© 2015 – Routledge
506 pages | 57 B/W Illus.
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 the following:
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.
"Highly recommended for graduate students in the social or biological sciences, or education, Nussbaum clearly describes modern techniques for nonparametric and categorical data analysis using accessible language, numerous examples, and thought-provoking questions. The accompanying PowerPoint slides are invaluable." – Jason E. King, Baylor College of Medicine, USA
"This book is unique in its coverage of categorical and nonparametric techniques, step-by-step examples, presentation of learning aids, and of internet resources. It is an important textbook on these topics as well as a useful reference book." – Joanne Peng, Indiana University, USA
"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, IE
"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
"The writing style makes it much easier for readers to appreciate the various nonparametric methods. Different from most other nonparametric books that use heavy mathematical notations, this book has simplified explanations followed by well worked out examples. The content and writing style are so inviting." – Haiyan Wang, Kansas State University, USA
"This book is well-written and at the right level. … The author hit the right balance between technical detail and comprehensibility for this audience. … Many texts written by statisticians do not make the grade in this area." – David Rindskopf, CUNY Graduate Center, USA
"The author has struck a nice middle ground here between showing the actual usage of the methods and giving theoretical underpinnings and background for each method. …. The level of the book is the major strength … [it is] a practical intermediate textbook. … Advanced undergraduate statistics majors could also use this book. … I would strongly consider it for my graduate level course in categorical data analysis." - Randall H. Rieger, West Chester University, USA
"[It is] a thorough intro to the basics of categorical and nonparametric data analysis. … Overall … well written and clear to … a wide range of students. … SPSS screenshots are a nice touch." - Sara Tomek, University of Alabama, USA
1. Levels of Measurement, Probability, and the Binomial Formula 2. Estimation and Hypothesis Testing 3. Random Variables and Probability Distributions 4. Contingency Tables: The Chi-Square Test and Associated Effect Sizes 5. Contingency Tables: Special Situations 6. Basic Nonparametric Tests for Ordinal Data 7. Nonparametric Tests for Multiple or Related Samples 8. Advanced Rank Tests (for Interactions and Robust ANOVA) 9. Linear Regression and Generalized Linear Models 10. Binary Logistic Regression 11. Multinomial Logistic, Ordinal, & Poisson Regression 12. Loglinear Analysis 13. General Estimating Equations 14. Estimation Procedures 15. Choosing the Best Statistical Technique. Answers to Odd Numbered Problems