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Understanding Mixed-Effects Models Through Data Simulation

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DeBruine, Lisa M., Barr, Dale J.·2021·Advances in Methods and Practices in Psychological Science

DOI 10.1177/2515245920965119

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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 analysis of variance on aggregated responses because researchers are not confident specifying and interpreting mixed-effects models. This Tutorial explains how to simulate data with random-effects structure and analyze 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 not only can enhance understanding of how these models work, but also enables researchers to perform power calculations for complex designs. All materials associated with this article can be accessed at https://osf.io/3cz2e/ .

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Authors: DeBruine, L. M., & Barr, D. J.
Affiliations: University of Glasgow (GB)
Corresponding author: DeBruine, Lisa M.
ORCID: DeBruine, Lisa M.
Barr, Dale J.
Venue: Advances in Methods and Practices in Psychological Science
Publisher: SAGE Publications
Published: 2021-01
Volume: 4
Issue: 1
ISSN: 2515-2459 (ISSN-L)
2515-2467
DOI: 10.1177/2515245920965119
OpenAlex: W2951982695
Cited by: 148
References: 21
Citations by year: 2026: 15 · 2025: 35 · 2024: 34 · 2023: 30 · 2022: 22 · 2021: 11
FWCI: 18.09
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (unspecified)
Locations: Advances in Methods and Practices in Psychological Science (publishedVersion, cc-by)
ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (acceptedVersion, cc-by-sa)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
OSF Preprints (OSF Preprints) (submittedVersion, cc-by-sa)
Funders: H2020 European Research Council (647910)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Physical Sciences › Mathematics › Statistics and Probability › Statistics Education and Methodologies
Life Sciences › Agricultural and Biological Sciences › Agronomy and Crop Science › Agriculture, Soil, Plant Science
Keywords: mixed models, data simulation, random effects, linear mixed models, lme4, power analysis, experimental design, model interpretation

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Understanding Mixed-Effects Models Through Data Simulation

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