Cited by 474Meta-analysis and systematic reviewsPsychology Research and BibliometricsStatistical Methods and Bayesian Inference

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Effect Sizes for Research

Grissom, Robert J., Kim, John J.·2012·Routledge eBooks

DOI 10.4324/9780203803233

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Abstract

Noted for its comprehensive coverage, this greatly expanded new edition now covers the use of univariate and multivariate effect sizes. Many measures and estimators are reviewed along with their application, interpretation, and limitations. Noted for its practical approach, the book features numerous examples using real data for a variety of variables and designs, to help readers apply the material to their own data. Tips on the use of SPSS, SAS, R, and S-Plus are provided. The book's broad disciplinary appeal results from its inclusion of a variety of examples from psychology, medicine, education, and other social sciences. Special attention is paid to confidence intervals, the statistical assumptions of the methods, and robust estimators of effect sizes. The extensive reference section is appreciated by all. With more than 40% new material, highlights of the new editon include: three new multivariate chapters covering effect sizes for analysis of covariance, multiple regression/correlation, and multivariate analysis of variance more learning tools in each chapter including introductions, summaries, "Tips and Pitfalls" and more conceptual and computational questions more coverage of univariate effect sizes, confidence intervals, and effect sizes for repeated measures to reflect their increased use in research more software references for calculating effect sizes and their confidence intervals including SPSS, SAS, R, and S-Plus the data used in the book are now provided on the web along with new data and suggested calculations with IBM SPSS syntax for computational practice. Effect Sizes for Research covers standardized and unstandardized differences between means, correlational measures, strength of association, and parametric and nonparametric measures for between- and within-groups data. Intended as a resource for professionals, researchers, and advanced students in a variety of fields, this book is also an excellent supplement for advanced statistics courses in psychology, education, the social sciences, business, and medicine. A prerequisite of introductory statistics through factorial analysis of variance and chi-square is recommended.

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“…Rosenthal (1991), p. 20, formula 2.21 2 d ¼ rpb corrected=½ð1 r2pb correctedÞ× ðp1 × p2Þ 1=2 Lipsey and Wilson (2001), p. 199, formula 10 3 d ¼ ð2 × rpb correctedÞ× f½ðN 2Þ=N 1=2=ð1 r2pb correctedÞ 1=2g Grissom and Kim (2012), p. 130, formula 4.17 4 d ¼ frpb corrected × ½1=ðp1 × p2Þ1=2 g=ð1…”
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Appendix B. Exemplary material for communicative tasks
(2026) · Processability approaches to language acquisition research & teaching · doi:10.1075/palart.10.app2
Dedication
(2026) · Processability approaches to language acquisition research & teaching · doi:10.1075/palart.10.ded
Introduction
(2026) · Processability approaches to language acquisition research & teaching · doi:10.1075/palart.10.c1
Acknowledgements
(2026) · Processability approaches to language acquisition research & teaching · doi:10.1075/palart.10.ack
The study
Nottbeck, E. (2026) · Processability approaches to language acquisition research & teaching · doi:10.1075/palart.10.c3
Appendix D. Example interview with advanced learner
(2026) · Processability approaches to language acquisition research & teaching · doi:10.1075/palart.10.app4
Appendix C. Example interviews with beginning learners
(2026) · Processability approaches to language acquisition research & teaching · doi:10.1075/palart.10.app3
Index
(2026) · Processability approaches to language acquisition research & teaching · doi:10.1075/palart.10.index
Turn-Taking in Second Language Acquisition
Nottbeck, E. (2026) · Processability approaches to language acquisition research & teaching · doi:10.1075/palart.10
Appendix A. Questionnaire for stimulus design
(2026) · Processability approaches to language acquisition research & teaching · doi:10.1075/palart.10.app1
Results and analysis
(2026) · Processability approaches to language acquisition research & teaching · doi:10.1075/palart.10.c4
Theory
(2026) · Processability approaches to language acquisition research & teaching · doi:10.1075/palart.10.c2
List of tables
(2026) · Processability approaches to language acquisition research & teaching · doi:10.1075/palart.10.lot
List of abbreviations
(2026) · Processability approaches to language acquisition research & teaching · doi:10.1075/palart.10.abbreviations
Prelim pages
(2026) · Processability approaches to language acquisition research & teaching · doi:10.1075/palart.10.prelim
List of figures
(2026) · Processability approaches to language acquisition research & teaching · doi:10.1075/palart.10.lof
References
(2026) · Processability approaches to language acquisition research & teaching · doi:10.1075/palart.10.refs
Table of contents
(2026) · Processability approaches to language acquisition research & teaching · doi:10.1075/palart.10.toc
Discussion
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Conclusion
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Authors: Grissom, R. J., & Kim, J. J.
Affiliations: San Francisco State University (US)
ORCID: Kim, John J.
Venue: Routledge eBooks
Publisher: Routledge
Published: 2012-04-23
DOI: 10.4324/9780203803233
OpenAlex: W2892211387
Cited by: 470
References: 0
Citations by year: 2026: 41 · 2025: 27 · 2024: 27 · 2023: 31 · 2022: 37 · 2021: 23 · 2020: 34 · 2019: 40 · 2018: 44 · 2017: 24 · 2016: 47 · 2015: 31 · 2014: 33 · 2013: 23 · 2012: 8
FWCI: 12.17
Type: book
Language: en
Open access: closed
Locations: Routledge 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 and Bayesian Inference
Keywords: effect size, confidence intervals, standardized mean difference, correlation measures, associative strength, covariance analysis, multiple linear regression, multivariate analysis of variance, repeated measures, robust estimators, statistical assumptions

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