NONLINEAR PRINCIPAL COMPONENT ANALYSIS IN PATH ANALYSIS WITH LATENT VARIABLES MIXED DATA
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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Universitas Pattimura
2024-03-01
|
| Series: | Barekeng |
| Subjects: | |
| Online Access: | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/10410 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849402549888614400 |
|---|---|
| author | Rindu Hardianti Solimun Solimun Nurjannah Nurjannah |
| author_facet | Rindu Hardianti Solimun Solimun Nurjannah Nurjannah |
| author_sort | Rindu Hardianti |
| collection | DOAJ |
| description | 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%. |
| format | Article |
| id | doaj-art-57c773f0b42941458ca2204aee086325 |
| institution | Kabale University |
| issn | 1978-7227 2615-3017 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | Universitas Pattimura |
| record_format | Article |
| series | Barekeng |
| spelling | doaj-art-57c773f0b42941458ca2204aee0863252025-08-20T03:37:31ZengUniversitas PattimuraBarekeng1978-72272615-30172024-03-011810373038210.30598/barekengvol18iss1pp0373-038210410NONLINEAR PRINCIPAL COMPONENT ANALYSIS IN PATH ANALYSIS WITH LATENT VARIABLES MIXED DATARindu Hardianti0Solimun Solimun1Nurjannah Nurjannah2Departement of Statistics, Faculty of Mathematics & Natural Sciences, University of Brawijaya, IndonesiaDepartement of Statistics, Faculty of Mathematics & Natural Sciences, University of Brawijaya, IndonesiaDepartement of Statistics, Faculty of Mathematics & Natural Sciences, University of Brawijaya, IndonesiaThis 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%.https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/10410nonlinear principal component analysispath analysislatent variablesmixed data |
| spellingShingle | Rindu Hardianti Solimun Solimun Nurjannah Nurjannah NONLINEAR PRINCIPAL COMPONENT ANALYSIS IN PATH ANALYSIS WITH LATENT VARIABLES MIXED DATA Barekeng nonlinear principal component analysis path analysis latent variables mixed data |
| title | NONLINEAR PRINCIPAL COMPONENT ANALYSIS IN PATH ANALYSIS WITH LATENT VARIABLES MIXED DATA |
| title_full | NONLINEAR PRINCIPAL COMPONENT ANALYSIS IN PATH ANALYSIS WITH LATENT VARIABLES MIXED DATA |
| title_fullStr | NONLINEAR PRINCIPAL COMPONENT ANALYSIS IN PATH ANALYSIS WITH LATENT VARIABLES MIXED DATA |
| title_full_unstemmed | NONLINEAR PRINCIPAL COMPONENT ANALYSIS IN PATH ANALYSIS WITH LATENT VARIABLES MIXED DATA |
| title_short | NONLINEAR PRINCIPAL COMPONENT ANALYSIS IN PATH ANALYSIS WITH LATENT VARIABLES MIXED DATA |
| title_sort | nonlinear principal component analysis in path analysis with latent variables mixed data |
| topic | nonlinear principal component analysis path analysis latent variables mixed data |
| url | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/10410 |
| work_keys_str_mv | AT rinduhardianti nonlinearprincipalcomponentanalysisinpathanalysiswithlatentvariablesmixeddata AT solimunsolimun nonlinearprincipalcomponentanalysisinpathanalysiswithlatentvariablesmixeddata AT nurjannahnurjannah nonlinearprincipalcomponentanalysisinpathanalysiswithlatentvariablesmixeddata |