Social Psychology of Interaction and Group Dynamics: The Problem of Interdependence

社会互动的研究是社会和行为科学研究的核心部分。尽管其中心地位，统计方法还没有得到很好的发展，以处理这些数据。现有统计技术的问题在于，它们要求观测是独立的。然而，在涉及社会互动的研究中，观察结果由于它们的互动而是非独立的。例如：两个兄弟姐妹相互影响对方的行为;工作组的成员相互影响对方的表现;一群猴子在对方在场时表现出特定的梳理行为;陪审员在审议中相互影响对方的裁决。本研究旨在开发一类统计模型，将允许从社会互动研究的数据分析。它提出了一个新的框架，成对的方法，其中涉及社会互动的设计可以进行测试和评估。其概念是将数据结构的非独立性直接嵌入到数据集中，然后相应地对数据进行建模。这些统计技术将允许研究社会和行为科学中的许多问题，这些问题没有得到太多的研究关注，主要是因为没有适当的方法来分析数据。这些技术可以广泛应用，并将有助于NSF的知识和分布式智能（KDI）倡议的知识网络的重点。

The study of social interaction is a central part of research in the social and behavioral sciences. Despite its centrality, statistical methods have not been well developed for handling such data. The problem with existing statistical techniques is that they require observations to be independent. However, in research involving social interaction, the observations are nonindependent by virtue of their interaction. For instance: two siblings influence each other's behavior; the members of a work group influence each other's performance; a group of monkeys exhibit particular grooming behaviors in each other's presence; and jurors in deliberation influence each other's verdicts. This research seeks to develop a class of statistical models that will permit analyses of data from social interaction research. It presents a new framework, the pairwise approach, in which a broad class of designs involving social interaction can be tested and evaluated. The notion is to embed the nonindependence of the data structure directly into the data set, and then model the data accordingly. These statistical techniques will permit the study of many problems in the social and behavioral sciences that have not received much research attention primarily because there have been no appropriate methods for analyzing the data. The techniques can be broadly applied, and will contribute to the Knowledge Networking focus of NSF's Knowledge and Distributed Intelligence (KDI) initiative.