Mediation, Moderation, and Conditional Process Analysis II

About

Statistical mediation and moderation analyses are among the most widely used data analysis techniques. 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 ʺinteraction.ʺ 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., ʺmoderated mediationʺ).

In 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 and third editions of the book as well as new material including recently published methodological research.

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.
Mediation, Moderation, and Conditional Process Analysis in Two-Instance Repeated-Measures Designs
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.

Course leader

Amanda Montoya

Target group

Master | PhD | Postdoc | Professional

Fee info