In principal components EFA, the prior communalities equal 1. An eigenvector is a representation of linear dependence in R . The number of eigenvectors is equal to the number of variables in R .
Preliminary eigenvalues are sufficient for deciding how many factors are present in R .
Assessments of Fit Maximum likelihood EFA will provide assessments of fit for the model. Two hypotheses are tested using an estimate of chi-square as the test statistic. The first null hypothesis is that R contains no factors against the alternative that R contains at least one common factor. Note in this example that two factors explain all the linear dependency in R . This measure is less likely to include trivial factors than chi-square or Akaike's measure.