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From |
Fatih Yilmaz <fyilmaz@ucalgary.ca> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Sampling weights (pweights) and regression analysis |

Date |
Thu, 12 Jul 2012 18:25:45 -0600 |

Thanks Steve, This is very helpful. fatih On 2012-07-12, at 6:16 PM, Steve Samuels wrote: > On Jul 11, 2012, at 4:15 PM, Fatih Yilmaz wrote: > > >> I am having trouble with using sampling weights in my simple regression >> analysis. >> >> Here is the story: >> >> The survey data I have is not representative, where some groups were >> deliberately over or under-sampled. >> The weights I was provided ara computed as follows: >> >> For group one (strata), population weight is 60% >> sample weight is 40% >> Final Pweight = 60%/40%=1.5 >> >> My questions: >> >> 1- I needed to drop some of the observations from the survey data: outliers, >> missings obs and also unrelated data. >> so, can I still use the old (initial) weights or do I have to re-weight the >> data with respect to the dropped observations? >> Or how problematic could it be to use old weights? >> > > > You should reweight for non-response.. Not doing so could be quite problematic. > How you do thisdepends on what you know about the population. See the sections > on nonresponse weighting in the books by Lohr or Groves et al. and in the PEAS page > referenced below. If you are dropping observations because of missing data for > some variables, you have a couple of choices. Probably best is to treat these as > "nonrespondents". Better would be to impute missing variables with Stata's > multiple imputation commands (see the help for -mi svyset-), but this would take > your analysis out of the realm of the "simple". > > Note that if you want to analyze a subgroup, it is an error to discard > members of the sample who are not in the subgroup. Doing so risks standard > errors that are too small. See the section on "subpopulations" in > Stata's survey manual and in Lohr's book (reference) > > >> 2- Since, my weights were computed as w=(pop%)/(sample%) (in general, some other >> researchers may compute them as w=(sample%)/(pop%) ), >> when I estimate weighted OLS should I use "reg y x [pw=1/w]" or ""reg y x >> [pw=w]". >> > Other researchers may, but they would be wrong. From your description, I think that > you have the right weights. You can check by seeing if the stratum weight totals > add up to the known stratum population sizes. ("total w, over(stratum)" > > To do survey regression in Stata, you -svyset- the data and identify weights, > sampling strata, and clusters, if any. The regression estimation command is > s -svy, subpop(): regress- > > >> Could you pls also recommend some resources on sampling weights and regression >> analysis (preferably practical sources ), >> > Resources: > > Lohr, S. L. (1999 1st Ed & 2009 2nd Ed). Sampling: Design and Analysis (2nd > ed.). Boston, MA: Cengage Brooks/Cole. > > Groves, R. M., Fowler, F. J., Couper, M. P., Lepkowski, J. M., Singer, E., & > Tourangeau, R. (2004 1st Ed, 2009, 2nd). Survey methodology. Hoboken, N.J.: Wiley. > > http://www.restore.ac.uk/PEAS/about.php > especially http://www.restore.ac.uk/PEAS/theory.php > with sections on weighting and non-response > and the exemplars page > http://www.restore.ac.uk/PEAS/aboutex2.php > > > http://www.statcan.gc.ca/edu/power-pouvoir/ch13/5214895-eng.htm. See especially: > http://www.statcan.gc.ca/edu/power-pouvoir/ch13/estimation/5214893-eng.htm. > > help.pop.psu.edu/help-by-statistical-method/weighting > > http://www.ats.ucla.edu/stat/stata/seminars/applied_svy_stata11/default.htm > > > Steve > sjsamuels@gmail.com > * > * 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/

**References**:**st: Sampling weights (pweights) and regression analysis***From:*Fatih Yilmaz <fyilmaz@ucalgary.ca>

**Re: st: Sampling weights (pweights) and regression analysis***From:*Steve Samuels <sjsamuels@gmail.com>

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