THE ORDINAL LOGISTIC REGRESSION MODEL WITH SAMPLING WEIGHTS ON DATA FROM THE NATIONAL SOCIO-ECONOMIC SURVEY
Ordinal logistic regression is a method describing the relationship between an ordered categorical response variable and one or more explanatory variables. The parameter estimation of this model uses the maximum likelihood estimation having assumption that each sample unit having an equal chance of...
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Universitas Pattimura
2022-12-01
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| Series: | Barekeng |
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| Online Access: | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/6464 |
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| author | Reni Amelia Indahwati Indahwati Erfiani Erfiani |
| author_facet | Reni Amelia Indahwati Indahwati Erfiani Erfiani |
| author_sort | Reni Amelia |
| collection | DOAJ |
| description | Ordinal logistic regression is a method describing the relationship between an ordered categorical response variable and one or more explanatory variables. The parameter estimation of this model uses the maximum likelihood estimation having assumption that each sample unit having an equal chance of being selected, or using simple random sampling (SRS) design. This study uses data from the National Socio-Economic Survey (SUSENAS) having two-stage one-phase sampling (not SRS). So, the parameter estimation should consider the sampling weights. This study describes the parameter estimation of the ordinal logistic regression with sampling weight using the pseudo maximum likelihood method, especially in SUSENAS sampling design framework. The variance estimation method uses Taylor linearization. This study also provides numerical examples using ordinal logistic regression with sampling weight. Data used is 121,961 elderly spread over 514 districts/cities. Testing data (20%) is used to obtain the accuracy of the prediction results. The variables used in this study are the health status of the elderly as the response variable, and nine explanatory variables. The results of this study indicate that the ordinal logistic regression model with sampling weights is more representative of the population and more capable to predict minority categories of the response variable (poor and moderate health status) than is without sampling weights. |
| format | Article |
| id | doaj-art-3f0bb557e044412bbbb62ceb2ea1f98c |
| institution | Kabale University |
| issn | 1978-7227 2615-3017 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Universitas Pattimura |
| record_format | Article |
| series | Barekeng |
| spelling | doaj-art-3f0bb557e044412bbbb62ceb2ea1f98c2025-08-20T03:37:34ZengUniversitas PattimuraBarekeng1978-72272615-30172022-12-011641355136410.30598/barekengvol16iss4pp1355-13646464THE ORDINAL LOGISTIC REGRESSION MODEL WITH SAMPLING WEIGHTS ON DATA FROM THE NATIONAL SOCIO-ECONOMIC SURVEYReni Amelia0Indahwati Indahwati1Erfiani Erfiani2Department of Statistics, Faculty of Mathematics and Natural Sciences, IPB UniversityDepartment of Statistics, Faculty of Mathematics and Natural Sciences, IPB UniversityDepartment of Statistics, Faculty of Mathematics and Natural Sciences, IPB UniversityOrdinal logistic regression is a method describing the relationship between an ordered categorical response variable and one or more explanatory variables. The parameter estimation of this model uses the maximum likelihood estimation having assumption that each sample unit having an equal chance of being selected, or using simple random sampling (SRS) design. This study uses data from the National Socio-Economic Survey (SUSENAS) having two-stage one-phase sampling (not SRS). So, the parameter estimation should consider the sampling weights. This study describes the parameter estimation of the ordinal logistic regression with sampling weight using the pseudo maximum likelihood method, especially in SUSENAS sampling design framework. The variance estimation method uses Taylor linearization. This study also provides numerical examples using ordinal logistic regression with sampling weight. Data used is 121,961 elderly spread over 514 districts/cities. Testing data (20%) is used to obtain the accuracy of the prediction results. The variables used in this study are the health status of the elderly as the response variable, and nine explanatory variables. The results of this study indicate that the ordinal logistic regression model with sampling weights is more representative of the population and more capable to predict minority categories of the response variable (poor and moderate health status) than is without sampling weights.https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/6464ordinal logistic regressionsampling weightsusenaspseudo maximum likelihoodtaylor linearization |
| spellingShingle | Reni Amelia Indahwati Indahwati Erfiani Erfiani THE ORDINAL LOGISTIC REGRESSION MODEL WITH SAMPLING WEIGHTS ON DATA FROM THE NATIONAL SOCIO-ECONOMIC SURVEY Barekeng ordinal logistic regression sampling weight susenas pseudo maximum likelihood taylor linearization |
| title | THE ORDINAL LOGISTIC REGRESSION MODEL WITH SAMPLING WEIGHTS ON DATA FROM THE NATIONAL SOCIO-ECONOMIC SURVEY |
| title_full | THE ORDINAL LOGISTIC REGRESSION MODEL WITH SAMPLING WEIGHTS ON DATA FROM THE NATIONAL SOCIO-ECONOMIC SURVEY |
| title_fullStr | THE ORDINAL LOGISTIC REGRESSION MODEL WITH SAMPLING WEIGHTS ON DATA FROM THE NATIONAL SOCIO-ECONOMIC SURVEY |
| title_full_unstemmed | THE ORDINAL LOGISTIC REGRESSION MODEL WITH SAMPLING WEIGHTS ON DATA FROM THE NATIONAL SOCIO-ECONOMIC SURVEY |
| title_short | THE ORDINAL LOGISTIC REGRESSION MODEL WITH SAMPLING WEIGHTS ON DATA FROM THE NATIONAL SOCIO-ECONOMIC SURVEY |
| title_sort | ordinal logistic regression model with sampling weights on data from the national socio economic survey |
| topic | ordinal logistic regression sampling weight susenas pseudo maximum likelihood taylor linearization |
| url | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/6464 |
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