Extending the actor–partner interdependence model for binary outcomes: A multilevel logistic approach

Relationship researchers face many challenges when samples consist of dyads. The actor–partner interdependence model provides a framework for the analysis of dyadic data (D. A. Kashy & D. A. Kenny, 2000). Binary variables like discrete health diagnoses present additional challenges that are not easily handled using existing models. The analysis was demonstrated using SAS PROC GLIMMIX and HLM6. An example is presented where couple's personality traits are used to predict discrete health outcomes. A series of Monte Carlo simulations were performed to compare PROC GLIMMIX to HLM. GLIMMIX performed acceptably compared to HLM for small dyad‐level sample sizes (100 or fewer), but HLM clearly outperformed in larger samples. Both had problems estimating the variance of the random intercept and its standard error.