Estimating power in (generalized) linear mixed models: An open introduction and tutorial in R

• Published in: Behavior research methods Vol. 53; no. 6; pp. 2528 - 2543
• Main Authors: Kumle, Levi, Võ, Melissa L.-H., Draschkow, Dejan
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2021; Springer Nature B.V
• Subjects: Behavioral Science and Psychology; Cognitive Psychology; Computer Simulation; Generalized linear models; Humans; Linear Models; Power; Psychology; Reproducibility of Results; Sample Size; Simulation; lme4; R; Simulation; mixedpower; Power; Mixed models
• ISSN: 1554-3528; 1554-351X; 1554-3528
• DOI: 10.3758/s13428-021-01546-0

Mixed-effects models are a powerful tool for modeling fixed and random effects simultaneously, but do not offer a feasible analytic solution for estimating the probability that a test correctly rejects the null hypothesis. Being able to estimate this probability, however, is critical for sample size planning, as power is closely linked to the reliability and replicability of empirical findings. A flexible and very intuitive alternative to analytic power solutions are simulation-based power analyses. Although various tools for conducting simulation-based power analyses for mixed-effects models are available, there is lack of guidance on how to appropriately use them. In this tutorial, we discuss how to estimate power for mixed-effects models in different use cases: first, how to use models that were fit on available (e.g. published) data to determine sample size; second, how to determine the number of stimuli required for sufficient power; and finally, how to conduct sample size planning without available data. Our examples cover both linear and generalized linear models and we provide code and resources for performing simulation-based power analyses on openly accessible data sets. The present work therefore helps researchers to navigate sound research design when using mixed-effects models, by summarizing resources, collating available knowledge, providing solutions and tools, and applying them to real-world problems in sample sizing planning when sophisticated analysis procedures like mixed-effects models are outlined as inferential procedures.