Data Analysis: A Model Comparison Approach to Regression, ANOVA, and Beyond is an integrated treatment of data analysis for the social and behavioral sciences. It covers all of the statistical models normally used in such analyses, such as multiple regression and analysis of variance, but it does so in an integrated manner that relies on the comparison of models of data estimated under the rubric of the general linear model.

Data Analysis also describes how the model comparison approach and uniform framework can be applied to models that include product predictors (i.e., interactions and nonlinear effects) and to observations that are nonindependent. Indeed, the analysis of nonindependent observations is treated in some detail, including models of nonindependent data with continuously varying predictors as well as standard repeated measures analysis of variance. This approach also provides an integrated introduction to multilevel or hierarchical linear models and logistic regression. Finally, Data Analysis provides guidance for the treatment of outliers and other problematic aspects of data analysis. It is intended for advanced undergraduate and graduate level courses in data analysis and offers an integrated approach that is very accessible and easy to teach.

Highlights of the third edition include:

a new chapter on logistic regression;

expanded treatment of mixed models for data with multiple random factors;

updated examples;

an enhanced website with PowerPoint presentations and other tools that demonstrate the concepts in the book; exercises for each chapter that highlight research findings from the literature; data sets, R code, and SAS output for all analyses; additional examples and problem sets; and test questions.

About the Author:

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. McClelland

is 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. Ryan
is 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.

"About this title" may belong to another edition of this title.

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