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Second language acquisition researchers often face particular challenges when attempting to generalize study findings to the wider learner population. For example, language learners constitute a heterogeneous group, and it is not always clear how a study's findings may generalize to other individuals who may differ in terms of language background and proficiency, among many other factors. In this paper, we provide an overview of how mixed‐effects models can be used to help overcome these and other issues in the field of second language acquisition. We provide an overview of the benefits of mixed‐effects models and a practical example of how mixed‐effects analyses can be conducted. Mixed‐effects models provide second language researchers with a powerful statistical tool in the analysis of a variety of different types of data.
Linck et al. (Thu,) studied this question.