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Hierarchical Meta-Analysis: A Simulation Study Comparing Classical Random Effects And Fully Bayesian Methods, Nancy R. Andiloro

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Meta-analytic data have a natural hierarchical structure to them, where individuals are nested within studies, and have both within-and between-study variation to model. A random-effects hierarchical linear model is useful to conduct a meta-analysis because it allows one to appropriately parse out the two components of variation that exist within and across studies to determine an observed effect. Empirical Bayes estimation considers the reliability of variance estimates; when the reliability of the effect size estimate for a study is high, substantial weight is placed on that estimate. However, problems with estimation arise when the number of studies and their sample …