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Re: st: weighted regression
At 11:28 AM 1/14/2004 +0100, Ernest Berkhout wrote:
When doing regressions, I always use aweights. I'm a bit ignorant about
pweights but my guess is that they give the same results. However iweights
are very different! When you
Weights are discussed on pgs. 278-282 of the Stata 8 Users Guide. -help
weights- from within Stata gives a lot of the same info but not as much
explanation as to rationale. A few points:
* If you have a probability weighted sample and are using aweights rather
than pweights, you are probably doing it wrong! Or so says the users
guide. You have the consolation of knowing you are not alone. As an SPSS
user, I believe I have always been using the equivalent of aweights, as
pweights are not an option.
* As I understand it, the main difference between aweights and pweights is
that with pweights, you get robust standard errors. aweights and pweights
produce the same point estimates but different standard errors. The users
guide explains the rationale for this.
* I don't understand the practical difference between fweights and
iweights. The guide says that iweights are usually used by programmers;
why they don't use fweights instead, I don't know.
* Back in December there was a discussion as to whether you should weight
at all; see Dick Campbell's message at
and the followups. Dick cited a very good 1994 article by Winship and
Radbill saying that the answer was usually no. Left unclear to me is
whether that is still true with Stata 2004 and its pweights and svy
commands. My reading of the Users Guide is that you should use pweights
rather than not weight at all. If there is a more contemporary discussion
of these issues I would like to see it.
* Finally, going back to the original question: the idea behind pweights
is that your sampling scheme deliberately oversampled some groups, e.g.
minorities are overrepresented. However, the original question made it
sound like underrepresentation of some groups just sort of happened; it
wasn't a matter of the research design. If nonresponse was random then I
suppose pweights may be ok; but if there are systematic biases in the
sample then I'm not sure that pweights really solves them. Another of my
weak areas is post-sampling adjustments, but my understanding is that this
is a weak area for Stata too, and something that is being worked
on. Again, I'd be interested in hearing more on this.
Richard Williams, Associate Professor
OFFICE: (574)631-6668, (574)631-6463
WWW (personal): http://www.nd.edu/~rwilliam
WWW (department): http://www.nd.edu/~soc
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