Mediation, Moderation, and Conditional Process Analysis with R
Aims, contents and method of the course
This data analysis seminar focuses on linear regression analysis to explore questions about mediated and moderated effects.
Computer applications will focus on R statistical language, the Rstudio environment (https://www.rstudio.com) and the PROCESS software by Andrew F. Hayes (http://processmacro.org).
Learning outcomes
By the conclusion of this course, students should be proficient in:
Executing and comprehending the outcomes of linear regression, moderation, mediation, and conditional process models.
Statistically testing competing theories of mechanisms through the evaluation of indirect effects in models that encompass multiple mediators.
Representing and investigating interactions in regression models to accurately interpret interaction effects.
Calculating and inferring about conditional indirect effects to estimate the contingencies of mechanisms.
Utilizing the R language and PROCESS to conduct, depict, and comprehend linear regression, moderation, mediation, and conditional process models.
Handbook
The handbook of the course and the primary source of most of the material here presented is:
Andrew F. Hayes. Introduction to Mediation, Moderation, and Conditional Process Analysis. A Regression-Based Approach. 2018. SECOND EDITION. THE GUILFORD PRESS, New York, London.