Hierarchical Modeling of Sequential Behavioral Data: Examining Complex Association Patterns in Mediation Models

Getachew A. Dagne, C. Hendricks Brown, George W. Howe

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


This article presents new methods for modeling the strength of association between multiple behaviors in a behavioral sequence, particularly those involving substantively important interaction patterns. Modeling and identifying such interaction patterns becomes more complex when behaviors are assigned to more than two categories, as is the case for most observational research. The authors propose multilevel empirical Bayes methods to overcome the challenges inherent in such data. Furthermore, these methods allow the study of how variation in interaction patterns can mediate the effects of antecedents or intervention on distal outcomes. New procedures are developed to compare alternative mediation models and pinpoint which random effects operate as mediators. These models are then applied to observational data taken from a study of the behavioral interactions of 254 couples.

Original languageEnglish (US)
Pages (from-to)298-316
Number of pages19
JournalPsychological Methods
Issue number3
StatePublished - Sep 2007


  • behavioral observation
  • empirical Bayes
  • loglinear model
  • mediation model
  • multilevel model

ASJC Scopus subject areas

  • Psychology(all)

Cite this