Understanding mixed effects models through data simulation


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.

DeBruine, L. M., & Barr, D. J. (2021). Understanding Mixed-Effects Models Through Data Simulation. Advances in Methods and Practices in Psychological Science. https://doi.org/10.1177/2515245920965119
Lisa DeBruine
Lisa DeBruine
Professor of Psychology

Lisa DeBruine is a professor of psychology at the University of Glasgow. Her substantive research is on the social perception of faces and kinship. Her meta-science interests include team science (especially the Psychological Science Accelerator), open documentation, data simulation, web-based tools for data collection and stimulus generation, and teaching computational reproducibility.