Cluster analysis

Shelley Staples, Douglas Biber

Research output: Chapter in Book/Report/Conference proceedingChapter

20 Scopus citations

Abstract

Cluster analysis is useful in studies where there is extensive variation among the individual cases within predefined categories. For example, many researchers compare students across proficiency level categories, defined by their performance on a test or holistic ratings. Cluster analysis is not a commonly used statistical procedure in L2 research, and it is rarely discussed in methodological textbooks written for L2 researchers. There are two main types of cluster analysis: hierarchical cluster analysis (HCA) and disjoint cluster analysis. Rather, the use of agglomeration schedule mostly determines the number of clusters that should be included in the final analysis. For this purpose, the focus on the Coefficients column, which indicates the within-cluster sum of squares at the point at which the last two clusters were joined. Two types of descriptive information are especially useful for this purpose: investigating the composition of each cluster and investigating the mean scores of the predictor variables for each cluster.

Original languageEnglish (US)
Title of host publicationAdvancing Quantitative Methods in Second Language Research
PublisherTaylor and Francis
Pages243-274
Number of pages32
ISBN (Electronic)9781317974093
ISBN (Print)9780415718332
DOIs
StatePublished - Jan 1 2015

ASJC Scopus subject areas

  • Arts and Humanities(all)
  • Social Sciences(all)

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