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Re: st: using post stratification weights

From   Stas Kolenikov <>
Subject   Re: st: using post stratification weights
Date   Sat, 11 Feb 2012 10:24:22 -0500


I am not sure what your question is. "Help me out" is too broad, and I
don't know how many people on this list are in the business of mind

We are probably talking about different types of weight adjustments
here. Afif quoted from the survey manual he's been using:

> The post-stratification weights are generated through iterative proportional fitting. Quote from the survey hand-book itself is below:

"The following benchmark distributions are utilized for this
post-stratification adjustment:
   Gender (Male, Female)
   Age (18-29, 30-44, 45-59, 60+)
   Race/Hispanic ethnicity
   Education category
   Metropolitan Area (Yes, No)
   Internet Access (Yes, No)

Comparable distributions are calculated using all completed cases from
the field data. Since study sample sizes are typically too small to
accommodate a complete cross-tabulation of all the survey variables
with the benchmark  variables, an iterative proportional fitting is
used for the post-stratification weighting adjustment. This procedure
adjusts the sample data back to the selected benchmark proportions.
Through an iterative convergence process,  the weighted sample data
are optimally fitted to the marginal distributions."

This is a different procedure than post-stratification in Stata terms.
Stata relies on the post-strata being mutually exclusive, and
obviously the above categories aren't. What your quote suggests is a
raking procedure, where the weights are adjusted along each of the
dimensions/categorical variables, so that the current variable is made
to agree with the known distribution perfectly, moving then to the
next margin, etc., until some sort of convergence is achieved.
Official Stata does not do this, although you should be able to find
third party programs written for this purpose. I use -maxentropy-
(which is cumbersome to use, but does the job quickly).

Post-stratification adjustments call for special variance estimation
methods. That's why Stata has post-stratification as an additional
option in -svyset-; without these adjustments, your standard errors
may be some 20-30% too small on descriptive statistics correlated with
the calibration variables. These adjustments are relatively easy to
implement with post-stratification over mutually exclusive strata (and
that's done in Stata), but are somewhat harder with multivariate
marginal adjustments. You won't be able to do these adjustments unless
you have both the original sampling weight and the post-stratified
weights, as well as the variables used for calibration (or,
equivalently, the population totals towards which the adjustment was

A typo correction in Cam's literature suggestions:

Holt, D., & Smith, T.M.F. (1979). Post stratification. Journal of the
Royal Statistical Society, Series A, 142, 33–46.

Stas Kolenikov, also found at
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