Complexities and Practical Issues in Conducting Structural Equation Modeling: A Review

Structural Equation Modeling (SEM) is widely used to analyze complex relationships among latent variables across scientific fields. However, its application is often affected by methodological and practical issues that may compromise result validity. This study provides a critical review of challenges in SEM, including model specification, construct validity, sampling, model evaluation, and reporting practices. A Systematic Literature Review (SLR) of 28 peer-reviewed articles (2019–2025) from the Scopus database was conducted using thematic synthesis and frequency analysis. The findings identify five dominant issue clusters and demonstrate that these challenges are associated with observable estimation consequences, such as non-convergence, parameter instability, inflated standard errors, and estimator sensitivity. These effects reflect underlying bias–variance trade-offs between CB-SEM and PLS-SEM. The study contributes to improving SEM practice by linking methodological shortcomings with their numerical implications.