Exploratory Structural Equation Modeling, Integrating CFA and EFA: Application to Students' Evaluations of University Teaching
Abstract
This study is a methodological-substantive synergy, demonstrating the power and flexibility of exploratory structural equation modeling (ESEM) methods that integrate confirmatory and exploratory factor analyses (CFA and EFA), as applied to substantively important questions based on multidimentional students' evaluations of university teaching (SETs). For these data, there is a well established ESEM structure but typical CFA models do not fit the data and substantially inflate correlations among the nine SET factors (median rs = .34 for ESEM, .72 for CFA) in a way that undermines discriminant validity and usefulness as diagnostic feedback. A 13-model taxonomy of ESEM measurement invariance is proposed, showing complete invariance (factor loadings, factor correlations, item uniquenesses, item intercepts, latent means) over multiple groups based on the SETs collected in the first and second halves of a 13-year period. Fully latent ESEM growth models that unconfounded measurement error from communality showed almost no linear or quadratic effects over this 13-year period. Latent multiple indicators multiple causes models showed that relations with background variables (workload/difficulty, class size, prior subject interest, expected grades) were small in size and varied systematically for different ESEM SET factors, supporting their discriminant validity and a construct validity interpretation of the relations. A new approach to higher order ESEM was demonstrated, but was not fu…
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ESEM was developed by Marsh et al. (2009) and is described as an overarching integration of the best aspects of CFA, SEM, and EFA.
“ESEM was developed by Marsh et al. (2009) and is described as “an overarching integration of the best aspects” of CFA, structural equation modeling (SEM), and exploratory factor analysis (EFA) (Marsh et al., 2014, p. 85).”
From Taking a future perspective by learning from the past – A systematic review of assessment instruments that aim to measure primary and secondary school students' ICT literacy (2016) · cites this paper as Marsh et al., 2009
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- Authors
- Marsh, H. W., Muthén, B., Asparouhov, T., Lüdtke, O., Robitzsch, A., Morin, A. J. S., & Trautwein, U.
- Affiliations
- University of Oxford (GB)
- University of California, Los Angeles (US)
- Muthén & Muthén (United States) (US)
- Max Planck Society (DE)
- Institute for Educational Progress
- Université de Sherbrooke (CA)
- ORCID
- Venue
- Structural Equation Modeling: A Multidisciplinary Journal
- Publisher
- Informa UK Limited
- Published
- 2009-07-14
- Volume
- 16
- Issue
- 3
- Pages
- 439-476
- ISSN
- 1070-5511 (ISSN-L)
- 1532-8007
- OpenAlex
- W2144487201
- Cited by
- 1,093
- References
- 4
- Citations by year
- 2026: 64 · 2025: 70 · 2024: 92 · 2023: 74 · 2022: 77 · 2021: 98 · 2020: 76 · 2019: 78 · 2018: 71 · 2017: 53 · 2016: 66 · 2015: 74 · 2014: 62 · 2013: 52 · 2012: 28
- FWCI
- 38.13
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Structural Equation Modeling A Multidisciplinary Journal (publishedVersion)
- Research Bank (Australian Catholic University) (submittedVersion)
- Max Planck Institute for Plasma Physics (submittedVersion)
- Max Planck Digital Library (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- Economic and Social Research Council (ES/F041292/1)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Social Sciences › Education › Evaluation of Teaching Practices
- Social Sciences › Education › Educational Assessment and Pedagogy
- Keywords
- exploratory structural equation modeling, measurement invariance, discriminant validity, latent growth curve models, multiple indicators multiple causes model, higher education teaching quality
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