[R-meta] fixed-effect multivariate model interpretation

[Viechtbauer, Wolfgang (SP)]

Hi Filippo,

You can *assume* that there is no residual heterogeneity, but there may be. That is what the test of residual heterogeneity is testing here (whether your assumption is correct or not).

Best,
Wolfgang

>-----Original Message-----
>From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-project.org] On
>Behalf Of Filippo Gambarota
>Sent: Monday, 03 January, 2022 16:42
>To: R meta
>Subject: [R-meta] fixed-effect multivariate model interpretation
>
>Hello!
>I'm fitting for the first time a multivariate fixed-effect model using
>metafor. The code is:
>
>```
>rma.mv(yi, V, mods = ~ 0 + outcome, data = data, test = "t")
>```
>Where V is the block variance-covariance matrix created with vcalc()
>that represents the covariance between different outcome levels within
>each study. The outcome is a factor that represents different effect
>sizes measured on the same participants within a study.
>The model as expected did not estimate tau for each outcome and test
>all coefficients (each outcome mean with this parametrization) against
>0 (both the omnibus test and each beta). My question is about the
>*residual heterogeneity* parameter and the associated Q test. Under
>this model, I should have assumed that there is no heterogeneity
>within each outcome level so I'm not sure how to interpret the
>residual heterogeneity in this case.
>Thank you!
>Filippo
>
>--
>Filippo Gambarota
>PhD Student - University of Padova
>Department of Developmental and Social Psychology
>Website: filippogambarota.netlify.app
>Research Group: Colab   Psicostat