- Series: Quantitative Applications in the Social Sciences (Book 91)
- Paperback: 104 pages
- Publisher: SAGE Publications, Inc; 1 edition (February 25, 1993)
- Language: English
- ISBN-10: 0803946643
- ISBN-13: 978-0803946644
- Product Dimensions: 5.5 x 0.2 x 8.5 inches
- Shipping Weight: 6.7 ounces (View shipping rates and policies)
- Average Customer Review: 3 customer reviews
- Amazon Best Sellers Rank: #264,338 in Books (See Top 100 in Books)
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Nonparametric Measures of Association (Quantitative Applications in the Social Sciences) 1st Edition
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About the Author
Gibbons, a retired professor from the University of Alabama who now lives in Florida, earned her Ph.D. in statistics from Virginia Tech in 1962. She says she made the gift as an effort to enable the university to recruit the nation’s best doctoral candidates in her field, and to help the United States remain the global leader in the discipline.
“Statistics is my love,” Gibbons said. “It’s my vocation, as well as my avocation. I was so delighted when I discovered statistics … and I think that it is a field that will always be of utmost importance.”
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Top customer reviews
It is true that the kind of analyses covered in Gibbon's instructive text are no longer ubiquitous in academic work. Nevertheless, depending on the availability of good quality data and on the kind of issue an analyst wants to address, they all retain a respectable place in the social or behavioral scientist's statistical tool kit.
I have found Gibbon's material to be especially useful for instructional purposes in classes I teach in basic statistics. For example, in most applications the ubiquitous measure of association Pearson's r requires a good deal of computational effort, too much to make it easy to use for in-class board work. However, Gibbon's discussion of Pearson's r as applied to tabular data involving the association between two dichotomous variables provides a conceptually comparable alternative which is easy to use and easy for students to understand.
Gibbon's treatment of Gamma (Yule's Q) as applied to a two-by-two table provides a measure of association that is easy to compute, has an intuitively appealing interpretation (as these things go), permits use of the normal distribution for its sampling distribution, and has an easy to compute standard error. As such, Gamma is invaluable in introducing the elusive idea of statistical significance and in demonstrating construction of confidence intervals.
Some may judge the material in Gibbon's book to be dated, even obsolete. I don't share that view. Instead, it provides us with a way of learning about basic statistical concepts in a comparatively painless but generalizable way. I'm glad this book is still in print and that I can refer to it from time to time when preparing lectures or when working with students on modest research projects that may best be served by easy-to-use, easy-to-understand statistical procedures.