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Re: st: pweight question
Steven Archambault <email@example.com>
Re: st: pweight question
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
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!
On Thu, Apr 29, 2010 at 6:37 PM, Steve Samuels <firstname.lastname@example.org> 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.
> On Thu, Apr 29, 2010 at 5:47 PM, Stas Kolenikov <email@example.com> wrote:
>> On Thu, Apr 29, 2010 at 3:03 PM, Michael I. Lichter
>> <firstname.lastname@example.org> 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
> 18 Cantine's Island
> Saugerties NY 12477
> Voice: 845-246-0774
> Fax: 206-202-4783
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