Title: Bayesian VAR-modeling: Unraveling emotion dynamics in multivariate, multisubject time series
Authors: Casper Albers - University of Groningen (Netherlands) [presenting]
Tanja Krone - University of Groningen (Netherlands)
Marieke Timmerman - University of Groningen (Netherlands)
Peter Kuppens - Leuven (Belgium)
Abstract: Emotion dynamic research typically aims at revealing distinct information on affective functioning and regulation. Herewith, one distinguishes various elementary emotion dynamic features, which are studied using intensive longitudinal data. Typically, each emotion dynamic feature is quantified separately, which hampers the study of relationships between various features. In emotion research, the length of the observed time series is limited, and often suffers from a high percentage of missing values. We propose a Bayesian vector autoregressive model (VAR) that is useful for emotion dynamic research. The model encompasses the six central emotion dynamic features at once, and can be applied with relatively short time series, including missing data. The individual emotion dynamic features covered are: long and short term variability, granularity, inertia, cross-lag correlation and the intensity. The model can be applied to both univariate and multivariate time series, allowing to model the relationships between emotions. Further, it may model multiple individuals jointly as well as external variables and non-Gaussian observed data, and can deal with missing data. We illustrate the usefulness of the model with an empirical example using relatively short time series of three emotions, with missing time points within the series, measured for three individuals.