EXTRACTING AND INTERPRETING SOCIOECONOMIC COMPONENTS USING PRINCIPAL COMPONENT ANALYSIS

Abstract

Principal component analysis (PCA) is a widely used multivariate statistical technique for reducing the dimensionality of large datasets while retaining most of the original information. In this study, PCA was applied to identify the underlying socioeconomic components of household characteristics using data from 368 randomly selected households in Villanueva, Misamis Oriental, drawn from a population of 8,293 households. The suitability of the dataset was assessed using the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Re- Bartlett's test of sphericity prior to analysis. The overall KMO value of 0.827 and a significant Bartlett's test ( \(\chi^{2}=1457, p