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.

中文翻译：

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在存在响应样式偏差的情况下对偏好数据进行聚类。

偏好数据（例如李克特量表数据）通常在基于问卷的调查中获得。根据调查项目对受访者进行聚类有助于发现潜在结构。但是，偏好数据的聚类分析可能会受到响应方式的影响，也就是说，响应者的系统响应趋势与项目内容无关。例如，一些受访者可能倾向于选择量表两端的评分，这被称为“极端回应风格”。具有极端反应风格的受访者群体可能被错误地识别为基于内容的群体。为了解决此问题，我们提出了一种新颖的方法，即根据受访者对一组项目的指示偏好将他们分类，同时纠正响应风格的偏见。我们首先介绍一个新的框架来检测和纠正 通过推广约束双比例缩放中使用的响应样式的定义来扩展响应样式。然后，我们同时校正响应样式，并根据校正后的偏好数据执行聚类分析。仿真研究表明，所提出的方法比现有方法具有更好的聚类精度。我们将该方法应用于来自四个不同国家的有关社会价值的经验数据。