Explaining the fundamentals of mediation and moderation analysis, this engaging book also shows how to integrate the two using an innovative strategy known as conditional process analysis. Procedures are described for testing hypotheses about the mechanisms by which causal effects operate, the conditions under which they occur, and the moderation of mechanisms. Relying on the principles of ordinary least squares regression, Andrew Hayes carefully explains the estimation and interpretation of direct and indirect effects, probing and visualization of interactions, and testing of questions about moderated mediation. Examples using data from published studies illustrate how to conduct and report the analyses described in the book. Of special value, the book introduces and documents PROCESS, a macro for SPSS and SAS that does all the computations described in the book. The companion website (www.afhayes.com) offers free downloads of PROCESS plus data files for the book's examples.
Unique features include: *Compelling examples (presumed media influence, sex discrimination in the workplace, and more) with real data; boxes with SAS, SPSS, and PROCESS code; and loads of tips, including how to report mediation, moderation and conditional process analyses. *Appendix that presents documentation on use and features of PROCESS. *Online supplement providing data, code, and syntax for the book's examples.
About the Author
Andrew F. Hayes, PhD, is Professor of Quantitative Psychology at The Ohio State University. His research and writing on data analysis has been published widely. Dr. Hayes is the author of Introduction to Mediation, Moderation, and Conditional Process Analysis and Statistical Methods for Communication Science, as well as coauthor, with Richard B. Darlington, of Regression Analysis and Linear Models. He teaches data analysis, primarily at the graduate level, and frequently conducts workshops on statistical analysis throughout the world. His website is www.afhayes.com.
"Mediation and moderation are two of the most widely used statistical tools in the social sciences. Students and experienced researchers have been waiting for a clear, engaging, and comprehensive book on these topics for years, but the wait has been worth it--this book is an absolute winner. With his usual clarity, Hayes has written what will become the default resource on mediation and moderation for many years to come."--Andy Field, PhD, School of Psychology, University of Sussex, United Kingdom
"Hayes provides an accessible, thorough introduction to the analysis of models containing mediators, moderators, or both. The text is easy to follow and written at a level appropriate for an introductory graduate course on mediation and moderation analysis. The book is also an extremely useful resource for applied researchers interested in analyzing conditional process models. One strength is the inclusion of numerous examples using real data, with step-by-step instructions for analysis of the data and interpretation of the results. This book's largest contribution to the field is its replacement of the confusing terminology of mediated moderation and moderated mediation with the clearer and broader term conditional process model."--Matthew Fritz, PhD, Department of Educational Psychology, University of Nebraska-Lincoln
"A welcome contribution. This book's accessible language and diverse set of examples will appeal to a wide variety of substantive researchers looking to explore how or why, and under what conditions, relationships among variables exist. Hayes has a unique ability to effectively communicate technical material to nontechnical audiences. He facilitates application of several cutting-edge statistical models by providing practical, well-oiled machinery for conducting the analyses in practice. I can use this book to enhance my graduate-level mediation class by extending the course to include more coverage on differentiating mediation versus moderation and on conditional process models that simultaneously evaluate both effects together."--Amanda Jane Fairchild, PhD, Department of Psychology, University of South Carolina
"This decidedly readable, informative book is perfectly suited for a range of audiences, from the novice graduate student not quite ready for SEM to the advanced statistics instructor. Even the seasoned quantitative methodologist will benefit from Hayes's years of accumulated wisdom as he expertly navigates this burgeoning--and at times inconsistent--literature. This book is particularly well suited for graduate-level courses. Hayes brings conditional process analysis to life with such passion that even the most 'stat-o-phobic' will become convinced that they too can master SPSS (or SAS) process. The thoughtful use of real-life examples, accompanied by SPSS and SAS syntax and output, makes the book highly accessible."--Shelley Brown, PhD, Department of Psychology, Carleton University, Canada
“This book elegantly presents both the basic and advanced issues of mediation and moderation analysis…it will be beneficial for graduate students and applied researchers who are interested in causal mechanisms using linear models….[T]his is a very good textbook for applied researchers in social sciences. It covers mediation and moderation analysis using regression techniques quite nicely. The online materials of this book provide the data and software code for SAS and SPSS, which are very helpful supplements. I think this book could be very useful for both preliminary and advanced readers who are interested in mediation and moderation analysis.” — Journal of American Statistical Association
“The book is very readable and conversational, providing many interesting and useful examples….I found this to be a very nice book that is readable enough for the intermediate statistics user but with enough technical detail to appeal to advanced users as well. The first three sections provide a thorough presentation of regression analysis and its use in answering questions of mediation and moderation. The inclusion of helpful SPSS macros and SAS programs for better estimation of these models is a very attractive quality….The final section on conditional process analysis is illuminating and thought-provoking, and for the reader unfamiliar with these topics will definitely peak interest. This book would make an excellent textbook for an advanced graduate-level multiple regression course, or just a great resource for the interested reader.” — Journal of Educational Measurement