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RE: st: Re: coefficient explanation


From   Kayla Bridge <kayla.bridge@outlook.com>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Re: coefficient explanation
Date   Sat, 15 Jun 2013 14:02:05 +0000

Thanks a lot, Joseph and John.

But I searched the previous emails, I did not find the post by Maarten Buis. Could you please send me the post? I appreciate.

Best,
Kayla

----------------------------------------
> From: stajc2@gmail.com
> To: statalist@hsphsun2.harvard.edu
> Subject: st: Re: coefficient explanation
> Date: Sat, 15 Jun 2013 17:10:11 +0900
>
> Kayla Bridge wrote:
>
> The model I am working with now is:
> y=beta1*x1+beta2*x2+u (here, beta1 is significant)
> However, I realize the correlation between y and x1 is due to some other factor
> which is not present in the model. Therefore, I add this critical variable that
> can best proxy for this factor, x3, in the model. Now the model is
> y=beta1*x1+beta2*x2+beta3*x3+u. In this case, beta1 should weaken when x3 is
> present. But my question is: beta1 should have smaller magnitude than before but
> still significant or beta1 should be insignificant when x3 is added? If beta1 is
> still significant but with smaller value when x3 is added, can I say x3 is a
> critical value which is ignored before or correlation between y and x1 is
> weakened?
>
> Any suggestion is appreciated.
>
> --------------------------------------------------------------------------------
>
> It sounds like you're analyzing data from an observational study. Maarten Buis
> has posted on this list before on what can happen to the magnitudes and signs of
> regression coefficients when additional variables are added to a regression
> model of an observational study. You might want to search the archives for some
> of his posts.
>
> You seem to suggest that your subject matter knowledge tells you that the
> apparent association between y and x1 is illusory, that in reality it only
> reflects the action of some other factor on both. If so, then is there a good
> reason to include x1 in the model at all, especially if you have in hand a
> halfway-decent measure of this other factor, namely, x3? If your subject matter
> knowledge allows, you might consider modeling the relationships between y, x1
> and x3 (and x2) by means of path analysis or even a structural equation model if
> your dataset has enough indicator variables to assure model identification.
> (Type "help sem" in Stata's command window for more information.)
>
> I assume that your model actually does have an intercept, that its omission in
> your post is inadvertent.
>
> Joseph Coveney
>
>
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