An Introduction to Bayesian Multilevel Models Using brms: A Case Study of Gender Effects on Vowel Variability in Standard Indonesian
Publication: Journal of Speech, Language, and Hearing Research
Volume 62, Number 5
Pages 1225-1242
Abstract
Purpose
Bayesian multilevel models are increasingly used to overcome the limitations of frequentist approaches in the analysis of complex structured data. This tutorial introduces Bayesian multilevel modeling for the specific analysis of speech data, using the brms package developed in R.
Method
In this tutorial, we provide a practical introduction to Bayesian multilevel modeling by reanalyzing a phonetic data set containing formant (F1 and F2) values for 5 vowels of standard Indonesian (ISO 639-3:ind), as spoken by 8 speakers (4 females and 4 males), with several repetitions of each vowel.
Results
We first give an introductory overview of the Bayesian framework and multilevel modeling. We then show how Bayesian multilevel models can be fitted using the probabilistic programming language Stan and the R package brms, which provides an intuitive formula syntax.
Conclusions
Through this tutorial, we demonstrate some of the advantages of the Bayesian framework for statistical modeling and provide a detailed case study, with complete source code for full reproducibility of the analyses (https://osf.io/dpzcb/).
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Information & Authors
Information
Published In
Journal of Speech, Language, and Hearing Research
Volume 62 • Number 5 • May 2019
Pages: 1225-1242
PubMed: 31082309
Copyright
Copyright © 2019 American Speech-Language-Hearing Association.
History
- Received: Jan 11, 2018
- Revised: Mar 26, 2018
- Accepted: Aug 15, 2018
- Published online: May 10, 2019
- Published in issue: May 21, 2019
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