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Re: st: Anova


From   David Airey <david.airey@vanderbilt.edu>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Anova
Date   Mon, 1 Dec 2008 19:43:56 -0600

If a variance component is negative and a small number, just assume it is not different from zero. But earlier did you not say all your variables were significant? So there may be some problem.

-Dave

On Dec 1, 2008, at 4:42 AM, aapdm wrote:

Dear John,

Thanks for this, the gfields is indeed very useful! I am now using it and it gives me pretty nice results. However I am surprised that the contribution of one of my explanatory variables is negative (although small). Any idea why this can be the case?

Many thanks, Alice.


--- On Fri, 28/11/08, John Antonakis <John.Antonakis@unil.ch> wrote:

From: John Antonakis <John.Antonakis@unil.ch>
Subject: Re: st: Anova
To: statalist@hsphsun2.harvard.edu
Date: Friday, 28 November, 2008, 4:32 PM
Simply estimate:

xi: reg y  x1 x2 x3 x4 x4 x6 i.dumm, beta.

If your variables are either binary or continuous variables
then you can examine the beta coefficient (i.e.,
standardized) for relative impact.

For other ways to look at coefficients download
"listcoef".

Also "gfields" is nice for breaking down
variance.

If you have categorical data with more than two categories
use the "test" command to test whether the dummies
of that variable dummies are jointly different from zero:
e.g., test I_dumm1 I_dumm2

I guess you could convert that F or chi-square that it
reports to an effect size.

HTH,
John Antonakis
University of Lausanne
Switzerland
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