Dear Stata users,

I hope this post finds you all in good health!

I am constructing several types of indices using PCA and MCA commands in Stata based upon various types of data inputs (e.g. continuous and/or categorical) in a survey. The general understanding is when data types are continuous, we should use Principal Component Analysis (PCA) and in cases where data types are categorical i.e. binary (0/1), we should use Multiple Correspondence Analysis (MCA) in developing an index through an induction method. My questions are as follows:
  • In PCA, what is the use of rotation (i.e. “rotate”) before “predict” and why the results vary (with/without rotate)? Why we do not tend to do rotation in case of MCA?
  • In case of an Asset index where respondents had reported in a YES/NO on availing 20 different items (e.g. radio/television/truck etc.) in absence of a TOTAL NUMBER; which method is more appropriate, PCA or MCA? [I am asking this because the commodities listed might not be indifferent like perception based choices].
Please do take care, maintain social distance and be safe!!

Sincerely yours,
Azreen Karim.