Factor analysis in Market Research I am doing a survey to find out customer experience/satisfaction for  a specific product. My  survey contains a scaling  question in which i asked satisfaction level (from 1 to 5) 5 being very satisfied with respect to various attributes(15 in number) of the product. Now i am confused about how to analyze these results. I want to use a statistical technique for  analysis.I came to know about factor analysis. Is it applicable here? 
If yes then how to interpret final results of factor analysis to say something about satisfaction level.
 A: Factorial Analysis is especially helpful when trying to visualize a high-dimension space (i.e dataset with lots of variables). In your case, that could be somewhat applicable. Factorial Analysis, such as PCA, is strictly speaking not compatible with ordinal variables (as it assumes that your variables are linear, and thus continuous) but lots of people in sensometrics still use it in your situation.
Using PCA you could visualize more easily how your individuals and variables 'behave' with one another. Meaning that you could see which variables hold the same information.
If you want to specifically explain which variables have an impact on satisfaction level (and even quantify that impact) then I would recommend linear modeling (such as ANOVA if all your attributes are factors).
A: If you have a structural model, e.g., the impact of customer experience (CX) on customer satisfaction, you could use PLS-SEM based analysis. A full guideline is available in this open-access article including the latest survey questions for customer satisfaction, customer experience, customer loyalty, trust, and share-of-wallet. (also see the web appendix of the article for details).
