Data Analysis

Joshua Correll is a professor of psychology and neuroscience in the College of Arts and Sciences at the University of Colorado at Boulder. His research examines face processing, stereotypes and data analysis.Abigail (Abby) M. Folberg is an assistant professor of psychology in the College of Arts and Sciences at the University of Nebraska at Omaha. Her research examines the impacts of stereotypes and prejudice on marginalized group members as well as how individuals and organizations can reduce prejudice and discrimination.Charles “Chick” M. Judd is Professor Emeritus 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. McClelland is Professor Emeritus 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 and data analysis, and measurement and scaling.Carey S. Ryan is Professor Emeritus in the Department of Psychology at the University of Nebraska at Omaha. Her research interests include stereotyping and prejudice, group processes, and program evaluation.

• Section A: Statistical Machinery 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 Estimates 5. Statistical Power: Power, Effect Sizes, and Confidence Intervals Section B: Increasingly Complex Models 6. Simple Regression: Models with a Single Continuous Predictor 7. Multiple Regression: Models with Multiple Continuous Predictors 8. Moderated and Nonlinear Multiple Regression models 9. One-Way ANOVA: Models with a Single Categorical Predictor 10. Factorial ANOVA: Models with Multiple Categorical Predictors and Product Terms 11. ANCOVA: Models with Continuous and Categorical Predictors Section C: Violations of Assumptions About Error 12. Repeated-Measures ANOVA: Models with Nonindependent Errors 13. Incorporating Continuous Predictors with Nonindependent Data: Towards Mixed Models 14. Outliers and Ill-Mannered Error 15. Logistic Regression: Dependent Categorical Variables

"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 hypotheses as formal comparisons between competing explanations. The first three 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"I’ve relied on previous editions of Data Analysis: A Model Comparison Approach to Regression, ANOVA, and Beyond for years in my graduate-level data analysis courses. The book’s clear, integrated approach to complex statistical models—coupled with its focus on practical application and ethical considerations—has made it an indispensable resource for both students and instructors. This latest edition continues to be a top choice for mastering advanced data analysis techniques."Markus Brauer, University of Wisconsin-Madison, USA