Discovering Psychological Dynamics in Time-Series Data

This paper provides a methodological overview of statistical network models in cross-sectional and time-series data. The increasing trend of modeling psychological data through networks attempts to highlight potential causal relationships between observed variables. When data are cross-sectional, it is becoming increasingly popular to estimate a Gaussian graphical model (GGM; a network of partial correlation coefficients). In a time-series analysis, networks are typically constructed through the use of (multilevel) vector autoregression (VAR). VAR estimates a directed network that encodes temporal predictive effects - the temporal network. We show that GGM and VAR models are closely related: VAR generalizes the GGM by taking violations of independence between consecutive cases into account. VAR analyses can also return a GGM that encodes relationships within the same window of measurement - the contemporaneous network, which has not yet been extensively utilized in the literature. When multiple subjects are measured, multilevel VAR estimates fixed and random temporal networks. Proper centering can disentangle within- and between-subject variance in such processes. We show, for the first time, that the between-subject effects can be summarized in a GGM network - the between-subjects network. We argue that such between-subjects effects can also indicate causal pathways. Furthermore, we propose a novel two-step, multilevel estimation procedure to obtain fixed and random effects for contemporaneous network structures. We have implemented this procedure in the R package mlVAR. We present a simulation study to show the performance of mlVAR and to showcase the method in an empirical example on personality inventory items and physical exercise.