The DOI matches its Crossref record; 331 of the 332 references it cites were found in a registry or an index.
The Essential Guide to Effect Sizes: Statistical Power, Meta-Analysis, and the Interpretation of Research Results
Abstract
This succinct and jargon-free introduction to effect sizes gives students and researchers the tools they need to interpret the practical significance of their results. Using a class-tested approach that includes numerous examples and step-by-step exercises, it introduces and explains three of the most important issues relating to the practical significance of research results: the reporting and interpretation of effect sizes (Part I), the analysis of statistical power (Part II), and the meta-analytic pooling of effect size estimates drawn from different studies (Part III). The book concludes with a handy list of recommendations for those actively engaged in or currently preparing research projects.
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“All training effects were in a range of Cohen’s d = 0.240 and d = 0.295, which are conventionally labelled small effects72.”
From Voluntary upregulation of heart rate variability through biofeedback is improved by mental contemplative training (2019) · cites this paper as [72]
In the citing paper
“this may have inflated the reported effect sizes used in the power analysis (Ellis, 2010)”
From A Feasibility and Pilot Randomised Dismantling Trial of the Efficacy of Self-As-Context During Acceptance and Commitment Therapy. (2021) · cites this paper as Ellis, 2010
In the citing paper
“the use of effect sizes, such as Cohen d, is based on statistical assumptions that change is linear.”
From Measurement of Symptom Change Following Web-Based Psychotherapy: Statistical Characteristics and Analytical Methods for Measuring and Interpreting Change (2018) · cites this paper as [27]
In the citing paper
“by employing effect sizes, researchers assume that the symptom change that follows treatment is average, constant, and representative of the average change experienced by any participating individual [18,27].”
From Measurement of Symptom Change Following Web-Based Psychotherapy: Statistical Characteristics and Analytical Methods for Measuring and Interpreting Change (2018) · cites this paper as [27]
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Metadata
- Authors
- Ellis, P. D.
- Affiliations
- Hong Kong Polytechnic University (HK)
- Corresponding author
- Ellis, Paul D.
- Venue
- Cambridge University Press eBooks
- Publisher
- Cambridge University Press
- Published
- 2010-07-01
- OpenAlex
- W578092267
- Cited by
- 2,461
- References
- 332
- Citations by year
- 2026: 114 · 2025: 161 · 2024: 170 · 2023: 178 · 2022: 187 · 2021: 269 · 2020: 248 · 2019: 204 · 2018: 188 · 2017: 147 · 2016: 143 · 2015: 137 · 2014: 133 · 2013: 83 · 2012: 57
- FWCI
- 33.44
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Cambridge University Press eBooks (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Psychology Research and Bibliometrics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- effect size, practical significance, statistical power, meta-analysis, effect size reporting, interpretation of research results
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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