E. Michael Nussbaum – författare

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.

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.

In an era of rampant misinformation, conspiracy theories, and political polarization, this book confronts the paradox between rational models of human cognition and seemingly irrational behavior. Drawing on cutting-edge research in psychology and other social sciences, it explores practical tools such as fostering digital literacy and cultivating 'wise deliberative spaces' grounded in argument, perspective taking, and moral inquiry. Written for graduate students, researchers, and general readers, E. Michael Nussbaum provides an accessible introduction to contemporary models of reasoning, motivation, and dialogue. With chapters on truth, talk, trust, and thinking, the volume presents a revised model of dual-process theory, linking it to deliberative dialogue while integrating insights from education, communication studies, philosophy, and political science. The result is a timely vision of cautious optimism for navigating today's post-truth challenges.

In an era of rampant misinformation, conspiracy theories, and political polarization, this book confronts the paradox between rational models of human cognition and seemingly irrational behavior. Drawing on cutting-edge research in psychology and other social sciences, it explores practical tools such as fostering digital literacy and cultivating 'wise deliberative spaces' grounded in argument, perspective taking, and moral inquiry. Written for graduate students, researchers, and general readers, E. Michael Nussbaum provides an accessible introduction to contemporary models of reasoning, motivation, and dialogue. With chapters on truth, talk, trust, and thinking, the volume presents a revised model of dual-process theory, linking it to deliberative dialogue while integrating insights from education, communication studies, philosophy, and political science. The result is a timely vision of cautious optimism for navigating today's post-truth challenges.

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.

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.

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.