A demonstration and evaluation of the use of cross-classified random-effects models for meta-analysis

A demonstration and evaluation of the use of cross-classified random-effects models for... It is common for the primary studies in meta-analyses to report multiple effect sizes, generating dependence among them. Hierarchical three-level models have been proposed as a means to deal with this dependency. Sometimes, however, dependency may be due to multiple random factors, and random factors are not necessarily nested, but rather may be crossed. For instance, effect sizes may belong to different studies, and, at the same time, effect sizes might represent the effects on different outcomes. Cross-classified random-effects models (CCREMs) can be used to model this nonhierarchical dependent structure. In this article, we explore by means of a simulation study the performance of CCREMs in comparison with the use of other meta-analytic models and estimation procedures, including the use of three- and two-level models and robust variance estimation. We also evaluated the performance of CCREMs when the underlying data were generated using a multivariate model. The results indicated that, whereas the quality of fixed-effect estimates is unaffected by any misspecification in the model, the standard error estimates of the mean effect size and of the moderator variables’ effects, as well as the variance component estimates, are biased under some conditions. Applying CCREMs led to unbiased fixed-effect and variance http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Behavior Research Methods Springer Journals

A demonstration and evaluation of the use of cross-classified random-effects models for meta-analysis

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Publisher
Springer US
Copyright
Copyright © 2018 by Psychonomic Society, Inc.
Subject
Psychology; Cognitive Psychology
eISSN
1554-3528
D.O.I.
10.3758/s13428-018-1063-2
Publisher site
See Article on Publisher Site

Abstract

It is common for the primary studies in meta-analyses to report multiple effect sizes, generating dependence among them. Hierarchical three-level models have been proposed as a means to deal with this dependency. Sometimes, however, dependency may be due to multiple random factors, and random factors are not necessarily nested, but rather may be crossed. For instance, effect sizes may belong to different studies, and, at the same time, effect sizes might represent the effects on different outcomes. Cross-classified random-effects models (CCREMs) can be used to model this nonhierarchical dependent structure. In this article, we explore by means of a simulation study the performance of CCREMs in comparison with the use of other meta-analytic models and estimation procedures, including the use of three- and two-level models and robust variance estimation. We also evaluated the performance of CCREMs when the underlying data were generated using a multivariate model. The results indicated that, whereas the quality of fixed-effect estimates is unaffected by any misspecification in the model, the standard error estimates of the mean effect size and of the moderator variables’ effects, as well as the variance component estimates, are biased under some conditions. Applying CCREMs led to unbiased fixed-effect and variance

Journal

Behavior Research MethodsSpringer Journals

Published: Jun 5, 2018

References

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