Analyzing sequential categorical data on dyadic interaction: A latent structure approach

William R. Dillon, Thomas J. Madden, Ajith Kumar

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


Demonstrates how a class of modeling techniques, commonly referred to as latent structure analysis, can be used in an informative way to study the character of sequential categorical data. Using this procedure, the authors show how to investigate (a) the lagged dependence between 2 actors, (b) dependency across populations, and (c) the issue of dominance and autodependence in reciprocal models of interaction sequences. Formal test statistics are utilized to select from an array of restricted and unrestricted latent class models fit to various sets of dyadic interaction data. (42 ref) (PsycINFO Database Record (c) 2006 APA, all rights reserved).

Original languageEnglish (US)
Pages (from-to)564-583
Number of pages20
JournalPsychological bulletin
Issue number3
StatePublished - Nov 1 1983
Externally publishedYes


  • latent structure modeling techniques, analysis of sequential categorical data on dyadic interaction

ASJC Scopus subject areas

  • Psychology(all)


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