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

互动和群体动力学的社会心理学：相互依赖的问题

基本信息

批准号：
9809261
负责人：
Richard Gonzalez
金额：
$ 20.07万
依托单位：
Regents of the University of Michigan - Ann Arbor
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
1998
资助国家：
美国
起止时间：
1998-09-15 至 2002-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=9809261&HistoricalAwards=false
关键词：
Social Psychology Interaction Group Dynamics

项目摘要

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.

社会互动的研究是社会和行为科学研究的核心部分。尽管具有中心性，但统计方法并未得到很好的开发来处理此类数据。现有统计技术的问题在于，它们需要观察到独立。但是，在涉及社会互动的研究中，观察结果是由于它们的相互作用而不是独立的。例如：两个兄弟姐妹影响彼此的行为；一个工作组的成员会影响彼此的表现；一群猴子在彼此的存在中表现出特殊的修饰行为；审议的陪审员影响彼此的判决。这项研究旨在开发一类统计模型，这些模型将允许从社会互动研究中分析数据。它提出了一种新的框架，即成对方法，其中可以测试和评估涉及社交互动的广泛设计。该概念是将数据结构的非独立性嵌入到数据集中，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，这些问题主要没有得到太多的研究关注，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知识和分布式智能（KDI）计划的知识网络重点。