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An introduction to bayesian multilevel models using brms : a case study of gender effects on vowel variability in standard Indonesian

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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/).
Keywords
INTRANASAL OXYTOCIN, CROSS-VALIDATION, MIXED MODELS, VARIANCE, SCHIZOPHRENIA, CONFIDENCE

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MLA
Nalborczyk, Ladislas et al. “An Introduction to Bayesian Multilevel Models Using Brms : a Case Study of Gender Effects on Vowel Variability in Standard Indonesian.” JOURNAL OF SPEECH LANGUAGE AND HEARING RESEARCH 62.5 (2019): 1225–1242. Print.
APA
Nalborczyk, L., Batailler, C., Loevenbruck, H., Vilain, A., & Burkner, P.-C. (2019). An introduction to bayesian multilevel models using brms : a case study of gender effects on vowel variability in standard Indonesian. JOURNAL OF SPEECH LANGUAGE AND HEARING RESEARCH, 62(5), 1225–1242.
Chicago author-date
Nalborczyk, Ladislas, Cedric Batailler, Helene Loevenbruck, Anne Vilain, and Paul-Christian Burkner. 2019. “An Introduction to Bayesian Multilevel Models Using Brms : a Case Study of Gender Effects on Vowel Variability in Standard Indonesian.” Journal of Speech Language and Hearing Research 62 (5): 1225–1242.
Chicago author-date (all authors)
Nalborczyk, Ladislas, Cedric Batailler, Helene Loevenbruck, Anne Vilain, and Paul-Christian Burkner. 2019. “An Introduction to Bayesian Multilevel Models Using Brms : a Case Study of Gender Effects on Vowel Variability in Standard Indonesian.” Journal of Speech Language and Hearing Research 62 (5): 1225–1242.
Vancouver
1.
Nalborczyk L, Batailler C, Loevenbruck H, Vilain A, Burkner P-C. An introduction to bayesian multilevel models using brms : a case study of gender effects on vowel variability in standard Indonesian. JOURNAL OF SPEECH LANGUAGE AND HEARING RESEARCH. Rockville: Amer Speech-language-hearing Assoc; 2019;62(5):1225–42.
IEEE
[1]
L. Nalborczyk, C. Batailler, H. Loevenbruck, A. Vilain, and P.-C. Burkner, “An introduction to bayesian multilevel models using brms : a case study of gender effects on vowel variability in standard Indonesian,” JOURNAL OF SPEECH LANGUAGE AND HEARING RESEARCH, vol. 62, no. 5, pp. 1225–1242, 2019.
@article{8624552,
  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/).},
  author       = {Nalborczyk, Ladislas and Batailler, Cedric and Loevenbruck, Helene and Vilain, Anne and Burkner, Paul-Christian},
  issn         = {1092-4388},
  journal      = {JOURNAL OF SPEECH LANGUAGE AND HEARING RESEARCH},
  keywords     = {INTRANASAL OXYTOCIN,CROSS-VALIDATION,MIXED MODELS,VARIANCE,SCHIZOPHRENIA,CONFIDENCE},
  language     = {eng},
  number       = {5},
  pages        = {1225--1242},
  publisher    = {Amer Speech-language-hearing Assoc},
  title        = {An introduction to bayesian multilevel models using brms : a case study of gender effects on vowel variability in standard Indonesian},
  url          = {http://dx.doi.org/10.1044/2018_JSLHR-S-18-0006},
  volume       = {62},
  year         = {2019},
}

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