Structural Modeling Overview

Principles

Definition

Structural modeling is a methodological approach involving the creation of abstract representations or models to investigate the underlying structure, interrelationships, and dynamics of systems or phenomena. This approach facilitates rigorous analysis, theoretical development, and empirical investigation by formalizing the hypothesized structural relationships between system components.

Ontological type

Core Methods

Theoretical Foundations

Model Validation

Key developments

Key Figures

Misspecification-Aware SEM era

Peter M. Bentler [1] was active across prominent American institutions during this era, notably the University of California, Los Angeles [3] and the University of California, Berkeley [4]. His contributions centered on evaluating model fit under misspecification and promoting robust covariance approaches, as reflected in the 1998 paper 'Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification' [7] and the 1998 work '9. Structural Equation Modeling with Robust Covariances' [8], which together advanced detection of misspecification and resilience in SEM. Kenneth A. Bollen [2] was affiliated with the University of Iowa [5] and the University of North Carolina at Chapel Hill [6] during this period. His landmark contribution, the 1995 paper 'Testing Structural Equation Models.' [9], introduced formal SEM testing methods that informed misspecification validation and reinforced cross-context evaluation and parsimony principles of the era.

Integrated SEM and Causal Focus era

Herbert W. Marsh [1] is a prominent scholar whose work in this era spans institutions such as the University of Oxford [3] and the University of Zurich [4]. His key contribution is the 2004 paper In Search of Golden Rules: Comment on Hypothesis-Testing Approaches to Setting Cutoff Values for Fit Indexes and Dangers in Overgeneralizing Hu and Bentler's (1999) Findings [7], which critiques traditional cutoff practices and emphasizes robust model-fit interpretation to underpin causal claims within SEM in Integrated SEM and Causal Focus. Kit-Tai Hau [2] is associated with the University of Hong Kong [5] and the Chinese University of Hong Kong [6] in this era. His key contribution in this era is co-authorship of the same 2004 paper [7], which advances methodological dialogue on fit-index cutoffs and the rigor of causal interpretation in SEM across engineering, management, and education.

Flexible Hybrid Modeling Synthesis era

Marko Sarstedt [1] is associated with Ludwig-Maximilians-Universität München [3] and the University of Newcastle Australia [4] in this era. His key contributions include clarifying when to use and how to report the results of PLS-SEM [6], articulating common beliefs and reality about PLS [7], and testing measurement invariance of composites using partial least squares [8], which collectively advanced rigorous reporting, measurement validity, and invariance checks essential for practical flexible hybrid modeling. Christian M. Ringle [2] is associated with Universität Hamburg [5] and the University of Newcastle Australia [4] in this era. Alongside Sarstedt, his contributions in the same papers—When to use and how to report the results of PLS-SEM [6], Common Beliefs and Reality About PLS [7], and Testing measurement invariance of composites using partial least squares [8]—helped establish actionable guidelines, resolve debates about PLS-SEM practice, and advance measurement-invariance approaches for small samples in flexible hybrid modeling.