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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
Subject   Re: st: Dummy Variables vs. Subgroup Models in Logistic Regression
Date   Fri, 22 Oct 2004 10:29:59 -0500

At 08:01 AM 10/22/2004 -0700, SamL wrote:
Even if you believe every group has a different coefficient, I would also
estimate the pooled model and use interactions to test whether the
coefficients really are different across groups.  If you only estimate
separate models for each group, you are unable to test the assumption
(unless you simply eyeball coefficients and make an ad hoc decision).
There are some other issues to consider (e.g., is it appropriate to assume
the same error structure for the different groups?) but the pooled model
seems a place to start, at least in my area of research and perhaps in
yours as well.

Hope this helps.
Sam
A variation on that concern: You'll occasionally see statements like "The effect of education is significant for whites but not for blacks" - which is true but when you look at the actual coefficients they are very similar for the 2 groups. They probably don't significantly differ from each other; the fact that the coefficient is significant in one group but not the other probably just reflects differences in sample size. When doing group comparisons, just looking at whether coefficients within each group are statistically significant can be very misleading if you then use that as a basis for making group comparisons. As Sam says, you should do formal tests of the group differences.

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Richard Williams, Notre Dame Dept of Sociology
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