Mediation is harder when dealing with non-linear models like -logit- than in linear models, so the type of tricks you used below won't work in your case. This is discussed in http://home.fsw.vu.nl/m.buis/wp/ldecomp.html , which discusses one way of doing this test using -ldecomp-, which you can download from SSC by typing in Stata -ssc install ldecomp-.
Hope this helps,
Maarten
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Maarten L. Buis
Institut fuer Soziologie
Universitaet Tuebingen
Wilhelmstrasse 36
72074 Tuebingen
Germany
http://home.fsw.vu.nl/m.buis/
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--- On Thu, 16/7/09, [email protected] wrote:
> I would be grateful for any assistance from anyone on
> proper use of SUEST command to test for mediation of the
> effect of one predictor on a dependent variable from another
> predictor. To be clear, I wish to test for mediation, not
> effect modification. I think x1 is a cause of y and x2 is
> also a cause of y. I also know that x1 is a cause of x2. By
> Barony and Kenney’s rules of mediation, one of the
> necessary criteria to say that x2 mediates the effect of x1
> on y is that the parameter estimate for x1 changes
> significantly when x2 is added to the regression. The data I
> have used is all from the same study sample.
>
> Here is the code I have used;
> • logit y x1 x2 x3
> • estimates store A
> • logit y x2 x3
> • estimates store B
> • suest A B
> • testnl [A]x1=[B]x2
>
> When I do this, the 95% confidence intervals of the
> parameter estimates for x1 when x2 is or is not included
> overlap considerably;
> 0.23 (95% C.I. 0.01 to 0.46) when x2 is included, and
> 0.33 (95% C.I. 0.11 to 0.54) when x2 is not included as a
> predictor.
>
> And yet the chi-square and p-value for the test,
> respectively, are 6.37 and 0.012. How can this test be
> significant when the confidence intervals overlap so much?
> Any ideas how I am misusing this test?
>
> I would appreciate any guidance or help anyone can give
> me.
>
> John Schousboe MD, PhD
>
>
> *
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>
*
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