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st: Probability and non-response weights; how can I create a composite weight?


From   Arturo Rodriguez <[email protected]>
To   [email protected]
Subject   st: Probability and non-response weights; how can I create a composite weight?
Date   Fri, 18 Nov 2011 15:13:37 -0600

Hi everybody,

I am working with survey data which was collected through a one-stage
cluster sample with stratification (i.e. health clinics in a region
were selected to be part of the study first and then villages
surrounding those clinics were randomly sampled, all households in the
selected villages and all women in those households were then
surveyed).

My question is regarding the proper use of a composite design weight.
Here are the details:

(1) DESIGN WEIGHTS
I have calculated probability weights for each h_th stratum ... (i.e.
pw = total_villages_h / sampled_villages_h)
I have also calculated unit non-response weights for each h_th stratum
... (i.e. unrw = total_eligible_households_h / sampled_households_h)

I then created a composite weights (sw) by multiplying pw * unrw = sw
and used the following command to define my survey data:
svyset village_number [pweight = sw], strata(clinic) fpc(total_villages_h)

I have read that using a composite weight (sw = pw*unrw) is not
recommended and that I should weight my sample first by pw and then
weight again by unrw.
My first question is: Can STATA do this automatically? How? If not, do
I have to multiply each of my 300+ variables by pw and then by unrw?
What is the most efficient way
to do this?

I asked STATA to calculate estimated population totals for each of the
areas surrounding the clinics but I am getting figures that are very
low compared to what is known to be true. Is there anything I can do
to correct this statistically? Or is it just that my sample is not
representative?

(2) POST-STRATIFICATION WEIGHTS
Will adding poststrata and postweight to my svyset make any difference
in obtaining better "population around clinic" point estimates? Or
will it only change my STD ERRORS?

Thanks in advance for all the wisdom.
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