Unraveling Structural Equation Modeling: Key Assumptions, Model Fit, and Trends

Structural equation modeling (SEM) has become a fundamental analytical tool across diverse disciplines, offering a robust framework for testing complex relationships. However, various assumptions underpinning SEM, including model fit, measurement invariance, missing data treatment, and the validity of causal inferences, remain subjects of debate. Despite significant advancements in SEM methodologies, gaps persist in addressing the reliability of parameter estimates, sensitivity to model modifications, and the integration of alternative analytical approaches. The study aims to critically examine key assumptions in SEM by synthesizing insights from recent empirical and theoretical contributions. A comprehensive review of 29 studies highlights emerging trends in SEM applications, including advancements in fit evaluation procedures, the interplay of SEM with fuzzy-set qualitative comparative analysis (fsQCA), and the incorporation of machine learning techniques. The findings reveal contradictions in the treatment of missing data and model respecification strategies, indicating inconsistencies in methodological approaches across disciplines. While quantitative assessments emphasize model fit indices' refinement, qualitative insights underscore the challenges of theoretical justification in structural modeling. Comparative analysis of SEM applications in psychology, finance, education, healthcare, and marketing further illustrates variations in assumption adherence, raising concerns about generalizability. The study underscores the need for methodological rigor in evaluating measurement invariance, particularly when dealing with cross-cultural data and ordinal variables. Moreover, findings suggest that integrating hybrid methodologies, such as combining SEM with system dynamics and network analysis, enhances predictive power while mitigating assumption violations. The study concludes that while SEM remains a powerful analytical tool, researchers must adopt a nuanced approach to assumption testing, ensuring theoretical coherence and empirical validity. Implications highlight the necessity for interdisciplinary collaboration in refining SEM practices, particularly in model validation and assumption testing. Limitations include the potential for publication bias in reviewed studies and the exclusion of unpublished methodological advancements. Future research should explore alternative fit evaluation techniques, assess the impact of assumption violations on SEM results, and investigate the role of AI in automating model diagnostics. By addressing these challenges, the field can move towards more reliable, replicable, and theoretically grounded applications of SEM.