NONLINEAR PRINCIPAL COMPONENT ANALYSIS IN PATH ANALYSIS WITH LATENT VARIABLES MIXED DATA - Hardianti, Rindu; Solimun, Solimun; Nurjannah, Nurjannah (BAREKENG: Jurnal Ilmu Matematika dan Terapan, 2024)
This study aims to obtain the main component score of the ability to pay latent variable, determine the strongest indicators forming the ability to pay on a mixed scale based on defined indicators, and model the ability to pay on time mediated by fear of paying using path analysis. The data used in this study is secondary data from mortgage-paying customers with a sample size of 100. The method used is nonlinear principal component analysis with path analysis modeling. The results of this study indicate that the eleven variables formed by PC1 or X1 are able to store diversity or information by 32.50%, while 67.50% of diversity or other information is not stored (wasted). The credit term is the strongest indicator that forms the ability to pay variable. The variable ability to pay mortgages has a significant effect on payments by mediating the fear of paying late with a coefficient of determination of 80.40%.