Mediation and Moderation Analysis
Within PhD projects, questions often arise about how effects become stronger or weaker due to some third variable (moderation), or how effects may be explained by some intermediate third variable (mediation). In this three-day course the core issues of mediation and moderation are explored and practiced.
- Target group
-
Lecturer
Researcher
Postdoctoral researcher
PhD candidate
Guest - Teachers
- Elise Dusseldorp (Coordinator/Associate professor) Jacqueline Zadelaar (Assistant professor)
- Method
-
Training course
- Hours
- 33
Registration deadline is Thursday 15 June 2023.
Description
The first day of the course is targeted at the basic concepts of mediation and moderation (causal steps approach, estimating and testing indirect effects, effect size, and post hoc probing of moderator effects). On the second day generalisations to multiple mediators or moderators are discussed. On the third day combinations of moderation and mediation (moderated mediation) will be discussed, not only as a direct extension, but also in the context of within-subjects designs. During both days, lectures are intermingled with computer practical sessions using SPSS in combination with SPSS macro PROCESS, and/or using R.
Course objectives
After this course, you are able to:
- explain the concepts of mediation, moderation, and moderated mediation, and related issues (e.g., mean centering, causal steps approach);
- choose the appropriate analysis for different research questions involving mediation and/or moderation;
- perform mediation, moderation, and moderated mediation analysis in SPSS, macro PROCESS, and R.
- interpret the results of such mediation, moderator, and moderated mediation analyses.
Preparation
Read chapters 1, 2, and 3 of Hayes' book (2022) to brush up on your knowledge. Bring your own device to class with SPSS or R installed on it.
Literature
-
Hayes, A. F. (2022). Introduction to mediation, moderation, and conditional process analysis. A regression-based approach (3rd Edition). New York: Guilford.
-
A few journal articles (to be announced).
Entry requirements
Basic knowledge about multiple regression analysis, and basic skills in working with SPSS or R.
Fees
|
Target group |
One day |
Two days |
Three days |
|
PhD candidates FSW |
FREE |
FREE |
FREE |
|
Staff FSW |
€300 |
€400 |
€500 |
|
Other Leiden University PhD candidates |
€215 |
€315 |
€415 |
|
Externals* |
€450 |
€600 |
€750 |
*Externals are PhD candidates related to staff members of FSW (buitenpromovendi) and/or staff members of other Leiden University Faculties.