top banner top banner
index
RegularArticles
ReplicationStudies
EditorialBoard
Instructions4Authors
JournalGuidelines
Messages
Submission

Search publications

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

Full text PDF
Bibliographic information: BibTEX format RIS format XML format APA style
Cited references information: BibTEX format APA style
Doi: 10.20982/tqmp.12.3.p154

Assink, Mark , Wibbelink, Carlijn J. M.
154-174
Keywords: meta-analysis , multilevel analysis
Tools: R, rma.mv, metafor
(no sample data)   (Appendix)

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.


Pages © TQMP;
Template last modified: 2017-27-09.
Page consulted on .
Be informed of the upcoming issues with RSS feed: RSS icon RSS