[R-meta] Does QE statistic in rma.mv() account for random effects as well?

Dear Simon,

if you compare the output of a simple fixed effect model to a simple
random effects model then you can see that the QE value is the same.

A reproducible example:

library(metafor)
rma(data=dat.bangertdrowns2004,yi,vi,mods=~length,method="FE")
rma(data=dat.bangertdrowns2004,yi,vi,mods=~length,method="REML")

"Test for Residual Heterogeneity:
QE(df = 44) = 96.2810, p-val < .0001"

In other words. Random effects do not influence the QE value.

You can also see it in the code that Wolfgang provides via github:
https://github.com/cran/metafor/blob/master/R/rma.mv.r (line 1500)


For some conceptual clarifications see also this paper:
Pastor, D. A., & Lazowski, R. A. (2017). On the multilevel nature of
meta-analysis: A tutorial, comparison of software programs, and
discussion of analytic choices. Multivariate Behavioral Research, 52(6),
789?804. https://doi.org/10.1080/00273171.2017.1365684



Best,
Lukasz
--
Lukasz Stasielowicz
Osnabr?ck University
Institute for Psychology
Research methods, psychological assessment, and evaluation
Seminarstra?e 20
49074 Osnabr?ck (Germany)

Am 21.01.2022 um 12:00 schrieb r-sig-meta-analysis-request at r-project.org:

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Dear Meta-Analysis Experts,

I'm running a multilevel model with metafor's rma.mv().

Does the QE statistic (Test for Residual Heterogeneity) reported by

rma.mv(),

in addition to the variance explained by moderators, also account for the
variance components explained by random-effects (Sigmas, Taus, & Gammas)?

In other words, a significant QE in such models suggests that the true
effects are heterogeneous beyond what has been explained by moderators

AND

random components OR just moderators?

Thank you,
Simon

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