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The performance of latent growth curve model-based structural equation model trees to uncover population heterogeneity in growth trajectories

Author

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  • Satoshi Usami

    (University of Tokyo)

  • Ross Jacobucci

    (University of Notre Dame)

  • Timothy Hayes

    (Florida International University)

Abstract

Behavioral researchers have shown growing interest in structural equation model trees (SEM Trees), a new recursive partitioning-based technique for detecting population heterogeneity. In the present research, we conducted a large-scale simulation to investigate the performance of latent growth curve model (LGCM)-based SEM Trees for uncovering between-individual differences in patterns of within-individual change. Simulation results showed that the correct estimation rates of the number of classes are most strongly related to the agreement rate of the covariate with its true latent profile, and the number of true classes also has a serious negative impact on correct estimation rates of the number of classes. SEM Trees is not always sensitive to the influence of model misspecification, and its impact differs according to a complex function of the types of misspecification as well as the statistical properties of the template model. On the whole, LGCM-based SEM Trees is a robust and stable approach under possible model misspecifications.

Suggested Citation

  • Satoshi Usami & Ross Jacobucci & Timothy Hayes, 2019. "The performance of latent growth curve model-based structural equation model trees to uncover population heterogeneity in growth trajectories," Computational Statistics, Springer, vol. 34(1), pages 1-22, March.
    • Handle: RePEc:spr:compst:v:34:y:2019:i:1:d:10.1007_s00180-018-0815-x
    DOI: 10.1007/s00180-018-0815-x
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    References listed on IDEAS

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    1. Hayes, Timothy & McArdle, John J., 2017. "Should we impute or should we weight? Examining the performance of two CART-based techniques for addressing missing data in small sample research with nonnormal variables," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 35-52.
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    3. Edgar Merkle & Achim Zeileis, 2013. "Tests of Measurement Invariance Without Subgroups: A Generalization of Classical Methods," Psychometrika, Springer;The Psychometric Society, vol. 78(1), pages 59-82, January.
    4. Rosseel, Yves, 2012. "lavaan: An R Package for Structural Equation Modeling," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i02).
    5. William Meredith & John Tisak, 1990. "Latent curve analysis," Psychometrika, Springer;The Psychometric Society, vol. 55(1), pages 107-122, March.
    6. Benjamin E. Leiby & Mary D. Sammel & Thomas R. Ten Have & Kevin G. Lynch, 2009. "Identification of multivariate responders and non‐responders by using Bayesian growth curve latent class models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(4), pages 505-524, September.
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    1. Vinícius Diniz Mayrink & Renato Valladares Panaro & Marcelo Azevedo Costa, 2021. "Structural equation modeling with time dependence: an application comparing Brazilian energy distributors," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(2), pages 353-383, June.

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