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st: Constructing socio-economic status scale using Principal Components Analysis


From   Ameya Bondre <ameyabondre.jhsph@gmail.com>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   st: Constructing socio-economic status scale using Principal Components Analysis
Date   Tue, 27 Nov 2012 18:59:50 -0800

Hello,

I have a data-set with about 37 variables that can assess household
socio-economic status in a sample of about 6000 households. These
include variables measuring household wealth, access to water and
sanitation, rural households owning animals, etc.

I used factor analysis (factor var1, var2, ...., pcf) and it gave me
10 factors with eigen values more than 1. But the percentage of
variation in the total data was best explained by the first factor -
"factor1" had the highest eigen value and explained 18% of the
variation.

Using the command "predict factor1, bar", I got the factor scores for
each of these 37 variables, for factor1.



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