Exploratory Structural Equation Modeling, Integrating CFA and EFA: Application to Students' Evaluations of University Teaching

By Herbert W. Marsh, B. Muthén, T. Asparouhov, O. Lüdtke, Alexander Robitzsch, Alexandre J. S. Morin, Ulrich Trautwein. 2009

1,079 citations recorded by the source corpus.

Subfields: Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/23c7024c7cb4ac192f8f4f1039a8b1f41b1e5c90

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In Management Science and Operations Research

Citations in the corpus

The relations between implicit intelligence beliefs, autonomous academic motivation, and school persistence intentions: a mediation model (2015, background): “However, some tested SEM/CFAmodels fail to reach commonly accepted levels of fit to the data usually advocated in SEM/CFA research (see Marsh et al. 2009 for more details).”
Comparing Exploratory Structural Equation Modeling and Existing Approaches for Multiple Regression with Latent Variables (2018, methodology): “…loadings, exploratory structural equation modeling (ESEM) was proposed as an alternative approach for latent variables analysis, which evaluates the measurement model of latent variables using exploratory factor analysis (EFA) instead of CFA (Marsh et al., 2009; Sass, 2011; Schmitt, 2011).”
Teaching to address diverse learning needs: development and validation of a Differentiated Instruction Scale (2013, methodology): “As recommended by Marsh et al. (2009), we performed first-order CFAs using Mplus 6.1 (Muthén and Muthén 2006) to further assess the adequacy of the factor structure and to confirm the bi-dimensionality of the DIS.”
Methodological Measurement Fruitfulness of Exploratory Structural Equation Modeling (ESEM): New Approaches to Key Substantive Issues in Motivation and Engagement (2011, methodology): “Following Marsh et al. (2010, 2009) we used an oblique geomin rotation (the default in Mplus) with an epsilon value of 0.5 and the full-information MLR estimator to correct for small amounts of missing data (due to the nature of data, the amount of missing data was very small—less than 1%).”
Revisiting the Model Size Effect in Structural Equation Modeling (2018, methodology): “In a 10-factor exploratory structural equation model (ESEM; Asparouhov & Muthén, 2009; Marsh et al., 2009) with only minimal constraints for model identification, the number of free parameters would be inflated to about 660.”
Evaluating Model Fit With Ordered Categorical Data Within a Measurement Invariance Framework: A Comparison of Estimators (2014, methodology): “Nevertheless, both TLI and RMSEA continue to be widely used in evaluating invariance, and Marsh and colleagues (Marsh et al., 2010; Marsh et al., 2009) noted that they are particularly valuable in that they contain a correction for parsimony that is such a critical issue in testing invariance.”
Motivation and coping with the stress of assessment: Gender differences in outcomes for university students (2017, methodology): “Goodness-of-fit of the measurement and structural models was evaluated according to the recommendations of Marsh et al. (2009): models were deemed to have acceptable fit with a comparative fit index (CFI) equal to or higher than .90 (and an excellent fit equal to or greater than .95) and a root…”
Evidence of a continuum structure of academic self-determination: A two-study test using a bifactor-ESEM representation of academic motivation (2017, methodology): “These guidelines have been established for CFA, and used in previous ESEM applications (e.g., Marsh et al., 2009, 2014; Morin et al., 2013, 2016a).”
Bayesian Structural Equation Modeling With Cross-Loadings and Residual Covariances (2015, background): “Recent statistical literature on measurement models including cross-loadings (Marsh et al., 2009, 2010, 2014; Morin et al., in press) show that factor correlations tend to be upwardly biased when true cross-loadings are constrained to be zero.”
Perceived information and communication technology (ICT) demands on employee outcomes: the moderating effect of organizational ICT support. (2012, methodology): “We conducted an Exploratory Structural Equation Modeling analysis (ESEM; Asparouhov & Muthén, 2009; Marsh et al., 2010; Marsh et al., 2009), using MPlus version 5.”
Development and initial validation of the Life Skills Scale for Sport (2017, methodology): “Changes in the TLI of < 0.01 indicate a similar fit with models involving a complex structure (Marsh et al., 2009; Morin et al., 2013).”