Inter-Individual Differences in Multivariate Time-Series: Latent Class Vector-Autoregressive Modeling

Research output: Contribution to journalArticleAcademicpeer-review

131 Downloads (Pure)

Abstract

Theories of emotion regulation posit the existence of individual differences in emotion dynamics. Current multi-subject time-series models account for differences in dynamics across individuals only to a very limited extent. This results in an aggregation that may poorly apply at the individual level. We present the exploratory method of latent class vector-autoregressive modeling (LCVAR), which extends the timeseries models to include clustering of individuals with similar dynamic processes. LCVAR can identify individuals with similar emotion dynamics in intensive time-series, which may be of unequal length. The method performs excellently under a range of simulated conditions. The value of identifying clusters in time-series is illustrated using affect measures of 410 individuals, assessed at over 70 time points per individual. LCVAR discerned six clusters of distinct emotion dynamics with regard to diurnal patterns and augmentation and blunting processes between eight emotions.

Original languageEnglish
Pages (from-to)482–491
Number of pages10
JournalEuropean Journal of Psychological Assessment
Volume36
Issue number3
DOIs
Publication statusPublished - 2020

Keywords

  • clustering
  • emotion dynamics
  • time series clustering

Cite this