The DOI matches its Crossref record; 8 of the 9 references it cites were found in a registry or an index.
Distribution Theory for Glass's Estimator of Effect Size and Related Estimators
Abstract
Glass's estimator of effect size, the sample mean difference divided by the sample standard deviation, is studied in the context of an explicit statistical model. The exact distribution of Glass's estimator is obtained and the estimator is shown to have a small sample bias. The minimum variance unbiased estimator is obtained and shown to have uniformly smaller variance than Glass's (biased) estimator. Measurement error is shown to attenuate estimates of effect size and a correction is given. The effects of measurement invalidity are discussed. Expressions for weights that yield the most precise weighted estimate of effect size are also derived.
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In the citing paper
“Raw effect sizes were corrected for bias (i.e., the upward-bias of effect sizes among small samples; Hedges, 1981); however, most correction factors were close to 1.0 given the large sample sizes.”
From Gender Differences in Depression in Representative National Samples: Meta-Analyses of Diagnoses and Symptoms (2017) · cites this paper as Hedges, 1981
In the citing paper
“we applied a correction to bias-correct estimates (resulting in what is often referred to as Hedge ' s g ).”
From The Impact of Peer Assessment on Academic Performance: A Meta-analysis of Control Group Studies (2020) · cites this paper as Hedges 1981
In the citing paper
“As standardized mean difference ( d ) is known to have a slight positive bias (Hedges 1981), we applied a correction to bias-correct estimates (resulting in what is often referred to as Hedge ' s g ).”
From The Impact of Peer Assessment on Academic Performance: A Meta-analysis of Control Group Studies (2020) · cites this paper as Hedges 1981
In the citing paper
“To correct for possible bias caused by small samples, Cohen's d was then used to calculate Hedges' g (Hedges 1981).”
From The gamification of learning: a meta-analysis (2020) · cites this paper as Hedges 1981
In the citing paper
“Effect sizes were computed as Hedges’ g standardised mean differences (SMDs) to correct for small-sample bias and improve comparability across tasks88.”
From Meta-analysis of computerised working memory training: behavioural gains, training parameters, transfer mechanisms, and neural correlates (2026) · cites this paper as [88]
In the citing paper
“Cholinesterase inhibitors and memantine appear ineffective. 13 , 14”
From Non-pharmacological interventions for agitation in dementia: systematic review of randomised controlled trials (2014) · cites this paper as [13]
In the citing paper
“Unlike Cohen's d, Hedge's G a helps to control for common overestimations of effect sizes in samples below 20 (Hedges 1981).”
In the citing paper
“Preliminary searching indicates that most samples are over 100 and none are under 50, so the small‐sample correction (to use Hedges' g) is not likely to be needed (Hedges, 1981).”
From PROTOCOL: Single-track year-round education for improving academic achievement in us k-12 schools: results of a meta-analysis (2018) · cites this paper as Hedges, 1981
In the citing paper
“We chose Hedges’ g as our measure of effect size because it is unitless, so it allows us to compare human–AI performance across different metrics, and it corrects for upward bias in the raw standardized mean difference (Cohen’s d)61.”
From When combinations of humans and AI are useful: A systematic review and meta-analysis (2024) · cites this paper as [61]
In the citing paper
“Risk of bias of the included RCTs was assessed using the Cochrane Collaboration’s Tool for Assessing Risk of Bias 120 , which were independently conducted by a minimum of two researchers (KJM, PA, and/or DH).”
In the citing paper
“Next, an estimation for Hedges' g (Hedges, 1981) was applied as a conservative measure to correct for sample size.”
In the citing paper
“Since differences in sample sizes between studies might introduce bias into the calculated effect sizes, Hedges's g was used to adjust the effect sizes to correct for small sample size biases (Hedges, 1981).”
From RETRACTED ARTICLE: The effect of ChatGPT on students’ learning performance, learning perception, and higher-order thinking: insights from a meta-analysis (2025) · cites this paper as Hedges, 1981
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Metadata
- Authors
- Hedges, L. V.
- Affiliations
- University of Chicago (US)
- Corresponding author
- Hedges, Larry V.
- ORCID
- Hedges, Larry V.
- Venue
- Journal of Educational Statistics
- Publisher
- JSTOR
- Published
- 1981
- Volume
- 6
- Issue
- 2
- Pages
- 107
- ISSN
- 0362-9791 (ISSN-L)
- OpenAlex
- W2059527510
- Cited by
- 5,695
- References
- 9
- Citations by year
- 2026: 623 · 2025: 486 · 2024: 543 · 2023: 414 · 2022: 463 · 2021: 405 · 2020: 359 · 2019: 306 · 2018: 264 · 2017: 222 · 2016: 198 · 2015: 175 · 2014: 169 · 2013: 132 · 2012: 118
- FWCI
- 61.80
- 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 in Epidemiology
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Keywords
- effect size, exact distribution, small sample bias, minimum variance unbiased estimator, measurement error, measurement error correction
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