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

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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.