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st: logistic regression with clustered SE vs. xtlogit


From   Adam Olszewski <adam.olszewski@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   st: logistic regression with clustered SE vs. xtlogit
Date   Sat, 15 Jun 2013 20:12:36 -0400

Dear listers,
I have a dataset with results from a 15-item questionnaire, with a
binary response to each question (the questions are felt to measure
the same underlying binary factor). I want to study the correlation of
demographic variables (age, gender) on whether the answer is 0 or 1.
The questions are obviously correlated between the 15 items filled out
by the same person.
After reading about different models, it seems that logistic
regression with clustered standard errors (-logit varlist, vce(cluster
ID)-) or random-effects logistic model (-xtset ID-, then: -xtlogit
varlist, re-) might be appropriate, and give similar, although not
identical results. I am not sure what is the conceptual difference
between them. Would one be preferred over the other in some
circumstances? Or did I even pick wrong tools for the problem? I
thought about regressing the mean of answers, but such a dependent
variable does not meet assumption for any model that I know of.
Sorry if this sounds basic, but I rarely wander beyond routine
logistic regression and I am a little puzzled by xtlogit.
Best regards,
Adam Olszewski
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