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RE: st: using post stratification weights
Afif Naeem <firstname.lastname@example.org>
RE: st: using post stratification weights
Sat, 11 Feb 2012 12:24:19 -0500
Thanks Stas for your response. I should have been more clear in my first email. What I meant is that I have weights in the data set generated through iterative proportional fitting, as described by the quote from the sruvey hand-book itself below.
What I can not figure out is how to utilize these weights to find out the summary statistics of variables in the data set. Also, is the a way to run simple Logit model where this weight variable is used to weigh individuals differently?
> Date: Sat, 11 Feb 2012 10:24:22 -0500
> Subject: Re: st: using post stratification weights
> From: email@example.com
> To: firstname.lastname@example.org
> 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 http://stas.kolenikov.name
> Small print: I use this email account for mailing lists only.
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