Introducing brms (Nalborczyk et al., 2019)

• DOI: 10.23641/asha.7973822.v1
• Publication Date: 2019-05-14
• Authors: Ladislas Nalborczyk; Cédric Batailler; Hélène Lœvenbruck; Anne Vilain; Paul‐Christian Bü...
• Keywords: FOS: Psychology; 170204 Linguistic Processes (incl. Speech Production and Comprehension); Statistics; FOS: Mathematics

Purpose: Bayesian multilevel models are increasingly used to overcome the limitations of frequentist approaches in analysis complex structured data. This tutorial introduces modeling for specific speech data, using brms package developed R.Method: In this tutorial, we provide a practical introduction by reanalyzing phonetic data set containing formant (F1 and F2) values 5 vowels standard Indonesian (ISO 639-3:ind), as spoken 8 speakers (4 females 4 males), with several repetitions each vowel.Results: We first give an introductory overview framework modeling. then show how can be fitted probabilistic programming language Stan R brms, which provides intuitive formula syntax.Conclusions: Through demonstrate some advantages statistical detailed case study, complete source code full reproducibility analyses (https://osf.io/dpzcb/).Supplemental Material S1. Moderation analysis; lognormal skew-normal models; session information. Nalborczyk, L., Batailler, C., Loevenbruck, H., Vilain, A., & Burkner, P.-C. (2019). An brms: A study gender effects on vowel variability Indonesian. Journal Speech, Language, Hearing Research, 62, 1225–1242. https://doi.org/10.1044/2018_JSLHR-S-18-0006