Categorical and Nonparametric Data Analysis

Now in its second edition, this book provides a comprehensive overview of categorical and nonparametric statistics, offering a conceptual framework for choosing the most appropriate test in various scenarios. Basic statistics and probability are reviewed for those needing a refresher.

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Now in its second edition, this book provides a focused, comprehensive overview of both categorical and nonparametric statistics, offering a conceptual framework for choosing the most appropriate test in various scenarios. The book’s clear explanations and Exploring the Concept boxes help reduce reader anxiety. Problems inspired by actual studies provide meaningful illustrations of these techniques. Basic statistics and probability are reviewed for those needing a refresher with mathematical derivations placed in optional appendices.

Highlights include the following:
• Three chapters co-authored with Edgar Brunner address modern nonparametric techniques, along with accompanying R code.
• Unique coverage of both categorical and nonparametric statistics better prepares readers to select the best technique for particular research projects.
• Designed to be used with most statistical packages, clear examples of how to use the tests in SPSS, R, and Excel foster conceptual understanding.
• Exploring the Concept boxes integrated throughout prompt students to draw links between the concepts to deepen understanding.
• Fully developed Instructor and Student Resources featuring datasets for the book's problems and a guide to R, and for the instructor PowerPoints, author's syllabus, and answers to even-numbered problems.

Intended for graduate or advanced undergraduate courses in categorical and nonparametric statistics taught in psychology, education, human development, sociology, political science, and other social and life sciences.