Assumptions for Interpretation and Statistical Inferenceģ.2. Multicategorical Antecedent VariablesĢ.8. Alternative Explanations for AssociationĢ.7. Fundamentals of Linear Regression AnalysisĢ.3. Statistical and Conceptual Diagrams, and Antecedent and Consequent VariablesĢ. Correlation, Causality, and Statistical Modelingġ.5. Questions of Whether, If, How, and Whenġ.4. *Discussion of testing for interaction between a causal antecedent variable X and a mediator M in a mediation analysis, and how to test this assumption in a new PROCESS feature.ġ.2. *Introduction of a bootstrap-based Johnson–Neyman-like approach for probing moderation of mediation in a conditional process model. *Discussion of a method for comparing the strength of two specific indirect effects that are different in sign. *Discussion of the meaning of and how to generate the correlation between mediator residuals in a multiple-mediator model, using a new PROCESS option. *Expanded discussion of effect scaling and the difference between unstandardized, completely standardized, and partially standardized effects. *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. The companion website ( provides data for all the examples, plus the free PROCESS download. Readers gain an understanding of the link between statistics and causality, as well as what the data are telling them. 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. Hayes illustrates each step in an analysis using diverse examples from published studies, and displays SPSS, SAS, and R code for each example. Using the principles of ordinary least squares regression, Andrew F. 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.
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