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From |
Kayla Bridge <[email protected]> |

To |
"[email protected]" <[email protected]> |

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: [email protected] > To: [email protected] > 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 > > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/faqs/resources/statalist-faq/ > * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: Re: coefficient explanation***From:*"Joseph Coveney" <[email protected]>

**References**:**st: coefficient explanation***From:*Kayla Bridge <[email protected]>

**st: Re: coefficient explanation***From:*Joseph Coveney <[email protected]>

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