Comparison of Ordinal Logistic Regression and Geographically Weighted Ordinal Logistic Regression (GWOLR) in Predicting Stunting Prevalence among Indonesian Toddlers

Authors

  • Silfiana Lis Setyowati Departement of Statistics and Science Data, School of Data Science, Mathematics and Informatics IPB University and Ministry of Higher Education, Science, and Technology
  • Indahwati Departement of Statistics and Science Data, School of Data Science, Mathematics and Informatics IPB University
  • Anwar Fitrianto Departement of Statistics and Science Data, School of Data Science, Mathematics and Informatics IPB University
  • Erfiani Departement of Statistics and Science Data, School of Data Science, Mathematics and Informatics IPB University
  • Muftih Alwi Aliu Departement of Statistics and Science Data, School of Data Science, Mathematics and Informatics IPB University

DOI:

https://doi.org/10.31851/sainmatika.v21i2.15416

Keywords:

Ordinal Logistic Regression, Geographically Weighted Ordinal Logistic Regression, Stunting

Abstract

Ordinal logistic regression is a type of logistic regression used for response variables with an ordinal scale, containing two or more categories with levels between them. This method is an extension of logistic regression where the observed response variable is ordinal with a clear order. It addresses spatial effects that can cause variance heterogeneity and improve parameter estimation accuracy compared to logistic regression. Geographically Weighted Regression (GWR) is a statistical analysis technique designed to account for spatial heterogeneity. GWOLR is an extension of OLS and GWR models that incorporates spatial elements into regression with categorical variables. This study compares the effectiveness of OLR and GWOLR in analyzing stunting prevalence in toddlers. Comparing OLR and GWOLR can help assess the spatial impact on stunting prevalence. This analysis could reveal that certain regions have a higher tendency for stunting prevalence, while others might have lower tendencies, thus helping in understanding regional disparities. Toddler height is a key indicator of health and nutrition in early growth. The prevalence of stunting for toddlers, according to WHO, is categorized into four levels: low, moderate, high, and very high. The Ordinal Logistic Regression model is better suited for modeling toddler stunting prevalence in Indonesia than the GWORL model. The Ordinal Logistic Regression model and the GWOLR both have a classification accuracy of 85.7%, but the OLR model has a lower AIC value. The GWOLR model is not suitable for analyzing stunting prevalence among Indonesian toddlers due to the lack of spatial variability in the data. The Breusch-Pagan test results indicate that there is no spatial heterogeneity in the data on stunting prevalence among Indonesian toddlers, as the p-value is less than the significance level of 0.05. The prevalence of undernourished toddlers is the main factor influencing stunting among Indonesian toddlers.

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Published

2024-12-09