[R-meta] Computing var-covariance matrix with correlations of six non-independent outcomes
Mika Manninen
m|xu89 @end|ng |rom gm@||@com
Tue Jul 7 18:51:31 CEST 2020
Wolfgang,
Hedges g is the effect size (SMD).
Sorry about excluding that information from the previous email and thank
you for your quick response.
Mika
ti 7. heinäk. 2020 klo 19.42 Viechtbauer, Wolfgang (SP) (
wolfgang.viechtbauer using maastrichtuniversity.nl) kirjoitti:
> 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
>
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