[R-meta] Comparing dependent, overlapping correlation coefficients
Anna-Lena Schubert
@nn@-len@@@chubert @ending from p@ychologie@uni-heidelberg@de
Mon Aug 13 14:32:11 CEST 2018
Hi Wolfgang,
thanks four sharing your rmat-code, that is extremely helpful!
If you say I need the var-cov matrix for each study, do you mean the
var-cov matrix for Y and Z? And if I have that, how do I feed this into
the meta-analysis? Do I add those matrices to a single large matrix that
contains all variances/covariances, or do I add each matrix separately
somehow? Would I do this in a similar vein as Berkey et al. (1998) --
http://www.metafor-project.org/doku.php/analyses:berkey1998 -- or do I
need a different approach?
Best,
Anna-Lena
Am 10.08.2018 um 17:02 schrieb Viechtbauer, Wolfgang (SP):
> Hi Anna-Lena,
>
> Do you have cor(Y,Z)? Then this allows you to compute the covariance between cor(X,Y) and cor(X,Z) -- which is what you need to do a proper test. Indeed, you want the data in long format as you have them, but you also need the 2x2 var-cov matrix for each study. You can compute this with:
>
> https://gist.github.com/wviechtb/700983ab0bde94bed7c645fce770f8e9
>
> See also this thread in the archives:
>
> https://stat.ethz.ch/pipermail/r-sig-meta-analysis/2018-January/000483.html
>
> Best,
> Wolfgang
>
> -----Original Message-----
> From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-project.org] On Behalf Of Anna-Lena Schubert
> Sent: Friday, 10 August, 2018 16:21
> To: r-sig-meta-analysis using r-project.org
> Subject: [R-meta] Comparing dependent, overlapping correlation coefficients
>
> Dear all,
>
> I want to run a meta-analysis that compares dependent, overlapping
> correlation coefficients (i.e., I want to see if X correlates more
> strongly with Y than it does with Z). I already ran a meta-analysis
> separately for both of these correlations and would now like to compare
> those two pooled effect sizes statistically. Confidence intervals of the
> two correlations do not overlap (r1 = .18 [.12; .24]; r2 = .32 [.25;
> .39]), but I wonder if there may be a more elegant way to compare these
> correlations than just based on CIs.
>
> I wonder, for example, if a factorial variable could be used to identify
> those correlations in a "long" data format style, and if I could test
> for a significant interaction between variable type (Y vs. Z) and the
> correlation in a meta-analysis:
>
> Study Variable r
> 1 Y .20
> 1 Z .30
> 2 Y .34
> 2 Z .43
>
> I would greatly appreciate if anyone could tell me if that's a good idea
> or could recommend other approaches. Thanks in advance for any offers of
> help!
>
> Best,
> Anna-Lena
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