Thanks Nick, this was also helful. I particularly like the advice to use -aweigth- to get the correct point estimates for the correlation coefficient with survey data. This should be public knowledge! Frankly speaking I never quite figured out why Stata needs to make these distinctions between different types of weight variables. It is very confusing, and I can't remember having this problem when using SAS or SPSS, so is it really necessary?
Best,
Jan Teorell
________________________________
Från: owner-statalist@hsphsun2.harvard.edu genom Nick Winter
Skickat: ti 2006-03-14 14:14
Till: statalist@hsphsun2.harvard.edu
Ämne: Re: SV: st: svy jackknife problems
Note that -correlate- does allow aweights, which
give the same point estimates as pweights or iweights would.
Note also my -corr_svy- on SSC, which implements
one approach to a linearized variance estimator
for correlations (see the help file for a
discussion of the method--basically it uses
-svyreg- to get significance levels.
Also note my -svr- package on SSC, which
implements BRR and jackknife. The package is
largely superseded by Stata 9's -svy-
capabilities in this area; however it does
include facilities for correlation
coefficients. (It also includes -svrest-, which
is a wrapper for (almost) any estimation command.)
Best,
Nick Winter
At 04:04 AM 3/14/2006, you wrote:
>Thanks Jeff, this was helpful. The results I
>reported were estimated with no pweight (or
>iweight) defined in svyset. When I svyset using
>the approrpiate pweight, svy jackknife, mse
>yields exactly the same result as my own
>estimator. And, if I svyset with a new pweight
>variable defined as 1 for all cases, I replicate
>the correct result from my example in the former email (se=.08737).
>
>So, as you say, there seems to be some problem
>with the mse estimator when no pweight is defined that needs to be fixed.
>
>While fixing this problem, couldn't you
>implement a jackknife svy estimator that allows
>the correlation command? I know the problem now
>is that corr doesn't allow pweights (or
>iweigths), but couldn't that be fixed in the
>first place (that is also very annoying, that
>you can't weight your survey data appropriately
>when running correlation coefficients without
>using the cumbersome fweights). I guess no
>well-defined linearized variance estimator
>exists for the correlation coefficient (at least
>I have never seen one), so this is an area where
>jackknife (or, if the design allows for it, BRR)
>would really be a necessary tool!
>
>All the best,
>
>Jan
>
>________________________________
>
>Från: owner-statalist@hsphsun2.harvard.edu genom Jeff Pitblado, StataCorp LP
>Skickat: må 2006-03-13 22:34
>Till: statalist@hsphsun2.harvard.edu
>Ämne: Re: st: svy jackknife problems
>
>
>
>Jan Teorell <jan.teorell@pol.gu.se> is having trouble reproducing hand-coded
>jackknife variance estimates using -svy jackknife-:
>
> > I'm having trouble with the svy jackknife
> command. I had earlier implemented
> > a crewd jackknife estimator myself, tailored for my particular complex
> > survey design including both stratification
> and multistage cluster sampling.
> > With Stata 9 I presumably should need to use this homemade program anymore,
> > since svy jackknife should do the job for me. However, the results from my
> > estimator and svy jackknife differs for reasons I am not quite clear of.
>
> > To take this down to a more concrete level, I tested the two commands on a
> > small subsample of my survey, using only 2 strata with 2 PSU:s each. I then
> > ran a simple regression, with the following
> estimates per replication (where
> > b(h,j)=the regression coefficient received
> when excluding PSU j from stratum
> > h):
>
> > b(1,1)=.4230769
> > b(1,2)=.5417409
> > b(2,1)=.5537783
> > b(2,2)=.4259508
>
> > The estimate from the entire sample is: b=.4866513
>
> > Plugging in these estimates into the formula for the mse estimator (Survey
> > Data Manual, p. 266) yields:
>
> > 1/2*[(.4230769-.4866513)^2+(.5417409-.4866513)^2] +
> > 1/2*[(.5537783-.4866513)^2+(.4259508-.4866513)^2]
>
> > which is aproximately equal to .0076336. The square root of this, that is,
> > the estimate of the standard error is: .0873703.
>
> > Incidentally, this is what my homemade jackknife estimator arrives at.
> > However, svy jackknife reaches a somewhat different conclusion: se =
> > .1070064
>
> > This is so despite the fact that the same estimated b(h,j)-coefficients go
> > into both procedures (I have checked this by running jackknife noisily).
> > There also appears to be nothing wrong with the weights: the "sum of wgt
> > is..." yields exactly similar results.
>
> > So what could be wrong? What could explain the difference?
>
>Although I can't be sure without seeing how Jan -svyset- the data, it seems
>the culprit here is -svyset- -iweight-s.
>
>As far as -svy- is concerned, -iweight-s and -pweight-s are the same except
>that -iweight-'s are allowed to be negative. However, looking into
>reproducing Jan's results, we found that -svyset-ting -iweight-s and using the
>-mse- option with -svy jackknife- can result in different variance/standard
>error estimates than -svyset-ting -pweight-s. This should be fixed in the
>next ado-file update.
>
>Until then Jan should be able to reproduce the correct results by -svyset-ting
>-pweight-s and re-running -svy jackknife-.
>
>--Jeff
>jpitblado@stata.com
>*
>* For searches and help try:
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>* http://www.stata.com/support/statalist/faq
>* http://www.ats.ucla.edu/stat/stata/
>
>
________________________________________________________
Nicholas J. G. Winter 607.255.8819 t
Assistant Professor 607.255.4530 f
Department of Government nw53@cornell.edu e
Cornell University falcon.arts.cornell.edu/nw53 w
308 White Hall
Ithaca, NY 14853-4601
*
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