DOI: https://doi.org/10.14710/medstat.14.1.79-88

A COMPARISON OF POLYTOMOUS MODEL WITH PROPORTIONAL ODDS AND NON-PROPORTIONAL ODDS MODEL ON BIRTH SIZE CASE IN INDONESIA

*Yenni Kurniawati orcid scopus  -  Universitas Negeri Padang, Indonesia
Anang Kurnia  -  Department of Statistics, IPB University, Indonesia
Kusman Sadik  -  Department of Statistics, IPB University, Indonesia
Open Access Copyright (c) 2021 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
The proportional odds model (POM) and the non-proportional odds model (NPOM) are very useful in ordinal modeling. However, the proportional odds assumption is often violated in practice. In this paper, the non-proportional odds model is chosen as an alternative model when the proportional odds assumption is not violated. This paper aims to compare Proportional Odds Model (POM) and Non-Proportional Odds Model (NPOM) in cases of birth size in Indonesia based on the 2017 Indonesian Demographic and Health Survey (IDHS) data. The results showed that in the POM there was a violation of the proportional odds assumption, so the alternative NPOM model was used. NPOM had better use than POM. The goodness of fit shows that the deviance test failed to reject H0, and the value of Mac Fadden R2 is higher than POM. The risk factors that have a significant influence on all categories of birth size are the residence and gender of the child.
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Keywords: Proportional Odds Model (POM); Non-Proportional Odds Model (NPOM); birth size; Likelihood Ratio test; Goodness of fit

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  1. Adeyemi, R. A., Zewotir, T., & Ramroop, S. (2016). Semiparametric multinomial ordinal model to analyze spatial patterns of child birth weight in Nigeria. International Journal of Environmental Research and Public Health, 13(11), 1–22. https://doi.org/10.3390/ijerph13111145
  2. Agresti, A. (2010). Modeling Ordinal Categorical Data. In Harvard University Statistic Department (pp. 345–358). www.stat.ufl.edu/∼aa/ordinal/ord.htm
  3. Agresti, A. (2015). Foundations Linear Generalized Linear Models. In John Wiley & Sons, Inc
  4. Ari, E., & Yildiz, Z. (2014). Parallel lines assumption in ordinal logistic regression and analysis approaches. International Interdisciplinary Journal of Scientific Research, 1(3), 8–23
  5. BPS, [Badan Pusat Statistik]. (2017). Survei Demografi dan Kesehatan Indonesia 2017. In Badan Pusat Statistik. Badan Pusat Statistik
  6. Budyanra, B., & Azzahra, G. N. (2017). Penerapan Regresi Logistik Ordinal Proportional Odds Model pada Analisis Faktor-Faktor yang Mempengaruhi Kelengkapan Imunisasi Dasar Anak Balita di Provinsi Aceh Tahun 2015. Media Statistika, 10(1), 37. https://doi.org/10.14710/medstat.10.1.37-47
  7. Dolgun, A., & Saracbasi, O. (2014). Assessing proportionality assumption. Statistics and Its Interface, 7, 275–295
  8. Fullerton, A. S., & Xu, J. (2012). The proportional odds with partial proportionality constraints model for ordinal response variables. Social Science Research, 41(1), 182–198. https://doi.org/10.1016/j.ssresearch.2011.09.003
  9. Kemenkes. (2015a). Profil Kesehatan Indonesia 2014. Kementrian Kesehatan RI
  10. Kemenkes. (2015b). Rencana Strategis Kementerian Kesehatan Tahun 2015-2019
  11. Khan, J. R., Islam, M. M., Awan, N., & Muurlink, O. (2018). Analysis of low birth weight and its co-variants in Bangladesh based on a sub-sample from nationally representative survey. BMC Pediatrics, 18(1), 1–9. https://doi.org/10.1186/s12887-018-1068-0
  12. Kurniawati, Y., Notodiputro, K. A., & Sartono, B. (2020). Unordered Features Selection of Low Birth WeightDatain Indonesiausing the LASSO and Fused LASSO Techniques. Conference on Statistics and Analytics, ICSA 2019. https://doi.org/10.4108/eai.2-8-2019.2290344
  13. Tutz, G., & Berger, M. (2020). Non Proportional Odds Models are Widely Dispensable -- Sparser Modeling based on Parametric and Additive Location-Shift Approaches. Stat.ME, 1–20
  14. Yasin, H., & Ispriyansti, D. (2017). Klasifikasi Data Berat Bayi Lahir Menggunakan Weighted Probabilistic Neural Network (WPNN) (Studi Kasus di Rumah Sakit Islam Sultan Agung Semarang). Media Statistika, 10(1), 61. https://doi.org/10.14710/medstat.10.1.61-70
  15. Yee, T. W. (2010). VGAM Family Functions for Categorical Data. http://www.stat.auckland.ac.nz/∼yee
  16. Yee, T. W. (2021). Package “VGAM.” https://doi.org/10.1007/978-1-4939-2818-7

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