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st: Interpreting Polychoric PCA results in STATA 11


From   kiran javaid <kiranjavaidr@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   st: Interpreting Polychoric PCA results in STATA 11
Date   Mon, 8 Aug 2011 13:56:41 +0500

Hello everybody,

I want to form a wealth index at household level by using variables
like housetype(mud=1, bricks_mud=2, bricks_cement=3), household owner
(rented=1 owned=2), electricity (no=0, yes=1), mobilephone (range is
from 0 - the number of mobile phones a household has, for my sample,
the maximum is 13), cycles (no cycle=0, yes cycle = 1,2,3... the
number of cycles owned) etc. As the variables are categorical I should
use polychoric pca instead of simple pca, right? One question I have
is that if i use polychoric pca then do i need to generate a seperate
variable for each category of these variables? for instance, in the
household type category, should i have one variable as housetype_mud
(mud house=1, not mud house=0), then another as housetype_bricks_mud
(bricks_mud house=1, not bricks_mud house=0) and similarly for
bricks_cement? but then i would have to leave one category out to
avoid multicollinearity, right? Furthermore, if this is the case then
what happens to the mobile phone and cycle variables? Do i still have
just one variable for mobile phone (going from 0 to 13) and one for
cycle as it currently is?

Secondly, the command i'm using for polychoric pca is: polychoricpca
housetype houseowner electricity mobilephone cycle, score(index)
nscore(3)
(ofcourse, if i need to generate seperate variables for all categories
then those will replace housetype, houseowner...)

My question is that this one command will give me the factors
generated? Since i typed nscore (3), it will give me 3 factor
components. However, the eigenvalues are greater than 1 for only 2
factor components. So in order to get one composite index (wealth
index) i should multiply index1 with the eigenvalue for index1, and
index2 with eigenvalue for index2 and then add these two up? like we
do in simple pca?

Any and all help will be greatly appreciated.

- Kiran

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