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- Title
The application of meta-analytic (multi-level) models with multiple random effects: A systematic review.
- Authors
Fernández-Castilla, Belén; Jamshidi, Laleh; Declercq, Lies; Beretvas, S. Natasha; Onghena, Patrick; Van den Noortgate, Wim
- Abstract
In meta-analysis, study participants are nested within studies, leading to a multilevel data structure. The traditional random effects model can be considered as a model with a random study effect, but additional random effects can be added in order to account for dependent effects sizes within or across studies. The goal of this systematic review is three-fold. First, we will describe how multilevel models with multiple random effects (i.e., hierarchical three-, four-, five-level models and cross-classified random effects models) are applied in meta-analysis. Second, we will illustrate how in some specific three-level meta-analyses, a more sophisticated model could have been used to deal with additional dependencies in the data. Third and last, we will describe the distribution of the characteristics of multilevel meta-analyses (e.g., distribution of the number of outcomes across studies or which dependencies are typically modeled) so that future simulation studies can simulate more realistic conditions. Results showed that four- or five-level or cross-classified random effects models are not often used although they might account better for the meta-analytic data structure of the analyzed datasets. Also, we found that the simulation studies done on multilevel meta-analysis with multiple random factors could have used more realistic simulation factor conditions. The implications of these results are discussed, and further suggestions are given.
- Subjects
RANDOM effects model; META-analysis; MULTILEVEL models; DATA structures
- Publication
Behavior Research Methods, 2020, Vol 52, Issue 5, p2031
- ISSN
1554-351X
- Publication type
Article
- DOI
10.3758/s13428-020-01373-9