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st: Weights in survey design


From   "Jason Ferris" <J.Ferris@latrobe.edu.au>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: Weights in survey design
Date   Mon, 19 Mar 2007 09:46:43 +1100

I have a large dataset with weights calculated as PPS based on household
size, stratified by sex.  The age group respondents are from 16-64.  

 

I am interested in looking at data only from those aged 16-24.  I can
use the subpop command "subpop(if age>=16 & age<=24)" for all the
commands.  But I am wondering if I can drop all other cases (keep if
age>=16 & age<=24) and the 'reset' my weights based only on those aged
16-24.

 

In the original form (with all data) I have the following summary data:
(note the survey design is quiet a simple one)

Svyset

 

      pweight: pps

          VCE: linearized

     Strata 1: sex

         SU 1: <observations>

        FPC 1: <zero>

 

. svy: tab sex

(running tabulate on estimation sample)

 

Number of strata   =         2        Number of obs      =      8664

Number of PSUs     =      8664        Population size    =      8664

                                      Design df          =      8662

 

-----------------------

      sex | proportions

----------+------------

   female |       .5046

     male |       .4954

          | 

    Total |           1

-----------------------

  Key:  proportions  =  cell proportions

 

If I select the subgroup (age 16-24):

. svy,subpop(if age<=24): tab sex

(running tabulate on estimation sample)

 

Number of strata   =         2        Number of obs      =      8664

Number of PSUs     =      8664        Population size    =      8664

Subpop. no. of obs =       999

Subpop. size       = 1438.7586

Design df          =      8662

 

-----------------------

      sex | proportions

----------+------------

   female |       .4599

     male |       .5401

          | 

    Total |           1

-----------------------

  Key:  proportions  =  cell proportions

 

 

When I reset my weights with data only representing those 16-24 years of
age (ie., as if this was the way I original designed my study) I get the
following results:

 

. svy: tab sex

(running tabulate on estimation sample)

 

Number of strata   =         2        Number of obs      =       999

Number of PSUs     =       999        Population size    =       999

Design df          =       997

 

-----------------------

      sex | proportions

----------+------------

   female |       .4655

     male |       .5345

          | 

    Total |           1

-----------------------

  Key:  proportions  =  cell proportions

 

As it can be seen there is now a difference in the proportions between
using subpop and resetting my weights.  Is this a problem?

 

 Jason


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