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Continuous Time Structural Equation Modeling with R Package ctsem

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  • Driver, Charles C.
  • Oud, Johan H. L.
  • Voelkle, Manuel C.

Abstract

We introduce ctsem, an R package for continuous time structural equation modeling of panel (N > 1) and time series (N = 1) data, using full information maximum likelihood. Most dynamic models (e.g., cross-lagged panel models) in the social and behavioural sciences are discrete time models. An assumption of discrete time models is that time intervals between measurements are equal, and that all subjects were assessed at the same intervals. Violations of this assumption are often ignored due to the difficulty of accounting for varying time intervals, therefore parameter estimates can be biased and the time course of effects becomes ambiguous. By using stochastic differential equations to estimate an underlying continuous process, continuous time models allow for any pattern of measurement occasions. By interfacing to OpenMx, ctsem combines the flexible specification of structural equation models with the enhanced data gathering opportunities and improved estimation of continuous time models. ctsem can estimate relationships over time for multiple latent processes, measured by multiple noisy indicators with varying time intervals between observations. Within and between effects are estimated simultaneously by modeling both observed covariates and unobserved heterogeneity. Exogenous shocks with different shapes, group differences, higher order diffusion effects and oscillating processes can all be simply modeled. We first introduce and define continuous time models, then show how to specify and estimate a range of continuous time models using ctsem.

Suggested Citation

  • Driver, Charles C. & Oud, Johan H. L. & Voelkle, Manuel C., 2017. "Continuous Time Structural Equation Modeling with R Package ctsem," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i05).
  • Handle: RePEc:jss:jstsof:v:077:i05
    DOI: http://hdl.handle.net/10.18637/jss.v077.i05
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    Cited by:

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    2. Oliver Weigelt & Petra Gierer & Christine J. Syrek, 2019. "My Mind is Working Overtime—Towards an Integrative Perspective of Psychological Detachment, Work-Related Rumination, and Work Reflection," IJERPH, MDPI, vol. 16(16), pages 1-27, August.
    3. Oliver Weigelt & Antje Schmitt & Christine J. Syrek & Sandra Ohly, 2021. "Exploring the Engaged Worker over Time—A Week-Level Study of How Positive and Negative Work Events Affect Work Engagement," IJERPH, MDPI, vol. 18(13), pages 1-27, June.
    4. Oscar García, 2019. "Estimating reducible stochastic differential equations by conversion to a least-squares problem," Computational Statistics, Springer, vol. 34(1), pages 23-46, March.
    5. Øystein Sørensen & Anders M. Fjell & Kristine B. Walhovd, 2023. "Longitudinal Modeling of Age-Dependent Latent Traits with Generalized Additive Latent and Mixed Models," Psychometrika, Springer;The Psychometric Society, vol. 88(2), pages 456-486, June.
    6. Nicolas Pellerin & Michael Dambrun & Eric Raufaste, 2022. "Selflessness Meets Higher and More Stable Happiness: An Experience Sampling Study of the Joint Dynamics of Selflessness and Happiness," Journal of Happiness Studies, Springer, vol. 23(6), pages 3127-3142, August.
    7. Oisín Ryan & Ellen L. Hamaker, 2022. "Time to Intervene: A Continuous-Time Approach to Network Analysis and Centrality," Psychometrika, Springer;The Psychometric Society, vol. 87(1), pages 214-252, March.
    8. Driver, Charles C, 2022. "Inference With Cross-Lagged Effects - Problems in Time and New Interpretations," OSF Preprints xdf72, Center for Open Science.

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