Analysis of Factors Affecting Early Married Women in East Java Province Using Binary Logistic Regression Method (A Case Study of Bondowoso, Probolinggo, Situbondo, and Jember Districts)

Amelia, Grace Marcella (2024) Analysis of Factors Affecting Early Married Women in East Java Province Using Binary Logistic Regression Method (A Case Study of Bondowoso, Probolinggo, Situbondo, and Jember Districts). Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Early marriage is a global problem that has broad implications for health, education and the economy, especially in developing countries like Indonesia. In East Java Province, the rate of early marriage has reached an alarming level. This study aims to identify factors that influence early marriage status among women in East Java Province, using binary logistic regression. Data comes from the 2022 National Socio-Economic Survey (SUSENAS) with 142 respondents. The results of the analysis show that 65% of women aged 20-24 years in Bondowoso, Probolinggo, Situbondo and Jember districts married early, with 62% of them having husbands aged 28 years or more. The majority of women who marry early have a junior high school education (41%) and 46% of their husbands have an elementary school education. Most husbands worked (97%), 58% of women had health insurance, and 70% came from rural areas. As many as 88% of households have adequate water sources, but 91% do not receive PKH assistance. In addition, 61% of women who marry early use the internet as entertainment. Two significant variables on early marriage are the woman's highest level of education and location of residence. Women with less education and those living in rural areas are more likely to marry early. The binary logistic regression model has a classification accuracy of 71.8%. This research is important for the government's initiative to reduce the rate of early marriage in East Java.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Early Age Marriage, SUSENAS, Woman, Binary Logistic Regression
Subjects:	A General Works > AC Collections. Series. Collected works A General Works > AC Collections. Series. Collected works
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Statistics > 49201-(S1) Undergraduate Thesis
Depositing User:	Grace Marcella Amelia
Date Deposited:	27 Aug 2024 05:00
Last Modified:	27 Aug 2024 05:00
URI:	http://repository.its.ac.id/id/eprint/114825

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