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# RE: st: calculating effect sizes when using svy command

 From "Vogt, Dawne" To "'statalist@hsphsun2.harvard.edu'" Subject RE: st: calculating effect sizes when using svy command Date Tue, 7 Dec 2010 16:11:28 -0500

```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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