Structural Equation Modeling with Generalized Structured Component Analysis (SEM-GSCA) Estimation on The Human Development Index (HDI) of Central Java Province Equipped

The Human Development Index (HDI) serves as a primary indicator in assessing the success of human development within a country. This study aims to analyze the latent variables influencing the HDI of Central Java in 2023 using the Generalized Structured Component Analysis (GSCA) method. GSCA is a variance-based Structural Equation Modeling (SEM) approach that does not require the assumption of multivariate normality and incorporates overall goodness-of-fit measures to evaluate the model’s alignment with the data This research utilizes population data sourced from the Central Bureau of Statistics (BPS) of Central Java Province and examines the key indicators constituting the HDI. The analytical findings indicate that all indicators significantly contribute to their respective latent variables. Moreover, the education and health variables exert a significant influence on the economic variable. Furthermore, the HDI is substantially affected by economic, health, and education factors. The model evaluation results demonstrate a Fit Index (FIT) value of 62.3%, reflecting the model’s explanatory capacity, and a Goodness of Fit Index (GFI) value of 96.9%, signifying a strong model fit. These findings provide a comprehensive understanding of the determinants of the HDI in Central Java and are expected to serve as a reference for policymakers in formulating strategies to enhance public welfare and overall quality of life.