Date: Wed, 16 Jul 2008 12:59:58 -0700
Reply-To: jimjohn <azam.khan@utoronto.ca>
Sender: "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From: jimjohn <azam.khan@utoronto.ca>
Subject: Re: Multicollinearity
In-Reply-To: <105244.69652.qm@web50909.mail.re2.yahoo.com>
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I have one example where I know there is a multicollinearity effect because
my coefficient comes out positive, when it is supposed to have a negative
effect on the dependent variable (in a pairwise correlation with the
dependent variable, its sign is negative). However, there are no condition
indexes greater than 20 or 30, so that test doesn't find any significant
collinearity. Also, the values of VIF and tolerance do not show a
significant effect either. Can someone plz explain how come these two tests
don't show any collinearity when there should be. Or, any suggestions? Thx!
Coefficients(a)
Unstandardized Coefficients Standardized Coefficients Collinearity
Statistics
Model B Std. Error Beta t Sig. Tolerance VIF
1 (Constant) .260 .013 20.486 .000
%VIRM Pacific (Branch) .013 .049 .028 .259 .797 .661 1.513
LEADS(@3monthBA1monthBA,11) -.455 .084 -.534 -5.383 .000 .809 1.236
LEADS(@3monthOISCORRA,9) -.147 .037 -.419 -3.947 .000 .708 1.413
a. Dependent Variable: Pacific (Branch)
Collinearity Diagnostics(a)
Variance Proportions
Model Dimension Eigenvalue Condition Index (Constant) %VIRM Pacific (Branch)
LEADS(@3monthBA1monthBA,11) LEADS(@3monthOISCORRA,9)
1 1 2.747 1.000 .02 .02 .05 .03
2 .754 1.908 .04 .01 .00 .66
3 .424 2.546 .05 .02 .92 .09
4 .075 6.039 .89 .96 .04 .22
a. Dependent Variable: Pacific (Branch)
SR Millis wrote:
>
> VIF (and tolerance) have limitations: the inability to distinguish among
> several coexisting near-dependencies and the lack of a meaningful
> guideline to differentiate high VIF from low.
>
> To diagnose collinearity, it is much better to first use the condition
> indexes: pick out those that are large, say >20 or >30. For those large
> condition indexes, see if there are large variance-decomposition
> proportions (> .50) associated with each high condition index: this
> identifies those variables that have high collinearity.
>
>
> Scott R Millis, PhD, MEd, ABPP (CN,CL,RP), CStat
> Professor & Director of Research
> Dept of Physical Medicine & Rehabilitation
> Wayne State University School of Medicine
> 261 Mack Blvd
> Detroit, MI 48201
> Email: smillis@med.wayne.edu
> Tel: 313-993-8085
> Fax: 313-966-7682
>
>
> --- On Wed, 7/2/08, azam.khan@utoronto.ca <azam.khan@utoronto.ca> wrote:
>
>> From: azam.khan@utoronto.ca <azam.khan@utoronto.ca>
>> Subject: Re: Multicollinearity
>> To: SPSSX-L@LISTSERV.UGA.EDU
>> Date: Wednesday, July 2, 2008, 10:16 AM
>> Thanks so much! I see that SPSS has collinearity
>> diagnostics:
>> Tolerance and VIF. Can anyone recommend generally what
>> values of
>> tolerance and VIF should indicate there is a
>> multicollinearity
>> problem. I am seeing many different responses in different
>> lectures/books. some say a tolerance < .1 or a VIF >
>> 10 indicate
>> collinearity. others say tolerance < .2 and VIF > 4).
>> and then ive
>> also seen tolerance < .4. Any ideas? thx.
>>
>>
>
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