Bayesian Multivariate Linear Mixed-Effects Models for Speech Research: A Tutorial Using brms.
Purpose: When studying how factors influence multiple outcomes (e.g., acoustic measures in phonetics), researchers often analyze each outcome separately using a univariate approach. However, this approach ignores relationships between outcomes, which can reduce estimate accuracy and make it difficul...
| Publicado en: | Journal of Speech, Language & Hearing Research Vol. 69; no. 8; pp. 3515 - 3541 |
|---|---|
| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
American Speech-Language-Hearing Association
Aug2026
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=196185566&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 196185566 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10924388 1SM jtl: Journal of Speech, Language & Hearing Research issn: 10924388 maglogo: N pubinfo: dt: Aug2026 vid: 69 iid: 8 pid: 42 pub: American Speech-Language-Hearing Association artinfo: ui: 196185566 10.1044/2026_JSLHR-25-00880 ppf: 3515 ppct: 26 formats: fmt: @attributes: type: P size: 11.4MB tig: atl: Bayesian Multivariate Linear Mixed-Effects Models for Speech Research: A Tutorial Using brms. aug: au: Hu, Na Bürkner, Paul-Christian Arvaniti, Amalia affil: Department of Modern Languages and Cultures, Radboud University, Nijmegen, the Netherlands Department of Statistics, TU Dortmund University, Germany su: Germany Phonetics Statistical models Repeated measures design Pearson correlation (Statistics) Research funding Multiple regression analysis Multivariate analysis Descriptive statistics Physiological aspects of speech Speech evaluation Factor analysis Data analysis software sug: subj: Phonetics Germany Statistical models Repeated measures design Pearson correlation (Statistics) Research funding Multiple regression analysis Multivariate analysis Descriptive statistics Physiological aspects of speech Speech evaluation Factor analysis Data analysis software ab: Purpose: When studying how factors influence multiple outcomes (e.g., acoustic measures in phonetics), researchers often analyze each outcome separately using a univariate approach. However, this approach ignores relationships between outcomes, which can reduce estimate accuracy and make it difficult to examine how effects are related across outcomes. A multivariate approach addresses these issues by modeling all outcomes jointly. This tutorial illustrates how to fit Bayesian multivariate linear mixed-effects models using the R package brms. Method: We present three example applications in phonetic research using a corpus of Greek utterances. We focus on a rising accent, that is, a deliberate fundamental frequency movement temporally aligned with a word's stressed syllable and used to highlight that word in speech. Specifically, we examine whether the phonetic characteristics of the rising accent depend on two factors: (a) the presence of a preceding accent within the same utterance and (b) the location of the stress in the accented word relative to its final syllable. Results: The multivariate approach reduced uncertainty in population-level effect estimates compared to univariate models and provided a convenient way to examine correlations among effects across outcomes. Conclusion: This tutorial provides guidance on implementing Bayesian multivariate linear mixed-effects models and demonstrates their potential. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|