Factors Influencing Individual Income Level in Cikanyere Village Using Ordinal Logistic Regression Model

Authors

  • Hagni Wijayanti Universitas Pakuan
  • Herman Herman Universitas Pakuan

DOI:

https://doi.org/10.20956/j.v20i2.31964

Keywords:

Tingkat Pendapatan, Regresi Logistik Ordinal, Tingkat Pendidikan, Usia, Jumlah Tanggungan

Abstract

Income is an element in the process of economic development that serves as an indicator of the standard of living for individuals, families, or the population. Cikanyere is a village located in, West Java, Cianjur, which still has low economic growth. Economic growth can be observed from the level of income obtained by the population. Based on the aforementioned issue, the original purpose of this research is to identify the factors that influence the level of individual income in Cikanyere Village. One type of regression analysis is ordinal logistic regression, which is used to test the correlation between independent variables and dependent variables that have multiple categories or polychotomous, meaning variables that have two or more categories and are in ordinal scale. Ordinal logistic regression is used because the dependent variable in this study is the income level, while the independent variables include education level, age, marital status, number of dependents, and gender. All these independent variables are measured on an ordinal scale. In this study, the influence of each component on the income level is measured partially using the Pearson Chi-Square test. The results show that age, education level, and the number of dependents to the components that affect the income level in Cikanyere Village. Gender and marital status do not affect the income level. The obtained ordinal logistic regression model provides the likelihood of individual income improvement based on changes in age, education level, and the number of dependents.

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Published

2023-12-24

How to Cite

Wijayanti, H., & Herman, H. (2023). Factors Influencing Individual Income Level in Cikanyere Village Using Ordinal Logistic Regression Model. Jurnal Matematika, Statistika Dan Komputasi, 20(2), 398-415. https://doi.org/10.20956/j.v20i2.31964

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Research Articles