Beyond Significance Testing: Reforming Data Analysis Methods in Behavioral Research (Hardcover)

by Rex B. Kline (Author)

Editorial Reviews

Product Description
Practices of data analysis in psychology and related disciplines are changing. This is evident in the longstanding controversy about statistical tests in the behavioral sciences and the increasing number of journals requiring effect size information. Beyond Significance Testing offers integrative and clear presentations about the limitations of statistical tests and reviews alternative methods of data analysis, such as effect size estimation (at both the group and case levels) and interval estimation (i.e., confidence intervals). Written in a clear and accessible style, the book is intended for applied researchers and students who may not have strong quantitative backgrounds. Readers will learn how to measure effect size on continuous or dichotomous outcomes in comparative studies with independent or dependent samples. They will also learn how to calculate and correctly interpret confidence intervals for effect sizes. Numerous research examples from a wide range of areas illustrate the application of these principles and how to estimate substantive significance instead of just statistical significance.

Product Details

• Hardcover: 325 pages
• Publisher: American Psychological Association (APA); 1 edition (April 2004)
• Language: English
• ISBN-10: 1591471184
• ISBN-13: 978-1591471189
• Product Dimensions: 10.1 x 7.2 x 1.2 inches
• Shipping Weight: 2 pounds (View shipping rates and policies)
• Average Customer Review: 4.0 out of 5 stars See all reviews (2 customer reviews)
• Amazon.com Sales Rank: #714,804 in Books (See Bestsellers in Books)

Customer Reviews

Most Helpful Customer Reviews

16 of 17 people found the following review helpful:

5.0 out of 5 stars The days of significance testing are numbered..., January 11, 2005

By	Ivan "just your average degenerate psychologist" (Newark, DE) - See all my reviews

First off, this book is written in plain English for those of us with less than stellar mathematical backgrounds. Also, the level at which it is written suggests it would make a great masters level stats course supplimental reading choice. The author walks us through the history of statistical testing and shows us where social scientists have gone wrong. Tests of statistical significance are often considered the most important indication of publication worthiness, or of experimental success whereas they really are not. For instance, what good is finding a significant difference between two groups (or in regression, finding an independant varaible significantly predicting a dependant varaible) if the effect size is so small as to be trivial? Kline discusses this in terms of "statistical significance vs. practical significance". This is one example of the very logical arguments he presents regarding aspects of behavioral statistics that most of us probably take for granted.
He argues (convincingly) that there will come a day when tests of statistical significance are no longer the be all end all of our work. Effect sizes, confidence intervals etc... will be the kings. For those of us just getting into the social scientific world, these new techniques will be vaguely familiar, but generally unexplored (remember when your professor talked about confidence intervals for 15 minutes that day you learned about t-tests?). For older behavioral scientists, (at least those that I know) the suggestions made will appear to be heresy (don't test for significance? Preposterous!). Both groups can benefit from reading the compelling arguments that Kline makes (and he is not alone) regarding the discontinuation of statistical testing, or rather, the limited use of it. This new wave in our experimental lives will take us from the barren island of null hypothesis testing to set us down in an ancient, less explored wonderland of confidence intervals, effect sizes, and more. This will be our new statistical paradise. (Yes, it was corny, but I write technical papers all day so I need the outlet).
In closing, a great read, if only to think about what will happen and where we have gone wrong in the past. I have a love of the history of statistics and its evolution, and found this book to link that aspect of my interests with the practical information about how the field is changing and how to begin approaching an acceptance of those changes. The book is reccomended for those with at least an elementary stats background. I would suggest you have taken one masters level stats course (know about p values, R-squared, basic regression, ANOVA, the null hypothesis etc...). While someone with an undergrad behavioral stats course might be familar with these terms, I find it less likely that they have puzzled about such wondrously dull topics as "what does the t-test mean" or "how do between and within subjects variance relate to one another". Such prior puzzling would help the reader fully grasp concepts Dr. Kline presents the first time through.

1 of 2 people found the following review helpful:

3.0 out of 5 stars Some great points, but does not "grab" the reader, January 3, 2008

By	D. Collingridge (Utah) - See all my reviews (REAL NAME)

Whenever I launch into a new book, be it fiction or scholarly non-fiction, I want the author to grab my attention. I was initially very excited about this book given the recent debate surrounding the over-reliance on significance testing and p-values in the social sciences.

I expected Kline to start out with some exciting prose about how his book would help set the social sciences on a new path towards breaking free from excessive significance testing. Well, I am sorry to say that he did not take that approach. The book rather reads like a statistics textbook - an informative textbook, but a textbook nonetheless. This will make the reading "tough going" for scholars who already understand many of the issues. The continual presentation of formula and descriptions of statistical variables etc. will challenge your ability to stay awake.

I am also concerned about incorrect formula. For example, the odds ratio (OR) formula on page 157 is way off the mark. He is missing at least three division signs. I corrected it in my book, but this error left me questioning whether other formula, especially those I am less familiar with, are incorrect as well. For instance, I used one of his formula for standard error to calculate effect sizes for dichotomous data. The process was not so easy to follow, so naturally I was skeptical of my results, so I checked them against SPSS (sats package) - they differed substantially. I double checked my math but could not reconcile the differences. Makes me wonder.

Also, I wish he would have not used the APA referencing style. It makes reading difficult - with all the last names and dates you have to skip ahead to find the end of the sentence. This is a book, not an article being prepared for peer review in a scientific journal.

This book makes a contribution, a contribution that could have taken on a different approach. Looking over the last half of the book which I still have to read, my focus and ability to stay awake will be challenged.

A bit overpriced at $40.00 (I think all the copies are hardback). Probably worth half that amount.