Understanding mixed effects models through data simulation

DeBruine, L. M. and Barr, D. J. (2021) Understanding mixed effects models through data simulation. Advances in Methods and Practices in Psychological Science, 4(1), pp. 1-15. (doi: 10.1177/2515245920965119)

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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. All materials associated with this article can be accessed at https://osf.io/3cz2e/.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Barr, Dr Dale and DeBruine, Professor Lisa
Authors: DeBruine, L. M., and Barr, D. J.
College/School:College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience
Journal Name:Advances in Methods and Practices in Psychological Science
Publisher:SAGE Publications
ISSN (Online):2515-2459
Published Online:23 March 2021
Copyright Holders:Copyright © 2021 The Authors
First Published:First published in Advances in Methods and Practices in Psychological Science 4(1): 2515245920965119
Publisher Policy:Reproduced under a Creative Commons License
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
172112KINSHIP: How do humans recognise kin?Lisa DebruineEuropean Research Council (ERC)647910NP - Centre for Cognitive Neuroimaging (CCNi)