Re: st: calculating effect sizes when using svy command

[Nick Winter <nwinter@virginia.edu>]

Re: svy and (bivariate) correlation, this FAQ talks about how to do the 
equivalent of the nonexistent -svy: correlate-:

http://www.stata.com/support/faqs/stat/survey.html

The short version is that the point estimate is -correlate- with 
aweights, and the p-value as you discuss below.

My -corr_svy- from SSC implements this approach, though it is a Stata 
version 7 program so it does not take advantage of Stata's current, more 
extensive -svy- features.

 -Nick Winter


On 12/7/2010 4:11 PM, Vogt, Dawne wrote:
> Thanks. So it sounds like I can take the square root of the R squared value to get the correlation coefficient for a regression with 1 predictor. But how do I get effect size indicators (preferably in the form of correlation coefficients) for each predictor in a regression with multiple predictors?
>
>
> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Steven Samuels
> Sent: Tuesday, December 07, 2010 4:00 PM
> To: statalist@hsphsun2.harvard.edu
> Subject: Re: st: calculating effect sizes when using svy command
>
> --
> -
> I should have added:  The  relation of (partial) r-squares to t-
> statistics holds only for ordinary least squares, not for the
> estimation formulas of survey regression. So, neither of your
> calculated r's is correct.
>
> Steve
>
> On Dec 7, 2010, at 3:31 PM, Vogt, Dawne wrote:
>
> I have two questions related to calculating effect sizes using svyreg
> (pweights):
>
> First, when doing unweighted regressions in SPSS, I like to provide
> effect sizes for each predictor by calculating a correlation
> coefficient value (r) from the t values provided in the output. I like
> using r because it is easy for most people to interpret. Can I do the
> same using svyreg output?
>
> My second question is related to the first. Since there is no
> correlation option under the svy commands, I have been computing
> regressions of Y on X and X and Y and using the largest p value of the
> two sets of results, as recommended elsewhere. I've having trouble
> figuring out how to convert the results provided in the output to a
> correlation coefficient though.  I noticed that the r value I get by
> taking the square root of the R squared is different from my own hand
> calculation of r derived from the t value provided in the regression
> output [sqrt of (t squared divided by t squared + df). I'm not sure
> which r is correct (or if either of them are correct).
>
> Thanks in advance for any guidance others may be able to offer.
>
>
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Assistant Professor                             434.924.3359 f
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