International Journal of Statistics and Applications
p-ISSN: 2168-5193 e-ISSN: 2168-5215
2022; 12(3): 77-82
doi:10.5923/j.statistics.20221203.03
Received: Aug. 2, 2022; Accepted: Aug. 17, 2022; Published: Aug. 30, 2022
Wilson da C. Vieira, José A. Ferreira Neto, Mariane P. B. Roque, Bianca D. da Rocha
Department of Agricultural Economics, Federal University of Viçosa, Brazil
Correspondence to: Wilson da C. Vieira, Department of Agricultural Economics, Federal University of Viçosa, Brazil.
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Copyright © 2022 The Author(s). Published by Scientific & Academic Publishing.
This work is licensed under the Creative Commons Attribution International License (CC BY).
http://creativecommons.org/licenses/by/4.0/
This article presents a simple and effective procedure for the construction of socioeconomic status indices using principal component analysis. The methodological approach consists of obtaining principal components of the correlation matrix from a sample of random variables. For the calculation of the index, a weighted average of selected principal components is used. The proposed method is sufficiently general and can be applied to obtain other types of composite indices. To illustrate the versatility of the method, we provide in this article the calculation of a social vulnerability index for the municipalities of an area of the São Francisco river basin, Brazil, based on data from the demographic census.
Keywords: Socioeconomic status index, Principal component analysis, Methodology
Cite this paper: Wilson da C. Vieira, José A. Ferreira Neto, Mariane P. B. Roque, Bianca D. da Rocha, Using Principal Component Analysis to Build Socioeconomic Status Indices, International Journal of Statistics and Applications, Vol. 12 No. 3, 2022, pp. 77-82. doi: 10.5923/j.statistics.20221203.03.
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Figure 1. São Francisco River basin and study area |
Figure 2. Social vulnerability for the municipalities of an area of the São Francisco River basin |