Beyond Significance Testing: Statistics Reform in the Behavioral Sciences, Second Edition Second Edition Edition, Kindle Edition

by Rex B. Kline (Author)

3 customer reviews

ISBN-13: 978-1433812781

ISBN-10: 1433812789

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Length: 328 pages
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This accessibly written book reviews the controversy about significance testing, which has now crossed various disciplines as diverse as psychology, ecology, commerce, education, and biology, among others. It also introduces readers to alternative methods, especially effect size estimation (at both the group and case levels) and interval estimation (confidence intervals) in comparative studies. Basics of bootstrapping and Bayesian estimation are also considered. Research examples from substance abuse, education, learning, and other areas illustrate how to apply these methods.

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Editorial Reviews

About the Author

Rex B. Kline is an associate professor of psychology at Concordia University in Montreal, Canada. Since earning a PhD in psychology, his areas of research and writing have included the psychometric evaluation of cognitive abilities, child clinical assessment, structural equation modelling, and usability engineering in computer science.

Product details

File Size: 22817 KB
Print Length: 328 pages
Publisher: American Psychological Association; Second Edition edition (March 31, 2013)
Publication Date: October 27, 2014
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B00OY844NI
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Do you really need the null hypothesis?

ByFreddy A. Paniaguaon June 25, 2017

Format: HardcoverVerified Purchase

This book will teach you why you should avoid an emphasis on the rejection of null hypothesis if you want to make
significant scientific contributions. Why do you need to formulate a null hypothesis if you know statistical tricks to reject it?
This is the central point in an article I am now writing for a peer-reviewed journal, and Dr. Kline's book is a very helpful text in my goal
to show that the null hypothesis can, indeed, prevents scientific advances and particularly when null results are important but rejected
by the peer-review process that only agrees with papers rejecting the null hypothesis. If you conduct an experiment and your "experimental hypothesis" claims that the Method A will be a good method to teach English to immigrant children from Latin America Countries, your null hypothesis claims the opposite of your expected results (i.e., Method A is not a good method to teach English ). If your results do not support your experimental hypothesis, you would have trouble finding a peer-reviewed journal to publish your null results. However, your results are still very important for the school district in your community because they inform that this district does not need to spend lots of money producing a method for teaching English that does not actually work or is not effective in achieving the goal of this district, namely, teaching English to immigrant children from Latin American countries. In this example, the administration of that school district will be very happy to know that your results did not reject the null hypothesis!

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Much needed corrective

ByK. Eameson May 18, 2013

Format: HardcoverVerified Purchase

Problems with significance testing have been noted for years, yet elementary college textbooks and many, many social science publications rely heavily on it. Dr. Kline's offers an excellent overview of the problems with NHST and how other measures can offer more accurate insight into data.

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Could have been an important contribution if it were only written better

ByDonon January 3, 2014

Format: Hardcover

Beyond Significance Testing raises important points about how data are often misinterpreted and how valuable resources are squandered in laboratory research and clinical trials because of inappropriate use of statistical analyses. This is an urgent issue with wide application and interest. Because BST is a work of worthwhile and original ideas, I am perplexed and disappointed by the author’s decision to cloak those ideas in turgid, textbook-like exposition. It really is a slog to get through this book.

The very idea that there are issues in statistics will be startling to many, and could be a powerful hook to draw in the potentially large audience of readers who use statistics in their profession. BST seems to be addressed to the author’s statistician colleagues rather than to researchers, clinicians, practitioners, policy-makers and students in many fields who could profit greatly from understanding the issues BST raises. This book, as it stands, is more likely to be a turn-off than a turn-on and that’s a shame.

Clearly, the field of statistics and its methods of analysis need to change. I hope for the next edition of this book the author will give some thought to how to increase the impact of his important and provocative ideas by presenting them in a way that can be more easily absorbed by those who can benefit most from his wisdom. In his own arresting formulation, "If psychologists are so smart, why are they so confused? Why is statistics carried out like compulsive handwashing?" (p. 95). Could it be that they, along with researchers in many other fields, need a supple, compelling, accessible, lucid, user-friendly account of statistics that can help them to think critically about, understand the meaning behind, and apply, statistical methods? It has been my experience and general impression (in over 45 years of doing biomedical research) that most statisticians are more interested in demonstrating their sagacity than in taking the time to teach effectively and well. This is as true of their writing as it is of their publications. Someone clearly needs to do better.

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