DOI: https://doi.org/10.14710/medstat.17.1.81-92

ANALYSIS OF MULTILEVEL STRUCTURAL EQUATION MODELING WITH GENERALIZED STRUCTURED COMPONENT ANALYSIS METHOD

*Fitri Amanah  -  Master of Mathematics Study Programme, Universitas Gadjah Mada, Yogyakarta, Indonesia
Abdurakhman Abdurakhman  -  Department of Mathematics, Universitas Gadjah Mada, Yogyakarta, Indonesia
Open Access Copyright (c) 2024 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
Generalized Structured Component Analysis (GSCA) is a component-based SEM. One of the developments of GSCA is the GSCA method for multilevel data known as multilevel GSCA. Multilevel data is data that has a nested, grouped, or nested structure. This study aims to apply multilevel GSCA to the data on factors that affect poverty. The data used is on Indonesia's health, education and poverty in 2023.. The result is that all indicators are significant to the latent variables. The structural model shows that the quality of health has a negative and significant effect on poverty, education has a negative and significant effect on poverty, and the quality of health has a positive and significant effect on education. The results of between group show that health quality has a positive and significant effect on education in all regions, health quality has a negative and significant effect on poverty in Bali & Nusa Tenggara, Sulawesi, as well as Maluku and Papua, education has a negative and significant effect on poverty in Sumatra, Java, and Maluku & Papua. The overall goodness of fit value (FIT) is 0.622, meaning the model can explain 62.2% of data variation.
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Keywords: SEM; GSCA; Multilevel GSCA; ALS. poverty.

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