Nonlinear Principal Component Analysis with Mixed Data Formative Indicator Models in Path Analysis
This research aims to obtain the main component score of the latent variable ability to pay, determine the strongest indicators forming the ability to pay on a mixed scale based on predetermined indicators, and model the ability to pay on time as mediated by fear of paying using path analysis. The d...
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Universitas Muhammadiyah Mataram
2024
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Online Access: | http://ir.unimas.my/id/eprint/45754/1/17559-67118-1-PB.pdf http://ir.unimas.my/id/eprint/45754/ https://journal.ummat.ac.id/index.php/jtam/article/view/17559 https://doi.org/10.31764/jtam.v8i1.17559 |
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my.unimas.ir.457542024-08-22T05:40:09Z http://ir.unimas.my/id/eprint/45754/ Nonlinear Principal Component Analysis with Mixed Data Formative Indicator Models in Path Analysis Rindu, Hardianti Solimun, Solimun2 Nurjannah, Nurjannah Rosita, Hamdan HA Statistics This research aims to obtain the main component score of the latent variable ability to pay, determine the strongest indicators forming the ability to pay on a mixed scale based on predetermined indicators, and model the ability to pay on time as mediated by fear of paying using path analysis. The data used is secondary data obtained through distributing questionnaires with a mixed data scale. The sampling technique used in the research was purposive sampling. The number of samples used in the research was 100 customers. The method used is nonlinear principal component analysis with path analysis modeling. The results of this research show that of the five indicators formed by the Principal Component, 74.8% of diversity or information is able to be stored, while 25.20% of diversity or other information is not stored (wasted). Credit term is the strongest indicator that forms the ability to pay variable. The variable ability to pay mortgage has a significant effect on payments by mediating the fear of being late in paying with a coefficient of determination of 73.63%. Universitas Muhammadiyah Mataram 2024 Article PeerReviewed text en http://ir.unimas.my/id/eprint/45754/1/17559-67118-1-PB.pdf Rindu, Hardianti and Solimun, Solimun2 and Nurjannah, Nurjannah and Rosita, Hamdan (2024) Nonlinear Principal Component Analysis with Mixed Data Formative Indicator Models in Path Analysis. JTAM (Jurnal Teori dan Aplikasi Matematika), 8 (1). pp. 195-205. ISSN 2597-7512 https://journal.ummat.ac.id/index.php/jtam/article/view/17559 https://doi.org/10.31764/jtam.v8i1.17559 |
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HA Statistics Rindu, Hardianti Solimun, Solimun2 Nurjannah, Nurjannah Rosita, Hamdan Nonlinear Principal Component Analysis with Mixed Data Formative Indicator Models in Path Analysis |
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This research aims to obtain the main component score of the latent variable ability to pay, determine the strongest indicators forming the ability to pay on a mixed scale based on predetermined indicators, and model the ability to pay on time as mediated by fear of paying using path analysis. The data used is secondary data obtained through distributing questionnaires with a mixed data scale. The sampling technique used in the research was purposive sampling. The number of samples used in the research was 100 customers. The method used is nonlinear principal component analysis with path analysis modeling. The results of this research show that of the five indicators formed by the Principal Component, 74.8% of diversity or information is able to be stored, while 25.20% of diversity or other information is not stored (wasted). Credit term is the strongest indicator that forms the ability to pay variable. The variable ability to pay mortgage has a significant effect on payments by mediating the fear of being late in paying with a coefficient of determination of 73.63%. |
format |
Article |
author |
Rindu, Hardianti Solimun, Solimun2 Nurjannah, Nurjannah Rosita, Hamdan |
author_facet |
Rindu, Hardianti Solimun, Solimun2 Nurjannah, Nurjannah Rosita, Hamdan |
author_sort |
Rindu, Hardianti |
title |
Nonlinear Principal Component Analysis with Mixed Data Formative Indicator Models in Path Analysis |
title_short |
Nonlinear Principal Component Analysis with Mixed Data Formative Indicator Models in Path Analysis |
title_full |
Nonlinear Principal Component Analysis with Mixed Data Formative Indicator Models in Path Analysis |
title_fullStr |
Nonlinear Principal Component Analysis with Mixed Data Formative Indicator Models in Path Analysis |
title_full_unstemmed |
Nonlinear Principal Component Analysis with Mixed Data Formative Indicator Models in Path Analysis |
title_sort |
nonlinear principal component analysis with mixed data formative indicator models in path analysis |
publisher |
Universitas Muhammadiyah Mataram |
publishDate |
2024 |
url |
http://ir.unimas.my/id/eprint/45754/1/17559-67118-1-PB.pdf http://ir.unimas.my/id/eprint/45754/ https://journal.ummat.ac.id/index.php/jtam/article/view/17559 https://doi.org/10.31764/jtam.v8i1.17559 |
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1808981515777867776 |
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13.19449 |