Emphasizing conceptual understanding over mathematics, this user-friendly text introduces linear regression analysis to students and researchers across the social, behavioral, consumer, and health sciences. Coverage includes model construction and estimation, quantification and measurement of multivariate and partial associations, statistical control, group comparisons, moderation analysis, mediation and path analysis, and regression diagnostics, among other important topics. Engaging worked-through examples demonstrate each technique, accompanied by helpful advice and cautions. The use of SPSS, SAS, and STATA is emphasized, with an appendix on regression analysis using R. The companion website (www.afhayes.com) provides datasets for the book's examples as well as the RLM macro for SPSS and SAS.
Pedagogical Features:
*Chapters include SPSS, SAS, or STATA code pertinent to the analyses described, with each distinctively formatted for easy identification.
*An appendix documents the RLM macro, which facilitates computations for estimating and probing interactions, dominance analysis, heteroscedasticity-consistent standard errors, and linear spline regression, among other analyses.
*Students are guided to practice what they learn in each chapter using datasets provided online.
*Addresses topics not usually covered, such as ways to measure a variable's importance, coding systems for representing categorical variables, causation, and myths about testing interaction.
Emphasizing conceptual understanding over mathematics, this user-friendly text introduces linear regression analysis to students and researchers across the social, behavioral, consumer, and health sciences.
This is a thorough and accessible introduction to regression analysis as conducted and reported in the psychology research literature. In addition to the basics, there is up-to-date coverage of more advanced topics--for example, interaction effects, path analysis, and mediation. Accompanying examples of statistical software code and output enable students to quickly utilize linear models in the analysis of their own data. This is the right textbook for first-year psychology graduate students, and I plan to continue using it."--Daniel Ozer, PhD, Department of Psychology, University of California, Riverside "This fantastic introduction to the general linear model takes the reader from first principles through to widely used techniques such as mediation and path analysis. The clear writing makes it a pleasure to read. Students will find the book an invaluable resource. There are plenty of insights, too, for even seasoned researchers and data analysts. Instructors and students will appreciate the logical structure and chapters that break the material up into manageable chunks."--Andy Field, PhD, Professor of Child Psychopathology, University of Sussex, United Kingdom "If you want to get the most bang for your buck out of your statistical training, investing in learning regression and linear models is the way to go. Nonetheless, many people find linear modeling to be confusing at first. This book breaks down all walls to mastering this fundamental analysis by providing a complete guide in an approachable, conversational style. The book begins with a comprehensive introduction to linear models and continues on to cover the most useful advanced topics, such as logistic regression and mediation and path analysis. A 'must-have' desk reference for entry-level learners and long-time practitioners alike."--Elizabeth Page-Gould, PhD, Canada Research Chair in Social Psychophysiology, University of Toronto "A terrific addition to the regression literature. I am often asked, 'How do I determine which regressor(s) is/are the most important?' The treatment of this topic is excellent, and the authors have done a fantastic job of bringing important issues to light. The applied nature of the text and the interweaving of software syntax and output are major improvements over similar books. I like the fact that the book has software package information for SPSS, SAS, and STATA. It has a nice balance; not too technical on the statistical side, but not simply a 'how to' on the software side. I could see this book being used as the main text in our department's graduate-level regression course."--Scott C. Roesch, PhD, Department of Psychology, San Diego State University "This is a great textbook for students who have only basic knowledge of statistics yet would like to gain a deep conceptual understanding of regression. The book is up to date in current methods in regression, with strong examples using SAS/SPSS/STATA.--Chris Oshima, PhD, Department of Educational Policy Studies, Georgia State University-
