Multilevel DSEM of Dyadic Data: Challenges and Opportunities
Individual change processes rarely occur in a social vacuum. Intensive longitudinal data from dyads allow investigators to ask important (and possibly) new questions that cannot be addressed in corresponding studies of independent individuals. The challenges of analyzing intensive longitudinal data from dyads come from at least two sources: (a) adequately conceptualizing the dyadic causal influence process under investigation, and (b) specifying any residual statistical dependencies that occur in data obtained from two individuals who share daily life together. Using data from an intensive longitudinal study of intimate couples, I will discuss how a multilevel DSEM approach allows for several unique and fundamental modeling opportunities when studying dyadic processes that unfold over time.