In a psychology study of N = 149, I was testing for moderation using a three-step hierarchical regression analysis using SPSS. I had two independent variables, X1 and X2, an outcome variable, Y, and the moderator, M. Step 1 uses the variables X1, X2, the interaction X1X2, and 5 covariates. Step 2 adds M. Step 3 adds the interaction variables X1M and X2M.

In my collinearity statistics, VIF is all under 10 for Steps 1 & 2 (VIF of 6 is found between X2 and X1X2 in both steps). For Step 3, VIF is high for X1, X2, M, X1M, and X2M. When I go look at the collinearity diagnostics box, the variance proportions are high for the constant, X1, M, and X1M. I'm understanding that there is multicollinearity.

My question is, what does it mean when the constant shows a high VIF? What would it mean if only one predictor variable and the constant coefficient were collinear?

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