Free Ebook The Essential Guide to Effect Sizes: Statistical Power, Meta-Analysis, and the Interpretation of Research Results
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The Essential Guide to Effect Sizes: Statistical Power, Meta-Analysis, and the Interpretation of Research Results
Free Ebook The Essential Guide to Effect Sizes: Statistical Power, Meta-Analysis, and the Interpretation of Research Results
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Review
"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
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Book Description
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.
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Product details
Paperback: 192 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:
4.5 out of 5 stars
32 customer reviews
Amazon Best Sellers Rank:
#401,964 in Books (See Top 100 in Books)
Exceptionally lucid overview of power analysis, effect sizes, sample size calculations and related topics. Along the way, the book also provides a good overview of the null hypothesis significance testing controversy, the problems with familywise alpha corrections like Bonferroni, publication bias, the almost-universal misinterpretation of p-values and other methodological issues. Heartily recommended as a practical guide for researchers and statisticians, but also for readers with an interest in the foundations of statistics or the sociology of science.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.
I am such a nerd; I actually emailed friends to tell them I was excited that this book was on my summer reading list. But it is, and I'm enjoying it very much. This is the clearest presentation of three very complex and important subjects: Effect size, power analysis, and meta analysis. This treatment is not comprehensive or detailed to the degree that other texts are; if you want an introduction that introduces these topics, this is it. If you want the background math and theory in detail, consult the references listed at the back.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.
An excellent primer or review of statistics for studies. The writer gets to the point and avoids the basics of stats, instead narrowing in on the problem of low power studies causing type II errors and navigating the reader on how to balance between their expected effect size, power, alpha, measurement error and the availability bias of published results.
I found this book to be an excellent primr that helped me grasp the concept of effect size. Prior reviewers of this book have done an excellent job documenting the content so I need not re-type a content description. I will add that Mr. Ellis done an exceptional job of making the concepts accessible to the lay reader interested in meta-analysis and effect size. His examples and explanations help grasp the concepts and appreciate the nuances of the parameters he is describing. I would recommend this book as a great place to start to understand these concepts.
This is a very good book! A bit more oriented toward academic/publishing perspective but full of practical insights that business users / practitioners such as myself can truly benefit from. With the proliferation of "a/b testing" with online world and commonplace, yet important questions on sample sizes, proper way of setting up experiments, all the way to data mining (pitfalls of opportunistic "finding something") - this book, to me, gave lot of food for thought.
In healthcare, effect size understanding is necessary during informing decision analysis (Lang & Secic). Well written, easy to understand with a basic knowledge of statistics.
Is a useful reference.It would be nice if it also included a section on Bayes factors.
This book makes clear what Effect Sizes and Power is about in hypothesis testing. Any student who has had Stat 101 should find this a handy guide to interpretation. I recommend it for anyone from the undergraduate level on up who needs additional material to understand these concepts.
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