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Re: st: pweight question


From   Randall Lewis <randallalewis@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: pweight question
Date   Thu, 29 Apr 2010 17:31:26 -0700 (PDT)

Hi Stas--

Should I really think about pweights as stratified sampling (i.e., is this how people generally think about them)? Or should I think of them as 1/f(i) where f(i) is the probability of drawing that individual from the population? You seem to have described it in the former way, rather than the latter. Or does it matter? (I'm pretty sure it should matter for some estimators--like if you were trying to compute the median of an RV, x, and had stratified sampled 100 observations according to each percentile, your estimate of the median would have a much different S.E. than if you had just sampled individuals randomly, via simple random sampling.

Any thoughts?

--Randall



----- Original Message -----
From: "Stas Kolenikov" <skolenik@gmail.com>
To: statalist@hsphsun2.harvard.edu
Sent: Thursday, April 29, 2010 2:47:37 PM GMT -08:00 US/Canada Pacific
Subject: Re: st: pweight question

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.

-- 
Stas Kolenikov, also found at http://stas.kolenikov.name
Small print: I use this email account for mailing lists only.
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