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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. Models with Continuous and Categorical Predictors: ANCOVA. 11.Repeated-Measures ANOVA: Models with Nonindependent Errors. 12. Continuous Predictors with Nonindependent Observations. 13. Outliers and Ill-Mannered Error.
Charles "Chick" Judd is Professor of Distinction in the College of Arts and Sciences at the University of Colorado at Boulder. He received his Ph.D. in 1976 from Columbia University. A Fellow of the American Psychological Association, the Society of Personality and Social Psychology, and the Society for the Psychological Study of Social Issues, Dr. Judd's research focuses on social cognition and attitudes, intergroup relations and stereotypes, judgment and decision making, and methods of behavioral science research and data analysis. Gary McClelland is Professor of Psychology at the University of Colorado at Boulder. He received his Ph.D. in 1974 from The University of Michigan. A Faculty Fellow at the Institute of Cognitive Science, Dr. McClelland's research interests include judgment and decision making, psychological models of economic behavior, statistics and data analysis, measurement and scaling, and mathematical psychology. Carey S. Ryan is a Professor in the Department of Psychology at the University of Nebraska at Omaha. After earning her Ph.D. at the University of Colorado at Boulder, Professor Ryan taught at the University of Pittsburgh and joined the University of Nebraska faculty in 2001. She has research interests in stereotyping and prejudice, group processes, and program evaluation.
"This text's focus on a unifying framework and nonthreatening style sets it apart. I would certainly recommend this book to a student or colleague struggling to gain an intuitive understanding of the concepts. The original edition was popular and influential. I expect the second edition will be as well." - Kristopher J. Preacher, University of Kansas "I am absolutely delighted to report that in virtually every way I could hope for, the new edition is better. The scholarship is top-notch, but in a very accessible way. The authors write beautifully ! I will absolutely adopt and recommend the second edition. I love it." - J. Michael Bailey, Northwestern University "After reviewing eight potential textbooks for my ! graduate statistics course, I chose this one ! The thoroughness with which the book teaches the model comparison approach allows students to understand and develop meaningful analyses of their own data." -Deborah M. Clawson, Catholic University, USA "This second edition is the perfect book for today's data analysts." -David Kenny, University of Connecticut, USA