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.
