Categorical and Nonparametric Data Analysis

E. Michael Nussbaum is a Professor of Educational Psychology at The University of Nevada, Las Vegas, USA. Dr. Nussbaum holds a PhD from Stanford University and an MPP from the University of California, Berkeley. He is the author of numerous research publications and serves on the editorial boards of the Journal of Educational Psychology and the Educational Psychologist.

• 1. Levels of Measurement, Probability, and the Binomial Formula2. Estimation and Hypothesis Testing3. Random Variables and Probability Distributions4. Contingency Tables: The Chi-Square Test of Independence and Associated Effect Sizes5. Contingency Tables: Special Situations6. Basic Nonparametric Tests for Ordinal Data7. Nonparametric Tests for Multiple Independent Samples8. Nonparametric Tests for Related Samples9. Linear Regression and Generalized Linear Models10. Binary Logistic Regression11. Multinomial Logistic, Ordinal, and Poisson Regression12. Loglinear Analysis13. General Estimating Equations14. Estimation Procedures15. Choosing the Best Statistical TechniqueAnswers to Odd Numbered Problems

"This is an excellent, hands-on, introduction to the core concepts of nonparametric statistics, with additional sections allowing for a gentle, in-depth study of advanced material. I was especially delighted when I found the nonparametric tests for factorial designs and repeated measurements. Widely disregarded for years, I am so happy to see these topics finally being covered in a textbook for psychology students, including cutting-edge results such as pseudorank-based methodology for unbalanced designs. The large number of applied examples and exercises fosters deep processing, comprehension of the concepts, and routine use of statistical software. It is obvious that Nussbaum and Brunner used their vast experience in teaching and research to provide a comprehensive overview on nonparametric statistics topics while keeping a realistic estimate with respect to academic formats and students' skills." Matthias Gondan-Rochon, University of Innsbruck, Austria"Highly recommended for graduate students in the social or biological sciences or education fields. Nussbaum clearly describes modern techniques for nonparametric and categorical data analysis using accessible language, numerous examples, and thought-provoking questions. The accompanying PowerPoint slides and newly added R code are invaluable."Jason E. King, Baylor College of Medicine, USA"Categorical and Nonparametric Data Analysis: Choosing the Best Statistical Technique (2nd ed.) is an exceptional book about the why and how of nonparametric data analysis. It gives a comprehensive overview of the most important statistical tests, illustrating their use with careful explanations and examples. What makes this book stand out to me, however, is the amount of care the authors took to help readers appreciate the reasoning behind the statistical tests. In particular, the book provides a lot of guidance that makes it easy to understand which test is appropriate in a given scenario and why. When there are multiple tests that could be appropriate, the book provides explicit algorithms that help to decide when and why which test to use. Summaries wrap up the key points to make sure the reader always keeps sight of the main points rather than getting lost in the details."Nikos Bosse, London School of Hygiene and Tropical Medicine, UK"This is a timely, up-to-date introduction to essential social science research tools that makes the complex accessible, and provides budding researchers with the tools they need—from the simple to the state of the art—in a consistent framework." Brendan Halpin, University of Limerick, Ireland"The book uses real examples, step-by-step explanations and straightforward language to help the reader not only understand the statistical methods available for categorical and non-parametric data analysis, but also how to implement them in practice… Also important, the book includes comprehensive explanations about computational and estimation methods often neglected in other texts."Irini Moustaki, London School of Economics, UK