SmartPLS-SEM

Structural Equation Modeling (SEM) is a statistical technique used to test and estimate relationships among observed (measured) and latent (unobserved) variables. It combines elements of factor analysis and multiple regression, allowing researchers to test complex theoretical models about how variables influence each other. Here’s a concise overview: 1. Latent Variables (Constructs): These are variables that cannot be directly measured — for example, “driver behavior” or “project management efficiency.” They are represented by several observable indicators (survey items or measurements). 2. Observed Variables: These are the actual items or indicators you collect data on, such as questionnaire responses or measured quantities. 3. Measurement Model: This part examines how well the observed variables represent the latent constructs (using confirmatory factor analysis – CFA). 4. Structural Model: This part tests the hypothesized relationships between constructs (e.g., whether road design affects traffic safety through driver behavior). 5. Model Fit: Fit indices (like RMSEA, CFI, TLI, or SRMR) show how well your theoretical model matches the data. 6. Applications: SEM is widely used in engineering, psychology, and social sciences — for example, in your field, to analyze how road design, materials, and management factors influence construction delays or safety outcomes.