## Introduction to statistical mediation (2023)

This series of videos provides a demonstration and comparison of R packages for statistical mediation. It was created as part of a statistics class for first-year psychology PhD students in 2023. It consists of seven videos listed on this page and in a Youtube playlist.

In the videos, I draw a distinction between conceptual mediation and statistical mediation. I use the phrase "conceptual mediation" to refer to the idea that we can learn about a causal process by identifying an intermediate step. I use the phrase "statistical mediation" to refer the practice of fitting a series of regression models (represented in the triangular diagram below) on the data obtained from a single experiment. Conceptual mediation is difficult to test. Statistical mediation techniques provide clues about some aspects of the overall conceptual mediation story, not direct evidence for the whole thing. Nonetheless, it is important for psychology students to be familiar with statistical mediation techniques.

The diagram above is a symbolic representation of statistical mediation. It is covered in the videos.

These videos are designed to be viewed in order. For example, later videos continue discussing examples from earlier videos. These videos also assume that students have prior knowledge of linear regression models and some exposure to the lm function in R. My main statistics page links to several series of videos about linear regression that could provide the necessary background.

The R code files and example datasets used in this video series are available here: Code for mediation examples [zip]. I show some slides in a few of the videos. Those slides are available here: Mediation slides [zip]. The slides will not make sense without the context given in the videos, and you do not need the slides to watch the videos.

## List of Videos

The videos linked below are also available in a single Youtube playlist.

Part 1: Introduction

Part 2: Three key regression models

Part 3: Intro to bootstrapping

Part 4: Conceptual illustration in R

Part 5: Four R packages

Part 6: Using PROCESS in SPSS

Part 7: Suppression and parallel mediation