Exploring the dynamics of dyadic interactions via hierarchical segmentation

Fushing Hsieh, Emilio Ferrer, Shu Chun Chen, Sy-Miin Chow

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)351-372
Number of pages22
JournalPsychometrika
Volume75
Issue number2
DOIs
StatePublished - Mar 23 2010

Fingerprint

Segmentation
Recurrence
Interaction
Multivariate Time Series
Synchrony
Time series
Dynamic models
Change Point
Multivariate Data
Stationarity
Dynamic Model
Partition
Simulation Study

All Science Journal Classification (ASJC) codes

  • Psychology(all)
  • Applied Mathematics

Cite this

Hsieh, Fushing ; Ferrer, Emilio ; Chen, Shu Chun ; Chow, Sy-Miin. / Exploring the dynamics of dyadic interactions via hierarchical segmentation. In: Psychometrika. 2010 ; Vol. 75, No. 2. pp. 351-372.
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Exploring the dynamics of dyadic interactions via hierarchical segmentation. / Hsieh, Fushing; Ferrer, Emilio; Chen, Shu Chun; Chow, Sy-Miin.

In: Psychometrika, Vol. 75, No. 2, 23.03.2010, p. 351-372.

Research output: Contribution to journalArticle

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