Clustering preference data in the presence of response‐style bias

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 syste...

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Veröffentlicht in:British journal of mathematical & statistical psychology 2019-11, Vol.72 (3), p.401-425
Hauptverfasser: Takagishi, Mariko, Velden, Michel, Yadohisa, Hiroshi
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container_title British journal of mathematical & statistical psychology
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creator Takagishi, Mariko
Velden, Michel
Yadohisa, Hiroshi
description 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.
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source MEDLINE; Access via Wiley Online Library
subjects Bias
categorical data
Cluster Analysis
Clustering
constraint least squares
Empirical analysis
k‐means
preference data
Preferences
Psychology, Social
Research - statistics & numerical data
Research Design
response style
smoothing
splines
Surveys and Questionnaires
title Clustering preference data in the presence of response‐style bias
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