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Multilevel factor score regression

Ines Devlieger (UGent) and Yves Rosseel (UGent)
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Abstract
Multilevel SEM is an increasingly popular technique to analyze data that are both hierarchical and contain latent variables. The parameters are usually jointly estimated using a maximum likelihood estimator (MLE). This has the disadvantage that a large sample size is needed and misspecifications in one part of the model may influence the whole model. We propose an alternative stepwise estimation method, which is an extension of the Croon method for factor score regression. In this article, we extend this method to the multilevel setting. A simulation study was used to compare this new estimation method to the standard MLE. The Croon method outperformed MLE with regard to convergence rate, bias, MSE, and coverage, in particular when models contained a structural misspecification. In conclusion, the Croon method seems to be a promising alternative to MLE.
Keywords
Multilevel factor score regression, multilevel SEM, factor scores, stepwise estimation method, Bayesian-approach, sample-size, latent, models, level, variables

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Please use this url to cite or link to this publication:

MLA
Devlieger, Ines, and Yves Rosseel. “Multilevel Factor Score Regression.” MULTIVARIATE BEHAVIORAL RESEARCH, 2020.
APA
Devlieger, I., & Rosseel, Y. (2020). Multilevel factor score regression. MULTIVARIATE BEHAVIORAL RESEARCH.
Chicago author-date
Devlieger, Ines, and Yves Rosseel. 2020. “Multilevel Factor Score Regression.” MULTIVARIATE BEHAVIORAL RESEARCH.
Chicago author-date (all authors)
Devlieger, Ines, and Yves Rosseel. 2020. “Multilevel Factor Score Regression.” MULTIVARIATE BEHAVIORAL RESEARCH.
Vancouver
1.
Devlieger I, Rosseel Y. Multilevel factor score regression. MULTIVARIATE BEHAVIORAL RESEARCH. 2020;
IEEE
[1]
I. Devlieger and Y. Rosseel, “Multilevel factor score regression,” MULTIVARIATE BEHAVIORAL RESEARCH, 2020.
@article{8632612,
  abstract     = {Multilevel SEM is an increasingly popular technique to analyze data that are both hierarchical and contain latent variables. The parameters are usually jointly estimated using a maximum likelihood estimator (MLE). This has the disadvantage that a large sample size is needed and misspecifications in one part of the model may influence the whole model. We propose an alternative stepwise estimation method, which is an extension of the Croon method for factor score regression. In this article, we extend this method to the multilevel setting. A simulation study was used to compare this new estimation method to the standard MLE. The Croon method outperformed MLE with regard to convergence rate, bias, MSE, and coverage, in particular when models contained a structural misspecification. In conclusion, the Croon method seems to be a promising alternative to MLE.},
  author       = {Devlieger, Ines and Rosseel, Yves},
  issn         = {0027-3171},
  journal      = {MULTIVARIATE BEHAVIORAL RESEARCH},
  keywords     = {Multilevel factor score regression,multilevel SEM,factor scores,stepwise estimation method,Bayesian-approach,sample-size,latent,models,level,variables},
  language     = {eng},
  pages        = {25},
  title        = {Multilevel factor score regression},
  url          = {http://dx.doi.org/10.1080/00273171.2019.1661817},
  year         = {2020},
}

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