I'm currently reading a research paper entitled Relationships between national economic culture, institutions, and accounting: Implications for IFRS and I am looking at trying to replicate the analysis of the paper (just for my understanding).
The paper has a couple of data sets which are combined. The authors then run a "principal component analysis utilizing a varimax rotation" to evaluate the separate constructs and confirm validity of their constructs. They check the following assumptions for the analysis:
sampling adequacy, and
Paraphrasing the paper results:
The six variables loaded on the first component, which accounted for x% of the variance, while the other variables loaded on the second component, which accounted for an additional X% of the variance. This validated discriminant validity.
My question is, are these not the assumptions and results of exploratory factor analysis, and not of principal component analysis?
My understanding is that PCA is a data reduction technique with the end result being uncorrelated principal components. The assumptions of PCA are linear relationship between all variables, sampling adequacy, variables are somewhat correlated and no significant outliers. Whereas exploratory factor analysis is used as a method to evaluate construct validity which seems to be happening here.
I could include the original paragraph but the paper sits behind a paywall so I am not sure if its allowed.