"Nonparametric Statistics is a short and sweet introduction to the five most familiar nonparametric location tests and associated confidence intervals and multiple comparisons. . . . This book is extremely limited in coverage, but none the worse for that. You won′t find anything on the Fisher Exact Test, or measures of association for the cases covered. What you will find is good, accurate, comprehensible accounts of location tests for two or more treatments. . . . The book is well-written throughout, with fewer than expected mistakes. . . . All in all, a good basic introduction to nonparametric tests, with few frills, as you would expect. The computer package comparisons sound very useful warning bells and are a welcome frill."
--British Journal of Mathematical and Statistical Psychology
Using actual research investigations that have appeared in recent social science journals, Jean Dickinson Gibbons shows you the specific methodology and logical rationale for many of the best-known and most frequently used nonparametric methods (that are applicable for most small and large sample sizes). The methods are organized according to the type of sample structure that produced the data, and the inference types covered are limited to location tests, such as the sign test, the Mann-Whitney-Wilcoxon test, the Kruskal-Wallis test, and Friedman′s test. Formal introductions to each test are followed by a data example, calculated first by hand and then by computer.
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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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