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

[Richard Williams <Richard.A.Williams.5@nd.edu>]

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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