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Gabi Huiber <email@example.com>
Sat, 7 Feb 2009 12:18:35 -0500
This result simply says that the marginal effect of whatever regressor
c is the slope of is not a constant. Instead, it depends on the size
of whatever regressor f is the slope of. Namely, it's equal to
c+c*f*[whatever regressor f is the slope of].
Was your theory suggesting otherwise? If not, pick (a). If yes, why
would these particular data say otherwise? Based on the answer to this
question you may be right to consider (b), but the other alternative
is that the data are fine and your theory's screwy.
On Sat, Feb 7, 2009 at 11:29 AM, Antonio Silva <firstname.lastname@example.org> wrote:
> 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
> Windows Live™: Keep your life in sync.
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