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st: RE:


From   "Benjamin Villena Roldan" <bvillena@troi.cc.rochester.edu>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: RE:
Date   Sat, 7 Feb 2009 12:00:08 -0500

Antonio,
The correct answer must come from the theoretical considerations of your
model. Do you a have a reasonable argument to justify this interaction term?
Does it make sense for your theory? Be aware that marginal response of your
dependent variable with respect to C depends on the level of your F
variable. What does it mean? 

-----Mensaje original-----
De: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] En nombre de Antonio Silva
Enviado el: Saturday, February 07, 2009 11:29 AM
Para: Stata list
Asunto: 

Hello Statlist:
I have an OLS model that looks like this: y = constant + b + c + d + e + f.
c is the variable in which I am most interested. In the basic model, c turns
out NOT to be significant (it is not even close).
However, when I include an interaction term in the model, c*f, c turns out
to be highly significant.
So the new model looks like this: y = constant + b + c + d + e + f + c*f.
The interaction term, c*f, is highly significant as well (though in many
versions f is NOT significant).
My question is this: Is it defensible JUST to report the results of the
fully specified model--that is, the one with the interaction? I kind of feel
bad knowing that the first model does not produce the results I desire (I am
very happy c ends up significant in the full model--it helps support my
hypothesis). I have heard from others that if the variable of interest is
NOT significant without the interaction term in the model but IS significant
WITH the interaction term, I should either a) report the results of both
models; or b) assume the data are screwy and back away...
What do you all think?
Thanks so much.
Antonio Silva
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09


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