Hello Statalist Forum Users,

We wish to summarize several dichotomous variables of individual level data into an index using Principle Component Analysis (PCA). The code we use is below. We have two questions.

Code:
tetrachoric TeamDept Company Shares Benefits FixedInc, stats(rho obs) posdef
mat def pay_mat = r(Rho)
local n = r(N)
pcamat pay_mat, n( `n' )
Q1: We have survey data which includes several countries and wish to weight observations by their sampling weight. However, the pcamat command only allows aweights and fweights (not pweights). Does anyone know why? Would it be unwise to manually weight observations? With manual weighting I mean duplicating observations to approximate their importance in the sample.

Q2: We wish to ensure the same Principle Component emerges in every country. This would mean (in our opinion) that comparable item loadings and eigenvalues are extracted across countries. The Stata documentation on PCA mentions we could test eigenvalues and loadings using testparm. However the data must have a multivariate normal distribution. Our data is not. Is there another way to test eigenvalues and item loadings across countries?