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 to d = 0.295, which are conventionally labelled small effects.
“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]
The small sample size may have inflated the reported effect sizes used in the power analysis.
“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
The use of effect sizes, such as Cohen d, is based on statistical assumptions that change is linear.
“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]
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.
“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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“Nevertheless, what seems to be lacking is also a practical interpretation of effect size (Ellis 2010) that would be useful for clinicians and parents.”
Background
- 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
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