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

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.