Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach 3rd edition

"Lauded for its easy-to-understand, conversational discussion of the fundamentals of mediation, moderation, and conditional process analysis, this book has been fully revised with 50% new content, including sections on working with multicategorical antecedent variables, the use of PROCESS version 3 for SPSS and SAS for model estimation, and annotated PROCESS v3 outputs. Using the principles of ordinary least squares regression, Andrew F. Hayes carefully explains procedures for testing hypotheses about the conditions under and the mechanisms by which causal effects operate, as well as the moderation of such mechanisms. Hayes shows how to estimate and interpret direct, indirect, and conditional effects; probe and visualize interactions; test questions about moderated mediation; and report different types of analyses. Data for all the examples are available on the companion website ((ital]www.afhayes.com(/ital]), along with links to download PROCESS"--
This text introduces mediation, moderation, and conditional process analysis in the social, behavioral, and health sciences using the principles of ordinary least squares regression. It focuses on basic principles primarily using data from simple experimental or cross-sectional studies covered in elementary statistics and research design courses. It includes SPSS, SAS, and R code for each example. This edition has a rewritten appendix on using PROCESS; expanded discussion of effect scaling and the difference between unstandardized, completely standardized, and partially standardized effects; discussion of the meaning of and how to generate the correlation between mediator residuals in a multiple-mediator model, using new PROCESS options; discussion of a method for comparing the strength of two specific indirect effects that are different in sign; an introduction to a bootstrap-based Johnson-Neyman-like approach for probing moderation of mediation in a conditional process model; and discussion of testing for interaction between a causal antecedent variable X and a mediator M in a mediation analysis, using a new PROCESS feature. It has new code for R users with every example, an expanded section on power analysis and sample size, and moves the appendix describing MCMED online. Annotation ©2022 Ringgold, Inc., Portland, OR (protoview.com)
Acclaimed for its thorough presentation of mediation, moderation, and conditional process analysis, this book has been updated to reflect the latest developments in PROCESS for SPSS, SAS, and, new to this edition, R. Using the principles of ordinary least squares regression, Andrew F. Hayes illustrates each step in an analysis using diverse examples from published studies, and displays SPSS, SAS, and R code for each example. Procedures are outlined for estimating and interpreting direct, indirect, and conditional effects; probing and visualizing interactions; testing hypotheses about the moderation of mechanisms; and reporting different types of analyses. Readers gain an understanding of the link between statistics and causality, as well as what the data are telling them. The companion website (www.afhayes.com) provides data for all the examples, plus the free PROCESS download. New to This Edition *Rewritten Appendix A, which provides the only documentation of PROCESS, including a discussion of the syntax structure of PROCESS for R compared to SPSS and SAS. *Expanded discussion of effect scaling and the difference between unstandardized, completely standardized, and partially standardized effects. *Discussion of the meaning of and how to generate the correlation between mediator residuals in a multiple-mediator model, using a new PROCESS option. *Discussion of a method for comparing the strength of two specific indirect effects that are different in sign. *Introduction of a bootstrap-based Johnson–Neyman-like approach for probing moderation of mediation in a conditional process model. *Discussion of testing for interaction between a causal antecedent variable (ital]X(/ital] and a mediator (ital]M(/ital] in a mediation analysis, and how to test this assumption in a new PROCESS feature.