicolinearity problem?