Applying Mixed-Effects Models in Research on Second Language Acquisition
A Tutorial for Beginners
Bibliographic Data
| ID | 5901231 |
|---|---|
| Authors | Marc Brysbaert (0000-0002-3645-3189, Ghent University, corresponding author) |
| Year | 2025 |
| Volume | 10 |
| Issue | 2 |
| Pages | 20 |
| Publication date | 2025-01-23 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Languages (JOURNAL) |
| Journal identifiers | ISSN: 2226-471X • E-ISSN: 2226-471X |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/languages10020020 |
| OpenAlex | W4406756621 |
| Language | EN |
| Citations received | 1 |
| References cited | 22 |
Mixed-effects models have become indispensable tools for analyzing data in second language acquisition (SLA) research. This tutorial offers a step-by-step guide to conducting mixed-effects analyses for simple designs using the gamlj package in jamovi, a user-friendly, free statistical software. We begin by discussing the advantages of mixed-effects modeling over traditional methods, particularly for SLA data, and the rationale for focusing on simple designs. Subsequently, we introduce the gamlj package, highlighting its intuitive interface and error-prevention features. To illustrate the application of the package, we employ toy datasets that can be easily replicated and used with other statistical software. By providing a clear and accessible approach, this tutorial empowers SLA researchers to effectively analyze their data and draw meaningful conclusions
Mathematics education · Computer Science · EFL/ESL Teaching and Learning · Neurobiology of Language and Bilingualism · Psychology · Second Language Acquisition and Learning
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Tidy Data
Linear mixed-effects models and the analysis of nonindependent data
Categorical data analysis
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How reliable are standard reading time analyses? Hierarchical bootstrap reveals substantial power over-optimism and scale-dependent Type I error inflation
A Crash Course in Good and Bad Controls
| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 1 |