Data Analysis

This essential textbook provides an integrated treatment of data analysis for the social and behavioral sciences. It covers all the key statistical models in an integrated manner that relies on the comparison of models of data estimated under the rubric of the general linear model.

Autorentext

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.

Inhalt

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