Posted by
Kathryn Gardner on
Jun 29, 2006; 10:25am
URL: http://spssx-discussion.165.s1.nabble.com/Suppressor-variables-in-moderated-multiple-regression-tp1069335p1069339.html
Hi Keith,
Thanks for the further advice. I've been looking at collinearity diagnostics and I was wondering how the condition index is used? You said "Look for rows with a low eigenvalue (near zero), and then across the row for the columns that have high proportions (near one). That will tell you which variables are collinear with each other". But I was wondering whether I need to look at the condition index as well? I read that a condition index over 30 suggests serious collinearity problems and an index over 15 indicates possible collinearity problems. If a factor (component) has a high condition index, one looks in the variance proportions column to see if two or more variables have a variance partition of .50 or higher. But this text makes no reference to the eigenvalues. Can you shed any light on how eigenvalues, condition indices and variance portions are used all together or relate?
Thanks
Kathryn
> Date: Tue, 27 Jun 2006 11:04:36 -0400> From:
[hidden email]> To:
[hidden email]> Subject: Re: Re: Suppressor variables in moderated multiple regression> CC:
[hidden email]> > HI Kathryn,> > I am glad you made progress. VIF and tolerance are often enough to do> the trick, but as Stephen mentioned there is also the Variance> Proportions table in the Collinearity Diagnostics.> > Look for row with a low eigenvalue (near zero), and then across the> row for the columns that have high proportions (near one). That will> tell you which variables are collinear with each other. Deleting> variables is not the only choice, you could combine them in some way> (add them or average them when it makes sense) or create factors using> factor analysis.> > Good luck, Keith> > On 6/27/06, Kathryn Gardner <
[hidden email]> wrote:> >> >> > Hi Keith,> >> > Thanks a lot for your help. I have centered my variables that are to be used> > to create product terms and have also examined the collinearity diagnostics.> > I ended up deleting one variable with a VIF of 11.90 and I now have more> > acceptable VIF levels. I still have many variables which have non-sig zero> > order correlations but sig B coefficients in the regression model though, so> > perhaps they are suppressors. Actually some of my VIFs are still over 5 so> > you may be right in that they are suppressors.> >> > Thank you for your advice regarding my second question which was also> > helpful.> > Best wishes,> > Kathryn> >> >> > ________________________________> >> > > Date: Mon, 26 Jun 2006 13:01:57 -0400> > > From:
[hidden email]> > > Subject: Re: Suppressor variables in moderated multiple regression> > > To:
[hidden email]> >> > >> > > Hi All,> > >> > > I think there is evidence of suppression here, but> > there are a number> > > of things I would check that you don't mention. I> > don't know which> > > you have tried, so I will list them in response to 'a'.> > >> > > If you have not centered the variables, you might want> > to do that.> > > That is, you subtract the average of a variable> > from itself so that> > > zero is the average. This is important when creating> > the interactions> > > and polynomials.> > >> > > Request the collinearity diagnostics. Small> > tolerance values (below> > > .1) would indicate a problem and add to the evidence> > that suppression> > > is present. Since you ran 3 steps it would be> > interesting to see when> > > (if) the tolerance radically lowers.> > >> > > VIF would also be a sign. If the Variance Inflation> > Factor becomes> > > large, you might have suppression (5+ or so). If you> > have not> > > centered and the VIF jumps on step three, then I> > would center and run> > > it again. It might help a lot.> > >> > > In answer to 'b', I don't see any harm in looking at> > the standarized> > > beta on the interactions to check for one detail. See> > if the value> > > falls outside its normal range - that is it shouldn't> > be above 1 or> > > below -1. If it is, that would also indicate> > suppression.> > >> > > HTH. Good Luck.> > >> > > Keith> > > keithmccormick.com> > >> > > On 6/26/06, Kathryn Gardner <
[hidden email]> wrote:> >> > > > Dear List,> > > >> > > > I've conducted moderated multiple regression> > analysis with the main effects> > > > on steps 1 and 2 and product (interaction) terms on> > step 3. After recently> > > > reading & learning about suppressor variables,> > I examined the zero-order> > > > correlations between all predictors (including> > interaction terms) and the> > > > criterion variable & compared them to the regression> > model beta> > > > coefficients for inconsistencies in sizes and signs.> > I found that some> > > > bivariate correlations between predictors &> > the criterion are non-> > > > significant, but they are significant predictors in> > the regression> > > > analysis. I have read that this may be a sign of> > classical suppression & I> > > > was wondering if anyone could advise on:> > > >> > > > a) whether this is a sign of suppression, & even if it> > is, what else> > > > could these results suggest other than suppression?> > > > b) the literature on suppressor variables suggests> > looking for> > > > inconsistencies in signs and sizes between the> > bivariate correlations and> > > > standardized regression coefficients (beta).> > However, I have read that only> > > > the unstandardized coefficients (B) should be> > interpreted when interpreting> > > > interaction effects. Is it therefore OK to> > examine inconsistencies between> > > > bivariate correlations and unstandardized> > coefficients and are there any> > > > issues I need to be aware of?> > > >> > > > Many thanks.> > > >> > > > Kathryn> > > >> >> >> > ________________________________> > Be one of the first to try Windows Live Mail.
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