Web Survey Bibliography
Title Improving Likert Scale Raw Scores Interpretability with K-means Clustering
Author Michalopoulou, C.; Symeonaki, M.
Source Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique; 135, 1, pp. 101-109
Year 2017
Database SAGE Journals Online
Access date 23.08.2017
Abstract In this article, by applying k-means clustering, cut-off points are obtained for the recoding of raw scale scores into a fixed number of groupings that preserve the original scoring. The method is demonstrated on a Likert scale measuring xenophobia that was used in a large-scale sample survey conducted in Northern Greece by the National Centre for Social Research. Applying split-half samples and fuzzy c-means clustering, the stability of the proposed solution is validated empirically. Testing its performance against three single indicators of xenophobia shows that it differentiates well between non-xenophobic and xenophobic respondents. The proposed method may be easily applied to facilitate interpretation by providing a more concise and meaningful “profile” of Likert scale (or subscale) raw scores especially the negative and positive ends of the scale for evaluation and social policy purposes.
Access/Direct link Journal Homepage (abstract) / (full text)
Year of publication2017
Bibliographic typeJournal article
Web survey bibliography (4086)
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