Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences is the classic text on multiple regression. It is noted for its non-mathematical, applied, and data-analytic approach intended to teach the reader "how to do it." Students and researchers profit from its verbal-conceptual exposition and frequent use of concrete examples. The applied emphasis provides clear illustrations of the principles and provides worked examples of the types of applications that are possible. Researchers learn how to specify regression models that directly address their research questions of interest. Early in the text an overview of the fundamental ideas of multiple regression and a review of bivariate correlation and regression and other elementary statistical concepts provide a strong foundation for a solid understanding of the rest of the text. The third edition reflects both the current and developing state-of-the-art practices in the field: *An increased emphasis on graphics provides greater understanding of data. *An increased emphasis on the use of confidence intervals and effect size measures provides more information about the size and precision of relationships. *An accompanying CD contains data for most of the numerical examples along with the computer code for SPSS, SAS, and SYSTAT. These computer scripts can serve as templates for the analysis of the student's own data. *Five entirely new chapters are included: Assumptions of the regression model and remedies when they are not met (Ch. 4), detection and treatment of the potential problems of outliers and multicollinearity (Ch. 10), alternative regression models that may be used when the dependent variable is binary, ordered category, or count in form, including logistic, ordinal logistic, Poisson regression, and the generalized linear model (Ch. 13), multilevel models for data collected in groups or other clusters (Ch. 14), and the analysis of longitudinal data (Ch. 15). *Extensively revi

"I think that the coverage of the text is excellent. It carves out a seriously neglected area and it very thoroughly covers the topic. The authors are very knowledgeable concerning the literature. This is an excellent text that provides a detailed, yet comprehensible account of how to estimate, test, and probe interactions in regression models."

--David A. Kenny, University of Connecticut

"Leona S. Aiken and Stephen G. West do an excellent job of structuring, testing, and interpreting multiple regression models containing interactions, curvilinear effects, or a combination of both. Procedures for testing and graphical displays of interactions between categorical variables have been done for years but none seems to have provided a comprehensive treatment or guideline for the analysis of interactions between continuous variables. . . . Aiken and West, however, address those issues quite effectively and thoroughly. . . . An aid to any graduate and/or researcher in their analysis of continuous variables. Highly recommended for graduate libraries."

--Choice

"The book would serve very well as a reference for applied researchers and methodologists. . . . In particular, this would be an excellent reference for anyone who encounters a multivariable prediction problem and has reason to believe that either a nonlinear model or a model including a variable product term would be appropriate."

--Contemporary Psychology

Researchers in a variety of disciplines frequently encounter problems in which interactions are predicted between two or more continuous variables. However, the current literature regarding how to analyze, interpret, and present interactions in multiple regression has been confusing. In this comprehensive volume, Leona S. Aiken and Stephen G. West provide academicians and researchers with a clear set of prescriptions for estimating, testing, and probing interactions in regression models. Including the latest research in the area, such as Fuller's work on the corrected/constrained estimator, the book is appropriate for anyone who uses multiple regression to estimate models or for those enrolled in courses on multivariate statistics.