Printer Friendly Version | Back

Comparison of Ordinal Logistic Regression with Tree-Based Methods in Predicting Socioeconomic Classes in the Philippines

Year: 2016       Vol.: 65       No.: 1      

Authors: Michael Daniel C. Lucagbo

Abstract:

The task of classifying Philippine households according to their socioeconomic class (SEC) has been tackled anew in a collaborative work between the Marketing and Opinion Research Society of the Philippines (MORES), the former National Statistics Office (NSO) and the University of the Philippines School of Statistics. This new system of classifying Philippine households has been introduced in the 12th National Convention on Statistics, in a paper entitled 1SEC 2012: The New Philippine Socioeconomic Classification. To predict the SEC of a household, certain household characteristics are used as predictors. The 1SEC Instrument, whose scoring system is based on the ordinal logistic regression model, is then used to predict the household’s SEC. Recently, the statistical literature has seen the development of novel tree-based learning algorithms. This paper shows that the ordinal logistic regression model can still classify households better than three popular tree-based statistical learning methods: bootstrap aggregation (or bagging), random forests, and boosting. In addition, this paper identifies which clusters are easier to predict than others.

Keywords: socioeconomic classification, ordinal logistic regression, bagging, random forests, boosting

Download this article:

Back to top