Date: Tue, 8 Mar 2011 07:08:41 -0800
Reply-To: Bruce Weaver <firstname.lastname@example.org>
Sender: "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From: Bruce Weaver <email@example.com>
Subject: Re: Multicoliniarity
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That kind of multicollinearity will largely go away if you center your
variables on some plausible in-range value before computing the product
Many people center on the mean in knee-jerk fashion. But you do not have to
center on the mean--the value you use is arbitrary. The mean changes from
sample to sample, so if you mean-center, you are centering on a different
value every time. If instead you pick a nice round value near the mean, and
use it in every analysis, you have much better comparability across data
sets. (Another option I like is centering on a value near the minimum so
that the intercept gives the fitted value for someone near the minimum on
David Moore-10 wrote:
> Dear Listers,
> I am hoping that someone might be able to help with a problem I am having.
> I am running a few regression models at the moment and an wondering about
> multicoliniarty, I believe from what I've read that ideally we need a VIF
> <10? this is ok for the first stage of our model however at a second stage
> we have entered some interaction terms (created from the first stage of
> model) understandably these correlate highly with some of the other
> variables (as they are products of these) resulting in VIF's as high as 40
> what does this mean for the analysis, can we continue or is there
> I am missing.
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