ArticleStructural equation modeling : a multidisciplinary journal2022
Fitting Multilevel Vector Autoregressive Models in Stan, JAGS, and Mplus.
Article in Structural equation modeling : a multidisciplinary journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Planned missingness in intensive longitudinal studies: Extensions and comparisons of multiform designs.Behavior research methods · 2026Article
- Simultaneous detection of gradual and abrupt structural changes in Bayesian longitudinal modelling using entropy and model fit measures.The British journal of mathematical and statistical psychology · 2026Article
- Modeling qualitative between-person heterogeneity in time series using latent class vector autoregressive models.Behavior research methods · 2025Article
- Modeling within-level latent interaction effects in multilevel vector-autoregressive models.Behavior research methods · 2025Article
- Healthy Mom Zone Adaptive Intervention With a Novel Control System and Digital Platform to Manage Gestational Weight Gain in Pregnant Women With Overweight or Obesity: Study Design and Protocol for a Randomized Controlled Trial.JMIR research protocols · 2025Article
- Are Bayesian regularization methods a must for multilevel dynamic latent variables models?Behavior research methods · 2025Article
- A Growth of Hierarchical Autoregression Model for Capturing Individual Differences in Changes of Dynamic Characteristics of Psychological Processes.Structural equation modeling : a multidisciplinary journal · 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
- Temporal network of experience sampling methodology identifies sleep disturbance as a central symptom in generalized anxiety disorder.BMC psychiatry · 2024Article
- Dynamic structural equation models with binary and ordinal outcomes in Mplus.Behavior research methods · 2024Article
- Dynamic Structural Equation Models with Missing Data: Data Requirements onStructural equation modeling : a multidisciplinary journal · 2024Article
- Conceptualizing and Examining Change in Communication Research.Communication methods and measures · 2023Article
- Multiple imputation of missing data in multilevel ecological momentary assessments: an example using smoking cessation study data.Frontiers in digital health · 2023Article
- Bayesian Forecasting with a Regime-Switching Zero-Inflated Multilevel Poisson Regression Model: An Application to Adolescent Alcohol Use with Spatial Covariates.Psychometrika · 2022Article
- Quit Stage and Intervention Type Differences in the Momentary Within-Person Association Between Negative Affect and Smoking Urges.Frontiers in digital health · 2022Article
- Multilevel Latent Differential Structural Equation Model with Short Time Series and Time-Varying Covariates: A Comparison of Frequentist and Bayesian Estimators.Multivariate behavioral researchArticle
- Multiple Imputation with Factor Scores: A Practical Approach for Handling Simultaneous Missingness Across Items in Longitudinal Designs.Multivariate behavioral researchArticle
Corrections and comments
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5 authors.
Funding
Abstract
The influx of intensive longitudinal data creates a pressing need for complex modeling tools that help enrich our understanding of how individuals change over time. Multilevel vector autoregressive (mlVAR) models allow for simultaneous evaluations of reciprocal linkages between dynamic processes and individual differences, and have gained increased recognition in recent years. High-dimensional and other complex variations of mlVAR models, though often computationally intractable in the frequentist framework, can be readily handled using Markov chain Monte Carlo techniques in a Bayesian framework. However, researchers in social science fields may be unfamiliar with ways to capitalize on recent developments in Bayesian software programs. In this paper, we provide step-by-step illustrations and comparisons of options to fit Bayesian mlVAR models using Stan, JAGS and Mplus, supplemented with a Monte Carlo simulation study. An empirical example is used to demonstrate the utility of mlVAR models in studying intra- and inter-individual variations in affective dynamics.
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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.