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Exploring the Dynamics of Dyadic Interactions via Hierarchical Segmentation

Published online by Cambridge University Press:  01 January 2025

Fushing Hsieh ,
Emilio Ferrer ,
Shu-Chun Chen  and
Sy-Miin Chow
Fushing Hsieh
Affiliation:
University of California, Davis
Emilio Ferrer*
Affiliation:
University of California, Davis
Shu-Chun Chen
Affiliation:
University of California, Davis
Sy-Miin Chow
Affiliation:
University of North Carolina, Chapel Hill
*
Requests for reprints should be sent to Emilio Ferrer, Department of Psychology, University of California, One Shields Ave., Davis, CA 95616-8686, USA. E-mail: eferrer@ucdavis.edu
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Abstract

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In this article we present an exploratory tool for extracting systematic patterns from multivariate data. The technique, hierarchical segmentation (HS), can be used to group multivariate time series into segments with similar discrete-state recurrence patterns and it is not restricted by the stationarity assumption. We use a simulation study to describe the steps and properties of HS. We then use empirical data on daily affect from one couple to illustrate the use of HS for describing the affective dynamics of the dyad. First, we partition the data into three periods that represent different affective states and show different dynamics between both individuals’ affect. We then examine the synchrony between both individuals’ affective states and identify different patterns of coherence across the periods. Finally, we discuss the possibilities of using results from HS to construct confirmatory dynamic models with multiple change points or regime-specific dynamics.

Keywords

dynamic systemsmultivariate analysisexploratory data analysisdyadic interactions
Type
Original Paper
Information
Psychometrika , Volume 75 , Issue 2 , June 2010 , pp. 351 - 372
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NC
This is an open access article distributed under the terms of the Creative Commons Attribution Noncommercial License (https://creativecommons.org/licenses/by-nc/2.0), which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
Copyright
Copyright © 2010 The Psychometric Society

Footnotes

R code for the HS algorithm is available upon request.

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