A Model for Emotion Dynamics: A multivariate statistical model for emotion dynamics

Krone, T., Albers, C. J., Kuppens, P. & Timmerman, M. 11-Sep-2017 (Accepted/In press) In : Emotion. 46 p.

Research output: Scientific - peer-reviewArticle

In emotion dynamic research 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. Further, the length of the observed time series in emotion research is
limited, and often suffers from a high percentage of missing values. In this paper we
propose a vector autoregressive Bayesian dynamic model, that is useful for emotion
dynamic research. The model encompasses six elementary properties of emotions, and can
be applied with relatively short time series, including missing data. The individual
elementary properties covered are: within person variability, innovation variability, inertia,
granularity, cross-lag regression and average intensity. The model can be applied to both
univariate and multivariate time series, allowing to model the relationships between
emotions. One may include external variables and non-Gaussian observed data. We illustrate the usefulness of the model on data involving 50 participants self-reporting on their experience of three emotions across the period of one week using experience sampling
Original languageEnglish
Number of pages46
StateAccepted/In press - 11-Sep-2017

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