We present a review of second language researchers’ use of cluster analysis, an advanced statistical method still uncommon but increasingly used to identify groups or patterns in a dataset and to examine group differences. After describing key methodological considerations in conducting cluster analysis, we present a methodological synthesis of 65 studies published between 1989 and 2018 that employed cluster analysis. We specifically review the use of cluster analysis for themes of usage and reporting practices. Our findings indicate that hierarchical cluster analysis and K‐means cluster analysis were the most commonly used cluster methods, but the widespread use of these two methods tended not to be accompanied by sound reporting practices, particularly when justifying cluster solutions. In our analysis, we highlight concerns related to reporting and evaluation. For future use and to inform methodological practices in second language research, we briefly report on a sample study of cluster analysis that uses open data.

中文翻译：

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第二语言研究中聚类分析的方法学综合

我们对第二语言研究人员对聚类分析的使用进行了综述，聚类分析是一种仍不常见的高级统计方法，但是越来越多地用于识别数据集中的组或模式并检查组差异。在描述进行聚类分析的主要方法学考虑因素之后，我们提出了1989年至2018年发表的65项采用聚类分析的研究的方法学综合。我们专门针对使用情况和报告实践主题审查了聚类分析的使用。我们的发现表明，层次聚类分析和K-means聚类分析是最常用的聚类方法，但是这两种方法的广泛使用往往没有合理的报告实践，尤其是在证明聚类解决方案合理时。在我们的分析中 我们重点介绍与报告和评估有关的问题。为了将来使用并为第二语言研究提供方法学信息，我们简要报告了使用开放数据的聚类分析的样本研究。