Contrasting Fixed- and Mixed-Effects Modeling in Vocabulary Research: Reanalyzing Laufer (2024) and Mclean et al. (2020)
Saved in:
| Title: | Contrasting Fixed- and Mixed-Effects Modeling in Vocabulary Research: Reanalyzing Laufer (2024) and Mclean et al. (2020) |
|---|---|
| Language: | English |
| Authors: | Christopher Nicklin (ORCID |
| Source: | Language Learning. 2026 76(1):211-248. |
| Availability: | Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us |
| Peer Reviewed: | Y |
| Page Count: | 38 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Vocabulary, Language Research, Second Languages, Hierarchical Linear Modeling, Effect Size |
| DOI: | 10.1111/lang.12715 |
| ISSN: | 0023-8333 1467-9922 |
| Abstract: | Analyses in vocabulary research should avoid the language-as-a-fixed-effect fallacy, whereby no statistical evidence is provided to support claimed generalizations beyond the words tested in the sample. Although mixed-effects models are widely adopted in social sciences to avoid this fallacy, second language vocabulary researchers primarily conduct potentially problematic fixed-effects analyses. In the present study, two published vocabulary studies relying on fixed-effects modeling were re-analyzed with generalized linear mixed-effects models (GLMMs). Consistent with prior research comparing these approaches, effect sizes in the GLMMs were reduced by 36% to nearly 80%. Crucially, one study's claims were not fully substantiated with GLMM re-analysis. The findings suggest that second language vocabulary researchers should strongly consider mixed-effect models to avoid the language-as-a-fixed-effect fallacy. Furthermore, replications of earlier studies that employed fixed-effects only analyses should be conducted to verify that their effect sizes were not overstated. |
| Abstractor: | As Provided |
| Notes: | https://osf.io/a5xf7 |
| Entry Date: | 2026 |
| Accession Number: | EJ1496724 |
| Database: | ERIC |
Be the first to leave a comment!
