I asked expert raters to evaluate several subject on six dimensions of creativity. Now, I am using factor analysis (factanal()) and PCA(princomp()) to see of these dimensions are measuring distinct or same aspects of creativity. Factor analysis shows that five out of the six dimensions load heavily (>0.5) on to one factor and the last dimension loads heavily on the second factor.
Factor analysis results
PCA with varimax rotation shows that the six dimensions load six components but each dimension loads heavily (>0.5) on only one component. The results are even more stark with PCA with promax rotation, where each dimension loads separately on the six components?
How do I reconcile the results from FA and PCA? I thought that based on the results of FA, the loadings of PCA would not be as starkly distinct.
I realize that FA and PCA do different things: Factor analysis is meant to understand the latent factors that account for the common variance among dimensions. In contrast, PCA is finding the eigen vectors to capture the direction of maximal variance.
Is FA might be more appropriate given my goals?Do the results suggests that the first five dimensions should be collapsed into one construct? thank you