Bayesian data analysis in the phonetic sciences: A tutorial introduction

• Published in: Journal of phonetics Vol. 71; pp. 147 - 161
• Main Authors: Vasishth, Shravan, Nicenboim, Bruno, Beckman, Mary E., Li, Fangfang, Kong, Eun Jong
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01-11-2018
• Subjects: Bayesian data analysis; Gender effects; Linear mixed models; Voice onset time; Vowel duration; Linear mixed models; Bayesian data analysis; Vowel duration; Voice onset time; Gender effects; vowel duration; gender effects; voice onset time
• ISSN: 0095-4470; 1095-8576
• DOI: 10.1016/j.wocn.2018.07.008

•A hands-on introduction to using the brms package for Bayesian data analysis.•Examples discussing how to decide on priors, and how to carry out sensitivity analyses.•Examples showing how to evaluate model fit using posterior predictive checks.•Examples showing different approaches to inference: reporting posterior distributions, Bayes factor, cross-validation.•Example using measurement error models demonstrating the flexibility of Bayesian modeling.
This tutorial analyzes voice onset time (VOT) data from Dongbei (Northeastern) Mandarin Chinese and North American English to demonstrate how Bayesian linear mixed models can be fit using the programming language Stan via the R package brms. Through this case study, we demonstrate some of the advantages of the Bayesian framework: researchers can (i) flexibly define the underlying process that they believe to have generated the data; (ii) obtain direct information regarding the uncertainty about the parameter that relates the data to the theoretical question being studied; and (iii) incorporate prior knowledge into the analysis. Getting started with Bayesian modeling can be challenging, especially when one is trying to model one’s own (often unique) data. It is difficult to see how one can apply general principles described in textbooks to one’s own specific research problem. We address this barrier to using Bayesian methods by providing three detailed examples, with source code to allow easy reproducibility. The examples presented are intended to give the reader a flavor of the process of model-fitting; suggestions for further study are also provided. All data and code are available from:https://osf.io/g4zpv.