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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 10:03:09 +0000

For once I disagree partially with Maarten.

On reading this again I have further comments:

1. The difference between -factor, pcf- and -pca- is small and
arguably immaterial as far as the results here are concerned. In
practice, the techniques are associated, however,  with very different
attitudes, -factor- often with a theology of latent variables and
-pca- often with a mechanistic aim of data reduction.

2. However, it doesn't seem much of a gain for interpretation to
discard interpretable variables and replace them with a very fuzzy
concept of socio-economic status (SES), even if numbers are attached.

3. This is not just an attitude, as the factor analysis results show
that the technique has not been especially successful (18% of variance
captured by first factor).

4. If Ameya's variables are typical of data like this that I have
seen, most marginal distributions will be skewed and clumpy and the
correlation structure extremely sensitive to whether data are left as
they come or transformed in some suitable way(s).

5. Ameya's main concern is presumably to do the best job with the
dataset in hand, but this kind of procedure is not highly reproducible
by others working in similar territory, except naturally with the same
dataset of "about 37 variables". It is usually better to try to
identify say 5-10 socio-economic variables and use those as predictors
in a regression-like model.

That said, much depends on the main aim of this project, which is not
clear. (Presumably, the measure of SES is not an end in itself.)


On Wed, Nov 28, 2012 at 9:18 AM, Maarten Buis <> wrote:
> On Wed, Nov 28, 2012 at 3:59 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)
> I would say that factor analysis is incorrect for this problem. Factor
> analysis assumes that the latent concepts influence the observed
> variables. This makes sense for something like an intelligence test:
> someone is more or less smart (the latent variable) and that
> influences the probability of answering a set of questions correctly
> (the observed variables). Conceptually, socio-economic status is just
> a pool of resources available to a person, family, or household: so it
> is the number and kind of animals, the wealth, a house with a concrete
> floor, etc. (the observed variables) that influence, or add up to, the
> socio-economic status (the latent variable).
> Some of the possible solutions available in Stata are discussed here:
> <>.
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