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# Re: st: Fwd: Comparing marginal effects of two subsamples

 From Maarten Buis <[email protected]> To [email protected] Subject Re: st: Fwd: Comparing marginal effects of two subsamples Date Fri, 21 Oct 2011 09:35:09 +0200

```On Thu, Oct 20, 2011 at 10:16 PM, Jianhong Chen wrote:
> I am conducting two-way interaction with negative binomial model. The
> reviewers asked us to do marginal effects because of non-linear model.
> So, I splitted the sample according to the mean level of the
> moderator.

I would not use marginal effects in this case. The exponentiated
coefficients are incidence rate ratios, and are as easy to interpret
as marginal effects but without any of the disadvantages related to
marginal effects.

Consider the example below:

The dependent variable (art) is the number of articles published in
last three years of PhD (I believe these are biologists, but I am not
certain).Women in an average status school (z_phd = 0) produce (1-
.80)*100%= -20% less articles than men. This effect of being a women
increases, i.e. becomes less negative, when the schools has higher
status. For every standard deviation increase in status of the school
the effect of women increases by a factor 1.14, i.e.
(1-1.14)*100%=14%. In other words the effect of being a women in a
school with 1 standard deviation more status than average is
1.1440*.7996= .91. Which means that women is such a school produce
only 9% less articles than men.

*--------------------- begin example ---------------------
use http://www.stata-press.com/data/lf2/couart2, clear
gen byte baseline = 1
sum phd if !missing(art,fem,ment,kid5,mar)
gen z_phd = (phd - r(mean))/r(sd)
nbreg art i.fem##c.z_phd c.ment##c.ment kid5 mar baseline, irr nocons
*---------------------- end example -----------------------
(For more on examples I sent to the Statalist see:
http://www.maartenbuis.nl/example_faq )

For a more general discussion on how to interpret interactions in such
non-linear models see:
M.L. Buis (2010) "Stata tip 87: Interpretation of interactions in
non-linear models", The Stata Journal, 10(2), pp. 305-308.

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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```