Cited by 148Statistical Methods and Bayesian InferenceStatistics Education and MethodologiesAgriculture, Soil, Plant Science

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

Published version of a preprint

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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Crossref · 10.1177/2515245920965119

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21 of 21 references verified0 citation errors · 0 not checkable

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  • A-priori power analysis for a single-predictor multilevel linear regression model using parameters from a previous study indicated that a minimum of 40 participants, each completing 10 pointing task trials, would achieve 83% power.

    “Using parameters estimated from own previous study (i.e., fixed effects beta coefficients and random effects’ variance; Kapaj et al., 2023), we found that a minimum of 40 participants, each completing 10 pointing task trials would achieve an 83% power (see data availability statement for study preregistration).”

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Authors
DeBruine, L. M., & Barr, D. J.
Affiliations
University of Glasgow (GB)
Corresponding author
DeBruine, Lisa M.
Venue
Advances in Methods and Practices in Psychological Science
Published
2021-01
Volume
4
Issue
1
ISSN
  • 2515-2459 (ISSN-L)
  • 2515-2467
OpenAlex
W2951982695
Cited by
148
References
21
Citations by year
2026: 15 · 2025: 35 · 2024: 34 · 2023: 30 · 2022: 22 · 2021: 11 · 2020: 1
FWCI
18.09
Type
journal article
Language
en
Open access
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 › Ecology, Evolution, Behavior and Systematics › 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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