Mediation, Moderation, and Conditional Process Analysis I



Next course


Further and more detailed information, including the schedule, can be found in the current course tables in the syllabus of the respective course, if the course is offered in the next sessions. The following text serves as information on what can be expected in terms of content in the course.

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.