# 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.

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## 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.

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## 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.

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## 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.

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## 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.

## Class Files

## Class Files

### Lecture Notes


### R Code


### Data Sets


## Resources


## Today's Reading

 
## R Lab Files

 

## Additional Notes
 
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