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Re: st: Creating post-stratification weights for use in Stata & other software |

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Tue, 22 Sep 2009 17:03:20 -0400 |

Michael, post-stratification is pretty easy, because it deals with only one set of control totals; and so the theory is basically that of stratified sampling, with added variability because sample sizes in the post-strata are not fixed in advance. The standard sampling book treatments treat this well. However most practitioners will balance on more than one set of control totals at a time ("raking") or will calibrate to population totals using Generalized Regression (GREG). For raking, the best guide I've found is: Battaglia, M.P., Izrael,D., Hoaglin,D.C., and Frankel, M.R.(2006) Practical Considerations in Raking Survey Data. Available at: http://www.abtassociates.com/presentations/raking_survey_data_2_JOS.pdf For some theoretical background, see: RJA Little and M-M Wu (1991). Models for Contingency Tables with Known Margins When Target and Sampled Populations Differ. Journal of the American Statistical Association, 86:412, 87-95. What I miss from most post-survey weight adjustments, especially non-response adjustment by modeling, is an assessment of variability added by having to estimate the weights. On Tue, Sep 22, 2009 at 3:41 PM, Michael I. Lichter <[email protected]> wrote: > Steven, > > Do you know of a text or article that has a good, practical discussion of > poststratification beyond the 2-3 pages it gets (at best) in many texts? > This would include not only the calculation of weights and variances, but > also detecting and dealing with problems (cells too small, too much variance > inflation, etc.). Thanks. > > Michael > > [email protected] wrote: >> >> The formula in the archive is not OK for all purposes. The weights >> computed by it will not sum to population totals and will not equal >> the weights produced by Stata. >> >> For a simple random sample, the post-stratified weight for an >> observation in post-straum h is : N_h divided by n_h where N_h is >> the population total in the stratum and n_h is the sample number in >> the post-stratum. You should prove the formula from the section on >> post-stratification in one of your sampling books or in the Stata >> manual. I would certainly not regard my post here as authoritative >> enough to serve as a publication reference. >> >> Note that if your colleagues treat the post-stratification weights as >> ordinary pweights, they might not get the same standard errors as >> Stata does. >> >> I'm going to quote Mike Hanson's instructions to his advanced >> econometrics class again: >> "Never push a button or type a command you do not fully understand.“ >> (Statalist May 8, 2009) >> >> -Steve >> >> On Mon, Sep 21, 2009 at 2:45 PM, Michael I. Lichter >> <[email protected]> wrote: >> >>> >>> Carolina, >>> >>> I didn't look very closely at the e-mail in the archive, but it seems OK. >>> It >>> would be easier, however, to use the undocumented -svygen poststratify- >>> command in Stata 10 and 11 or the user-written -survwgt- package ("findit >>> survwgt") (which also does raking and is a bit more flexible if also more >>> complex). >>> >>> Note that if you use a sub-package that supports pweights, like the SPSS >>> complex samples (CS*) routines, you should get the same results as in >>> Stata >>> if your tabulations are for the whole sample. For subsamples, results may >>> differ because Stata svy poststratification will adjust the weights for >>> the >>> subsetting in a way that the other package will not. >>> >>> ----- >>> sysuse auto >>> gen count = 100 if foreign ==0 >>> replace count = 120 if foreign == 1 >>> svyset, poststrata(foreign) postweight(count) >>> svy: tab foreign, count >>> svygen poststratify pswt, poststrata(foreign) postweight(count) >>> svyset [pw=pswt] >>> svy: tab foreign, count >>> ----- >>> >>> Michael >>> >>> Carolina Herrera wrote: >>> >>>> >>>> Hello everyone, >>>> I am working with a very simple random sample that we've post-stratified >>>> using the standard commands in Stata (poststrata postweight fpc). A >>>> colleague would also like to use the dataset, but he doesn't work in >>>> Stata >>>> and wanted a version that could be used in any other statistical package >>>> (SPSS, SAS, R, etc.). >>>> >>>> After hunting around on the statalist archives I found a post explaining >>>> how to manually calculate post-stratification weights: >>>> (http://www.stata.com/statalist/archive/2008-11/msg00152.html) which, I >>>> think suggested I treat these post-stratification weights like pweights >>>> and >>>> that these pweights could then be implemented in Stata (or elsewhere) to >>>> get >>>> the same point-estimators and standard errors. >>>> Is that the correct way to implement simple post-stratification without >>>> using Stata's post-stratification commands? >>>> >>>> many thanks, Carolina >>>> >>>> >>>> Carolina Herrera >>>> Statistician >>>> Center for the Health Professions >>>> UCSF >>>> -- Steven Samuels [email protected] 18 Cantine's Island Saugerties NY 12477 USA 845-246-0774 * * 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: Creating post-stratification weights for use in Stata & other software***From:*"Michael I. Lichter" <[email protected]>

**References**:**st: Creating post-stratification weights for use in Stata & other software***From:*"Carolina Herrera" <[email protected]>

**Re: st: Creating post-stratification weights for use in Stata & other software***From:*"Michael I. Lichter" <[email protected]>

**Re: st: Creating post-stratification weights for use in Stata & other software***From:*[email protected]

**Re: st: Creating post-stratification weights for use in Stata & other software***From:*"Michael I. Lichter" <[email protected]>

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