Clustering preference data in the presence of response style bias
Clustering preference data in the presence of response style bias
复制标题
在存在响应风格偏差的情况下对偏好数据进行聚类
DOI：
10.1111/bmsp.12170
复制
发表时间：
2019
影响因子：
2.6
通讯作者：
Hiroshi Yadohisa
中科院分区：
文献类型：
--
作者：
Mariko Takagishi;Michel van de Velden;Hiroshi Yadohisa
偏好数据，如李克特量表数据，通常在基于问卷的调查中获得。基于调查项目的被调查者聚类有助于发现潜在结构。然而，偏好数据的聚类分析可能会受到回应风格的影响，即被调查者的系统回应倾向与项目内容无关。例如，一些受访者可能倾向于在量表的末端选择评分，这被称为“极端反应风格”。具有极端回应风格的受访者集群可能被错误地识别为基于内容的集群。为了解决这个问题，我们提出了一种基于受访者对一组项目的偏好来聚类受访者的新方法，同时纠正了反应风格偏差。我们首先引入了一个新的框架，通过推广约束对偶标度中使用的响应样式的定义来检测和纠正响应样式。然后，我们同时校正响应风格，并根据校正后的偏好数据进行聚类分析。仿真研究表明，该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四个不同国家关于社会价值观的实证数据。
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.