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Linguistic Approaches to Bilingualism: Online-First Articles

A tutorial on generalised additive mixed effects models for bilingualism research

ORCID logo | University of Edinburgh
ORCID logo | Rutgers University
While recent years have seen a shift towards random effects modelling, particularly in areas of linguistics in which nested structure is the norm (e.g., trial repetitions nested within participants), an over-reliance on standard linear modelling prevails, particularly in the cases of dynamic phenomena that may not constitute a linear relationship, e.g., vowel trajectories, pitch contours, acquisition processes, etc. Generalised Additive (Mixed) Models (GAMMs) are now commonly employed in phonetic research (given the naturally dynamic nature of speech data) and this is reflected by the availability of several tutorials which focus on phonetic data. This tutorial aims at making GAMMs accessible to researchers from other fields within linguistics. In particular, this tutorial is written for researchers in bilingualism and multilingualism who wish to be able to start using GAMMs for non-linear data, which is very common in developmental and learning phenomena. While only the basics will be covered here, we hope that researchers will get the necessary foundations to be able to learn GAMMs from existing resources.
Keywords: generalised additive mixed models, GAMMs, non-linear data, dynamic analysis
Article outline
  • 1.Motivation
    • 1.1Generalised additive (mixed) models
  • 2.Pre-requisites
  • 3.Case study 1: U-shaped learning
    • 3.1U-shaped learning
    • 3.2The data
    • 3.3Modelling a non-linear effect
    • 3.4Multiple smooth terms
  • 4.Case study 2: L2 vowel production in simultaneous and late bilinguals
  • 5.Summary
  • 6.Data availability statement
  • References
This content is being prepared for publication; it may be subject to changes.
References (40)
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