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Indeks Sosio-ekonomi Menggunakan Principal Component Analysis
Authors: Iwan Ariawan
Source: Kesmas: National Public Health Journal, 1(2):83; DOI: http://dx.doi.org/10.21109/kesmas.v1i2.317
Topic(s): Economics
Country: Asia
  Indonesia
Published: OCT 2006
Abstract: In household survey, we could measure socio-economic status through income, expenditure and ownership of valuable goods. Measuring income and ex- penditure in developing countries has many weaknesses, therefore many researchers prefer to use the ownership of valuable goods as proxy of socio-eco- nomic status. Using ownership of valuable goods as proxy indicator creates another problem of having many variables for the socio-economic proxy. To show how to simplify many variables of ownership of valuable goods into 1 socio-economic index. Using prinicpal component analysis with Stata. Using Indonesia Demographic & Health Survey 2002-2003 data, 7 binomial variables of ownership of valuable goods and 3 ordinal variables of housing condition to construct socio-economic indices using principal component analysis (PCA), tetrachoric and polychoric correlation.We used Stata to construct the socio-economic in- dex. Correlation matrices were derived using tetrachoric command for tetrachoric correlation and polychoric command for polychoric correlation. Two socio- economic indices were constructed, 1 index was based only on 7 binomial variables of ownership of valuable goods and 1 index was based on 7 binomial variables of ownership of valuable goods and 3 ordinal variables of housing conditions. PCA was used to construct those 2 indices. In 7 variables model, the socio-economic index could explain 57% variance and in 10 variables model, the socio-economic index could explain 54% variance. We also showed the use of xtile command to regroup the subjects based on quintile of socio-economic indices. PCA, tetrachoric and polychoric correlation could be used to con- struct socio-economic indices based on information of ownership of valueable goods and housing conditions. Key words: Socio-economic indices, principal component analysis, tetrachoric correlation, polychoric correlation.
Web: http://journal.fkm.ui.ac.id/kesmas/article/view/317/316