Multiple Regression and Beyond

This book is designed to provide a conceptually-oriented introduction to multiple regression. It is divided into two main parts. The author concentrates on multiple regression analysis in the first part and structural equation modeling in the second part. Provides an introduction to multiple regression analysis, path analysis, confirmatory factor analysis, and structural equation modeling. Students gain a foundation in multiple regression. Multiple regression is used as a jumping-off place for structural equation modeling with measured variables (path analysis). Latent variables are introduced as a way of obviating some of the threats to path models, and SEM with latent variables is presented as the culmination of the previous topics. (not so sure about this one) Conceptual, rather tha

Multiple Regression and Beyond offers a conceptually oriented introduction to multiple regression (MR) analysis, along with more complex methods that flow naturally from multiple regression: path analysis, confirmatory factor analysis, and structural equation modeling. By focusing on the concepts and purposes of MR and related methods, rather than the derivation and calculation of formulae (the “plug and chug” approach), students learn more clearly, in a less threatening way. As a result, they are more likely to be interested in conducting research using MR, CFA, or SEM — and are more likely to use the methods wisely.

This is undoubtedly the most readable book about multiple regression I have ever used. My students found it clear and understandable. Keith writes in a clear style that is designed to engage students rather than alienate them. The emphasis is on conceptual understanding rather than mathematical proofs. Formulas are used when necessary, but Keith takes care not to drown students in a sea of algebra. The aim throughout is to empower students to make the decisions that they will need to make in thier own research.
Larry Greil, Alfred University

Keith's approach is a “conceptually oriented introduction” to multiple regression. None of the negative connotations of that phrase apply here. Keith's coverage de-emphasizes complex mathematics yet is committed to a rigorous, model-building use of multiple regression in research data analysis. . . . Material that can be quite difficult and confusing for students is covered with sufficient depth and clarity so that many issues will make considerably more sense to students than they usually do.
Robert J. Crutcher, University of Dayton