The two statistical approaches commonly used in the analysis of dyadic and group data, multilevel modeling and structural equation modeling, are reviewed. Next considered are three different models for dyadic data, focusing mostly on the very popular actor–partner interdependence model (APIM). We further consider power analyses for the APIM as well as the partition of nonindependence. We then present an overview of the analysis of over-time dyadic data, considering growth-curve models, the stability-and-influence model, and the over-time APIM. After that, we turn to group data and focus on considerations of the analysis of group data using multilevel modeling, including a discussion of the social relations model, which is a model of dyadic data from groups of persons. The final topic concerns measurement equivalence of constructs across members of different types in dyadic and group studies.

Developmental theories propose that children learn emotion regulation through dynamic interactions with their parents over time. Emotion regulation is transmitted intergenerationally through environmental and genetic pathways, yet both parents and children evoke regulatory capacities from each other. Further, these interactive effects iterate on a micro -level day by day, as well as on a longer time scale, influenced by factors like parenting style and dyadic conflict. Thus, dyadic parent–child emotion regulation is a bidirectional developmental process requiring careful study design, measurement, and analysis. Yet there are fewer research studies on these dynamic processes than one would expect, perhaps due to the aforementioned complexity. In this chapter, we provide a brief theoretical background on interactive emotion regulation between parent–child dyads, review example studies that have addressed these processes, identify conceptual and methodological barriers to conducting this research, and provide resources for researchers. Finally, we highlight the Actor–Partner Interdependence Model to derive interactive and bidirectional inferences into parent–youth emotion regulation.