Hostname: page-component-6766d58669-bp2c4 Total loading time: 0 Render date: 2026-05-21T00:31:06.708Z Has data issue: false hasContentIssue false
  • English
  • Français

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.
Get access Rights & Permissions [Opens in a new window]

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

Information

Type
Original Paper
Information
Psychometrika , Volume 53 , Issue 2 , June 1988 , pp. 261 - 282
Copyright
Copyright © 1988 The Psychometric Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

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.

References

Agresti, A. (1984). Analysis of ordinal categorical data, New York: John Wiley & Sons.Google Scholar
Allison, P. D., Liker, J. D. (1982). Analyzing sequential categorical data on dyadic interactions. Psychological Bulletin, 91, 393–403.CrossRefGoogle Scholar
Arabie, P. (1984). Validation of sociometric structure by data on individuals' attributes. Social Networks, 6, 373–403.CrossRefGoogle Scholar
Arabie, P., Carroll, J. D. (1980). MAPCLUS: A mathematical programming approach to fitting the ADCLUS model. Psychometrika, 45, 211–236.CrossRefGoogle Scholar
Baker, R. J., Nelder, J. A. (1978). The GLIM system, Release 3: Generalized linear interactive modeling, Oxford: The Numerical Algorithms Group.Google Scholar
Berkowitz, S. D. (1982). An introduction to structural analysis: The network approach to social research, Toronto: Butterworths.Google Scholar
Bernard, H. R., Killworth, P. D. (1979). Deterministic models of social networks. In Holland, P. W., Leinhardt, S. (Eds.), Perspectives on social network research (pp. 165–186). New York: Academic Press.CrossRefGoogle Scholar
Bishop, Y. M. M., Fienberg, S. E., Holland, P. W. (1975). Discrete multivariate analysis: Theory and practice, Cambridge, MA: The MIT Press.Google Scholar
Budescu, D. V. (1984). Tests of lagged dominance in sequential dyadic interaction. Psychological Bulletin, 96, 402–414.CrossRefGoogle Scholar
Burt, R. S. (1980). Models of network structure. Annual Review of Sociology, 6, 79–141.CrossRefGoogle Scholar
Fienberg, S. E. (1980). The Analysis of cross-classified, categorical data 2nd ed.,, Cambridge, MA: The MIT Press.Google Scholar
Fienberg, S. E. (1985). Multivariate directed graphs in statistics. In Kotz, S., Johnson, N. L. (Eds.), Encyclopedia of statistical sciences (pp. 40–43). New York: John Wiley & Sons.Google Scholar
Fienberg, S. E., Meyer, M. M., Wasserman, S. (1985). Statistical analysis of multiple sociometric relations. Journal of the American Statistical Association, 80, 51–67.CrossRefGoogle Scholar
Fienberg, S. E., Wasserman, S. (1981). Categorical data analysis of single sociometric relations. In Leinhardt, S. (Eds.), Sociological methodology 1981 (pp. 156–192). San Francisco: Jossey-Bass.Google Scholar
Gottman, J. M. (1979). Marital interactions: Experimental investigations, New York: Academic Press.Google Scholar
Gottman, J. M., Ringland, J. T. (1981). The analysis of dominance and bidirectionality in social development. Child Development, 52, 393–412.CrossRefGoogle Scholar
Haberman, S. J. (1978). Analysis of qualitative data, (Vol. 1), New York: Academic Press.Google Scholar
Haberman, S. J. (1979). Analysis of qualitative data (Vol. 2), New York: Academic Press.Google Scholar
Hage, P., Harary, F. (1983). Structural models in anthropology, Cambridge, England: Cambridge University Press.Google Scholar
Holland, P. W., Leinhardt, S. (1977). A dynamic model for social networks. Journal of Mathematical Sociology, 5, 5–20.CrossRefGoogle Scholar
Holland, P. W., Leinhardt, S. (1981). An exponential family of probability distributions for directed graphs. Journal of the American Statistical Association, 76, 33–50.CrossRefGoogle Scholar
Hubert, L. J. (1978). Generalized proximity function comparisons. British Journal of Mathematical and Statistical Psychology, 31, 179–192.CrossRefGoogle Scholar
Hubert, L. J. (1979). Generalized concordance. Psychometrika, 44, 135–142.CrossRefGoogle Scholar
Hubert, L. J., Baker, F. B. (1978). Evaluating the conformity of sociometric measurements. Psychometrika, 43, 31–41.CrossRefGoogle Scholar
Hubert, L. J., Schultz, J. V. (1976). Quadratic assignment as a general data analysis strategy. British Journal of Mathematical and Statistical Psychology, 29, 190–241.CrossRefGoogle Scholar
Iacobucci, D., Wasserman, S. (1987). Dyadic social interactions. Psychological Bulletin, 102, 293–306.CrossRefGoogle ScholarPubMed
Iacobucci, D., Wasserman, S. (1988). A general framework for the statistical analysis of sequential dyadic interaction data. Psychological Bulletin, 103, 279–390.CrossRefGoogle Scholar
Katz, L., Powell, J. H. (1953). A proposed index for the conformity of one sociometric measurement to another. Psychometrika, 18, 249–256.CrossRefGoogle Scholar
Katz, L., Proctor, C. H. (1959). The concept of configuration of interpersonal relations in a group as a time-dependent stochastic process. Psychometrika, 24, 317–327.CrossRefGoogle Scholar
Knoke, D., Kuklinski, J. H. (1982). Network Analysis, Beverly Hills, CA: Sage Publications.Google Scholar
Koehler, K., Larntz, K. (1980). An empirical investigation of goodness-of-fit statistics for sparse multinomials. Journal of the American Statistical Association, 75, 336–344.CrossRefGoogle Scholar
Meyer, M. M. (1982). Transforming contingency tables. Annals of Statistics, 10, 1172–1181.CrossRefGoogle Scholar
Noma, E., Smith, D. R. (1985). Benchmark for the blocking of sociometric data. Psychological Bulletin, 97, 583–591.CrossRefGoogle Scholar
Payne, C. D. (1985). The GLIM system release 3.77: Generalized linear interactive modelling manual, Oxford: The Numerical Algorithms Group.Google Scholar
Rice, R. E., Richards, W. D. Jr. (1985). An overview of network analysis methods and programs. In Cervin, B., Voigt, M. J. (Eds.), Progress in communication sciences (pp. 105–165). Norwood, NJ: Ablex.Google Scholar
Sampson, S. F. (1968). A Novitiate in a period of change: An experimental and case study of social relationships. Unpublished doctoral dissertation, Department of Sociology, Cornell University.Google Scholar
Shepard, R. N., Arabie, P. (1979). Additive clustering: Representation of similarities as combinations of discrete overlapping properties. Psychological Review, 86, 87–123.CrossRefGoogle Scholar
Wampold, B. E. (1984). Tests of dominance in sequential categorical data. Psychological Bulletin, 96, 424–429.CrossRefGoogle Scholar
Wampold, B. E., Margolin, G. (1982). Nonparametric strategies to test the independence of behavioral states in sequential data. Psychological Bulletin, 92, 755–765.CrossRefGoogle Scholar
Wasserman, S. (1978). Models for binary directed graphs and their applications. Advances in Applied Probability, 10, 803–818.CrossRefGoogle Scholar
Wasserman, S. (1980). Analyzing social networks as stochastic processes. Journal of the American Statistical Association, 75, 280–294.CrossRefGoogle Scholar
Wasserman, S. (1987). Conformity of two sociometric relations. Psychometrika, 52, 3–18.CrossRefGoogle Scholar
Wasserman, S., Anderson, C. (1987). Stochastica posteriori blockmodels: Construction and assessment. Social Networks, 9, 1–36.CrossRefGoogle Scholar
Wasserman, S., Iacobucci, D. (1986). Statistical analysis of discrete relational data. British Journal of Mathematical and Statistical Psychology, 39, 41–64.CrossRefGoogle Scholar