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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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 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 |
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Status: | Published |
Refereed: | Yes |
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: | 2515-2459 |
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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