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# RE: st: Interaction in logit

 From "Mustillo, Sarah A" <[email protected]> To "[email protected]" <[email protected]> Subject RE: st: Interaction in logit Date Mon, 25 Oct 2010 08:51:32 -0400

```In addition to Maartin's Stata Tip #87, UCLA also has some nice tutorials on using -margins- to interpret interactions in logistic regression.

For continuous by continuous:

http://www.ats.ucla.edu/stat/stata/faq/logitconcon.htm

For categorical by continuous:

http://www.ats.ucla.edu/stat/stata/faq/logitcatcon11.htm

For multiple interactions:

http://www.ats.ucla.edu/stat/stata/faq/margins_mlogcatcon.htm

Sarah

-----Original Message-----
From: [email protected] [mailto:[email protected]] On Behalf Of Maarten buis
Sent: Monday, October 25, 2010 8:44 AM
To: [email protected]
Subject: Re: st: Interaction in logit

--- On Mon, 25/10/10, [email protected] wrote:
> My colleagues and I are working on an analysis where we
> have a dummy moderator and three continuous IVs which the
> dummy is hypothesised to moderate. The dependent variable is
> a dummy and we estimated a logit model.
>
> We have run the analysis both using the -inteff- command by
> Ed Norton and his colleagues and by analysing the odds
> ratios, as advised by Jaccard (2001).
<snip>
> Does anyone have more experience with this type of analysis
> and with these two approaches to analysing interactions in
> binary regression?

Please give complete references. This is a multi-disciplinary
list, and literature you assume so universally well known that
a "name-year" reference will suffice is likely to be completely
unknown in other disciplines.

Interactions in terms of odds ratios and marginal effects are
subtly different, in that odds ratios are effects in relative
terms with respect to the baseline odds while marginal effects
are absolute effects. This means that the interaction effects
as computed by -inteff- are sensitive to differences in the
baseline odds, while the interaciton effects in terms of the
ratio of odds ratios is not sensitive to this difference.
Sensitivity with respect to differences in the baseline odds
is not necesarily a bad thing, it is a substantive quesiton
whether or not you want to control for that. I wrote an
example of that in:

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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