Posted by
Jason McNellis on
Aug 01, 2006; 8:54pm
URL: http://spssx-discussion.165.s1.nabble.com/Multicollinearity-in-SPSS-tp1070043p1070044.html
It's hard to give you a real specific recommendation without knowing more
about the variables at question are coded or what you are trying to model. I
would assume country of origin would be coded as a series of dummy
variables, but based on your post that doesn't seem to be the case so I am a
little confused. (I would think the same thing about race, unless it is
applied at the population level.)
Based on my experience your values do raise a possible warning, but I don't
know enough about the problem to give many suggestions without knowing more.
Have a great day, Jason
-----Original Message-----
From: SPSSX(r) Discussion [mailto:
[hidden email]] On Behalf Of
Love (sent by Nabble.com)
Sent: Tuesday, August 01, 2006 2:15 PM
To:
[hidden email]
Subject: Multicollinearity in SPSS
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 rest of the ouput
but focus on the Coefficients table 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) or the VIF is 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 condition values are very high in
the collinearity diagnostics table. Should I care at all about these values
or should just look at the other statistics like tolerance and VIF and
correlation values?
- Also, 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.
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