Categorical and Nonparametric Data Analysis

Choosing the Best Statistical Technique

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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: Unique coverage of categorical and nonparametric statistics better prepares readers to select the best technique for their particular research project; however, 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 and 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. Emphasis on selecting tests that maximize power helps readers avoid "marginally" significant results. Website (www.routledge.com/9781138787827) features datasets for the book's examples and problems, and for the instructor, PowerPoint slides, sample syllabi, answers to the even-numbered problems, and Excel data sets for lecture purposes. 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.

Weniger Details

"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

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