Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach (Methodology in the Social Sciences Series)

Review
“I know I speak for organizational researchers and graduate students everywhere when I say how much PROCESS, and prior editions of this book, have contributed to making some of the more difficult parts of the research process accessible and fun. I look forward to using the third edition in my own research, and (again) buying a copy for all my graduate students. Adding to the appeal of the third edition are features such as the new code for R users--now available for every example in the book--and techniques to analyze the strength of two specific direct effects that differ in sign. Hayes has made an immense contribution with his continual updates to PROCESS, and shows in his writing and his workshops that he is a gifted teacher.”--Julian Barling, PhD, FRSC, Distinguished University Professor and Borden Chair of Leadership, Smith School of Business, Queen’s University, Canada
"This book would make an excellent companion text to accompany a course on regression analysis that also addresses mediation and moderation, two topics of enormous practical utility. It can also serve as a useful reference for more experienced researchers and methodologists wanting to learn about mediation, moderation, and advanced applications. Reading this book is like taking an immersive workshop on mediation and moderation analysis, with the author right there to explain everything."--Kristopher J. Preacher, PhD, Department of Psychology and Human Development, Peabody College, Vanderbilt University
"This book is a staple on my bookshelf and a text that I recommend to all my students who are interested in quantitative research. The impressive third edition now includes code and examples for R. Making the incredibly flexible and useful analytic tools of PROCESS available for a free, open-source statistical software program is a huge contribution to the field. This is a most useful book for advanced graduate courses that focus on regression, as well as for faculty.”--Michael D. Broda, PhD, School of Education, Virginia Commonwealth University
"I have used this text for several years in my graduate-level statistics classes. It makes the teaching of mediation and moderation much easier, and the associated PROCESS code makes conducting these analyses much less tedious. Colleagues have found this book and PROCESS very helpful in their research endeavors, and several of my students have used PROCESS in their theses and dissertations. The third edition has all of the things I liked about the earlier editions, plus some nice new stuff--the inclusion of R code will be helpful to those who do not have access to SAS or SPSS, and I especially enjoyed the more detailed discussion of unstandardized, standardized, and partially standardized coefficients. I recommend this book without reservation."--Karl L. Wuensch, PhD, Department of Psychology, East Carolina University
“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....This book would make an excellent textbook for an advanced graduate-level multiple regression course, or just a great resource for the interested reader.” (on the first edition)
―
Journal of Educational Measurement Published On: 2014-08-01
“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.” (on the first edition)
―
Journal of the American Statistical Association Published On: 2014-09-01
About the Author
Andrew F. Hayes, PhD, is Distinguished Research Professor at the Haskayne School of Business at the University of Calgary, Alberta, Canada. His research and writing on data analysis has been published widely, and he is the author of
Introduction to Mediation, Moderation, and Conditional Process Analysis, Third Edition, and
Statistical Met