Re: st: Spearman correlations with survey data

["Roger B. Newson" <r.newson@imperial.ac.uk>]

I would second that "Amen". And I would recommend the use of Kendall's 
tau-a as an alternative, because (a) the Central Limit Theorem works a 
lot faster for Kendall's tau-a than for Spearman's rho and (b) Kendall's 
tau-a is interpretable (in words) as a difference between 2 
probabilities, namely a probability of concordance and a probability of 
discordance. I find this difference between probabilities easier to 
understand than a measure of rank linearity (which is what Spearman's 
rho is), and also more useful to know, as I would prefer a good 
non-linear monotonic predictor to a second-rate linear predictor. 
Spearman's rho, by contrast, is MUCH easier to estimate without a 
computer, which was an important issue before we had computers.

To estimate Kendall's tau-a with confidence limits in Stata with 
clustering and/or sampling-probability weighting, use the -somersd- 
package, downloadable from SSC. As in:

somersd x y [pwei=mysampwt], taua tdist transf(z) cluster(mypsu)

I have not yet introduced confidence intervals allowing for sampling 
strata. However, sampling-probability weights and clustering are 
available. And the -somersd- package comes with .pdf manuals, and with 
hypertext references  in the on-line help to further documentation of 
the methiods and formulas.

I hope this helps.

Best wishes

Roger


Roger B Newson BSc MSc DPhil
Lecturer in Medical Statistics
Respiratory Epidemiology and Public Health Group
National Heart and Lung Institute
Imperial College London
Royal Brompton Campus
Room 33, Emmanuel Kaye Building
1B Manresa Road
London SW3 6LR
UNITED KINGDOM
Tel: +44 (0)20 7352 8121 ext 3381
Fax: +44 (0)20 7351 8322
Email: r.newson@imperial.ac.uk
Web page: http://www.imperial.ac.uk/nhli/r.newson/
Departmental Web page:
http://www1.imperial.ac.uk/medicine/about/divisions/nhli/respiration/popgenetics/reph/

Opinions expressed are those of the author, not of the institution.

On 08/11/2012 17:26, Lachenbruch, Peter wrote:
> Amen!  In fact, tests on Spearman coefficients are notoriously sensitive to normality. An article by Egon Pearson in Biometrika in the 1970s showed this clearly.  Sorry i don't have the reference at hand.
>
> Peter A. Lachenbruch,
> Professor (retired)
> ________________________________________
> From: owner-statalist@hsphsun2.harvard.edu [owner-statalist@hsphsun2.harvard.edu] on behalf of Nick Cox [njcoxstata@gmail.com]
> Sent: Thursday, November 08, 2012 1:58 AM
> To: statalist@hsphsun2.harvard.edu
> Subject: Re: st: Spearman correlations with survey data
>
> Spearman correlation is just Pearson correlation applied to ranks, so
> ranking first (use -egen-) gets you from one to the other. Otherwise
> P-values for correlations are over-rated in my view, whether in -svy-
> contexts or otherwise.
>
> Others should have comments on the -svy- aspects.
>
> Nick
>
> On Thu, Nov 8, 2012 at 5:42 AM, Lee Grenon <lgrenon@sfu.ca> wrote:
>
> > I am interested in calculating Spearman correlations for complex survey data. As I understand, I can calculate Pearson correlations using corr with aweight for the coefficients and then calculate the p-values using svy: regress y x and svy: regress x y then selecting the larger p-value. Is there a way of calculating Spearman correlations using a survey weight and bootstrap weights?
>
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