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“This book is an excellent choice for a first course in general linear models because it prioritizes students' conceptual understanding of regression and its underlying architecture. Key concepts are developed through conceptual formulas, geometric representations, clear explanations, and substantive examples. The second edition further advances this goal with expanded descriptions of complex concepts. Along the way, the authors address controversies and common misunderstandings in the application and interpretation of regression models and offer defensible paths forward. By presenting important concepts in a variety of ways, the text helps students build a deeper and more intuitive understanding of regression modeling.”

—Timothy R. Konold, PhD, Curry Memorial Professor of Education, University of Virginia


“I am a big fan of the first edition, which I’ve used for several years in teaching general linear models to graduate students in the educational and social sciences. I am happy to see the authors continue their outstanding work in a second edition that now includes R syntax in addition to SPSS, SAS, and Stata. This comprehensive yet highly readable text provides a modern, thoughtful treatment with many highlights, such as useful coding options for categorical predictors into regression, how interaction terms generate conditional slopes, many options for nonlinear relations, and a thorough comparison of effect size measures (including a pie analogy I readily borrow). I recommend this text highly for use in any class focused on the meaningful application of general linear models for research purposes.”

—Lesa Hoffman, PhD, Eugene T. Moore Distinguished Professor, Department of Education and Human Development, Clemson University


“I have used the first edition of this book continuously since 2017, and I am delighted to see the second edition. The greater focus on R content in each chapter is an important upgrade given the ubiquity of R in modern data analysis. There is also stronger integration of PROCESS into this edition, which deepens the connection between this text and common practices of testing mediation and moderation. This text is an important resource for graduate students and professionals looking to understand the basic principles of regression as well as extensions to categorical predictors, tests of moderation, and analyses of indirect effects.”

—Jeffrey H. Kahn, PhD, Department of Psychology, Illinois State University

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About the Authors

Andrew F. Hayes, PhD, is Distinguished Research Professor at the Haskayne School of Business, University of Calgary, Alberta, Canada, where he is also affiliated with the Department of Psychology. His research and writing on data analysis has been published widely. Dr. Hayes is author or coauthor of books including Introduction to Mediation, Moderation, and Conditional Process Analysis, Third Edition, and Regression Analysis and Linear Models, Second Edition. A Fellow of the Association for Psychological Science and a recipient of the Methodological Innovator Award from the Society for Personality and Social Psychology, he has been recognized year after year as a Clarivate Analytics Highly Cited Researcher. Dr. Hayes teaches data analysis at the University of Calgary, including regression analysis and various other multivariate analysis courses, and conducts workshops on statistical analysis online and in person throughout the world. His website is www.afhayes.com.

• Author's website
• All titles by Andrew F. Hayes

Richard B. Darlington, PhD, is Emeritus Professor of Psychology at Cornell University. Dr. Darlington is a Fellow of the American Association for the Advancement of Science. He has published most extensively on regression and related methods, the cultural bias of mental tests, the long-term effects of preschool programs, and, most recently, the neuroscience of brain development and evolution.

• All titles by Richard B. Darlington

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Audience

Graduate students and instructors in psychology, human development, education, sociology, public health, and management; applied researchers who want to refresh their skills.

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Course Use

Serves as the main text for graduate-level courses such as Regression Analysis, Quantitative Methods and Analysis, Multivariate Statistics, Linear Modeling, and Multiple Regression, or as a supplementary text for courses such as Research Design and Experimental Design.

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Previous editions published by Guilford:

First Edition, © 2017
ISBN: 9781462521135

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New to this edition:

• New material on estimating, probing, and visualizing interactions in a regression model; simplifying a mediation analysis, especially for the construction of confidence intervals for inference about indirect effects; conducting a qualitative dominance analysis; and more.
• Incorporates use of the PROCESS macro for regression analysis, which makes it easier for users of different software to learn and share syntax across computing platforms.
• R code is now available throughout the book, along with SPSS, SAS and Stata.