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Re: st: comparing logistic regression coefficients between samples


From   Sunniva Andersen <andersensunniva6@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: comparing logistic regression coefficients between samples
Date   Wed, 6 Jul 2011 11:24:45 +0200

Thank you Maarten.
Sunniva


2011/7/6 Maarten Buis <maartenlbuis@gmail.com>:
> On Tue, Jul 5, 2011 at 11:31 PM, Sunniva Andersen wrote:
>> How can I do this in stata 11.   I have estimated the same
>> logistic regression model in two different samples, A and B.
>> I compute AME after the logit. Can someone suggest a method by which I
>> can test the hypothesis that var1 (b1) in sample A is different from
>> var1(b1) in sample B.
>
> That is an interaction effect, and those are hard when you want to do
> a logistic regression and marginal effects. In essence they will be so
> different from observation to observation that your conclusion will be
> that for some persons var1 will have a significantly larger effect in
> sample A than B, for others var1 will have a significantly smaller
> effect in sample A than B, and for the remaining individuals there is
> no significant difference. If this variability in interaction effect
> across individuals represented something present in the data, than
> there could be situations where this might be useful. However, it is
> just the result of the fact that marginal effects fit a linear model
> on top of your logit model in order to summarize your logit model, and
> that linear model does not fit very well. Averaging is not going to
> solve it as these effects are just too different from one another in
> order to be meaningfully summarized with one number.
>
> You have two options:
> 1) If you want to continue using logistic regression, you'll have to
> interpret your results as odds ratios and the interaction term as a
> ratio of odds ratios. See: M.L. Buis (2010) "Stata tip 87:
> Interpretation of interactions in non-linear models", The Stata
> Journal, 10(2), pp. 305-308.
>
> 2) If you want to continue interpreting your coefficients as a
> difference in probabilities rather than ratios of odds than you can
> use a linear probability model (i.e. just use -regress- in combination
> with the -vce(robust)- option instead of -logit-).
>
> Hope this helps,
> Maarten
>
> --------------------------
> Maarten L. Buis
> Institut fuer Soziologie
> Universitaet Tuebingen
> Wilhelmstrasse 36
> 72074 Tuebingen
> Germany
>
>
> http://www.maartenbuis.nl
> --------------------------
>
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