A practical and understandable approach to nonparametric statistics for researchers across diverse areas of study As the importance of nonparametric methods in modern statistics continues to grow, these techniques are being increasingly applied to experimental designs across various fields of study. However, researchers are not always properly equipped with the knowledge to correctly apply these methods. Nonparametric Statistics for Non-Statisticians: A Step-by-Step Approach fills a void in the current literature by addressing nonparametric statistics in a manner that is easily accessible for readers with a background in the social, behavioral, biological, and physical sciences.
Each chapter follows the same comprehensive format, beginning with a general introduction to the particular topic and a list of main learning objectives. A nonparametric procedure is then presented and accompanied by context-based examples that are outlined in a step-by-step fashion. Next, SPSSŪ screen captures are used to demonstrate how to perform and recognize the steps in the various procedures. Finally, the authors identify and briefly describe actual examples of corresponding nonparametric tests from diverse fields.
Using this organized structure, the book outlines essential skills for the application of nonparametric statistical methods, including how to:
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Test data for normality and randomness
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Use the Wilcoxon signed rank test to compare two related samples
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Apply the Mann-Whitney U test to compare two unrelated samples
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Compare more than two related samples using the Friedman test
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Employ the Kruskal-Wallis H test to compare more than two unrelated samples
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Compare variables of ordinal or dichotomous scales
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Test for nominal scale data
A detailed appendix provides guidance on inputting and analyzing the presented data using SPSSŪ, and supplemental tables of critical values are provided. In addition, the book's FTP site houses supplemental data sets and solutions for further practice.
Extensively classroom tested, Nonparametric Statistics for Non-Statisticians is an ideal book for courses on nonparametric statistics at the upper-undergraduate and graduate levels. It is also an excellent reference for professionals and researchers in the social, behavioral, and health sciences who seek a review of nonparametric methods and relevant applications.
Gregory W. Corder is adjunct instructor of undergraduate physics and general science for the College of Science and Mathematics at James Madison University and adjunct instructor of graduate educational statistics for the School of Education and Human Development in the College of Arts and Sciences at Shenandoah University.
Dale I. Foreman is associate professor in the School of Education and Human Development in the College of Arts and Sciences at Shenandoah University, where his teaching is focused on research, measurement, and statistics.
