A Multilevel Structural Equation Model for Dyadic Data

Dyadic data involving couples, twins, or parent-child pairs are common in the social sciences, but available statistical approaches are limited in the types of hypotheses that can be tested with dyadic data. A novel structural modeling approach, based on latent growth curve model specifications, is proposed for use with dyadic data. The approach allows researchers to test more sophisticated causal models, incorporate latent variables, and estimate more complex error structures than is currently possible using hierarchical linear modeling or multilevel structural equation models. A brief overview of multilevel regression and latent growth curve models is given, and the equivalence of the statistical model for nested and longitudinal data is explained. Possible expansion of the strategy for application with small groups and with unbalanced data is briefly explored.