Applications, Challenges, Methodological Advancements and Insights of Structural Equation Modelling: A Meta-Analysis

Abstract

Structural Equation Modelling (SEM) has proven to be a crucial tool for researchers analyzing complex networks of relationships among latent constructs. This cutting-edge review of SEM applications, enduring methodological issues and advances and findings is accomplished via a meta-analysis of 75 peer-reviewed articles (2015-2025). The review identifies SEM is being applied mainly for theory testing, scale validation and mediation/moderation analysis, thus solidifying its place across disciplines ranging from engineering management to psychology. But its potency is all too frequently neutralized by recurring methodological fallacies, like specification errors in model specification, sample size deficiency, neglect of measurement invariance, uncritical reliance on fit indices and misuse of non-normal and missing data.

Above all, the analysis highlights that these issues primarily occur not due to statistical complexity but due to insufficient methodological discipline and theoretical acumen. The review also highlights significant advancements like Bayesian SEM, Partial Least Squares SEM, machine learning integration and improved reporting standards that are strengthening and making the technique more flexible by building a brighter future for the method. This integration concludes that the optimal application of SEM requires effective compliance with good theory, well-thought-through research design and open practices.