DOI: https://doi.org/10.14710/jfma.v3i2.7811

ANALISIS REGRESI LOGISTIK ORDINAL MENGENAI FAKTOR-FAKTOR YANG MEMPENGARUHI TINGKAT PENDIKAN ANAK DI DESA SAYANG-SAYANG

Baiq Dewi Kusuma Ananda  -  Universitas Pendidikan Mandalika, Indonesia
Zulmi Insani  -  Universitas Pendidikan Mandalika, Indonesia
*Baiq Rika Ayu Febrilia  -  Universitas Pendidikan Mandalika, Indonesia
Dwi Utami Setyawati  -  Universitas Pendidikan Mandalika, Indonesia

Citation Format:
Abstract
The economic condition of the parents and their level of education are the determining factors for the level of education of the children. The purpose of this study was to analyze the factors affecting the level of education of children (socioeconomic status of parents and parents' latest education). The subjects in this study were 100 people of Sayang-Sayang Village who were determined by simple random sampling technique. Data collection was carried out online using a survey sheet or questionnaire developed with the help of Google Form. The method in analyzing this causal relationship uses ordinal logistic regression analysis. The results showed that the ordinal logistic regression analysis provided two logit models, in which socioeconomic status had a significant effect on the child's education level, while the parents' last education level did not have a significant effect on the child's education level. The first logit model is a logit model that explains the causal relationship between socioeconomic status and the last level of education of high-category children, while the second model is for the middle category level. Research is expected to be one of the references both in the use of ordinal logistic regression and in observing the factors that affect the level of children's education.
Fulltext View|Download
Keywords: Ordinal Logistics Regression; Factors; Level of Children’s Education

Article Metrics:

Article Info
Section: FUNDAMENTAL MATHEMATICS AND APPLICATIONS
Language : EN
Recent articles
SOLVING THE 106 YEARS OLD 3^k POINTS PROBLEM WITH THE CLOCKWISE-ALGORITHM MODELING PREDICTIVE TRACKING CONTROL FOR MAX-PLUS LINEAR SYSTEMS IN MANUFACTURING OPTIMAL CONTROL MODELLING OF COVID-19 OUTBREAK IN SEMARANG CITY INDONESIA More recent articles
  1. N. P. Suciningrum and E. S. Rahayu, “Pengaruh Status Sosial Ekonomi Orang Tua dan Motivasi Belajar Terhadap Minat Melanjutkan Studi ke Perguruan Tinggi pada Kelas XI di SMA Pusaka 1 Jakarta,” J. Pendidik. Ekon. dan Bisnis, vol. 3, no. 1, pp. 1–21, 2015
  2. D. Areva, “Pengaruh Status Sosial Ekonomi Orang Tua dan Motivasi Orang Tua Terhadap Tingkat Pendidikan Anak Pedagang Toko/Kios di Pasar Raya Kota Padang,” Econ. J. Econ. Econ. Educ., vol. 4, no. 1, pp. 52–60, 2015
  3. Fitrianingsih, B. Genjik S., and R. Rosyid, “Pengaruh Pendapatan Orang Tua Terhadap Tingkat Pendidikan Anak Desa Sungai Asam Kabupaten Kubu Raya,” J. Pendidik. dan Pembelajaran, vol. 5, no. 5, pp. 1–11, 2016
  4. S. Nurhayati, “Pengaruh Kondisi Ekonomi Terhadap Tingkat Pendidikan Anak di Desa Sinar Tebudak Kecamatan Tujuh Belas,” Pendidik. dan Pembelajaran, vol. 6, no. 7, pp. 1–8, 2017
  5. M. Z. Abdullah, T. S. Putro, and Syapsan, “Pengaruh Kondisi Sosial Ekonomi Terhadap Tingkat Pendidikan Anak Nelayan di Desa Bekawan Kecamatan Mandah Kabupaten Indragiri Hilir,” J. Ekon., vol. 25, no. September, pp. 71–84, 2017
  6. B. Saputra and Y. Suasti, “Aspirasi Orang Tua Tentang Pendidikan Anak,” J. Buana, vol. 3, no. 1, pp. 7–14, 2019
  7. A. C. J. Emor, A. L. Lonto, and T. Pangalila, “Pengaruh Tingkat Pendidikan Orang Tua Terhadap Pendidikan Anak di Kelurahan Pinasungkulan Kecamatan Ranowulu Kota Bitung,” J. Civ. Educ., vol. 3, no. 1, pp. 45–57, 2019
  8. P. Lu, H. Wang, and D. Tolliver, “Prediction of Bridge Component Ratings Using Ordinal Logistic Regression Model,” Math. Probl. Eng., vol. 2019, pp. 1–11, 2019
  9. E. Bozpolat, “Investigation of the self-regulated learning strategies of students from the faculty of education using ordinal logistic regression analysis,” Kuram ve Uygulamada Egit. Bilim., vol. 16, no. 1, pp. 301–318, 2016
  10. A. Larasati, C. DeYong, and L. Slevitch, “Comparing Neural Network and Ordinal Logistic Regression to Analyze Attitude Responses,” Serv. Sci., vol. 3, no. 4, pp. 304–312, 2020
  11. D. R. S. Saputro and P. Widyaningsih, “Limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) method for the parameter estimation on geographically weighted ordinal logistic regression model (GWOLR),” AIP Conf. Proc., vol. 18, no. 68, pp. 040009-1-040009–9, 2017
  12. M. Nusrang, R. Bakri, A. S. Ahmad, and Asfar, “Analisis Regresi Logistik Ordinal Terhadap Faktor-Faktor yang Mempengaruhi Predikat Kelulusan Mahasiswa S1 Universitas Negeri Makasar,” in Prosiding Seminar Nasional Lembaga Penelitian UNM, 2017, vol. 2, no. 1, pp. 655–661
  13. F. M. Ali et al., “Mapping of the DLQI scores to EQ-5D utility values using ordinal logistic regression,” Qual. Life Res., vol. 26, no. 11, pp. 3025–3034, 2017
  14. V. F. Rochmah, “Analisis Faktor-Faktor yang Mempengaruhi Keberhasilan Nilai Raport Siswa SMP Kyai Hasyim Surabaya dengan Menggunakan Metode Regresi Logistik Ordinal,” Institut Teknologi Sepuluh November, 2017
  15. A. O. Adejumo and A. A. Adetunji, “Application of Ordinal Logistic Regression in the Study of Students’ Performance,” Math. Theory Model., vol. 3, no. 11, pp. 10–19, 2013
  16. V. Singh, S. N. Dwivedi, and S. V. S. Deo, “Ordinal logistic regression model describing factors associated with extent of nodal involvement in oral cancer patients and its prospective validation,” BMC Med. Res. Methodol., vol. 20, no. 1, pp. 1–8, 2020
  17. E. Ari and Z. Yildiz, “Paralel Lines Assumption in Ordinal Logisic Regression and Parallel Lines Assumption in Ordinal Logistic,” Int. Interdiscip. J. Sci. Res., vol. 1, no. January 2014, pp. 8–23, 2016
  18. J. O. Moraes et al., “An ordinal logistic regression approach to predict the variability on biofilm formation stages by five Salmonella enterica strains on polypropylene and glass surfaces as affected by pH, temperature and NaCl,” Food Microbiol., vol. 83, no. April, pp. 95–103, 2019
  19. A. Ghasemzadeh, B. E. Hammit, M. M. Ahmed, and R. K. Young, “Parametric Ordinal Logistic Regression and Non-Parametric Decision Tree Approaches for Assessing the Impact of Weather Conditions on Driver Speed Selection Using Naturalistic Driving Data,” Transp. Res. Rec., vol. 2672, no. 12, pp. 137–147, 2018
  20. R. P. F. Astuti, “Pengaruh Status Sosial Ekonomi dan Life Style Terhadap Perilaku Konsumsi Mahasiswa Jurusan Pendidikan Ekonomi IKIP PGRI Bojonegoro,” J. Edutama, vol. 3, no. 2, pp. 49–58, 2016
  21. A. S. Anjani, S. Dahlan, and S. Mayasari, “Hubungan Tingkat Pendidikan dan Tingkat Penghasilan Orang Tua terhadap Aspirasi Melanjutkan Studi,” ALIBKIN (Jurnal Bimbing. Konseling), vol. 7, no. 2, 2019
  22. H. M. Fenta, D. L. Workie, D. T. Zike, B. W. Taye, and P. K. Swain, “Determinants of stunting among under-five years children in Ethiopia from the 2016 Ethiopia demographic and Health Survey: Application of ordinal logistic regression model using complex sampling designs,” Clin. Epidemiol. Glob. Heal., vol. 8, no. 2, pp. 404–413, 2020

Last update:

No citation recorded.

Last update:

No citation recorded.