Fitting three-level meta-analytic models in R: A step-by-step tutorial

Applying a multilevel approach to meta-analysis is a strong method for dealing with dependency of effect sizes. However, this method is relatively unknown among researchers and, to date, has not been widely used in meta-analytic research. Therefore, the purpose of this tutorial was to show how a thr...

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Veröffentlicht in:Tutorials in quantitative methods for psychology Jg. 12; H. 3; S. 154 - 174
Hauptverfasser: Assink, Mark, Wibbelink, Carlijn J. M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Université d'Ottawa 01.10.2016
Schlagworte:
meta-analysis
multilevel analysis
R, rma.mv, metafor
ISSN:1913-4126, 1913-4126
Online-Zugang:Volltext
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Zusammenfassung:Applying a multilevel approach to meta-analysis is a strong method for dealing with dependency of effect sizes. However, this method is relatively unknown among researchers and, to date, has not been widely used in meta-analytic research. Therefore, the purpose of this tutorial was to show how a three-level random effects model can be applied to meta-analytic models in R using the rma.mv function of the metafor package. This application is illustrated by taking the reader through a step-by-step guide to the multilevel analyses comprising the steps of (1) organizing a data file; (2) setting up the R environment; (3) calculating an overall effect; (4) examining heterogeneity of within-study variance and between-study variance; (5) performing categorical and continuous moderator analyses; and (6) examining a multiple moderator model. By example, the authors demonstrate how the multilevel approach can be applied to meta-analytically examining the association between mental health disorders of juveniles and juvenile offender recidivism. In our opinion, the rma.mv function of the metafor package provides an easy and flexible way of applying a multi-level structure to meta-analytic models in R. Further, the multilevel meta-analytic models can be easily extended so that the potential moderating influence of variables can be examined.
ISSN:1913-4126
1913-4126
DOI:10.20982/tqmp.12.3.p154