The risk of misclassifying subjects within principal component based asset index
1 icddr,b, 68 Shahid Tajuddin Ahamed Sarani, Mohakhali, 1212 Dhaka, Bangladesh
2 Department of Statistics, University of Rajshahi, 6205 Rajshahi, Bangladesh
3 School of Public Health, University of California, Berkeley, USA
4 Global Disease Detection Branch, Division of Global Health Protection, Center for Global Health, Centers for Disease Control and Prevention, Georgia, USA
Emerging Themes in Epidemiology 2014, 11:6 doi:10.1186/1742-7622-11-6Published: 18 June 2014
The asset index is often used as a measure of socioeconomic status in empirical research as an explanatory variable or to control confounding. Principal component analysis (PCA) is frequently used to create the asset index. We conducted a simulation study to explore how accurately the principal component based asset index reflects the study subjects’ actual poverty level, when the actual poverty level is generated by a simple factor analytic model. In the simulation study using the PC-based asset index, only 1% to 4% of subjects preserved their real position in a quintile scale of assets; between 44% to 82% of subjects were misclassified into the wrong asset quintile. If the PC-based asset index explained less than 30% of the total variance in the component variables, then we consistently observed more than 50% misclassification across quintiles of the index. The frequency of misclassification suggests that the PC-based asset index may not provide a valid measure of poverty level and should be used cautiously as a measure of socioeconomic status.