I am currently researching silence in the social sciences and am reviewing surveys and statistical methods implemented by researchers to get an idea methods in both survey design and the analysis currently being used in the field.
I am a reading a paper where the authors perform a "A principal component factor analysis with oblimin rotation" where they identified nine factors with loadings. They used a scree test to determine the number of factors
This to me seems like they implemented an exploratory factor analysis, as a PCA – as far as my understanding goes – is a data reduction technique which produces uncorrelated principle components and not factors.
Based on the information above can someone confirm my understanding? It just isn't clear to me in the paper why they have called it a principal component factor analysis.
Best Answer
PCA is different to EFA. In PCA, we don't have a hypothesis of the underlying structure of the data. We use PCA to simplify the dimension of the data, for each eigenvector. In EFA, we have a number of latent constructs which we want to run the analysis. Apart from the overall goal, the analysis and calculation is very similar.
In the paper, I think the authors performed a PCA with a rotation for the factors to improve the interpretation of the components. To quote @ttnphns in his answer on PCA vs FA: