Reply With QuoteGenerally, a tolerance value less than .3 and a VIF value over 3 indicates multicollinearity.
Hi guys,
I just found about this forum today and I am really happy for that. I am writing a PhD thesis and could not get much help from my advisor so far.
I have a dataset with categories to run a logistic regression. However, i want to check for multicollinearity before I run the log. regression. A book on SPSS says to run a linear regression and ignore the the rest of the ouput but focus on the Coefficients ta ble and the columns labelled collinearity Statistics. My questions are:
The correlation between two variables ( fathers’ Spanish origin and mother’s Spanish origin) is -0.714. Another warning sign, the Pearson correlation between these variables also suggests multicollinearity (Pearson correlation of 0.808). However I am not sure if tolerance value is small enough to raise concern (0.293) and the VIF is also higher than the cut-off point advisable for a logistic regression (3.416). Some researchers say the cut-off for the tolerance is 0.1 or 0.2 and the VIF is 4, but one book says "Values of VIF exceeding 10 are often regarded as indicating multicollinearity, but in weaker models, which is often the case in logistic regression, values above 2.5 may be a cause for concern"
So, what do you guys think? Should I drop one of the variables? I also have a similar case with mother's race and father's race, only a little less correlation (0.619)
Another question is: Although the book said to ignore the other outputs, I couldn't help and see that some of the eigenvalues are high in the collinearity table. Should I care at all about these values or should just look at the other statistics like tolerance and VIF?
- Is there any other way to check for multicolinearity in this type of dataset in SPSS?
Please reply to any questions you might know the answer!
Many thanks, Love.
Generally, a tolerance value less than .3 and a VIF value over 3 indicates multicollinearity.
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