Journal article
Estimating power in (generalized) linear mixed models: An open introduction and tutorial in R
- Abstract:
- 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.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, 1.7MB, Terms of use)
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- Publisher copy:
- 10.3758/s13428-021-01546-0
Authors
- Publisher:
- Springer
- Journal:
- Behavior Research Methods More from this journal
- Volume:
- 53
- Issue:
- 6
- Pages:
- 2528-2543
- Publication date:
- 2021-05-05
- Acceptance date:
- 2021-01-14
- DOI:
- EISSN:
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1554-3528
- ISSN:
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1554-351X
- Language:
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English
- Keywords:
- Pubs id:
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1175060
- Local pid:
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pubs:1175060
- Deposit date:
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2021-05-08
Terms of use
- Copyright holder:
- Kumle et al.
- Copyright date:
- 2021
- Rights statement:
- © The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
- Licence:
- CC Attribution (CC BY)
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