ArticleStructural equation modeling : a multidisciplinary journal2024
Dynamic Structural Equation Models with Missing Data: Data Requirements on
Article in Structural equation modeling : a multidisciplinary journal, 2024. 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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Who cites it
7 citing papers in PubMed.
- A primer on intensive longitudinal psychometrics.Behavior research methods · 2026Review
- Sleep and smartphone use: Within and between-person relationships from an objective longitudinal smartphone and wearable data donation study.PLOS digital health · 2026Article
- Dynamic Modeling with Intensive Longitudinal Data: One-Step and Two-Step DSEM Approaches.Structural equation modeling : a multidisciplinary journal · 2026Article
- Leveraging transformer-based artificial intelligence for enhanced anesthetic decision-making in orthopedic surgery.Frontiers in medicine · 2026Article
- A Latent Variable Approach to Affect Variability in Daily Life Accurately Predicts Psychopathology, Especially Depression Symptoms in a Non-Clinical Sample.Journal of emotion and psychopathology · 2025Article
- Couple synchrony in physical activity: effects on individuals with knee osteoarthritis.Annals of behavioral medicine : a publication of the Society of Behavioral Medicine · 2025Article
- Exploring the influence factors and operating mechanisms of age-friendly communities in urban fringe areas from the resilience perspective: a case study in Shangjie Township, Southeast China.Frontiers in public health · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Dynamic structural equation modeling (DSEM) is a useful technique for analyzing intensive longitudinal data. A challenge of applying DSEM is the missing data problem. The impact of missing data on DSEM, especially on widely applied DSEM such as the two-level vector autoregressive (VAR) cross-lagged models, however, is understudied. To fill the research gap, we evaluated how well the fixed effects and variance parameters in two-level bivariate VAR models are recovered under different missingness percentages, sample sizes, the number of time points, and heterogeneity in missingness distributions through two simulation studies. To facilitate the use of DSEM under customized data and model scenarios (different from those in our simulations), we provided illustrative examples of how to conduct Monte Carlo simulations in M
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.