Thanks Stas,
I will give post-stratification some consideration. I think you are
right...it is just that I have an over-representation of one gender,
and certain age groups in the data, and would like to correct for
this. The data does not have any weighting built in.
Cheers,
Janelle
On 8/11/07, Stas Kolenikov <[email protected]> wrote:
> On 8/10/07, Janelle Knox <[email protected]> wrote:
> > I am trying to create a sample weight for a dataset, which will
> > correct for variations in gender, age, etc from population means.
> > Does anyone know how to do this, or where I can find information for
> > setting up a sample weight.... pweight=?
>
> Yet another route to take is to use Stata's post-stratification
> adjustments, as it looks more like what you are doing there. The
> [pweights] are supposed to be the inverse probabilities of selection,
> and are treated as such by Stata's -svy- procedures... leading to
> Horvitz-Thompson estimators, etc. The post-stratification applies to
> the already collected data with known design features, and works by an
> additional ex-post re-weighting of the data, so that the re-weighted
> sample margins equate the known population ones -- which seems to be
> exactly your problem. So read up [SVY] post-stratification to see how
> it is done; rather than matching the means and such, Stata's
> implementation of post-stratification requires categorical information
> on the total groups sizes. My understanding is that if you produce
> weights through post-stratification procedures, and start using them
> as probability weights, you would get incorrect inference, although I
> am not 100% positive on that -- I have not studied that area of survey
> statistics. Here's a couple of articles on the general topic of
> post-stratification to help you out:
> http://www.citeulike.org/user/ctacmo/tag/poststratification.
>
> --
> Stas Kolenikov, also found at http://stas.kolenikov.name
>
> Small print: Please do not reply to my Gmail address as I don't check
> it regularly.
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