Abstract: Structural Equation Modeling (SEM) is widely used to analyze relationships among latent and observed variables in social and behavioral research. However, researchers face methodological, statistical, and practical challenges that threaten model validity and interpretability. This study conducts a systematic literature review of 35 peer-reviewed articles (2020–2025) using the PRISMA framework. The findings identify five major challenges: model misspecification, measurement-model instability, data quality and sample-size limitations, statistical and estimation issues, and practical implementation constraints. Common problems include overfitting, under-identification, low factor loadings, non-normality, and convergence failures, which are linked to violations of identification conditions, distributional assumptions, and finite-sample properties. Software heterogeneity and incomplete reporting further limit replicability. The results emphasize that robust SEM inference depends on strong theoretical grounding, appropriate statistical assumptions, and transparent reporting practices.

Keywords: Structural Equation Modeling, Practical Concerns in SEM, Methodological Issues from SEM, Model Comparison, Validity, Statistical Challenges, Model Identification, Measurement Model

Pages: 447-467