How does FA differ from PCA? Overall, as indicated in the chapter introduction, PCA accounts for the total variance of the variables in terms of the linear combinations of the original variables, while FA accounts for the correlations of the observed variables by positing latent factors. Here are some contrasts on how you would approach the respective analyses in SPSS Statistics FACTOR.
You can employ PCA on either covariances or correlations. Likewise, you can employ FA on either covariances (for extraction methods PAF or IMAGE) or correlations. The analysis in this chapter analyzes correlation matrices because correlations implicitly put variables on a common scale, and that is often needed for the data with which we work.
Following are a few of the important parameters in the discussion of PCA and FA:
- Regarding...
