Repercussions of Multicollinearity in Binary Logistic Regression

The type I error rates for the binary logistic regression model were examined across varying levels of multicollinearity. Population data sets were created using the statistical software package R and then used to create data suitable for binary logistic regression models. The results showed the type I error rates did not differ across multicollinearity levels, the percentage of accurately classified cases were unaffected, and the variance inflation factors were affected by more than just correlation between the independent variables. These results show that multicollinearity may have limited effects on the type I error rates of the binary logistic regression model; however, these effects should not be ignored