An Introduction to Linear Mixed-Effects Modeling in R
This Tutorial serves as both an approachable theoretical introduction to mixed-effects modeling and a practical introduction to how to implement mixed-effects models in R. The intended audience is researchers who have some basic statistical knowledge, but little or no experience implementing mixed-e...
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Veröffentlicht in: | Advances in methods and practices in psychological science 2021-01, Vol.4 (1) |
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Format: | Artikel |
Sprache: | eng |
Online-Zugang: | Volltext |
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Zusammenfassung: | This Tutorial serves as both an approachable theoretical introduction to mixed-effects modeling and a practical introduction to how to implement mixed-effects models in R. The intended audience is researchers who have some basic statistical knowledge, but little or no experience implementing mixed-effects models in R using their own data. In an attempt to increase the accessibility of this Tutorial, I deliberately avoid using mathematical terminology beyond what a student would learn in a standard graduate-level statistics course, but I reference articles and textbooks that provide more detail for interested readers. This Tutorial includes snippets of R code throughout; the data and R script used to build the models described in the text are available via OSF at https://osf.io/v6qag/, so readers can follow along if they wish. The goal of this practical introduction is to provide researchers with the tools they need to begin implementing mixed-effects models in their own research. |
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ISSN: | 2515-2459 2515-2467 |
DOI: | 10.1177/2515245920960351 |