If you search Google for "spss exploratory factor analysis" there are many results there, including video tutorials.

Principal Components Analysis is the default in SPSS, so if you don't specify something else, that is what you will get. However, for most social science data, it is more appropriate to use other methods of exploratory factor analysis.

I would recommend Maximum Likelihood with oblique rotation to allow for correlated factors.

SPSS allows to use Principle axis factoring and Maximum Likelihood methods for EFA. However,  in EFA we need to take care of type of rotation ( for example David mentioned oblique rotation) and numbers of factors to be retained as well. 

I will strongly suggest you to have a look at:

Osborne, J. W., & Costello, A. B. (2009). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Pan-Pacific Management Review, 12(2), 131-146.

I found this paper very enlightening especially in deciding method, type of rotation and how to identify number of factors to be retained. One technique of identifying number of factors to be retained is "parallel analysis",  which I found very appropriate, is not covered in above paper and can be searched for in Google scholar,  for example.