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Contrasting Fixed- and Mixed-Effects Modeling in Vocabulary Research: Reanalyzing Laufer (2024) and Mclean et al. (2020)

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Title: Contrasting Fixed- and Mixed-Effects Modeling in Vocabulary Research: Reanalyzing Laufer (2024) and Mclean et al. (2020)
Language: English
Authors: Christopher Nicklin (ORCID 0000-0002-8945-0678), Stuart McLean (ORCID 0000-0002-7035-378X), Joseph P. Vitta (ORCID 0000-0002-5711-969X)
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
FullText Text:
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  Data: Contrasting Fixed- and Mixed-Effects Modeling in Vocabulary Research: Reanalyzing Laufer (2024) and Mclean et al. (2020)
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  Data: <searchLink fieldCode="AR" term="%22Christopher+Nicklin%22">Christopher Nicklin</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-8945-0678">0000-0002-8945-0678</externalLink>)<br /><searchLink fieldCode="AR" term="%22Stuart+McLean%22">Stuart McLean</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-7035-378X">0000-0002-7035-378X</externalLink>)<br /><searchLink fieldCode="AR" term="%22Joseph+P%2E+Vitta%22">Joseph P. Vitta</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-5711-969X">0000-0002-5711-969X</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Language+Learning%22"><i>Language Learning</i></searchLink>. 2026 76(1):211-248.
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  Data: 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
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  Data: 38
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  Data: 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.
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  Data: https://osf.io/a5xf7
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