Insight Into Individual Differences in Emotion Dynamics With Clustering

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Abstract

Studying emotion dynamics through time series models is becoming increasingly popular in the social sciences. Across individuals, dynamics can be rather heterogeneous. To enable comparisons and generalizations of dynamics across groups of individuals, one needs sophisticated tools that express the essential similarities and differences. A way to proceed is to identify subgroups of people who are characterized by qualitatively similar emotion dynamics through dynamic clustering. So far, these methods assume equal generating processes for individuals per cluster. To avoid this overly restrictive assumption we outline a probabilistic clustering approach based on a mixture model that clusters on individuals’ vector autoregressive (VAR) coefficients. We evaluate the performance of the method and compare it to a non-probabilistic method in a simulation study. The usefulness of the methods is illustrated using 366 ecological momentary assessment time series with external measures of depression and anxiety.
Original languageEnglish
Pages (from-to)1186-1206
Number of pages21
JournalAssessment
Volume28
Issue number4
Early online date13-Sep-2019
DOIs
Publication statusPublished - Sep-2021

Keywords

  • Inter-individual differences
  • intensive longitudinal data
  • ecological momentary assessment
  • VAR models
  • Finite mixture model
  • TIME-SERIES
  • NEGATIVE AFFECT
  • SIMULATING DATA
  • BETWEEN-PERSON
  • MODEL
  • CLASSIFICATION
  • PERFORMANCE
  • SYMPTOMS
  • STRESS
  • CHULL

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