Experience Sampling Method (ESM) has become an essential tool for studying daily life dynamics. In dyadic research, ESM enables the examination of real-time interdependencies between partners, shedding light on emotional linkage, regulatory processes, communication, empathy, and relationship functioning. However, modeling and analyzing dyadic ESM data poses unique methodological challenges, requiring advanced statistical approaches to account for interdependence, time dynamics, and individual variability. This contribution provides an overview of the theory behind dyadic analysis and related modeling approaches, with a particular focus on network analysis applied to dyadic ESM data. In the analysis of dyadic data, network analysis offers a complementary perspective to the traditional Actor-Partner Interdependence Model, allowing to account for all interdependences within and between partners. To illustrate these methods, we present findings from a 30-day ESM study of 77 Italian romantic couples. The study examined the emotional linkage between partners using both multilevel models and network analysis. Results revealed overall trends of emotional interdependence, with gender differences in affective patterns. The study highlighted distinct emotional pathways, showing that female partners' emotions were predicted by both the positive and negative affect of their male partners, whereas male partners responded primarily to the positive emotions of their female partners. Network analysis provides a powerful framework for studying dyadic ESM data, allowing for the visualization and quantification of complex interdependencies beyond traditional modeling approaches. This method can enhance our understanding of emotional dynamics in relationships, offering new insights into patterns of connection, regulation, and variability in dyadic interactions. In this contribution, we will discuss practical considerations and future directions for applying network analysis in dyadic ESM research.
Join at: imt.lu/aula2