- 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: 31 customer reviews
- Amazon Best Sellers Rank: #204,383 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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Most statistics courses gloss over power analysis, and after all, there's only five or six equations (or one piece of software) that you need to learn how to use, but it absolutely pays to have a methodological grasp of what it is you're doing when calculating a sample size or estimating power. Again, wonderful book.
It would be nice if it also included a section on Bayes factors.