title: Hierarchical modeling of sequential behavioral data: an empirical Bayesian approach.
abstract: The authors review the common methods for measuring strength of contingency between 2 behaviors in a behavioral sequence, the binomial z score and the adjusted cell residual, and point out a number of limitations of these approaches. They present a new approach using log odds ratios and empirical Bayes estimation in the context of hierarchical modeling, an approach not constrained by these limitations. A series of hierarchical models is presented to test the stationarity of behavioral sequences, the homogeneity of sequences across a sample of episodes, and whether covariates can account for variation in sequences across the sample. These models are applied to observational data taken from a study of the behavioral interactions of 254 couples to illustrate their use.
authors.affiliation: Department of Epidemiology and Biostatistics, University of South Florida, Tampa 33612, USA. gdagne@hsc.usf.edu
authors.forename: Getachew A
authors.initials: GA
authors.lastname: Dagne
authors.forename: George W
authors.initials: GW
authors.lastname: Howe
authors.forename: C Hendricks
authors.initials: CH
authors.lastname: Brown
authors.forename: Bengt O
authors.initials: BO
authors.lastname: Muthén
mesh_terms: D001499:Bayes Theorem; D006801:Humans; D008960:Models, Psychological