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 Detecting Mulitcollinearity

 Detecting Mulitcollinearity

Multicollinearity is not desirable. It means that the variance our independent variables explain in our dependent variable are are overlapping with each other and thus not each explaining unique variance in the dependent variable. The way to check this is to calculate a Variable Inflation Factor (VIF) for each independent variable after running a multivariate regression. The rules of thumb for the VIF are as follows:

  • VIF < 3: not a problem
  • VIF > 3; potential problem
  • VIF > 5; very likely problem
  • VIF > 10; definitely problem

The tolerance value in SPSS is directly related to the VIF, and values less than 0.10 are strong indications of multicollinearity issues. For particulars on how to calculate the VIF in SPSS, watch the step by step video tutorial. The easiest method for fixing multicollinearity issues is to drop one of problematic variables. This won’t hurt your R-square much because that variable doesn’t add much unique explanation of variance anyway.

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