I suggest to use factor analysis over principal components analysis. Components analysis is only a data reduction method. It was common many decades ago when computers were slow. I know it is the default method in many statistical applications but factor analysis seems to be superior.
you can take a look to the following article where more information about this technique is provided:
Costello, A. B., & Osborne, J. W. (2005). Best Practices in Exploratory Factor Analysis: Four Recommendations for Getting the Most From Your Analysis. Practical Assessment, Research & Evaluation, 10(7). Retrieved from http://pareonline.net/getvn.asp?v=10&n=7
If you need further guidance don't hesitate to contact me
I agree with Yoilan experientially. I had considered pca for a research project I was contemplating but found factor analysis to be a better direction. I was attempting to see how descriptive variables of individual factors loaded against a focal independent variable, and the interaction results were very accessible with factor analysis.
Respected sir,
I have attached below links these are useful for you. thanks
http://support.sas.com/publishing/pubcat/chaps/55129.pdf
http://what-when-how.com/social-sciences/principal-components-social-science/
The question is "how is this statistical technique used...?" Not choose another statistic. Use depends on the problem/question at hand.
The very best way to understand the use of a statistic is to 1) understand what it is and 2) read articles that employ the statistic. I recommend a search in google that explain the statistic and identify scholarly articles that use the statistic. (In response to one of the other posters, under some conditions, including the design of the research question, intended analysis, and type of data that are collected, one statistic, e.g., factor analysis, may be a better choice than another, e.g., principal component analysis.
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