VIF interactions [duplicate]

I would like to check for multicollinearity in logistic regression analysis. Independent variables are categorical (always binary) and continuous. Sample has limited size (N=176, 36 events), so I can use 2, max. 3 variables.

Can I use VIF as an indicator for multicollinearity in final model? What if I have in final model 2 categorical (binary) and one continuous? Does it make sense to use VIF? Are there any others SIMPLE options? (SPSS)

I have searched all over internet but could not find an SIMPLE (excluding complicated formulas) answer concerning VIF with categorical variables.

I am using SPSS 22.0 and my field is heart surgery, not statistic. Any suggestions? (instead of finding a statistician).

• Commented May 29, 2020 at 19:58

$$VIF_i = \frac{1}{1-R_i^2}$$
where $$R_i^2$$ is the $$R^2$$ of a linear model calculated for the regression of $$X_j$$ on the other covariates.
So you can calculate VIF for a quantitative variable with a categorial, but you can't do the contrary! That's because the regression of $$Y$$ categorial on $$X$$ (quantitative o categorial) would imply the use of logistic regression whose $$R^2$$ is just a pseudo-$$R^2$$.
From this question How to calculate pseudo-$R^2$ from R's logistic regression? I found a useful sentence for you:
"Technically, $$R^2$$ cannot be computed the same way in logistic regression as it is in OLS regression. The pseudo-$$R^2$$, in logistic regression, is defined as 1 - L1/L0, where L0 represents the log likelihood for the "constant-only" model and L1 is the log likelihood for the full model with constant and predictors."