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Re: st: Dummy Variables vs. Subgroup Models in Logistic Regression


From   Richard Williams <Richard.A.Williams.5@nd.edu>
To   statalist@hsphsun2.harvard.edu, statalist@hsphsun2.harvard.edu
Subject   Re: st: Dummy Variables vs. Subgroup Models in Logistic Regression
Date   Fri, 22 Oct 2004 09:59:59 -0500

At 02:21 PM 10/22/2004 +0000, maartenbuis wrote:
Dear Brian,

Estimating seperate logistic regressions for each group is equivalent
of estimating a single logistic regresion with dummy variables for
each group and interactionterms of each group dummy with all other
explanatory variables. So the `seperate logistic regression model' is
larger (uses more degrees of freedom) than only adding the dummies
for group membership.
One followup comment: You can pretty much always think of subgroup models that you could run. You could run separate models by gender; by race; by religion; and then you could run separate models by various joint characteristics, e.g. white male Catholics, white female Catholics, etc. But if you just do that automatically, you can easily be overwhelmed with numbers and create samples that are so small that nothing comes up significant (or you start finding across-group differences just by chance because you are estimating so many parameters). So in general, I wouldn't start estimating subgroup models unless I had good theoretical reasons for doing so.

Also, my theory might point to a specific variable whose effects may differ by group, e.g. I have good reason for suspecting that the effect of education differs by gender. If so, I can include an interaction term that reflects this. But if I have no good theoretical reasons for suspecting that the effects of other variables differ by gender, then I shouldn't go and estimate subgroup models which, in effect, allow for interactions of gender with everything.

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Richard Williams, Notre Dame Dept of Sociology
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