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# Re: st: sampling weight

 From Stas Kolenikov To statalist@hsphsun2.harvard.edu Subject Re: st: sampling weight Date Wed, 26 Sep 2012 08:44:20 -0500

```If Lynn obtained her sample in a rigorous way by enumerating the
dwellings, she should have all the inputs into the probability of
selection, and the baseline sampling weight is the inverse of that.
Then she would want to correct for non-response, which would be the
fraction of those responding to the survey among those sampled.

If Lynn is interested in a specific population (females of
reproductive age, say), and that's who the survey collected the data
on, then she would need to get the total population counts for that
specific population (which may prove even more difficult).

If she does not have these figures, then I don't really know what to
do. As they say, when you approach a statistician with collected data
in hand, they can only tell you what killed your study.

--
-- Stas Kolenikov, PhD, PStat (SSC)  ::  http://stas.kolenikov.name
-- Senior Survey Statistician, Abt SRBI  ::  work email kolenikovs at
srbi dot com
-- Opinions stated in this email are mine only, and do not reflect the
position of my employer

On Wed, Sep 26, 2012 at 8:15 AM, JVerkuilen (Gmail)
<jvverkuilen@gmail.com> wrote:
> On Wed, Sep 26, 2012 at 2:49 AM, Lynn Lee <lynn09v@gmail.com> wrote:
>
>> Any suggestion to suggest which weight is better? Or, other types of weights
>> may be better than population weights?
>
> Do you have a few accurately observed variables such as the population
> age and gender breakdown? If so you can often create
> post-stratification weights (through a process called "raking") that
> make your samples align with the associations observed in those
> tables.
>
> A quick -findit raking- turned up a program -ipfraking- written by
> Stas Kolenikov and available from his website. Hopefully he'll chime
> in.
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```