Date:         Tue, 8 Mar 2011 07:08:41 -0800
Reply-To:     Bruce Weaver <bruce.weaver@hotmail.com>
Sender:       "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From:         Bruce Weaver <bruce.weaver@hotmail.com>
Subject:      Re: Multicoliniarity
In-Reply-To:  <E608FB96900AC4C238D2E8B2@mppc-0038.campus.bath.ac.uk>
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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
terms.

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
that variable.)



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
> the
> 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
> something
> I am missing.
>
> Best
> David
>
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-----
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Bruce Weaver
bweaver@lakeheadu.ca
http://sites.google.com/a/lakeheadu.ca/bweaver/

"When all else fails, RTFM."

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