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From |
Timothy Mak <tshmak@hku.hk> |

To |
"statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> |

Subject |
st: RE: logistic regression with clustered SE vs. xtlogit |

Date |
Tue, 18 Jun 2013 10:08:20 +0800 |

Hi Adam, This sounds like an Item Response Theory problem to me. http://en.wikipedia.org/wiki/Item_response_theory However, usually they assume an underlying continuous score rather than an underlying binary factor. If you definitely want a binary underlying factor, then the model to consider is a latent class model. If you want to assume an underlying continuous score, then the Rasch model may be appropriate. Your random-effects logistic model can be thought of as a very simplistic Rasch model - i.e. it assumes all questions have the same probability of being 0 or 1. See http://www.stata.com/support/faqs/statistics/rasch-model/ for a very useful discussion of the Rasch model and how to do it in Stata. The clustered SE approach is the same as doing -xtlogit, pa- with the corr(independent) and vce(robust) option. The difference between -xtlogit, re- and -xtlogit, pa- is explained in http://www.stata.com/support/faqs/statistics/random-effects-versus-population-averaged/ I wouldn't recommend using -xtlogit, pa-, since (a) it's not commonly (if ever) used for this kind of analysis, and (b) it's inefficient even if it's appropriate. That's my twopence... Tim -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Adam Olszewski Sent: 16 June 2013 08:13 To: statalist@hsphsun2.harvard.edu Subject: st: logistic regression with clustered SE vs. xtlogit 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 * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**st: logistic regression with clustered SE vs. xtlogit***From:*Adam Olszewski <adam.olszewski@gmail.com>

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