- Paperback: 188 pages
- Publisher: Cambridge University Press; 1 edition (August 16, 2010)
- Language: English
- ISBN-10: 0521142466
- ISBN-13: 978-0521142465
- Product Dimensions: 6.8 x 0.4 x 9.7 inches
- Shipping Weight: 14.9 ounces (View shipping rates and policies)
- Average Customer Review: 32 customer reviews
- Amazon Best Sellers Rank: #99,647 in Books (See Top 100 in Books)
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The Essential Guide to Effect Sizes: Statistical Power, Meta-Analysis, and the Interpretation of Research Results 1st Edition
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"Assessing the substantive significance of research is essential for both scientific progress and practical implications. This authoritative and well-written book gives relevant examples of key issues and offers practical guidelines for assessing the importance of research findings. The book concludes with clear recommendations for designing and carrying out good research and for assessing and reporting research findings."
William H. Starbuck, Professor-in-Residence, University of Oregon, and Professor Emeritus, New York University
"Paul Ellis writes with a light touch, explains well, and uses numerous practical examples. He focuses on four of the issues that are central to the statistical changes now sweeping many disciplines - effect sizes, confidence intervals, power, and meta-analysis. This is a highly readable, highly practical book. It will be invaluable to anyone who wishes to contribute to - or even just understand - the research of the future."
Geoff Cumming, Emeritus Professor, School of Psychological Science, La Trobe University, Australia
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, it explains the reporting and interpretation of effect sizes, the analysis of statistical power, and the meta-analytic pooling of effect size estimates.
Top customer reviews
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Too many stats texts (if not the majority) seem to focus on p values and alpha testing, versus computing and interpreting effect size. I've taught statistics and research design at Psychology Departments at various universities over the past 25+ years before retiring from that. If I still taught, I would definitely include this book, even at the undergraduate level, and refer to it at least when dealing with effect size.
It would be nice if it also included a section on Bayes factors.