Web Survey Bibliography

Title Improving Likert Scale Raw Scores Interpretability with K-means Clustering
Year 2017
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.
Year of publication2017
Bibliographic typeJournal article

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