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Nonparametric Measures of Association (Quantitative Applications in the Social Sciences) Paperback – February 25, 1993

ISBN-13: 978-0803946644 ISBN-10: 0803946643 Edition: 1st

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Nonparametric Measures of Association (Quantitative Applications in the Social Sciences) + Nonparametric Statistics: An Introduction (Quantitative Applications in the Social Sciences) + Correlation: Parametric and Nonparametric Measures (Quantitative Applications in the Social Sciences)
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Product Details

  • 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: 8.5 x 5.4 x 0.3 inches
  • Shipping Weight: 5 ounces (View shipping rates and policies)
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon Best Sellers Rank: #573,673 in Books (See Top 100 in Books)

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2 of 2 people found the following review helpful By not a natural on October 6, 2009
Format: Paperback
It is extremely unusual to find a statistics text that is both useful and a pleasure to read. Gibbons' book is a welcome exception. The fact that the text is fun to read stems in part from the suitable simplicity of the measures discussed, namely measures of association for ranked and tabular data. In addition, however, the pleasure that comes from reading the book speaks well of the quality of the author's prose and her skill in organizing a discussion of material that most readers, especially those with limited statistical training, would find fairly technical.

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.
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