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 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.
|Persistent URL||dx.doi.org/10.1111/bmsp.12170, hdl.handle.net/1765/120677|
|Journal||British Journal of Mathematical and Statistical Psychology|
Takagishi, M, van de Velden, M, & Yadohisa, H. (2019). Clustering preference data in the presence of response‐style bias. British Journal of Mathematical and Statistical Psychology. doi:10.1111/bmsp.12170