Nonparametric Statistics: An Introduction / Edition 1

by Jean D. Gibbons Fielden

Overview

Through the use of actual research investigations that have appeared in recent social science journals, Gibbons shows the reader the specific methodology and logical rationale for many of the best-known and most frequently used nonparametric methods that are applicable to most small and large sample sizes. The methods are organized according to the type of sample structure that produced the data to be analyzed, 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. The formal introduction of each test is followed by a data example, calculated first by hand and then by computer.

Product Details

ISBN-13:	9780803939516
Publisher:	SAGE Publications
Publication date:	10/07/1992
Series:	Quantitative Applications in the Social Sciences , #90
Edition description:	New Edition
Pages:	96
Product dimensions:	5.50(w) x 8.50(h) x 0.23(d)

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.”

Table of Contents

Introduction
Location Tests for Single and Paired Samples (Sign Test and Wilcoxon Signed Rank Test)
Confidence Interval Estimates of the Median and Median Difference for Single and Paired Samples
Location Tests and Confidence Intervals for Two Independent Samples (Mann-Whitney-Wilcoxon Test)
Location Tests and Multiple Comparisons for k>3 Mutually Independent Samples (Kruskal-Wallis Test)
Location Tests and Multiple Comparisons for k>3 Related Samples (Friedman's Test)
Summary