• Structural Equation Modeling (SEM) fit evaluation and misspecification sensitivity have been central, with multifaceted fit concepts, Monte Carlo assessments, and cross-context validation across psychometrics and education [1], [8], [12], [4], [11].

• Structural Equation Modeling (SEM) methodology expanded to robust covariances, EM-based estimations for multilevel designs, and alternative approaches like partial least squares, enhancing resilience to outliers and unbalanced data [17], [10], [15], [16].

• Structural Equation Modeling (SEM) refinement and respecification strategies emphasize controlling type I error in exploratory modifications and the role of model parsimony and sampling error in fit [7], [18], [3], [6].

• Structural Equation Modeling (SEM) diagnostics center on local influence and cross-validation/stability checks, enabling data-driven evaluation of observations and model components [4], [11], [13].

• Structural Equation Modeling (SEM) foundational concepts and future directions synthesize core ideas with perspectives and debates, guiding the evolution of SEM methods and applications [19], [20], [3], [6].