PCA makes a different set of assumptions than Factor Analysis, and then within FA, there are a number of different ways to implement that particular set of assumptions. Just because SPSS makes PCA the default choice does not mean that it is in any "sense" the best one -- especially for social science data, which seldom meets the assumptions necessary for PCA.

I took at look at the article Heiko recommended, and I think it is quite useful.

I am not sure, but maybe this will be helpful:

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), 2. http://pareonline.net/getvn.asp?v=10&n=7

PCA makes a different set of assumptions than Factor Analysis, and then within FA, there are a number of different ways to implement that particular set of assumptions. Just because SPSS makes PCA the default choice does not mean that it is in any "sense" the best one -- especially for social science data, which seldom meets the assumptions necessary for PCA.

I took at look at the article Heiko recommended, and I think it is quite useful.

Namal, there are some differences between the outputs of PCA and EFA. PCA uses principal components, which means that each component is a linear combination of the items. As the items are weighted and added to form the component, the variance of the component includes measurement error. This is not the case for EFA, because the latent variables only include true scores and not measurement errors.

The consequence is that the component loadings are inflated, i.e. higher than the factor loadings, because the same error terms are in the manifest items and in the component. Another consequence is, that the correlations between the components - if an oblique rotation was applied - are deflated, i.e., lower than the correlations between the factors.

In addition to the article suggested by Heiko and David, there is another article which deals with the differences between PCA and EFA:

Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272-299.

http://ww.w.statpower.net/Content/312/Handout/Fabrigar1999.pdf

Regards, Karin