Abstract
Many relationship theories assume some form of interdependence between relationship partners. Partners are thought to continuously influence each other and to be influenced by each other over time. These influences are not expected to be constant, but dynamic (sometimes partners influence each other a lot, and sometimes they do not influence each other). To investigate such changes in interpersonal dynamics, we showcase the value of using a change point detection approach, which can be used to monitor virtually any preferred quantification of interpersonal dynamics across time. Concretely, we introduce the KCP-RS method, which scans times series for changes in user-specified statistics, in interpersonal emotion dynamic research. We used KCP-RS to investigate changes in 96 couples’ emotional experiences during two 10-minute conversations, which were meant to elicit a negative and a positive interaction context. Based on participants’ continuous reports of the valence of their emotional experience, we looked for changes in three statistical measures, aiming to capture emotional similarity between partners (i.e., does their valence fluctuate together). Specifically, we investigated the occurrence, frequency, and direction of change in partners’ linear correlations, instantaneous derivative matching (IMD), and signal matching (SM). While correlation changes were only observed in 2% of the couples, IDM changes were detected for about one third of the couples (34%), and SM changes were detected in about half of them (49%). Most couples demonstrated one change point, and the direction of the change differed depending on the specific emotional similarity measure. In a first validation of this method, we demonstrated how such change points can pinpoint to subtle but meaningful dynamic processes in couples. We end by discussing the added value of change point detection analyses for relationship research and interpersonal research in general.
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