Indeks Sosioekonomi 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 socioeconomic 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 socioeco nomic status. Using ownership of valuable goods as proxy indicator creates another problem of having many variables for the socioeconomic proxy. To show how to simplify many variables of ownership of valuable goods into 1 socioeconomic index. Using prinicpal component analysis with Stata. Using Indonesia Demographic & Health Survey 20022003 data, 7 binomial variables of ownership of valuable goods and 3 ordinal variables of housing condition to construct socioeconomic indices using principal component analysis (PCA), tetrachoric and polychoric correlation.We used Stata to construct the socioeconomic 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 socioeconomic index could explain 57% variance and in 10 variables model, the socioeconomic index could explain 54% variance. We also showed the use of xtile command to regroup the subjects based on quintile of socioeconomic indices. PCA, tetrachoric and polychoric correlation could be used to con struct socioeconomic indices based on information of ownership of valueable goods and housing conditions.
Key words: Socioeconomic indices, principal component analysis, tetrachoric correlation, polychoric correlation. 
Web: 
http://journal.fkm.ui.ac.id/kesmas/article/view/317/316 
