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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 09:42:05 -0500

At 01:45 PM 10/22/2004 +0000, brian.h.nathanson@att.net wrote:

Dear Stata Users,

I'm creating a logistic regression model with many dichotomous variables along with one term that has 8 categories coded 1,2,..8. I can create 7 dummy variables and have a very large model. Would it be legitimate if my sample sizes are large enough to create 8 separate models with each model representing one subgroup? Can anyone comment on the pros and cons of using dummy variables versus creating separate "subgroup" models based on the remaining independent variables? Thanks!
If you estimate separate models, you are allowing ALL parameters to differ across groups, e.g. the effect of education could be different in each group. If you just add dummies, you are allowing the intercept to differ in each group, but the effects of the other variables stay the same.

If you estimate separate models for each group, your models will certainly be much less parsimonious, i.e. you'll have a lot more parameters floating around. But the real question is, what is most appropriate given your theory and the empirical reality? If the effects of everything really is different across every group, then you should estimate separate models. But, if the effects do not differ across groups, then you are producing unnecessarily complicated models, and you are also reducing your statistical power, e.g. by not pooling groups when you should be pooling them you'll be more likely to conclude that effects do not differ from zero when they really do.

These sorts of issues are discussed in

http://www.nd.edu/~rwilliam/stats2/l51.pdf

http://www.nd.edu/~rwilliam/stats2/l92.pdf


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Richard Williams, Notre Dame Dept of Sociology
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