List: sorry for the earlier post that did not have a subject line. My mistake. Here is the original post:
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
Anyway, I received several good responses. And here are my responses to those responses. Any further feedback is appreciated.
First, OLS seems appropriate, though I udnerstand the desire to do something more. The DV is a continuous variable that is normally distributed.
Diagnostics show the model works well... So I really don't think any other method makes sense here.
Second, the interaction is exactly what the theory holds, which is nice. I guess my confusion lies here...why would the variable not
be significant without the interaction term included? Th etheory holds that c would affect everyone, but would affect
different values of f differently. So I would expect that the model without the interaction would also produce some good
results on c, but it does not.
Thanks again...
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