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From |
Joseph Coveney <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 * * 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:*Kayla Bridge <kayla.bridge@outlook.com>

**References**:**st: coefficient explanation***From:*Kayla Bridge <kayla.bridge@outlook.com>

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