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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Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Citations
Cites
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Bias in meta-analysis detected by a simple, graphical test
Egger, M., Smith, G. D., Schneider, M., & Minder, C. (1997) · BMJ · doi:10.1136/bmj.315.7109.629 · Cited by 56,722
Statistical Methods for Rates and Proportions
Fleiss, J. L., Levin, B., & Paik, M. C. (2003) · Wiley Series in Probability and Statistics · doi:10.1002/0471445428 · Cited by 18,646
Hartley, J. (2004) · Essential Guide to Qualitative Methods in Organizational Research · doi:10.4135/9781446280119.n26 · Cited by 15,461
(1963) · Medical Entomology and Zoology · Cited by 15,085
Psychometric theory (2nd ed.).
Sitgreaves, R. (1979) · Contemporary Psychology · doi:10.1037/018882 · Cited by 14,384
Estimating Nonresponse Bias in Mail Surveys
Armstrong, J. S., & Overton, T. S. (1977) · Journal of Marketing Research · doi:10.2307/3150783 · Cited by 12,222
Statistical Methods for Research Workers
Fisher, R. A. (1945) · American Journal of Clinical Pathology · doi:10.1093/ajcp/15.11.552b · Cited by 10,883
Estimating nonresponse bias in mail surveys
Armstrong, J. S., & Overton, T. S. (1977) · Journal of Marketing Research · doi:10.1177/002224377701400320 · Cited by 10,390
Wasserman, S. S., Hedges, L. V., & Olkin, I. (1988) · Journal of Educational Statistics · doi:10.2307/1164953 · Cited by 10,377
Cited by
Sorted by citations
Lakens, D. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00863 · Cited by 10,303
Effect size estimates: Current use, calculations, and interpretation.
Fritz, C. O., Morris, P. E., & Richler, J. J. (2012) · Journal of Experimental Psychology: General · doi:10.1037/a0024338 · Cited by 5,199
The New Statistics: Why and How
Cumming, G. (2014) · Psychological Science · doi:10.1177/0956797613504966 · Cited by 3,200
Effect size guidelines for individual differences researchers
Gignac, G. E., & Szodorai, E. T. (2016) · Personality and Individual Differences · doi:10.1016/j.paid.2016.06.069 · Cited by 2,860
Cross-national epidemiology of DSM-IV major depressive episode
Bromet, E., Andrade, L. H., Hwang, I., Sampson, N. A., Alonso, J., de Girolamo, G., de Graaf, R., Demyttenaere, K., Hu, C., Iwata, N., et al. (2011) · BMC Medicine · doi:10.1186/1741-7015-9-90 · Cited by 2,221
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
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