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st: sampling weight


From   "Lynn Lee" <lynn09v@gmail.com>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: sampling weight
Date   Thu, 27 Sep 2012 10:58:07 +0800

Dear Stas,

I just want to do simple sampling. 

Take "webuse total" for example. I am wondering how was "swgt" generated? I
guess: obs 1 has her corresponding sampling weight, swgt=25964, which is the
total population in her group; obs 4 has his corresponding sampling weight,
swgt=4312, which is the total population in his group;etc.  Is that right?

So, if I use this logic in my downloaded survey data sets, I can group all
the obs into different sampling weight over residence place and gender.
Like: I calculate total number of individuals who were in the dataset
according to their resident city , say, total number of individuals in city
1 is 1000 in dataset, total number of individuals in city n is 400 in the
data set, then, I generate this city-total-individuals as a new variable
(weight). (Or I can even be more detailed, total number of people in the
data set over city, gender, age.) In regression, I simply use command "reg y
x1 x2 x3 [pweight=total]". Can this way correct in part for unweighted data
set? 

Suppose the mean of total(weights) is 500, min is 100 and max is 800.Then,
weighted analysis will give at most 800/100 times the weights to potentially
under-sampled observations. Do I understand correctly? 

I appreciate for your suggestion in advance.

Best Regards,
Lynn Lee

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Stas Kolenikov
Sent: Wednesday, September 26, 2012 9:44 PM
To: statalist@hsphsun2.harvard.edu
Subject: Re: st: sampling weight

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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