Finite-Mixture Partial Least Squares Structural Equation Modeling of Multidimensional Poverty and Latent Regional Heterogeneity in Central Java
Kata Kunci:
PLS-SEM, FIMIX-PLS, latent heterogeneity, multidimensional poverty, Central Java, statistical modelingAbstrak
This study reformulates a historical regional-poverty application using partial least squares structural equation modeling (PLS-SEM) and finite-mixture PLS (FIMIX-PLS). The analysis covers 35 districts and municipalities in Central Java using 2025 socioeconomic indicators grouped into five latent constructs: education, health, human capital, economic conditions, and poverty. The measurement model contains 14 reflective indicators, while the recursive structural model links education to health, human capital, and economic conditions, and links human capital and economic conditions to poverty. The reported outer loadings range from 0.724 to 0.998, and all bootstrap t statistics for the indicators exceed 1.96. The poverty construct has an R-squared value of 0.552, indicating that 55.2% of its reported variance is explained by human-capital and economic-condition constructs. The strongest direct structural association with poverty is the economic-condition path (coefficient = 0.843; t = 5.041). FIMIX-PLS yields the highest normalized entropy at K = 5 (EN = 0.955), suggesting substantial separation among five latent regional segments. Because the raw data and original project file were not supplied, this article reproduces and critically reassesses the reported estimates rather than independently re-estimating them. The results demonstrate the statistical value of component-based structural modeling for multidimensional poverty while emphasizing the need for contemporary reliability, predictive, and segmentation checks.
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