Open AccessAffect and Personality
Author(s) -
Jonathan J. Park,
Sy Miin Chow,
Zachary F. Fisher,
Peter Molenaar
Publication year - 2020
Publication title -
european journal of psychological assessment
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.927
H-Index - 62
eISSN - 2151-2426
pISSN - 1015-5759
DOI - 10.1027/1015-5759/a000612
Subject(s) - affect (linguistics) , personality , econometrics , autoregressive model , psychology , monte carlo method , big five personality traits , cognitive psychology , machine learning , statistics , social psychology , computer science , mathematics , communication
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 approaches dynamic networks 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.