Re: st: RE: logistic regression with clustered SE vs. xtlogit

[Adam Olszewski <adam.olszewski@gmail.com>]

Hi Tim,
Thank you very much for the explanation and the links which led to
even more reading and my better understanding of the models.
The random-effects logistic model with dummies for survey questions
(extending to Rasch model) works well except for problems with
stability with different number of integration points (as assessed by
-quadchk-) - which as I understand may be due to highly correlated
nature of the data..
Best,
AO

On Wed, Jun 19, 2013 at 1:09 AM, Adam Olszewski
<adam.olszewski@gmail.com> wrote:
> Hi Tim,
> Thank you very much for the explanation and the links which led to even more
> reading and my better understanding of the models.
> The random-effects logistic model with dummies for survey questions
> (extending to Rasch model) works well except for problems with stability
> with different number of integration points (as assessed by -quadchk-) -
> which as I understand may be due to highly correlated nature of the data..
> Best,
> AO
>
>
> On Mon, Jun 17, 2013 at 10:08 PM, Timothy Mak <tshmak@hku.hk> wrote:
>>
>> 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
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