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一种双模聚类方法,用于捕获大型轮廓数据矩阵中主要相互作用模式的本质。

A two-mode clustering method to capture the nature of the dominant interaction pattern in large profile data matrices.

机构信息

Department of Psychology, Katholieke Universiteit Leuven, Leuven, Belgium.

出版信息

Psychol Methods. 2011 Sep;16(3):361-71. doi: 10.1037/a0024446.

Abstract

Profile data abound in a broad range of research settings. Often it is of considerable theoretical importance to address specific structural questions with regard to the major pattern as included in such data. A key challenge in this regard pertains to identifying which type of interaction (double ordinal, mixed ordinal/disordinal, double disordinal) most adequately fits the major pattern in a profile data set at hand. In the present article a novel methodology is proposed to deal with this challenge. This methodology is based on constrained and unconstrained versions of a recently introduced 2-mode clustering model, the real-valued hierarchical classes model. The methodology is illustrated using empirical Person × Situation profile data on altruism.

摘要

个人资料在广泛的研究环境中都有大量存在。通常,对于包含在这些数据中的主要模式,解决特定的结构问题具有相当大的理论重要性。在这方面的一个关键挑战是确定哪种类型的相互作用(双重有序、混合有序/无序、双重无序)最适合手头的个人资料数据集的主要模式。本文提出了一种新的方法来应对这一挑战。该方法基于最近引入的 2 模式聚类模型——实值层次类模型的约束和非约束版本。该方法使用利他主义的实证个人与情境资料进行了说明。

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