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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).

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$$VIF_i = \frac{1}{1-R_i^2}$$

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."

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