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From |
Stas Kolenikov <skolenik@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: using post stratification weights |

Date |
Sat, 11 Feb 2012 10:24:22 -0500 |

Afif, 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 reading. 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 made). 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. * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**RE: st: using post stratification weights***From:*Afif Naeem <afeef745@hotmail.com>

**References**:**st: using post stratification weights***From:*Afif Naeem <afeef745@hotmail.com>

**RE: st: using post stratification weights***From:*Cameron McIntosh <cnm100@hotmail.com>

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