Any regression is just a form of correaltional analysis (and does not imply causality in general).
In my view, svy: y x (or the other way around) is especially appropriate in case of within-correlation in panels to avoid inflation of standard errors that would occur in case of using a simple correlation command.
Make sure you et the options right.
Justina
-------- Original-Nachricht --------
> Datum: Thu, 8 Nov 2012 09:58:49 +0000
> Von: Nick Cox <njcoxstata@gmail.com>
> An: statalist@hsphsun2.harvard.edu
> Betreff: 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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