Checking for multicollinearity of independent variables is necessary in linear regression since multicollinearity increases the standard error, which in turn affects t-stats and p-values. But in logistic regression, p-values are based on Khi2, therefore multicollinearity has no effect on the p-value (this is at least my understanding, am I wrong?). If so: Question 1 - Why check for multicollinearity in logistic regression? Next, Question 2 - If checking for multicollinearity is necessary, then should the check be run for continuous as well as for dummy variables, or instead should multicollinearity be checked for continuous variables only? Question 3 - If the check is to be run for dummies as well, then is it OK to calculate such association coefficients as Pearson's Phi, Tschuprow's T and Cramer's V (as the dummies in question are nominal)? And finally, Question 4 - If calculating association coefficients is OK, can it be considered that there is no serious multicollinearity risk insofar as the coefficient does not exceed 60%? Or is there any alternative rule of thumb on this?