The use of dynamic network models has grown in recent years. These models allow researchers to capture both lagged and contemporaneous effects in longitudinal data typically as variations, reformulations, or extensions of the standard vector autoregressive (VAR) models. To date, many of these dynamic networks have not been explicitly compared to one another. We compare three popular dynamic network approaches – GIMME, uSEM, and LASSO gVAR – in terms of their differences in modeling assumptions, estimation procedures, statistical properties based on a Monte Carlo simulation, and implications for affect and personality researchers. We found that all three dynamic network approaches provided yielded group-level empirical results in partial support of affect and personality theories. However, individual-level results revealed a great deal of heterogeneity across approaches and participants. Reasons for discrepancies are discussed alongside these approaches’ respective strengths and limitations.
Subtitle: Ramifications of Modeling (Non-)Directionality in Dynamic Network Models
Author(s): Jonathan J. Park, Sy-Miin Chow, Zachary F. Fisher, Peter Molenaar
Publisher: Hogrefe Publishing
Publication Type: Academic Journal Article
Journal Title: European Journal of Psychological Assessment
Page Range: 1009-1023