Editing Factor analysis (section)

=== Arguments contrasting PCA and EFA ===
Fabrigar et al. (1999)<ref name=Fabrigar /> address a number of reasons used to suggest that PCA is not equivalent to factor analysis:

# It is sometimes suggested that PCA is computationally quicker and requires fewer resources than factor analysis. Fabrigar et al. suggest that readily available computer resources have rendered this practical concern irrelevant.
# PCA and factor analysis can produce similar results. This point is also addressed by Fabrigar et al.; in certain cases, whereby the communalities are low (e.g. 0.4), the two techniques produce divergent results. In fact, Fabrigar et al. argue that in cases where the data correspond to assumptions of the common factor model, the results of PCA are inaccurate results.
# There are certain cases where factor analysis leads to 'Heywood cases'. These encompass situations whereby 100% or more of the [[variance]] in a measured variable is estimated to be accounted for by the model. Fabrigar et al. suggest that these cases are actually informative to the researcher, indicating an incorrectly specified model or a violation of the common factor model. The lack of Heywood cases in the PCA approach may mean that such issues pass unnoticed.
# Researchers gain extra information from a PCA approach, such as an individual's score on a certain component; such information is not yielded from factor analysis. However, as Fabrigar et al. contend, the typical aim of factor analysis – i.e. to determine the factors accounting for the structure of the [[Correlation and dependence|correlations]] between measured variables – does not require knowledge of factor scores and thus this advantage is negated. It is also possible to compute factor scores from a factor analysis.