Data Analysis

Charles "Chick" M. Judd is Professor of Distinction in the College of Arts and Sciences at the University of Colorado at Boulder. His research focuses on social cognition and attitudes, intergroup relations and stereotypes, judgment and decision making, and behavioral science research methods and data analysis. Gary H. McClellandis Professor of Psychology at the University of Colorado at Boulder. A Faculty Fellow at the Institute of Cognitive Science, his research interests include judgment and decision making, psychological models of economic behavior, statistics & data analysis, and measurement and scaling. Carey S. Ryanis a Professor in the Department of Psychology at the University of Nebraska at Omaha. She has research interests in stereotyping and prejudice, group processes, and program evaluation.

'Data Analysis is a rare graduate statistics book that combines truth (scholarly strength) and beauty (a clear approach that builds from one chapter to the next). The book’s model comparisons give students a systematic way to think deeply about their hypotheses as well as the flexibility to answer meaningful questions about their own data that most textbooks address only briefly if at all.' - Deborah Clawson, Catholic University of America, USA'Most introductory statistics texts teach students how to apply specific tests in specific circumstances, with little room for generalizing knowledge to new settings. Data Analysis instead teaches students how to think like scientists, always framing hypothesis tests as formal comparisons between competing explanations. The first two editions were ahead of their time in their philosophical approach to data analysis, and this new edition retains and expands their unifying framework.' - Kristopher J. Preacher, Vanderbilt University, USA'I am delighted that both logistic regression and multilevel modeling are now included. Both topics are introduced using the authors’ clear, useful, and integrative approach. Not only does the new material help me to teach this to my students better, it also helps me to understand the topics better!' - J. Michael Bailey, Northwestern University, USA

• Preface 1. Introduction to Data Analysis 2. Simple Models: Definitions of Error and Parameter Estimates 3. Simple Models: Models of Error and Sampling Distributions 4. Simple Models: Statistical Inferences about Parameter Values 5. Simple Regression: Estimating Models with a Single Continuous Predictor 6. Multiple Regression: Models with Multiple Continuous Predictors 7. Moderated and Nonlinear Regression Models 8. One-Way ANOVA: Models with a Single Categorical Predictor 9. Factorial ANOVA: Models with Multiple Categorical Predictors and Product Terms 10. ANCOVA: Models with Continuous and Categorical Predictors 11. Repeated-Measures ANOVA: Models with Nonindependent Errors 12. Incorporating Continuous Predictors with Nonindependent Data: Towards Mixed Models 13. Outliers and Ill-Mannered Error 14. Logistic Regression: Dependent Categorical Variables References Appendix Author Index Subject Index