Exploratory Approaches for Studying Social Interactions, Dynamics, and Multivariate Processes in Psychological Science.

In this article, I argue for the need of more use of exploratory techniques to identify dynamics in social interactions. I describe several approaches as they are applied to multivariate time series data. The first approach is an algorithm that searches for periods of variability and stability at the individual level as well as for patterns of overlap in such periods between the two individuals in a couple. These patterns describe the daily ups and downs in the couples' affect and are predictive of the state of the couples 1 to 2 years later. The second approach, hierarchical segmentation, is based on the idea of partitioning the time series in segments with distinct data patterns. In the case of data from dyads, as in the illustration, the patterns can be compared in terms of coherence between the 2 individuals in the dyad. The third approach is based on network analysis, and its use is shown as a method to examine data transitions at the individual and dyadic level as well as system-wide coherence in multivariate systems. For each approach, I provide examples of its use with empirical data. The article ends with general guidelines and recommendations for researchers interested in using exploratory methods as a way to examine psychological processes.