Clustering Analysis of Time Series of Affect in Dyadic Interactions

Abstract

An important goal when analyzing multivariate time series is the identification of heterogeneity, both within and across individuals over time. This heterogeneity can represent different ways in which psychological processes manifest, either between people or within a person across time. In many instances, those differences can have systematic patterns that can be related to future outcomes. In close relationships, for example, the daily exchange of affect between two individuals in a couple can contain a particular structure that is different across people and can result in varying levels of relationship satisfaction. In this paper we use Louvain, a clustering method, as a tool to characterize heterogeneity in multivariate time series data. Using affect measures from dyadic interactions, we first determine that Louvain is adept at detecting homogeneous patterns that are distinct from one another. Additionally, these homogeneous points are linked, at some level, by time. Thus, we find that clustering via Louvain is useful to find time periods of stable, reoccurring patterns. However, using measures founded on information theory reveals that there is some level of information loss that is inevitable when clustering on levels of variable expression. Finally, we evaluate the predictive validity of the clustering method by examining the relation between the identified clusters of affect and measures outside the time series (i.e., relationship satisfaction and breakup taken one and two years later).

Keywords:

• Clustering
• affect
• dyads
• time series
• information theory

Article information

Conflict of interest disclosures: Each author signed a form for disclosure of potential conflicts of interest. No authors reported any financial or other conflicts of interest in relation to the work described.

Ethical principles: The authors affirm having followed professional ethical guidelines in preparing this work. These guidelines include obtaining informed consent from human participants, maintaining ethical treatment and respect for the rights of human or animal participants, and ensuring the privacy of participants and their data, such as ensuring that individual participants cannot be identified in reported results or from publicly available original or archival data.

Funding: This work was not supported.

Role of the funders/sponsors: None of the funders or sponsors of this research had any role in the design and conduct of the study; collection, management, analysis, and interpretation of data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

Acknowledgments: The authors would like to thank Sacha Epskamp, Michael Brusco, and an anonymous reviewer for their comments on prior versions of this manuscript. The ideas and opinions expressed herein are those of the authors alone, and endorsement by the authors’ institutions is not intended and should not be inferred.