The DOI matches its Crossref record; 27 of the 28 references it cites were found in a registry or an index.
Empirical bayes meta-analysis.
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.”
From The Power of Feedback Revisited: A Meta-Analysis of Educational Feedback Research (2020) · cites this paper as Raudenbush and Bryk, 1985
In the citing paper
“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
Citations
Cites
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Hedges, L. (1982) · Psychological Bulletin · doi:10.1037/0033-2909.92.2.490 · Cited by 1,030
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Dempster, A. P., Rubin, D. B., & Tsutakawa, R. K. (1981) · Journal of the American Statistical Association · doi:10.1080/01621459.1981.10477653 · Cited by 444
Comparing effect sizes of independent studies.
Rosenthal, R., & Rubin, D. B. (1982) · Psychological Bulletin · doi:10.1037//0033-2909.92.2.500 · Cited by 406
A random effects model for effect sizes.
Hedges, L. V. (1983) · Psychological Bulletin · doi:10.1037/0033-2909.93.2.388 · Cited by 399
Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability.
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Self-citationComparing effect sizes of independent studies.
Rosenthal, R., & Rubin, D. B. (1982) · Psychological Bulletin · doi:10.1037/0033-2909.92.2.500 · Cited by 289
Estimation in Parallel Randomized Experiments
Rubin, D. B. (1981) · Journal of Educational Statistics · doi:10.3102/10769986006004377 · Cited by 269
Sociological Methodology 1983-1984
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Self-citationDeely, J., & Lindley, D. V. (1981) · Journal of the American Statistical Association · doi:10.1080/01621459.1981.10477731 · Cited by 234
Fitting Continuous Models to Effect Size Data
Hedges, L. V. (1982) · Journal of Educational Statistics · doi:10.3102/10769986007004245 · Cited by 129
Evaluating the Effect of Coaching on SAT Scores: A Meta-Analysis
DerSimonian, R., & Laird, N. (1983) · Harvard Educational Review · doi:10.17763/haer.53.1.n06j5h5356217648 · Cited by 114
Regression Models in Research Synthesis
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Flagged first, then by citations
1 of the 28 references couldn’t be matched to a known paper.
Research works printed but not linked: 7
- 10.2307/1174772 · DOI · Paper page
- 10.3102/10769986006002107 · DOI · Paper page
- Light RJ, 1983, Evaluation studies review annual
- Mason WM, 1984, Sociological Methodology
- Raudenbush SW, 1984, Applications of a hierarchical linear model in educational research
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- Sternio JLF, 1981, Empirical Bayes estimation for a hierarchical linear model
Cited by
Sorted by citations
Conducting Meta-Analyses in R with the metafor Package
Viechtbauer, W. (2010) · Journal of Statistical Software · doi:10.18637/jss.v036.i03 · Cited by 20,464
Fixed-and random-effects models in meta-analysis.
Hedges, L. V., & Vevea, J. L. (1998) · Psychological Methods · doi:10.1037/1082-989x.3.4.486 · Cited by 2,977
A Re-Evaluation of Random-Effects Meta-Analysis
Higgins, J. P. T., Thompson, S. G., & Spiegelhalter, D. J. (2009) · Journal of the Royal Statistical Society Series A: Statistics in Society · doi:10.1111/j.1467-985x.2008.00552.x · Cited by 2,569
“Metaanalytic implementations have made use of the EM algorithm and bootstrapping (Raudenbush and Bryk, 1985; Laird and Louis, 1989).”
BackgroundMethodBayesian Approaches to Clinical Trials and Health‐Care Evaluation
Spiegelhalter, D. J., Abrams, K. R., & Myles, J. P. (2003) · Wiley eBooks · doi:10.1002/0470092602 · Cited by 1,687
Application of hierarchical linear models to assessing change.
Bryk, A. S., & Raudenbush, S. W. (1987) · Psychological Bulletin · doi:10.1037/0033-2909.101.1.147 · Cited by 1,505
Self-citation
Metadata
- Authors
- Raudenbush, S. W., & Bryk, A. S.
- Affiliations
- Michigan State University (US)
- University of Chicago (US)
- Venue
- Journal of Educational Statistics
- Published
- 1985-06
- Volume
- 10
- Issue
- 2
- Pages
- 75-98
- ISSN
- 0362-9791 (ISSN-L)
- 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
- 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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