An Introduction to Fitting and Evaluating Mixed-effects Models in R

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Date
2018-09-06
Authors
Nagle, Charles
Nagle, Charles
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World Languages and Cultures
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World Languages and Cultures
Abstract

Mixed-effects modeling is a multidimensional statistical analysis capable of modeling complex relationships between predictor and outcome variables while accounting for random variance in various dimensions of the data. Although this technique is gaining popularity in applied linguistics research, learning how to model, and how to do so in R, can be intimidating. This guide provides an introduction to fitting mixed-effects models in R (Version 3.5.3) using RStudio. It includes a written introduction describing the modeling process, a video tutorial that focuses on getting started in RStudio, a sample data set, and an R script containing code to analyze the data. By the end of this introduction, researchers should have developed a basic understanding of the modeling process and should be able to (1) read data into R and inspect its structure, (2) create a series of plots to visualize trends and/or primary variables, and (3) fit and evaluate models.

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This conference proceedings is published as Nagle, C. (2019). An introduction to fitting and evaluating mixed-effects models in R. In J. Levis, C. Nagle, & E. Todey (Eds.), Proceedings of the 10th Pronunciation in Second Language Learning and Teaching Conference, ISSN 2380-9566, Ames, IA, September 2018 (pp. 82-105). Ames, IA: Iowa State University. Posted with permission.

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