Bookmark and Share

Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

Re: st: comparing logistic regression coefficients between samples

From   Sunniva Andersen <>
Subject   Re: st: comparing logistic regression coefficients between samples
Date   Wed, 6 Jul 2011 11:24:45 +0200

Thank you Maarten.

2011/7/6 Maarten Buis <>:
> 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
> --------------------------
> *
> *   For searches and help try:
> *
> *
> *

*   For searches and help try:

© Copyright 1996–2018 StataCorp LLC   |   Terms of use   |   Privacy   |   Contact us   |   Site index