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From |
Steven Archambault <archstevej@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: pweight question |

Date |
Thu, 29 Apr 2010 18:52:31 -0600 |

Thanks for the responses thus far. I cannot say it is all clear to me now, but I am getting there. As for the strata and clustering, this is data that was to be taken as a representation of the population in several different "economic zones". The observations are taken from different villages in each zone. Actually, observations from each village have the exact same weight. I also know the population and area of the individual villages. I am assuming the "probability that an observation is in the sample" is based on the population density of that village or economic region. But, that isn't clear. Perhaps I could come up with my own weights retrospectively? I am also analyzing this for multilevel effects, using gllamm. So, I do expect the weights to matter. Any further guidance would be very helpful! Thanks, Steve On Thu, Apr 29, 2010 at 6:37 PM, Steve Samuels <sjsamuels@gmail.com> wrote: > I have other problems with these scaled weights. > > First, if they are all you have, it is difficult to identify weights > that are too small. (Ken Brewer, Combined Survey Sampling Inference, > Wiley, p. 133). > > Second, with these scaled weights one cannot recover the original ones > without information on the total, and the information is not always > available. In fact, for some samples, the population total isn't known > and the only estimate is based on the original probability weights. > > Third, I wonder about the accuracy of the scaled weights. If n is > moderate and the sampling fraction is small, most of the significant > figures could be far to the right of the decimal place. > > Finally, these weights just lead to confusion on the part of people > who were not in on their construction. The original poster was > confused on this occasion, and I was confused on another last year. > > Steve > > On Thu, Apr 29, 2010 at 5:47 PM, Stas Kolenikov <skolenik@gmail.com> wrote: >> On Thu, Apr 29, 2010 at 3:03 PM, Michael I. Lichter >> <mlichter@buffalo.edu> wrote: >>> The scale of the weights (what they sum to) doesn't tell you whether or not >>> they are pweights. >> >> That's not quite right. Properly scaled probability weights should sum >> up to the population size. This however is only relevant when you >> estimate -total-s. If you run pretty much any other analysis (means, >> ratios, proportions, any sort of regressions), then the scale of the >> weights cancels out. I would grind my teeth at the pweights that are >> scaled to the sample size, and maybe make some mental comments about >> the data provider, but won't be bothered very much by this nuisance. >> >> The scaling of the weights begins to matter again with multilevel >> data, in which the scaling is known to affect the accuracy of the >> variance component estimates. >> >> -- > > -- > Steven Samuels > sjsamuels@gmail.com > 18 Cantine's Island > Saugerties NY 12477 > USA > Voice: 845-246-0774 > Fax: 206-202-4783 > * > * 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: pweight question***From:*Steven Archambault <archstevej@gmail.com>

**References**:**st: pweight question***From:*Steven Archambault <archstevej@gmail.com>

**Re: st: pweight question***From:*"Michael I. Lichter" <mlichter@buffalo.edu>

**Re: st: pweight question***From:*Stas Kolenikov <skolenik@gmail.com>

**Re: st: pweight question***From:*Steve Samuels <sjsamuels@gmail.com>

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