Hello Andreas, this might help you:

http://en.wikipedia.org/wiki/Factor_analysis#Exploratory_factor_analysis_versus_principal_components_analysis

The principal component analysis is very useful when your variables are cuantitative, because the method requires that. In factorial analysis you can have nominal, ordinal and cuantitative variables, as far i know (I never have to run a factorial analysis, sorry for the incomplete answer).

This tutorial and videotutorial of a popular statistical software might help you to understand better the concepts and differences:

http://www.xlstat.com/en/learning-center/tutorials/running-a-factor-analysis-with-xlstat.html

Best regards,

Luís Enrique.

The principal component analysis is very useful when your variables are cuantitative, because the method requires that. But, in factorial analysis you can have any variables. In addition to, factor analysis is one method based on PCA. Also, to assess relationship among different characters you can use of factor analysis.

You can use Factor Analysis with PCA as a method to estimate the components. The most important variables may be selected based on loadings (say greater than 0.7) from the significant components (may by at most 10 in your case).

Thank you all very much for your responses. Balázs Kotosz when I am saying most important variables, I mean a smaller number of variables that will be useful for my research.

Thank you very much Predrag. That was very helpful.

https://www.researchgate.net/post/PCA_analysis_is_done_with_mean_values_of_environmental_variables_or_raw_values

usually refer PCA ......u may use PCA