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All Journal Pattimura International Journal of Mathematics (PIJMath) Parameter: Jurnal Matematika, Statistika dan Terapannya
Rahayuningsih, Yuliana
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Mathematics

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Indonesian Students Reading Literacy Score in Framework Hierarchical Data Structure Using Multilevel Regression Maya Santi, Vera; Rahayuningsih, Yuliana; Sumargo, Bagus
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 2 (2025): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv4i2pp353-368

Abstract

Education is essential for improving the quality of Indonesian society. Indonesia participated in the Programme International Students Assessment (PISA) survey to improve the quality of education. Based on the 2018 PISA survey data, Indonesia's reading literacy score has a hierarchical data structure, which means students at level 1 are nested by schools at level 2. The multilevel model is an appropriate approach to analyze such hierarchical structures. However, quantitative analysis of PISA data is still rarely carried out. This study aims to analyze the explanatory variables that significantly affect Indonesian students' reading literacy from the PISA survey using multilevel regression. This study examined student-level and school-level explanatory variables obtained from the Organization for Economic Cooperation and Development (OECD). Significant parameter tests revealed that, at the student level, factors such as socioeconomic status, teacher support in language learning, teacher-directed instruction, enjoyment of reading, perceived difficulty, competitiveness, mastery goal orientation, disciplined classroom climate in reading, general fear of failure, attitudes toward school, and perceived feedback significantly influence reading literacy. At the school level, school size was found to be a significant factor affecting reading literacy scores. Furthermore, the Intraclass Correlation Coefficient (ICC) indicated that schools accounted for 49% of the total variance.
Negative Binomial Regression in Overcoming Overdispersion in Extreme Poverty Data in Indonesia Santi, Vera Maya; Rahayuningsih, Yuliana
Pattimura International Journal of Mathematics (PIJMath) Vol 2 No 2 (2023): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pijmathvol2iss2pp43-52

Abstract

Indonesia's extreme poverty status in 2021 was recorded to be high at 4% or 10.86 million people. One of the efforts in poverty alleviation is to analyze the factors influencing extreme poverty. Although the number of studies on poverty in Indonesia continues to grow, the findings are inconclusive because they are often discussed qualitatively. This study aimed to analyze the factors that influence extreme poverty in Indonesia using negative binomial regression. The data used was the amount of extreme poverty in 34 provinces of Indonesia as the response variable. Then, the explanatory variables used consist of 8 from the Central Bureau of Statistics. The analysis stage sought data exploration, the correlation between variables, Poisson regression model specification and assumption test, handling overdispersion with negative binomial regression, and model feasibility test. Based on the AIC value and dispersion ratio, the negative binomial model obtained an AIC value of 920.03 with a dispersion ratio 1.372. It shows that the negative binomial regression model is good enough to model extreme poverty in Indonesia. Furthermore, the factors significantly influencing extreme poverty in Indonesia are households with proper drinking water, housing status, and families with access to appropriate sanitation.
Co-Authors Bagus Sumargo, Bagus Vera Maya Santi