Unraveling structural equation modeling: Key assumptions, model fit, and trends

Article ID: 3815

Keywords: structural equation modeling; model fit evaluation; measurement invariance; multicollinearity mitigation; latent variable analysis; endogeneity and causal inference; AI-integrated structural equation modeling; cross-disciplinary SEM applications

Abstract

Structural equation modeling (SEM) serves as a cornerstone analytical tool across disciplines, enabling robust tests of complex relationships. Yet, core assumptions, model fit, measurement invariance, missing data handling, and causal inference validity spark ongoing debates. Despite methodological progress, challenges linger in parameter estimate reliability, sensitivity to model changes, and integration with alternative approaches. This study critically synthesizes recent empirical and theoretical insights to scrutinize these assumptions. A review of contemporary studies spotlights trends like refined fit evaluation, SEM-fsQCA synergies, and machine learning incorporation. Findings expose inconsistencies in missing data treatment and model respecification, varying by discipline. Quantitative focus sharpens model fit indices, while qualitative views stress theoretical justification hurdles. Cross-disciplinary analysis (psychology, finance, education, healthcare, marketing) reveals uneven assumption adherence, questioning generalizability, especially for cross-cultural data and ordinal variables. Hybrid integrations, such as SEM with system dynamics or network analysis, boost predictive accuracy and curb violations. SEM endures as powerful but demands nuanced assumption testing for theoretical and empirical soundness. Implications urge interdisciplinary collaboration on validation. Limitations encompass publication bias and the omission of unpublished advances. Future work should probe alternative fit techniques, violation impacts, and AI-driven diagnostics, fostering reliable, replicable SEM applications.