Mediation, Moderation, and Conditional Process Analysis II (GSERM Ljubljana 2024)

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 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.

Course leader

Amanda Montoya from University of California, Los Angeles, United States

Target group

Prerequisites (knowledge of topic)

Participants should have a basic working knowledge of the principles and practice of multiple regression and elementary statistical inference. Because this is a second course, participants should either be familiar with the contents of the first edition of Introduction to Mediation, Moderation, and Conditional Process Analysis and the statistical procedures discussed therein or should have taken the first course through GSERM or elsewhere. Participants should also have experience using syntax in SPSS, SAS, or R, and it is assumed that participants will already have some experience using the PROCESS macro. No knowledge of matrix algebra is required or assumed, nor is matrix algebra ever used in the course.

Course aim

Learning objectives:

Apply and report on tests of moderated mediation using the index of moderated mediation
Identify models for which partial and conditional moderated mediation are appropriate.
Apply and report mediation analysis with multicategorical independent variables.
Test and probe an interaction involving a multicategorical independent variable or moderator.
Apply and report tests of moderated mediation involving a multicategorical independent variable.
Generalize the index of moderated mediation to models with serial mediation
Estimate and conduct inference in mediation, moderation, and moderated mediation contexts for two-instance repeated-measures designs.
Generate and specify custom models in PROCESS

Credits info

4 EC
At the end of the course, participants receive a Certificate of Attendance and a Transcript of Records (if taking part in final examination).

Fee info

CHF 1000: Flat early bird discount worth CHF 100.00 (valid until 31 October 2023)

Scholarships

No.