Interactions in linear regression models: Analysis of Variance (2023)

This video series is a demonstration of how to apply the Analysis of Variance (ANOVA) framing device to statistical interactions in linear regression models. It was created as part of a statistics class for first-year psychology PhD students in 2023. It consists of eight videos listed on this page and in a Youtube playlist.

ANOVA is a general term for a variety of models that partition the variance of one variable (the response variable or dependent variable) into portions associated with other variables, combinations of variables, and sources of noise or error. It can be thought of as a strategy for summarizing the results of one or more regression models. These videos focus on explaining between-subjects ANOVA from the starting point of regression interactions and defining "main effects" in the context of a statistical interaction.

These videos are designed to be viewed in order. For example, later videos continue discussing hypothetical data examples from earlier videos. These videos also assume that students have a conceptual understanding of interactions in linear regression models and some exposure to the lm function in R.

The R code files and example datasets used in this video series are available here: Code for ANOVA and hypothetical data [zip]. I show some slides in a few of the videos. Those slides are available here: ANOVA slides [pdf]. 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: Types of main effects

Part 3: Types of main effects, continued

Part 4: F-tests

Part 5: F-tests, continued

Part 6: Unitless measures of association (effect sizes)

Part 7: SPSS vs. R

Part 8: Conclusion