Estimation of effect size under nonrandom sampling: the effects of censoring studies yielding statistically insignificant mean differences
Abstract
Quantitative research synthesis usually involves the combination of estimates of the standardized mean difference (effect size) derived from independent research studies. In some cases, effect size estimates are available only if the difference between experimental and control group means is statistically significant. If the quantitative result of a study is observed only when the mean difference is statistically significant, the observed mean difference, variance, and effect size are biased estimators of the corresponding population parameters. The exact distribution of the sample effect size is derived for the case in which only studies yielding statistically significant results may be observed. The maximum likelihood estimator of effect size also is derived under the model in which only significant results are observed. The exact distribution of the maximum likelihood estimator is obtained numerically and is used to study the bias of the maximum likelihood estimator. An empirical sampling study is used to supplement the analytic results.
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From A historical review of publication bias (2020) · cites this paper as Hedges, 1984
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Ives, B. (2003) · Journal of Learning Disabilities · doi:10.1177/00222194030360060101 · Cited by 38 · see citing works
A Parsimonious Weight Function for Modeling Publication Bias
Citkowicz, M., & Vevea, J. L. (2017) · Psychological methods · doi:10.1037/met0000119 · Cited by 42 · see citing works
Chapter 11: Issues in Meta-Analysis
Hedges, L. (1986) · Review of Research in Education · doi:10.3102/0091732x013001353 · Cited by 85 · see citing works
Self-citationA Series of Meta-Analytic Tests of the Efficacy of Long-Term Psychoanalytic Psychotherapy
Woll-Weber, C. F. J., & Schönbrodt, F. D. (2019) · European Psychologist · doi:10.1027/1016-9040/a000385 · Cited by 37 · see citing works
Confidence Intervals and P‐Values for Meta‐Analysis with Publication Bias
Henmi, M., Copas, J. B., & Eguchi, S. (2006) · Biometrics · doi:10.1111/j.1541-0420.2006.00705.x · Cited by 35 · see citing works
Evaluating Replication ResultsPreprint
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Assessing heterogeneity and power in replications of psychological experiments.
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- Authors
- Hedges, L. V.
- Affiliations
- University of Chicago (US)
- Corresponding author
- Hedges, Larry V.
- ORCID
- Hedges, Larry V.
- Venue
- Journal of Educational Statistics
- Published
- 1984-03
- Volume
- 9
- Issue
- 1
- Pages
- 61-85
- ISSN
- 0362-9791 (ISSN-L)
- OpenAlex
- W2012120824
- Cited by
- 241
- References
- 27
- Citations by year
- 2026: 11 · 2025: 13 · 2024: 14 · 2023: 13 · 2022: 13 · 2021: 15 · 2020: 18 · 2019: 21 · 2018: 17 · 2017: 12 · 2016: 9 · 2015: 5 · 2014: 5 · 2013: 4 · 2012: 2
- FWCI
- 5.00
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Educational Statistics (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Spencer Foundation
- 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
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- effect size estimation, non-probability sampling, standardized mean difference, maximum likelihood estimator, estimation bias, exact distribution, meta-analysis, publication bias
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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