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

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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.

Originele taal-2English
Pagina's (van-tot)482–491
Aantal pagina's10
TijdschriftEuropean Journal of Psychological Assessment
Nummer van het tijdschrift3
StatusPublished - 2020

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