Mediation
Week 11: March 17, 2025
Today’s Reading
- Hayes Chapter 3
Introduction to the Topic
This week, we explore mediation analysis, a statistical approach used to understand the mechanisms through which an independent variable influences a dependent variable via a third variable, known as the mediator. Mediation analysis is essential for uncovering causal pathways in research.
Key Concepts:
- Direct Effects: The relationship between the independent variable and the dependent variable, excluding the mediator.
- Indirect Effects: The portion of the relationship explained through the mediator.
- Total Effects: The combined impact of direct and indirect effects.
- Bootstrapping: A resampling method for testing the significance of indirect effects.
Relevance:
- Students will learn how to identify and test mediating variables in a causal framework.
- Students will explore how to decompose total effects into direct and indirect components to better understand relationships among variables.
Why This Is Important:
- Mediation analysis allows researchers to move beyond simple relationships to uncover how and why effects occur.
- Understanding mediation is critical for testing theoretical models in fields like psychology, sociology, and public health.
How This Ties Into the Overall Course:
- Builds upon previous topics, such as regression and effect sizes, by extending these tools to explore causal pathways.
- Prepares students for advanced concepts like moderated mediation and structural equation modeling (SEM), where mediation analysis is a core component.
By the end of this week, students will be able to conduct mediation analyses, interpret direct and indirect effects, and evaluate the significance of mediators using bootstrapping methods.