Abstract
Although in recent years researchers have begun to utilize dyadic data analyses such as the actor-partner interdependence model (APIM), certain limitations to the applicability of these models still exist. Given the complexity of APIMs, most researchers will often use observed scores to estimate the model's parameters, which can significantly limit and underestimate statistical results. The aim of this article is to highlight the importance of conducting a confirmatory factor analysis (CFA) of equivalent constructs between dyad members (i.e. measurement equivalence/invariance; ME/I). Different steps for merging CFA and APIM procedures will be detailed in order to shed light on new and integrative methods.