Categorical and Nonparametric Data Analysis ― Choosing the Best Statistical Technique

Featuring in-depth coverage of categorical and nonparametric statistics, this book provides a conceptual framework for choosing the most appropriate type of test in various research scenarios. Class tested at the University of Nevada, the book‘s clear explanations of the underlying assumptions, computer simulations, and Exploring the Concept boxes help reduce reader anxiety. Problems inspired by actual studies provide meaningful illustrations of the techniques. The underlying assumptions of each test and the factors that impact validity and statistical power are reviewed so readers can explain their assumptions and how tests work in future publications. Numerous examples from psychology, education, and other social sciences demonstrate varied applications of the material. Basic statistics and probability are reviewed for those who need a refresher.Mathematical derivations are placed in optional appendices for those interested in this detailed coverage.

Highlights include:

Unique coverage of categorical and nonparametric statistics better prepares readers to select the best technique for their particular research project but some chapters can be omitted entirely if preferred.

Step by step examples of each test help readers see how the material is applied in a variety of disciplines. Although the book can be used with any program, examples of how to use the tests in SPSS & EXCEL foster conceptual understanding.

Exploring the concept boxes integrated throughout prompt students to review key material and draw links between the concepts to deepen understanding.

Problems in each chapter help readers test their understanding of the material.

Emphasizes selecting tests that maximize power to help readers avoid "marginally" significant results.

Website featuring datasets for the book‘s examples and problems, and for the instructor Power Points, author‘s course syllabus, and answers to the even numbered problems.

Chapters 1-3 cover basic concepts in probability, especially the binomial formula followed by two chapters that address the analysis of contingency tables. Chapters 6-8 address nonparametric tests involving at least one ordinal variable, including testing for nonparametric interaction effects, a topic omitted from other texts. The book then turns to situations that involve one metric variable. Chapter 9 reviews concepts that are foundational to CDA, including linear regression and generalized linear models. Chapters 10-11 cover logistic, ordinal, and Poisson regression. Chapters 12 and 13 review loglinear models and the General Estimating Equations (GEE) methodology for measuring outcomes from multiple time points. For a deeper understanding of how various CDA techniques work, chapter 14 covers estimation methods, such as Newton-Raphson and Fisher scoring. The book concludes with a summary of factors that need to be considered when choosing the best statistical technique.

Intended for individual or combined graduate or advanced undergraduate courses in categorical and nonparametric data analysis, cross-classified data analysis, advanced statistics and/or quantitative techniques taught in psychology, education, human development, sociology, political science, and other social and life sciences, the book also appeals to researchers in these disciplines. The nonparametric chapters can be deleted if preferred. Prerequisites include knowledge of t-tests and ANOVA.

E. Michael Nussbaum is a professor of educational psychology at the University of Nevada, Las Vegas.