"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."
"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."
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.