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.

中文翻译：

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词汇研究中固定效应和混合效应建模的对比：重新分析 Laufer （2024） 和 McLean 等人 （2020）


词汇研究中的分析应避免语言作为固定效果的谬误，即没有提供统计证据来支持样本中测试的单词之外的所声称的概括。尽管混合效应模型在社会科学中被广泛采用以避免这种谬误，但第二语言词汇研究人员主要进行可能存在问题的固定效应分析。在本研究中，使用广义线性混合效应模型 （GLMM） 重新分析了两项依赖于固定效应模型的已发表词汇研究。与先前比较这些方法的研究一致，GLMM 的效应量减少了 36% 至近 80%。至关重要的是，一项研究的主张并未得到 GLMM 重新分析的完全证实。研究结果表明，第二语言词汇研究人员应该强烈考虑混合效应模型，以避免语言作为固定效应的谬误。此外，应重复采用仅固定效应分析的早期研究，以验证其效应大小没有被夸大。