Search results for ""Author Andrew F. Hayes""

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.

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Emphasizing conceptual understanding over mathematics, this user-friendly text introduces linear regression analysis to students and researchers across the social, behavioral, consumer, and health sciences. Coverage includes model construction and estimation, quantification and measurement of multivariate and partial associations, statistical control, group comparisons, moderation analysis, mediation and path analysis, and regression diagnostics, among other important topics. Engaging worked-through examples demonstrate each technique, accompanied by helpful advice and cautions. The use of SPSS, SAS, and STATA is emphasized, with an appendix on regression analysis using R. The companion website (www.afhayes.com) provides datasets for the book's examples as well as the RLM macro for SPSS and SAS. Pedagogical Features: *Chapters include SPSS, SAS, or STATA code pertinent to the analyses described, with each distinctively formatted for easy identification. *An appendix documents the RLM macro, which facilitates computations for estimating and probing interactions, dominance analysis, heteroscedasticity-consistent standard errors, and linear spline regression, among other analyses. *Students are guided to practice what they learn in each chapter using datasets provided online. *Addresses topics not usually covered, such as ways to measure a variable’s importance, coding systems for representing categorical variables, causation, and myths about testing interaction.

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