Bayesian hierarchical modeling of heterogeneity in multiple contingency tables: An application to behavioral observation data

Intervention studies often rely on microcoded data of social interactions to pro-vide evidence of change due to development or treatment. Traditionally these data have been collapsed into small contingency tables. Such an approach can intro-duce spurious findings. Instead of treating each unit’s contingency table indepen-dently, or collapsing the tables into single aggregate table, it is more efficient to analyze associations in all units simultaneously using hierarchical models. This article presents Bayesian hierarchical models to analyze several two-way cate-gorical data with random effects that allow different levels of variation across sev-eral events. To illustrate this approach, the authors present an analysis of couples’ interaction data from a recent study investigating how couples cope when one part-ner has become unemployed