ArticleEuropean journal of psychological assessment : official organ of the European Association of Psychological Assessment2020
Affect and Personality: Ramifications of Modeling (Non-)Directionality in Dynamic Network Models.
Article in European journal of psychological assessment : official organ of the European Association of Psychological Assessment, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Towards a personalized happiness approach to capturing change in satisfaction.Nature human behaviour · 2025Article
- Unsupervised Model Construction in Continuous-Time.Structural equation modeling : a multidisciplinary journal · 2025Article
- Measurement Model Misspecification in Dynamic Structural Equation Models: Power, Reliability, and Other Considerations.Structural equation modeling : a multidisciplinary journal · 2025Article
- Multiple imputation of missing data in multilevel ecological momentary assessments: an example using smoking cessation study data.Frontiers in digital health · 2023Article
- Multiple Imputation with Factor Scores: A Practical Approach for Handling Simultaneous Missingness Across Items in Longitudinal Designs.Multivariate behavioral researchArticle
- Subgrouping with Chain Graphical VAR Models.Multivariate behavioral researchArticle
- Evaluating Discrete Time Methods for Subgrouping Continuous Processes.Multivariate behavioral researchArticle
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4 authors.
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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 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.
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