Comparing Exploratory Structural Equation Modeling and Existing Approaches for Multiple Regression with Latent Variables

By Yujiao Mai, Zhiyong Zhang, Zhonglin Wen. 2018

115 citations recorded by the source corpus.

Exploratory structural equation modeling (ESEM) is an approach for analysis of latent variables using exploratory factor analysis to evaluate the measurement model. This study compared ESEM with two dominant approaches for multiple regression with latent variables, structural equation modeling (SEM) and manifest regression analysis (MRA). Main findings included: (1) ESEM in general provided the least biased estimation of the regression coefficients; SEM was more biased than MRA given large cross-factor loadings. (2) MRA produced the most precise estimation, followed by ESEM and then SEM. (3) SEM was the least powerful in the significance tests; statistical power was lower for ESEM than MRA with relatively small target-factor loadings, but higher for ESEM than MRA with relatively large target-factor loadings. (4) ESEM showed difficulties in convergence and occasionally created an inflated type I error rate under some conditions. ESEM is recommended when non-ignorable cross-factor loadings exist.

Subfields: Computer Networks and Communications, Information Systems and Management, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/ad6db6e7a0578ef7ccfee79d134f309871b8d963

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In Computer Networks and Communications

  1. K. Hau
  2. Yue Xiao
  3. Hongyun Liu

In Management Science and Operations Research

  1. K. Hau
  2. Yue Xiao
  3. Hongyun Liu
  4. Herbert W. Marsh
  5. Eldad Davidov
  6. Alexandre J. S. Morin
  7. Carlo Salvato
  8. Zhonglin Wen

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