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From |
Steve Samuels <sjsamuels@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: using post stratification weights |

Date |
Sat, 11 Feb 2012 19:43:18 -0500 |

I should have added: and use Stata's survey commands. Note that weighting alone is not sufficient for valid inference. You must properly specify the first stage of sampling design in the -svyset- statement. Otherwise standard errors will be wrong. SS Specify the adjusted weights in the -svyset- statement by [pw = adjusted_wt] Steve sjsamuels@gmail.com On Feb 11, 2012, at 12:24 PM, Afif Naeem wrote: 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? Afif ---------------------------------------- > Date: Sat, 11 Feb 2012 10:24:22 -0500 > Subject: Re: st: using post stratification weights > From: skolenik@gmail.com > To: statalist@hsphsun2.harvard.edu > > 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/ * * 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/ * * 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>

**Re: st: using post stratification weights***From:*Stas Kolenikov <skolenik@gmail.com>

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

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