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

From   Nick Cox <>
Subject   Re: st: Constructing socio-economic status scale using Principal Components Analysis
Date   Wed, 28 Nov 2012 08:55:29 +0000

If factor analysis (or PCA) is apppropriate then the first factor (PC)
_is_ the best single measure you can get.

(Why call -0.005 very negative? It's nearly zero.)


On Wed, Nov 28, 2012 at 2:59 AM, Ameya Bondre
<> wrote:

> 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.
> From the articles I read about principal components analysis, I got to
> know that negative factor scores mean a lower SES and positive scores
> mean a higher SES. Some scores are strongly defining the factor (0.30,
> 0.17), some are very negative (-0.005) and most other scores are too
> low and positive or negative but close to 0.
> The above steps also create a new variable called "factor1", which
> gives "a score" to each data point (or each household). The scores for
> the data points range from -2 to 1.8.
> I want to construct a socio-economic status scale using factor1. Is
> that possible?
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