Bayesian data analysis in the phonetic sciences
- 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 areThis 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.…
Author details: | Shravan VasishthORCiDGND, Bruno NicenboimORCiDGND, Mary E. Beckman, Fangfang LiGND, Eun Jong Kong |
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DOI: | https://doi.org/10.1016/j.wocn.2018.07.008 |
ISSN: | 0095-4470 |
Pubmed ID: | https://pubmed.ncbi.nlm.nih.gov/30197458 |
Title of parent work (English): | Journal of phonetics |
Subtitle (English): | a tutorial introduction |
Publisher: | Elsevier |
Place of publishing: | London |
Publication type: | Article |
Language: | English |
Year of first publication: | 2018 |
Publication year: | 2018 |
Release date: | 2021/07/08 |
Tag: | Bayesian data analysis; Gender effects; Linear mixed models; Voice onset time; Vowel duration |
Volume: | 71 |
Number of pages: | 15 |
First page: | 147 |
Last Page: | 161 |
Funding institution: | NIHUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [DC02932]; Ohio State University Department of Linguistics Targeted Investment Award; University of Lethbridge; Volkswagen FoundationVolkswagen [89 953]; Deutsche ForschungsgemeinschaftGerman Research Foundation (DFG) [VA 482/8-1] |
Organizational units: | Humanwissenschaftliche Fakultät / Strukturbereich Kognitionswissenschaften / Department Linguistik |
DDC classification: | 4 Sprache / 41 Linguistik / 410 Linguistik |