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This tutorial demonstrates Bayesian linear mixed models for voice onset time (VOT) analysis in Mandarin Chinese and English. It highlights the advantages of Bayesian methods, including flexibility, uncertainty quantification, and prior knowledge integration.

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Area of Science:

  • Linguistics
  • Computational Linguistics
  • Statistical Modeling

Background:

  • Voice onset time (VOT) is a crucial phonetic feature differentiating languages.
  • Bayesian statistical modeling offers advanced analytical capabilities but can be complex to implement.
  • Reproducibility in statistical analysis is essential for scientific rigor.

Purpose of the Study:

  • To provide a practical tutorial on fitting Bayesian linear mixed models using Stan and brms.
  • To illustrate the advantages of the Bayesian framework for linguistic data analysis.
  • To lower the barrier for researchers applying Bayesian methods to unique datasets.

Main Methods:

  • Analysis of voice onset time (VOT) data from Dongbei Mandarin Chinese and North American English.
  • Application of Bayesian linear mixed models implemented via the R package brms and the Stan programming language.
  • Development of three detailed, reproducible examples with accompanying source code.

Main Results:

  • Demonstration of flexible model specification within the Bayesian framework.
  • Illustration of direct quantification of parameter uncertainty.
  • Showcasing the incorporation of prior knowledge into statistical models.

Conclusions:

  • Bayesian linear mixed models offer a powerful and flexible approach for phonetic analysis.
  • The provided tutorial and code facilitate the adoption of Bayesian methods in linguistic research.
  • Accessible implementation of Bayesian modeling enhances data analysis capabilities for researchers.