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Understanding The New Statistics: Effect Sizes, Confidence Intervals, and Meta-Analysis (Multivariate Applications Series), by Geoff Cummi
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This is the first book to introduce the new statistics - effect sizes, confidence intervals, and meta-analysis - in an accessible way. It is chock full of practical examples and tips on how to analyze and report research results using these techniques. The book is invaluable to readers interested in meeting the new APA Publication Manual guidelines by adopting the new statistics - which are more informative than null hypothesis significance testing, and becoming widely used in many disciplines.
Accompanying the book is the Exploratory Software for Confidence Intervals (ESCI) package, free software that runs under Excel and is accessible at www.thenewstatistics.com. The book’s exercises use ESCI's simulations, which are highly visual and interactive, to engage users and encourage exploration. Working with the simulations strengthens understanding of key statistical ideas. There are also many examples, and detailed guidance to show readers how to analyze their own data using the new statistics, and practical strategies for interpreting the results. A particular strength of the book is its explanation of meta-analysis, using simple diagrams and examples. Understanding meta-analysis is increasingly important, even at undergraduate levels, because medicine, psychology and many other disciplines now use meta-analysis to assemble the evidence needed for evidence-based practice.
The book’s pedagogical program, built on cognitive science principles, reinforces learning:
- Boxes provide "evidence-based" advice on the most effective statistical techniques.
- Numerous examples reinforce learning, and show that many disciplines are using the new statistics.
- Graphs are tied in with ESCI to make important concepts vividly clear and memorable.
- Opening overviews and end of chapter take-home messages summarize key points.
- Exercises encourage exploration, deep understanding, and practical applications.
This highly accessible book is intended as the core text for any course that emphasizes the new statistics, or as a supplementary text for graduate and/or advanced undergraduate courses in statistics and research methods in departments of psychology, education, human development , nursing, and natural, social, and life sciences. Researchers and practitioners interested in understanding the new statistics, and future published research, will also appreciate this book. A basic familiarity with introductory statistics is assumed.
- Sales Rank: #103311 in Books
- Published on: 2011-07-16
- Released on: 2011-08-17
- Original language: English
- Number of items: 1
- Dimensions: 9.02" h x 1.21" w x 5.98" l, 1.60 pounds
- Binding: Paperback
- 536 pages
Review
"This book is so skilfully written and suitably illustrated with graphical displays and statistical formulae that, even individuals with only a basic knowledge of statistics, should be able to follow the author's message. ... [It is] well designed as an introductory text to confidence intervals, effect sizes and meta-analyses for use by teachers and students of statistics. ... I would recommend this book to anyone interested in developing a better understanding and appreciation of these estimation techniques." - Patricia Wheaton, University of Adelaide, Australia, in the Australasian Physical & Engineering Sciences in Medicine
"[Understanding The New Statistics] provides a clear description of the difficulties people have in interpreting p- values, as well as an accessible description of how to combine evidence from multiple studies using meta-analysis. It will be valuable to researchers in the behavioural and social sciences, who wish to move beyond the superficial level in the statistical interpretation of their results." - David J. Hand, Imperial College, London, UK, in International Statistical Review
"The exceptional value of this book is in the jargon-free and accessible way in which Cumming defines significance testing and its pitfalls and introduces the new statistics. ... The overall achievement of [Understanding The New Statistics] is the convincing argument for why the new techniques should be preferred over traditional methods. A valuable contribution ― even the most novice of reader could not fail in understanding the new statistics." - Victoria R. Kroll, Nottingham Trent University, UK, in The Psychologist
"This is a book intended to teach. There are exercises at the end of every chapter, exercise commentaries at the end of the book, boxed asides to expand on import points or exemplary articles, helpful rules of thumb for interpretation and excellent figures. ... If you are an undergraduate, postgraduate or researcher in the social sciences ... this book will enlighten you." - Eoin O'Connell in Significance
"In this book Cumming does exactly what's needed -- he clearly explains significance-testing and confidence-interval techniques, and in the process leaves no doubt as to which should be preferred. I hope that this book will be read by anyone who plans to do research of any kind." - Geoffrey Loftus, University of Washington, Seattle, USA
"It is about time that the "new statistics" get the place they deserve in statistics education, to further true understanding instead of following statistical rituals. Geoff Cumming’s excellent book is a milestone towards reaching that goal." - Peter Sedlmeier, Chemnitz University of Technology, Germany
"Geoff Cumming is a preeminent author in statistics reform and his new book is an important work in this area. Written in a clear, accessible way, Cumming covers essential topics in data analysis, including effect size estimation, confidence intervals, and meta-analysis. Readers in the social and behavioral science will learn new ways to look at their own results and avoid common traps of traditional significance testing." - Rex Kline, Concordia University, Canada
"Cumming makes strong arguments for confidence intervals and meta-analysis as better ways of representing and thinking about research. His graphical, interactive approach makes the "new statistics" highly accessible and remarkably impactful on our statistical understanding." - Robin K. Henson, University of North Texas, USA
"Cumming will be the ‘breakthrough’ text that finally shows how to analyze and interpret data for many common statistical designs without having to rely on significance testing. ... The material ... [is] easy even for undergraduates ... an unusual characteristic for a statistics text." - Joseph Rossi, University of Rhode Island, USA
"Currently, a paradigm shift from the flawed null hypothesis testing model to an effect size and confidence interval approach is taking place. Unfortunately, students taking lower level courses have not been exposed. This book repairs that omission. A very timely and important book!" - Wayne F. Velicer, University of Rhode Island, USA
"This is as clear a presentation of new approaches for evaluating hypotheses and presenting statistical evidence as one could want. Cumming brings researchers into the new age of statistical discourse." - Patrick E. Shrout, New York University, USA
"The writing style is breezy and informal. … It is a unique book … and it meets an important need. The quality of scholarship is excellent; the author is probably the top world expert on this subject. …The [accompanying] software … allows people to ‘run their own studies’ and see … just how unstable research findings are across studies when sample size are in the typical small range." - Frank Schmidt, University of Iowa, USA
"I would recommend the Cumming book to students who want to be on the ‘cutting edge’ of how to write-up statistics. ... Strengths include the author’s passion and long history of research into the most effective methods of teaching statistics. ... The quality of the scholarship is excellent. ... I would definitely purchase the book … and recommend it to colleagues." - Alan Reifman, Texas Tech University, USA
"There is a real need for a statistics book that makes the ‘new statistics’ understandable and can be used as a teaching framework for undergraduates, or graduate students." - Dennis Doverspike, University of Akron, USA
"[This book will pave] the way for others to demonstrate further the potency of effect sizes and confidence intervals for both univariate and multivariate inferential procedures." - Lisa Harlow, University of Rhode Island, USA
About the Author
Geoff Cumming is an Emeritus Professor in the School of Psychological Science at La Trobe University in Melbourne, Australia. He has taught statistics for more than 40 years at every level from introductory to advanced, and at undergraduate and graduate levels. He has published numerous journal articles about teaching statistics. His research focuses on statistical cognition―the study of how students and researchers understand statistical concepts and how they interpret different ways to present results. He has also studied how statistical reform has been advocated and thwarted. He is frequently invited to give his highly successful workshop on the new statistics using his ESCI software at the American Psychological Association’s Annual Convention. A Rhodes Scholar, he received his Doctorate degree in experimental psychology from Oxford University.
Most helpful customer reviews
40 of 42 people found the following review helpful.
Excellent
By Dimitri Shvorob
This is how an Apple aficionado must feel about his shiny new iPad. Looking for any nit-picks to restrain my enthusiasm, I could perhaps question the title - a reader wondering about "new statistics" will discover that it is "old statistics", only correctly applied and interpreted - but the author builds a convincing case for his designation, documenting pervasive not-quite-understanding of fundamental frequentist statistical concepts by applied researchers, and effectively advocates more robust and informative ways of analysis and presentation of statistical evidence. At the book's core is a look at the logic, and the shortcomings, of the null-hypothesis significance testing (NHST) and concepts of statistical significance and power, confidence intervals, p-values, etc., with almost all of the action unfolding in the sandbox of testing a simple hypothesis about a population mean. The book should be required reading for any statistician, especially a teaching one, and if it does reach a wide audience - making this more likely are Geoff Cumming's terrific writing (accompanied by cute illustrations by Gideon Polya), the companion website and Excel-based software, and the book's moderate price - the effect can be as far-reaching and benign as iPad's has been. Kudos to the author.
PS. In the "if you like this, you may also like that" vein, there are the book by Grissom and Kim, and the volume co-edited by Lisa Harlow. The latter is mentioned in "Doing Bayesian data analysis" by John Kruschke, which contrasts NHST with the Bayesian approach, and is my next recommendation. Finally, there is the excellent "Data analysis using regression and multilevel/hierarchical models" by Andrew Gelman and Jennifer Hill - the title makes the book sound less general than it really is; do get it even if you are not interested in HLM (besides, you just might change your mind!) - which pays considerable attention to issues of significance and power, and makes extensive use of simulations, echoing those in "Replication" chapter.
24 of 25 people found the following review helpful.
Best overview of effect size and confidence intervals you could find
By David Erceg-Hurn
This is the best text about effect size and confidence intervals that I have come across. Cumming writes in a very engaging, conversational style. He does an excellent job of clearly illustrating what interval estimation involves, why it is important, how to compute effect sizes and confidence intervals, and how to interpret them. The book is second to none when it comes to giving readers a very good conceptual understanding of WHAT confidence intervals are, unlike most articles and books on confidence intervals which just teach people how to use formulas without providing a good grounding in where the formulas come from. Cumming makes frequent use of pictures to illustrate key concepts, and also encourages you at certain times to stop and use his `ESCI' software that comes with the book. The software is Excel based and very easy to use and I found completing the ESCI exercises alongside reading the chapters substantially enhanced my understanding of confidence intervals. The book also contains a good introduction to meta-analysis. In terms of limitations / criticisms, the only thing I would mention is that if you are someone seeking a book with detailed coverage of effect sizes for higher order designs you may be better off looking at the 2nd edition of Grissom and Kim's Effect Sizes for Research book (effect size for complex designs is covered in the New Statistics, just not in great depth). In summary, the New Statistics provides an outstanding introduction to interval estimation and meta-analysis. Researchers new to these topics as well as old hands will find much to like here. I highly recommend it.
16 of 17 people found the following review helpful.
Important for psychology researchers
By Neil R. Thomason
The new edition of the APA's Publication Manual substantially changed the statistics that social scientists must report. Effect sizes, confidence intervals, and meta-analyses are now central; p-values are de-emphasized. This long-overdue reform means that many social scientists and their students will need to learn new ways to understand experimental results.
Cumming's "Understanding the New Statistics -- Effect Sizes, Confidence Intervals, and Meta-Analysis" gives those scientists and their students what they need. Remarkably clearly written, it shows not only how to calculate the new statistics but what they mean and why they matter. It is an excellent, easily-accessible introduction to otherwise intimidating material. The accompanying software is first-rate, and works on both the Windows and Mac versions of Excel.
For one of the reasons the APA has reformed its statistics standards, see Cumming's You Tube video:[...] . It vividly shows why the p-value of one experiment generally doesn't predict the p-value of its careful replication.
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