The presentation aims to introduce how Structural Equation Modeling (SEM) transforms theoretical frameworks into empirical data validation. Compared to traditional regression, SEM effectively isolates measurement errors, handles multiple causal paths simultaneously, and provides native support for "latent variables" that cannot be directly measured. Key highlights of the presentation include: Theory First: Emphasizing that SEM is a "confirmatory" analysis, requiring a theoretical blueprint before using data to test the model's goodness-of-fit. Core Components: Introducing visual symbols and building blocks such as observed variables, latent variables, and error terms. Two-Stage Analysis: Distinguishing between the "measurement model" for testing measurement quality and the "structural model" for verifying causal relationships. Evaluation Metrics: Listing fit indices such as CFI and RMSEA as criteria for judging the quality of the model.

Implemented by Institute of Educational Administration and Evaluation
Date: 2026/03/30