Empirical bayes meta-analysis.

Raudenbush, Stephen W.; Bryk, Anthony S.

doi:10.3102/10769986010002075

Cited by 299Meta-analysis and systematic reviewsStatistical Methods and Bayesian InferenceEducational Research and Methods

Empirical bayes meta-analysis.

Raudenbush, Stephen W., Bryk, Anthony S.·1985·Journal of Educational Statistics

DOI 10.3102/10769986010002075

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Abstract

As interest in quantitative research synthesis grows, investigators increasingly seek to use information about study features—study contexts, designs, treatments, and subjects—to account for variation in study outcomes. To facilitate analysis of diverse study findings, a mixed linear model with fixed and random effects is presented and illustrated with data from teacher expectancy experiments. This strategy enables the analyst to (a) estimate the variance of the effect size parameters by means of maximum likelihood; (b) pose a series of linear models to explain the effect parameter variance; (c) use information about study characteristics to derive improved empirical Bayes estimates of individual study effect sizes; and (d) examine the sensitivity of all substantive inferences to likely errors in the estimation of variance components.

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Raudenbush and Bryk (1985) have recommended that the use of hierarchical linear modeling may be more optimal to account for the nesting of studies within a meta-analysis and this would improve the fidelity of the estimation.
“Raudenbush and Bryk (1985) have recommended that the use of hierarchical linear modeling may be more optimal to account for the nesting of studies within a meta-analysis and this would improve the fidelity of the estimation.”
From The Power of Feedback Revisited: A Meta-Analysis of Educational Feedback Research (2020) · cites this paper as Raudenbush and Bryk, 1985
A single parameter cannot adequately summarize heterogeneous effects, so focusing on the mean effect alone is usually insufficient.
“However, a single parameter cannot adequately summarize heterogeneous effects (Raudenbush and Bryk, 1985) and so focusing on μ alone is usually insufficient.”
From A Re-Evaluation of Random-Effects Meta-Analysis (2009) · cites this paper as Raudenbush and Bryk, 1985

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Authors: Raudenbush, S. W., & Bryk, A. S.
Affiliations: Michigan State University (US)
University of Chicago (US)
Venue: Journal of Educational Statistics
Publisher: American Educational Research Association (AERA)
Published: 1985-06
Volume: 10
Issue: 2
Pages: 75-98
ISSN: 0362-9791 (ISSN-L)
DOI: 10.3102/10769986010002075
OpenAlex: W2043466393
Cited by: 299
References: 28
Citations by year: 2026: 6 · 2025: 6 · 2024: 11 · 2023: 14 · 2022: 12 · 2021: 11 · 2020: 14 · 2019: 9 · 2018: 7 · 2017: 6 · 2016: 7 · 2015: 9 · 2014: 11 · 2013: 14 · 2012: 14
FWCI: 8.95
Type: journal article
Language: en
Open access: closed
Licence: journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations: Journal of Educational Statistics (publishedVersion)
RePEc: Research Papers in Economics (submittedVersion)
Funders: Spencer Foundation
Harvard University
Topics: Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Social Sciences › Education › Educational Research and Methods
Keywords: empirical Bayes, meta-analysis, linear mixed models, random effects, effect size heterogeneity, study characteristics, teacher expectations, variance component estimation, sensitivity analysis

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