Amanda K. Montoya

Statistical mediation and moderation analyses are among the most widely used data analysis techniques in social science, health, and business fields. Mediation analysis is used to test hypotheses about various intervening mechanisms by which causal effects operate. Moderation analysis is used to examine and explore questions about the contingencies or conditions of an effect, also called “interaction”. Increasingly, moderation and mediation are being integrated analytically in the form of what has become known as “conditional process analysis,” used when the goal is to understand the contingencies or conditions under which mechanisms operate. An understanding of the fundamentals of mediation and moderation analysis is in the job description of almost any empirical scholar. In this course, you will learn about the underlying principles and the practical applications of these methods using ordinary least squares (OLS) regression analysis and the PROCESS macro for R, SPSS and SAS. Topics covered in this five-day course include: – Path analysis: Direct, indirect, and total effects in mediation models. – Estimation and inference about indirect effects in single mediator models. – Models with multiple mediators – Mediation analysis in the two-condition within-subject design. – Estimation of moderation and conditional effects. – Probing and visualizing interactions. – Conditional Process Analysis (also known as “moderated mediation”) – Quantification of and inference about conditional indirect effects. – Testing a moderated mediation hypothesis and comparing conditional indirect effects As an introductory-level course, we focus primarily on research designs that are experimental or cross-sectional in nature with continuous outcomes. We do not cover complex models involving dichotomous outcomes, latent variables, models with more than two repeated measures, nested data (i.e., multilevel models), or the use of structural equation modeling.

Mediation analysis is used to test various intervening mechanisms by which causal effects operate. Moderation analysis is used to examine and explore questions about the contingencies or conditions of an effect, also called “interaction.” Conditional process analysis is the integration of mediation and moderation analysis and used when one seeks to understand the conditional nature of processes (i.e., “moderated mediation”) In his best-selling book, Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach (www.guilford.com/p/hayes3) Dr. Andrew Hayes describes the fundamentals of mediation, moderation, and conditional process analysis using ordinary least squares regression. He also explains how to use PROCESS, a freely-available and handy tool he invented that brings modern approaches to mediation and moderation analysis within convenient reach. This seminar– a second course –picks up where the first edition of the book and the first course offered by GSERM leaves off. After a review of basic principles, it covers material in the second edition of the book as well as new material in neither edition, reflecting new work recently published by the instructor. Topics covered include: •Review of the fundamentals of mediation, moderation, and conditional process analysis. •Testing whether an indirect effect is moderated and probing moderation of indirect effects. •Partial and conditional moderated mediation. •Mediation analysis with a multicategorical independent variable. •Moderation analysis with a multicategorical (3 or more groups) independent variable or moderator. •Conditional process analysis with a multicategorical independent variable •Moderation of indirect effects in the serial mediation model. •Advanced uses of PROCESS, such as how to modify a numbered model or customize your own model. We focus primarily on research designs that are experimental or cross-sectional in nature with continuous outcomes. We do not cover complex models involving dichotomous outcomes, latent variables, nested data (i.e., multilevel models), or the use of structural equation modeling.