Clustering preference data in the presence of response-style bias

Mariko Takagishi, Michel van de Velden, Hiroshi Yadohisa

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Preference data, such as Likert scale data, are often obtained in questionnaire-based surveys. Clustering respondents based on survey items is useful for discovering latent structures. However, cluster analysis of preference data may be affected by response styles, that is, a respondent's systematic response tendencies irrespective of the item content. For example, some respondents may tend to select ratings at the ends of the scale, which is called an ‘extreme response style’. A cluster of respondents with an extreme response style can be mistakenly identified as a content-based cluster. To address this problem, we propose a novel method of clustering respondents based on their indicated preferences for a set of items while correcting for response-style bias. We first introduce a new framework to detect, and correct for, response styles by generalizing the definition of response styles used in constrained dual scaling. We then simultaneously correct for response styles and perform a cluster analysis based on the corrected preference data. A simulation study shows that the proposed method yields better clustering accuracy than the existing methods do. We apply the method to empirical data from four different countries concerning social values.

Original languageEnglish
Pages (from-to)401-425
Number of pages25
JournalBritish Journal of Mathematical and Statistical Psychology
Issue number3
Publication statusPublished - Nov 1 2019
Externally publishedYes


  • categorical data
  • constraint least squares
  • k-means
  • preference data
  • response style
  • smoothing
  • splines

ASJC Scopus subject areas

  • Statistics and Probability
  • Arts and Humanities (miscellaneous)
  • General Psychology


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