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Re: st: Testing interaction terms


From   David Hoaglin <dchoaglin@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Testing interaction terms
Date   Wed, 19 Jun 2013 10:39:12 -0400

Verena,

Not exactly.  I would not have included var00, which has no data to support it.

Your data do not allow you to test whether the effects of var1 and
var2 are additive.  You can estimate the effect of var1 when var2==1
and the effect of var2 when var1==1, but you cannot estimate the
"marginal" effect of either var1 or var2 (averaging over the levels of
the other variable).  The two comparisons that you can make are var11
- var10 and var11 - var01.

I assume that having 0 observations out of 700 with var1==0 & var2==0
reflects some structural aspect of your data, rather than a chance
occurrence.  That structure allows you to estimate the effects of
var01, var10, and var11 (perhaps with one category as the reference
category), but it does not allow you to estimate the interaction of
var1 and var2 (in the usual sense).  Perhaps the structure provides a
framework for restating the question and interpreting the three
effects that you can estimate.

David Hoaglin

On Wed, Jun 19, 2013 at 9:27 AM, Verena Dill <dill@uni-trier.de> wrote:
> What you wrote is exactly what I did in my regression (output below, just
> for the matter of illustration I included the four categories;  var11:
> var1==1 & var2==1, var10: var1==1 & var2==0, var01: var1==0 & var2==1,
> var00: var1==0 & var2==0; because of the below mentioned structure of the
> data two of the categories are omitted).
> ------------------------------------------------------------------------------
>      partner |      Coef.   Std. Err.      z    P>|z|     [95% Conf.
> Interval]
> -------------+----------------------------------------------------------------
>        var11 |   .7478253   .3528458     2.12   0.034     .0562602
> 1.43939
>        var10 |   .9636673   .3315029     2.91   0.004     .3139335
> 1.613401
>        var01 |          0  (omitted)
>        var00 |          0  (omitted)
>        _cons |    -.63364   .3095231    -2.05   0.041    -1.240294
> -.0269858
>
> But my question is: how can I test if the coefficients _b[var11] and
> _b[var10] are equal taking the "interaction"-nature of the variables into
> account? Using only "test _b[var11]= _b[var10 ]"  does not account for that.
> Is there any other procedure I could use here (maybe similar to contrast)?
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