How Methodological Features Affect Effect Sizes in Education
Abstract
As evidence becomes increasingly important in educational policy, it is essential to understand how research design might contribute to reported effect sizes in experiments evaluating educational programs. A total of 645 studies from 12 recent reviews of evaluations of preschool, reading, mathematics, and science programs were studied. Effect sizes were roughly twice as large for published articles, small-scale trials, and experimenter-made measures, compared to unpublished documents, large-scale studies, and independent measures, respectively. Effect sizes were significantly higher in quasi-experiments than in randomized experiments. Excluding tutoring studies, there were no significant differences in effect sizes between elementary and middle/high studies. Regression analyses found that effects of all factors maintained after controlling for all other factors. Explanations for the effects of methodological features on effect sizes are discussed, as are implications for evidence-based policy.
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Published studies in higher rated journals may report larger effects than unpublished reports due to publication bias.
“In addition, published studies in higher rated journals may report larger effects than unpublished reports due to biases in publishing only the stronger associations and significant results (Cheung & Slavin, 2016).”
From Does Natural Mentoring Matter? A Multilevel Meta‐analysis on the Association Between Natural Mentoring and Youth Outcomes (2018) · cites this paper as Cheung & Slavin, 2016
Study characteristics such as year and quality of the study affect meta-analytic results.
“major factors that have consistently shown to affect meta‐analytic results in other fields of research are study characteristics, such as the year and quality of the study (Cheung & Slavin, 2016).”
From Does Natural Mentoring Matter? A Multilevel Meta‐analysis on the Association Between Natural Mentoring and Youth Outcomes (2018) · cites this paper as Cheung & Slavin, 2016
Pre-to post effects are generally much larger than in randomized controlled studies.
“pre-to post effects generally are much larger than in randomized controlled studies [33,34]”
From Virtual reality exposure therapy for fear of spiders: an open trial and feasibility study of a new treatment for arachnophobia (2024) · cites this paper as [33]
In their meta-analysis of 197 RCTs, Cheung and Slavin (2016) find an average effect size on academic achievement of 0.16 standard deviations.
“Similar effect sizes are substantial; Cheung and Slavin (2016) find, in their meta-analysis of 197 RCTs, an average effect size on academic achievement of 0.16 standard deviations.”
From The effect of school closures on standardised student test outcomes (2022) · cites this paper as Cheung and Slavin (2016)
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- Authors
- Cheung, A. C. K., & Slavin, R. E.
- Affiliations
- Chinese University of Hong Kong (HK)
- Johns Hopkins University (US)
- Venue
- Educational Researcher
- Published
- 2016-06
- Volume
- 45
- Issue
- 5
- Pages
- 283-292
- ISSN
- 0013-189X (ISSN-L)
- 1935-102X
- OpenAlex
- W2470242385
- Cited by
- 529
- References
- 37
- Citations by year
- 2026: 61 · 2025: 57 · 2024: 57 · 2023: 56 · 2022: 62 · 2021: 57 · 2020: 94 · 2019: 36 · 2018: 23 · 2017: 18 · 2016: 8
- FWCI
- 26.47
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Educational Researcher (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Social Sciences › Decision Sciences › Management Science and Operations Research › Evaluation and Performance Assessment
- Social Sciences › Education › Educational Research and Methods
- Keywords
- effect size, research design, methodological characteristics, educational program evaluation, randomized experiments, quasi-experiments, unpublished research, preschool programs, reading programs, mathematics programs, science programs, evidence-based policy, publication bias
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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.
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