Inter-individual differences in multivariate time-series: Latent class vector-autoregressive modeling.

We present the exploratory method of latent class vector-autoregressive modeling (LCVAR), which extends the time-series 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. (PsycInfo Database Record (c) 2020 APA, all rights reserved)
Source: European Journal of Psychological Assessment - Category: Psychiatry & Psychology Source Type: research