A Guide to Conducting a Meta-Analysis with Non-Independent Effect Sizes

By Mike W.-L. Cheung. Neuropsychology Review, 2019

502 citations recorded by the source corpus.

Conventional meta-analytic procedures assume that effect sizes are independent. When effect sizes are not independent, conclusions based on these conventional procedures can be misleading or even wrong. Traditional approaches, such as averaging the effect sizes and selecting one effect size per study, are usually used to avoid the dependence of the effect sizes. These ad-hoc approaches, however, may lead to missed opportunities to utilize all available data to address the relevant research questions. Both multivariate meta-analysis and three-level meta-analysis have been proposed to handle non-independent effect sizes. This paper gives a brief introduction to these new techniques for applied researchers. The first objective is to highlight the benefits of using these methods to address non-independent effect sizes. The second objective is to illustrate how to apply these techniques with real data in R and Mplus. Researchers may modify the sample R and Mplus code to fit their data.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c6852ad314ab220185524beae0e799c07db64028

Citations in the corpus

• Meditation, Mindfulness, and Attention: a Meta-analysis (2021, unknown): “…error respectively, where d is Cohen’s d, df is the degrees of freedom, and SEd is the standard error of Cohen’s d. g ¼ 1− 3 4 dfð Þ−1 *d ð4Þ SEg ¼ 1− 34 dfð Þ−1 *SEd ð5Þ All analyses were conducted using three-level meta-regressions (see Cheung 2019) with the metafor R package (Viechtbauer 2010).”