The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Dynamic Structural Equation Models
Open accessAbstract
This article presents dynamic structural equation modeling (DSEM), which can be used to study the evolution of observed and latent variables as well as the structural equation models over time. DSEM is suitable for analyzing intensive longitudinal data where observations from multiple individuals are collected at many points in time. The modeling framework encompasses previously published DSEM models and is a comprehensive attempt to combine time-series modeling with structural equation modeling. DSEM is estimated with Bayesian methods using the Markov chain Monte Carlo Gibbs sampler and the Metropolis–Hastings sampler. We provide a detailed description of the estimation algorithm as implemented in the Mplus software package. DSEM can be used for longitudinal analysis of any duration and with any number of observations across time. Simulation studies are used to illustrate the framework and study the performance of the estimation method. Methods for evaluating model fit are also discussed.
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Cites
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Self-citation
Research works printed but not linked: 3
- Asparouhov T., 2016, Advances in multilevel modeling for educational research: Addressing practical issues found in real-world applications, 163
- Greene W. H., 2014, Econometric analysis
- Hamaker E. L., 2017, submitted for publication
Cited by
Variable-Centered, Person-Centered, and Person-Specific Approaches: Where Theory Meets the Method
Howard, M. C., & Hoffman, M. E. (2018) · Organizational Research Methods · doi:10.1177/1094428117744021 · Cited by 1,121
Gabriel, A. S., Podsakoff, N. P., Beal, D. J., Scott, B. A., Sonnentag, S., Trougakos, J. P., & Butts, M. M. (2019) · Organizational Research Methods · doi:10.1177/1094428118802626 · Cited by 755
Hamaker, E. L., Asparouhov, T., Brose, A., Schmiedek, F., & Muthén, B. (2018) · Multivariate Behavioral Research · doi:10.1080/00273171.2018.1446819 · Cited by 549
Self-citationDelivering transformative action in paediatric pain: a Lancet Child & Adolescent Health Commission
Eccleston, C., Fisher, E., Howard, R. F., Slater, R., Forgeron, P., Palermo, T. M., Birnie, K. A., Anderson, B. J., Chambers, C. T., Crombez, G., et al. (2021) · The Lancet Child & Adolescent Health · doi:10.1016/s2352-4642(20)30277-7 · Cited by 328
Schultzberg, M., & Muthén, B. (2018) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1080/10705511.2017.1392862 · Cited by 293
Self-citation
Metadata
- Authors
- Asparouhov, T., Hamaker, E. L., & Muthén, B.
- Venue
- Structural Equation Modeling: A Multidisciplinary Journal
- Publisher
- Informa UK Limited
- Published
- 2017-12-27
- Volume
- 25
- Issue
- 3
- Pages
- 359-388
- ISSN
- 1070-5511 (ISSN-L)
- 1532-8007
- OpenAlex
- W2777635006
- Cited by
- 985
- References
- 31
- FWCI
- 26.39
- Type
- journal article
- Language
- en
- Open access
- green
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- Asparouhov, T., Hamaker, E. L., & Muthén, B. (2018). Dynamic Structural Equation Models. Structural Equation Modeling: A Multidisciplinary Journal. https://doi.org/10.1080/10705511.2017.1406803
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
