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Methodological Synthesis of Cluster Analysis in Second Language Research
Author(s) -
Crowther Dustin,
Kim Susie,
Lee Jongbong,
Lim Jungmin,
Loewen Shawn
Publication year - 2021
Publication title -
language learning
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.882
H-Index - 103
eISSN - 1467-9922
pISSN - 0023-8333
DOI - 10.1111/lang.12428
Subject(s) - cluster (spacecraft) , psychology , content analysis , hierarchical clustering , data science , computer science , cluster analysis , sociology , artificial intelligence , social science , programming language
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.