On Fri, Sep 4, 2009 at 11:01 AM, <[email protected]> wrote:
> It might also be worth simply running a 'naive regression' (just logit with pweights) to see the difference. (I'm not suggesting this is a valid empirical approach, of course)
>
> On a (somewhat) related topic, I have been working with the 'subpop' option of the -svy- commands for my logit models, and though I understand the theoretical basis for specifying a subpopulation instead of simply using specifying 'if var1 == 1', in my case I found it made next to no difference in my standard errors.
>
> It is sometimes interesting to run the simple, technically incorrect models in order to see what effect the more specialized specifications truly have on your particular dataset.
There was a paper a few Stata Journals ago on this -- see
http://www.stata-journal.com/article.html?article=st0153. A very
simplistic explanation is that if you have subpopulation members in
all PSUs, then you won't see any difference. If some of the PSUs
contain no subpopulation members, your d.f.s will start diverging, you
will get singleton PSUs, etc. Stata will (correctly) refuse to do
anything until you intervene.
--
Stas Kolenikov, also found at http://stas.kolenikov.name
Small print: I use this email account for mailing lists only.
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