During my research, I asked several companies to complete a questionnaire to measure they risk maturity. At the end of the questionnaire, each respondent receives a score from 1 to 5 (1=low maturity, 5=high maturity)

Now I want to analyze the results of the questionnaire. The data are then in form of a single vector, where each row is the "score" received by a single participant. On this vector, I want to perform a PCA analysis to find the first 3 PCs.

The idea I want to follow comes from the Arbitrage Pricing Theory (Ross 1976), where the author performs the PCA analysis on the returns of several stocks to understand how many factors influence these returns. 

I know that PCA is usually used when I have several dimensions and I want to reduce them finding "factors" as combination of those dimensions. However, Ross in his paper (you can find it attached to this post) uses just the returns to investigate the factors, and I would like to perform a similar analysis on my values to find how many factors can explain the variability of my sample. Even though I now what my objective is, I am not able to get there. Any suggestion?

http://www.top1000funds.com/wp-content/uploads/2014/05/The-Arbitrage-Theory-of-Capital-Asset-Pricing.pdf

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