Abstract:

In this presentation I will introduce a multilevel Ornstein-Uhlenbeck stochastic process model, cast in the Bayesian statistical framework, for capturing time dynamics of emotion experiences in daily life. I will focus on core affect, which is an integral blend of momentary valence (pleasantness) and arousal (activation) levels. We will quantify individual differences in terms of stochastic process model parameters of homeostatic baseline, regulation, and stochastic volatility. With the proposed model, we can decompose variation in the observed scores into measurement error variance and latent process based within-person variance. I will show how individual differences in the process model parameters systematically relate to trait-level characteristics of emotional functioning such as the deployment of emotion regulation strategies and emotional well-being.