Test of multicollinearity among independent variables in logistic regression I am using 10 independent variables in building logistic regression model. I am sure that some of these variable are correlated. Can anybody tell me how to check for multicollinearity among independent variables in this case. Thanks!
 A: You can use whatever method you would use for ordinary regression. The dependent variable is irrelevant to multicollinearity issues, so it doesn't matter if you used logistic regression or regular regression or whatever.  
A: You can take the reference of condition index as well. a value greater than 30 indicates there is a near dependency in most cases. you can then go by either the correlation matrix or durbin watson test.
A: You could construct a correlation matrix and look for high values. An alternative would indeed be the VIF values as already mentioned. 
Both are quite arbitrary and rely on rules of thumb. For example what's the threshold for a correlation to be 'dangerous'? 
You could try to use factor scores on the correlated variables and check whether your results (estimates) are robust/sensitive to this issue. Good luck!
A: Examining a correlation matrix is helpful, but it is not a sufficient check since variables may be correlated when taken together but not pairwise.  I recommend examining tolerance or Variance Inflation Factor diagnostics in regression using a weighted regression where the weights are set to be equal to phat x (1-phat) where phat are the predicted values obtained from the logistic regression model fit with the same variables.
