Exploratory Structural Equation Modeling

By T. Asparouhov, B. Muthén. The SAGE Encyclopedia of Research Design, 2009

1,954 citations recorded by the source corpus.

Exploratory factor analysis (EFA) is a frequently used multivariate analysis technique in statistics. Jennrich and Sampson (1966) solved a significant EFA factor loading matrix rotation problem by deriving the direct Quartimin rotation. Jennrich was also the first to develop standard errors for rotated solutions, although these have still not made their way into most statistical software programs. This is perhaps because Jennrich's achievements were partly overshadowed by the subsequent development of confirmatory factor analysis (CFA) by Jöreskog (1969). The strict requirement of zero cross-loadings in CFA, however, often does not fit the data well and has led to a tendency to rely on extensive model modification to find a well-fitting model. In such cases, searching for a well-fitting measurement model may be better carried out by EFA (Browne, 2001). Furthermore, misspecification of zero loadings usually leads to distorted factors with over-estimated factor correlations and subsequent distorted structural relations. This article describes an EFA-SEM (ESEM) approach, where in addition to or instead of a CFA measurement model, an EFA measurement model with rotations can be used in a structural equation model. The ESEM approach has recently been implemented in the Mplus program. ESEM gives access to all the usual SEM parameters and the loading rotation gives a transformation of structural coefficients as well. Standard errors and overall tests of model fit are obtained. Geomin and Target rotations are discussed. Examples of ESEM models include multiple-group EFA with measurement and structural invariance testing, test–retest (longitudinal) EFA, EFA with covariates and direct effects, and EFA with correlated residuals. Testing strategies with sequences of EFA and CFA models are discussed. Simulated and real data are used to illustrate the points.

Subfields: Computer Networks and Communications, Food Science, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/fcabd4413ff386afac8dbe37d2f242ab24161059

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Food Science

In Management Science and Operations Research

Citations in the corpus

Four facets of Facebook Intensity - The development of the Multidimensional Facebook Intensity Scale (2016, methodology): “Compared to the exploratory factor analysis, ESEM is considered to be preferable as it (1) allows the items to cross-load, (2) allows the exploratory testing of different factor structures, and (3) allows the direct comparison of the exploratory and confirmatory analyses (Asparouhov & Muthén, 2009).”
Recent Methods for the Study of Measurement Invariance With Many Groups (2018, positive): “Maximum likelihood estimation was used for the initial configural model as discussed in Asparouhov and Muthén (2014). Table 3 shows the (non)invariance results for the measurement intercepts and factor loadings.”
The Narcissism Spectrum Model: A Synthetic View of Narcissistic Personality (2018, positive): “…multidimensional and personality features represent simultaneous influences from distinct systems, this technique is either inappropriate or suboptimal (see Asparouhov & Muthén, 2009; Church & Burke, 1994; Marsh et al., 2009; see McCrae, Zonderman, Costa, Bond, & Paunonen, 1996, for discussion).”
Different motivations for knowledge sharing and hiding: The role of motivating work design (2019, methodology): “We examined the adequacy of our measurement model and our hypotheses with exploratory structural equation modeling (ESEM, Asparouhov & Muthén, 2009; Marsh et al., 2014; Morin et al., 2013), using Mplus 7.4 (Muthén & Muthén, 2010).”
Culture's Consequences on Student Motivation: Capturing Cross-Cultural Universality and Variability Through Personal Investment Theory (2014, background): “More recently, exploratory structural equation modeling (ESEM) may also hold promise for testing differential factor structures across cultures (Asparouhov & Muthen, 2009).”
On Detecting Systematic Measurement Error in Cross-Cultural Research: A Review and Critical Reflection on Equivalence and Invariance Tests (2018, background): “Associated analyses are EFA/PCA in combination with DIF analysis, and multigroup confirmatory factor analytical procedures (including Bayesian structural equation modeling [SEM]), as well as alignment and ESEM.”
Psychometric Evidence of the Brief Resilience Scale (BRS) and Modeling Distinctiveness of Resilience from Depression and Stress (2018, methodology): “MODEL 4 was a variation of Model 3 with the Exploratory Structural Equation Model method (ESEM; Asparouhov & Muthen, 2009).”
Testing Measurement Invariance and Comparing Latent Factor Means Within a Confirmatory Factor Analysis Framework (2011, methodology): “Although this exploratory approach is questionable without strong theory (Asparouhov & Muthén, 2009; Browne, 2001; Schmitt, 2011), it was adopted given the importance of a good fitting CI model (see Cheung & Rensvold, 2002) and avoiding misspecified models.”
Methodological Measurement Fruitfulness of Exploratory Structural Equation Modeling (ESEM): New Approaches to Key Substantive Issues in Motivation and Engagement (2011, methodology): “However, when more than one factor (m > 1) is posited, further constraints are required to achieve an identified solution (Asparouhov & Muthén, 2009; Marsh et al., 2009).”
Intuitive Eating (2020, background): “As Morin, Marsh, and Nagengast (2013) noted: "ESEM should generally be preferred to ICM-CFA when the factors are appropriately identified by ESEM, the goodness of fit is meaningfully better than for ICMCFA, and factor correlations are meaningfully smaller than for ICM-CFA" (p. 430; where ICM-CFA…”
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 5.2 (Muthén & Muthén, 2008).”
Introducing the Short Dark Triad (SD3) (2014, positive): “Rather than a traditional confirmatory factor analysis (CFA), our method of choice was exploratory structural equation modeling (ESEM; Asparouhov & Muthén, 2009).”
A Critical Examination of Common Beliefs About Partial Least Squares Path Modeling (2013, neutral): “In addition, new SEM-based methods for exploratory 442 Organizational Research Methods 16(3) analyses are actively developed (e.g., Asparouhov & Muthén, 2009).”
Gaussian Process Structural Equation Models with Latent Variables (2010, background): “Asparouhov and Muth´en (2009) and Silva et al. (2006) discuss relevant issues.”
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.”