[R-meta] Computing var-covariance matrix with correlations of six non-independent outcomes

[Viechtbauer, Wolfgang (SP)]

Dear Mika,

What effect size measure are you using for the meta-analysis?

Best,
Wolfgang

>-----Original Message-----
>From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-project.org]
>On Behalf Of Mika Manninen
>Sent: Tuesday, 07 July, 2020 18:10
>To: r-sig-meta-analysis using r-project.org
>Subject: [R-meta] Computing var-covariance matrix with correlations of six
>non-independent outcomes
>
>Hello,
>
>I am doing a meta-analysis looking at the effect of a teaching intervention
>(versus control) on six types of motivation/behavioral regulation.
>Theoretically and empirically these constructs form a continuum in which
>the continuum neighbors are most strongly positively correlated and the
>furthest from one another most negatively correlated.
>
>I have 95 effects. These effects come from 25 studies, each reporting
>scores for between 1-6 motivation types. The number of effects per
>motivation ranges from 22 to 13. In some studies, they have measured only
>one or two types whereas in others they have measured 5 or all 6 types of
>motivation.
>
>I originally ran a separate random-effects meta-analysis for all the six
>outcomes. However, I got feedback that the dependency of the motivation
>types should be taken into account and a 3-level meta-analysis was
>recommended. After looking into it, the 3-level model seems to be a worse
>approach than the multivariate approach.
>
>As is not usually the case, I have succeeded in gathering all correlations
>between all the motivation types for all studies (some from original
>reporting and some from previous meta-analysis findings).
>
>My question is, how do I compute the V-matrix for this data in order to run
>the multivariate analysis? I read the whole archive but I could not find a
>clear answer to the problem.
>
>Thank you very much in advance,
>
>Mika