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From |
"Vogt, Dawne" <[email protected]> |

To |
"'[email protected]'" <[email protected]> |

Subject |
RE: st: calculating effect sizes when using svy command |

Date |
Wed, 8 Dec 2010 12:45:42 -0500 |

Excellent. Thanks for your help! Dawne -----Original Message----- From: [email protected] [mailto:[email protected]] On Behalf Of Steven Samuels Sent: Wednesday, December 08, 2010 12:23 PM To: [email protected] Subject: Re: st: calculating effect sizes when using svy command Dawne Vogt: You should not expect the p-values from -pcorr- with aweights to come close to those from -svy: reg-. Using analysis weights is a mathematical trick to get -pcorr- to compute the proper weighted correlations, but the trick does not extend to estimating the standard errors. For that reason, Bill Sribney suggested in the FAQ that the p- value for the (simple) correlation come from the original survey regression, not from -correlate-. Note that the t-statistic from the full -svy: reg-- is the same as that for -svy: reg- of the partialed residuals. You can get another approximate partial correlation starting from the t statistic, as you originally did: Instead of using the survey sample size n in the formula, substitute n' = n/DEFF. DEFF is the design effect and can be computed by running "estat effects" after the original regression; n' is the SRS size that would have produced the same value of t. Steve Steven J. Samuels [email protected] 18 Cantine's Island Saugerties NY 12477 USA Voice: 845-246-0774 Fax: 206-202-4783 On Dec 8, 2010, at 10:54 AM, Vogt, Dawne wrote: Thanks to everyone who weighed in on this issue. I figured out how to compute the weighted partial correlations with aweights. I noticed, however, that the p values associated with these weighted partial correlations are different than the p values associated with the t values that are presented in the svyreg output. I understand that the relationship between partial rs and t values only hold for ordinary least squares, but I am having trouble reconciling cases where the p values associated with the t for a particular predictor is not significant (p <.05) but the p value associated with the partial r for the same variable is significant. I'm not sure what to make of it. Do people have thoughts or suggestions? -----Original Message----- From: [email protected] [mailto:[email protected] ] On Behalf Of Steven Samuels Sent: Wednesday, December 08, 2010 8:56 AM To: [email protected] Subject: Re: st: calculating effect sizes when using svy command -- The simplest way of computing the weighted partial correlation is to use -pcorr- with aweights, as suggested by Bill Sribney's FAQ. In the example, repeated below, the difference is R-square from regression of residuals = 0.259; partial correlation-squared from -pcorr- = 0.288. Steve **************************CODE BEGINS************************** sysuse auto, clear svyset rep78 [pw=trunk] /* Compute partial correlation of mpg and weight, controlling for turn */ svy: reg mpg weight turn svy: reg mpg turn predict r_mt, resid svy: reg weight turn predict r_wt, resid svy: reg r_mt r_wt //R-squared is partial r-square pcorr mpg weight turn [aw=trunk] ***************************CODE ENDS*************************** On Dec 7, 2010, at 5:39 PM, Steven Samuels wrote: Well, I got the details wrong, but the example right. If the multiple regression is: svy: reg y x z svy: reg y z, with residual r_yz svy: reg x z, with residual r_xz Thecorrelation of r_yz and r_xz is the partial correlation r_yx.z So the goal is to estimate the correlation of r_yz and r_xz with one of the methods in the post, including svy: reg r_yz r_xz Sorry for the confusion. Steve [email protected] On Dec 7, 2010, at 5:03 PM, Steven Samuels wrote: -- What Dawne called "correlation" is actually the partial correlation of y and x, controlling for other covariates z (r_yx.z). The partial correlation in OLS can be estimated by t^2/(df_error + t^2), but that is not true for survey regression. However it can be estimated in a three step process: -svy regress- y on x, with residual r_yx ; y on the z's with residual r_yz. Then compute the correlation of r_yx and r_yz by means of 1) Nick's program 2) the methods in Bill Sribney's FAQ; or 3) by running -svy: reg r_yx r_yz- and taking the R-squared reported by that command. The p-value issue discussed by Bill doesn't arise for Dawn, because she takes the p-value from the original regression. See below for an example. Steve **************************CODE BEGINS************************** sysuse auto, clear svyset rep78 [pw=trunk] /* Compute partial correlation of mpg and weight, controlling for turn */ svy: reg mpg weight turn svy: reg mpg turn predict r_mt, resid svy: reg weight turn predict r_wt, resid svy: reg r_mt r_wt //R-squared is partial r-square ***************************CODE ENDS*************************** On Dec 7, 2010, at 4:18 PM, Nick Winter wrote: 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: [email protected] [mailto:[email protected] > ] On Behalf Of Steven Samuels > Sent: Tuesday, December 07, 2010 4:00 PM > To: [email protected] > 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. > > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ -- -------------------------------------------------------------- Nicholas Winter 434.924.6994 t Assistant Professor 434.924.3359 f Department of Politics [email protected] e University of Virginia faculty.virginia.edu/nwinter w S385 Gibson Hall, South Lawn * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: calculating effect sizes when using svy command***From:*"Vogt, Dawne" <[email protected]>

**Re: st: calculating effect sizes when using svy command***From:*Steven Samuels <[email protected]>

**RE: st: calculating effect sizes when using svy command***From:*"Vogt, Dawne" <[email protected]>

**Re: st: calculating effect sizes when using svy command***From:*Nick Winter <[email protected]>

**Re: st: calculating effect sizes when using svy command***From:*Steven Samuels <[email protected]>

**Re: st: calculating effect sizes when using svy command***From:*Steven Samuels <[email protected]>

**Re: st: calculating effect sizes when using svy command***From:*Steven Samuels <[email protected]>

**RE: st: calculating effect sizes when using svy command***From:*"Vogt, Dawne" <[email protected]>

**Re: st: calculating effect sizes when using svy command***From:*Steven Samuels <[email protected]>

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