Data Analysis ─ A Model Comparison Approach

This completely rewritten classic text features many new examples, insights and topics including mediational, categorical, and multilevel models. Substantially reorganized, this edition provides a briefer, more streamlined examination of data analysis. Noted for its model-comparison approach and unified framework based on the general linear model, the book provides readers with a greater understanding of a variety of statistical procedures. This consistent framework, including consistent vocabulary and notation, is used throughout to develop fewer but more powerful model building techniques. The authors show how all analysis of variance and multiple regression can be accomplished within this framework. The model-comparison approach provides several benefits:

It strengthens the intuitive understanding of the material thereby increasing the ability to successfully analyze data in the future
It provides more control in the analysis of data so that readers can apply the techniques to a broader spectrum of questions
It reduces the number of statistical techniques that must be memorized
It teaches readers how to become data analysts instead of statisticians.
The book opens with an overview of data analysis. All the necessary concepts for statistical inference used throughout the book are introduced in Chapters 2 through 4. The remainder of the book builds on these models. Chapters 5 - 7 focus on regression analysis, followed by analysis of variance (ANOVA), mediational analyses, non-independent or correlated errors, including multilevel modeling, and outliers and error violations. The book is appreciated by all for its detailed treatment of ANOVA, multiple regression, nonindependent observations, interactive and nonlinear models of data, and its guidance for treating outliers and other problematic aspects of data analysis.
Intended for advanced undergraduate or graduate courses on data analysis, statistics, and/or quantitative methods taught in psychology, education, or other behavioral and social science departments, this book also appeals to researchers who analyze data. A protected website featuring additional examples and problems with data sets, lecture notes, PowerPoint presentations, and class-tested exam questions is available to adopters. This material uses SAS but can easily be adapted to other programs. A working knowledge of basic algebra and any multiple regression program is assumed.

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.

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.