[R-meta] Recommendations on quantifying heterogeneity in three-level meta-regression

[Jens Schüler]

Dear all,

 

I am currently working on a three-level meta-regression and I am a bit
unsure about how to properly/adequately report heterogeneity in such
mixed-effect models (whereas it is pretty clear cut for regular „two-level“
meta-regressions e.g. the recent best practice recommendations of
Gonzalez-Mule & Aguinis 2017
(http://journals.sagepub.com/doi/abs/10.1177/0149206317710723)

 

Currently the metafor rma.mv function provides us with variance estimates
for both levels and a Q-test on residual heterogeneity. Moreover, we are
provided with code on how to calculate I2 in multilevel models, which I did
(http://www.metafor-project.org/doku.php/tips:i2_multilevel_multivariate#mul
tilevel_models).

 

Turning towards the metaSEM package of Cheung, he reports variance estimates
(no predictors vs. predictors) for both levels and R2
(https://courses.nus.edu.sg/course/psycwlm/internet/metasem/3level.html#mode
l-1-type-as-a-covariate). However, not I2. In his book (Cheung 2015)
reports, on page 186, some formulas on how to calculate I2 and ICC in
three-level models but ends with a cautionary note that we lack further
insight in order to make reliable statements on that matter.

 

Hence, I am a bit left in the dark in terms of what is currently appropriate
(or how to defend such choices) and would like to pick your brains on that
issue.

 

 

Best regards

Jens

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