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Sequential Social Network Data

Published online by Cambridge University Press:  01 January 2025

Stanley Wasserman  and
Dawn Iacobucci
Stanley Wasserman*
Affiliation:
Department of Psychology, Department of Statistics, University of Illinois
Dawn Iacobucci
Affiliation:
Department of Marketing, J. L. Kellogg Graduate School of Management, Northwestern University
*
Requests for reprints should be sent to Stanley Wasserman, Department of Psychology and Department of Statistics, University of Illinois, 603 East Daniel Street, Champaign, IL 61820.
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Abstract

A new method is proposed for the statistical analysis of dyadic social interaction data measured over time. The data to be studied are assumed to be realizations of a social network of a fixed set of actors interacting on a single relation. The method is based on loglinear models for the probabilities for various dyad (or actor pair) states and generalizes the statistical methods proposed by Holland and Leinhardt (1981), Fienberg, Meyer, & Wasserman (1985), and Wasserman (1987) for social network data. Two statistical models are described: the first is an “associative” approach that allows for the study of how the network has changed over time; the second is a “predictive” approach that permits the researcher to model one time point as a function of previous time points. These approaches are briefly contrasted with earlier methods for the sequential analysis of social networks and are illustrated with an example of longitudinal sociometric data.

Keywords

sociometric measurementsequential dyadic interactionssociometrymultivariate directed graphlog linear model
Type
Original Paper
Information
Psychometrika , Volume 53 , Issue 2 , June 1988 , pp. 261 - 282
Copyright
Copyright © 1988 The Psychometric Society

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Footnotes

Research support provided by National Science Foundation Grant #SES84-08626 to the University of Illinois at Urbana-Champaign and by a predoctoral traineeship awarded to the second author by the Quantitative Methods Program of the Department of Psychology, University of Illinois at Urbana-Champaign, funded by ADAHMA, National Research Service Award #MH14257. We thank the editor and three anonymous referees for helpful comments.

This paper is based on research presented at the 1986 Annual Meeting of the Psychometric Society, Toronto, Ontario, June, 1986.

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