Understanding mixed effects models through data simulation
Lisa M. DeBruine & Dale J. Barr
This repository contains all materials needed to reproduce the manuscript, as well as supplemental scripts and shiny apps.
Abstract Experimental designs that sample both subjects and stimuli from a larger population need to account for random effects of both subjects and stimuli using mixed effects models. However, much of this research is analyzed using ANOVA on aggregated responses because researchers are not confident specifying and interpreting mixed effects models. The tutorial will explain how to simulate data with random effects structure and analyse the data using linear mixed effects regression (with the lme4 R package), with a focus on interpreting the output in light of the simulated parameters. Data simulation can not only enhance understanding of how these models work, but also enables researchers to perform power calculations for complex designs.
- PsyArXiv preprint
- HTML version
- Example code: Rmd / html
- Example code without tidyverse: Rmd / html
- More complex example: Rmd / html
- Shiny App Simulating LMEM
- Shiny App Crossed Random Effects