Affect and Personality: Ramifications of Modeling (Non-)Directionality in Dynamic Network Models

Jonathan J. Park, Sy Miin Chow, Zachary F. Fisher, Peter C.M. Molenaar

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1009-1023
Number of pages15
JournalEuropean Journal of Psychological Assessment
Volume36
Issue number6
DOIs
StatePublished - Nov 2020

All Science Journal Classification (ASJC) codes

  • Applied Psychology

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