Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2020 Oct;52(5):2031-2052.
doi: 10.3758/s13428-020-01373-9.

The application of meta-analytic (multi-level) models with multiple random effects: A systematic review

Belén Fernández-Castilla  1   2 , Laleh Jamshidi  3   4 , Lies Declercq  3   4 , S Natasha Beretvas  5 , Patrick Onghena  3 , Wim Van den Noortgate  3   4
Affiliations

The application of meta-analytic (multi-level) models with multiple random effects: A systematic review

Belén Fernández-Castilla et al. Behav Res Methods. 2020 Oct.

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.

Keywords: Systematic review; meta-analysis; multilevel models; multiple effect sizes.

PubMed Disclaimer

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

References marked with an asterisk (*) were included in systematic review
    1. *Acar, S., Chen, X., & Cayirdag, N. (2018). Schizophrenia and creativity: A meta-analytic review. Schizophrenia Research, 195, 23–31.
    1. *Acar, S., & Sen, S. (2013). A multilevel meta-analysis of the relationship between creativity and schizotypy. Psychology of Aesthetics, Creativity, and the Arts, 7, 214–228.
    1. *Acar, S., Sen, S., & Cayirdag, N. (2016). Consistency of the performance and nonperformance methods in gifted identification: A multilevel meta-analytic review. Gifted Child Quarterly, 60, 81–101.
    1. *Appuhamy, J. A. D. R. N., Judy, J. V., Kebreab, E., & Kononoff, P. J. (2016). Prediction of drinking water intake by dairy cows. Journal of Dairy Science, 99, 7191–7205.
    1. *Assink, M., Van der Put, C. E., Hoeve, M., de Vries, S. L. A., Stams, G. J. J. M., & Oort, F. J. (2015). Risk factors for persistent delinquent behavior among juveniles: A meta-analytic review. Clinical Psychology Review, 42, 47–61.

Publication types

LinkOut - more resources