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From |
Steve Samuels <[email protected]> |

To |
[email protected] |

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

Date |
Thu, 12 Jul 2012 20:16:25 -0400 |

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 [email protected] * * 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: Sampling weights (pweights) and regression analysis***From:*Fatih Yilmaz <[email protected]>

**References**:**st: Sampling weights (pweights) and regression analysis***From:*Fatih Yilmaz <[email protected]>

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