The Principal Components report contain the following information:
Eigenvalue
Eigenvalues for the covariance matrix.
Percent
Percent variation explained by the corresponding eigenvector. Also shows an accompanying bar chart.
Cum Percent
Cumulative percent variation explained by eigenvectors corresponding to the eigenvalues.
ChiSquare
Provides a test of whether the correlation remaining in the data is of a random nature. This is a Bartlett test of sphericity. When this test rejects the null hypothesis, this implies that there is structure remaining in the data that is associated with this eigenvalue.
DF
Degrees of freedom associated with the Chi-square test.
Prob > ChiSq
p-value for the test.
Eigenvectors
Table of eigenvectors corresponding to the eigenvalues. Note that each eigenvector is divided by the square root of its corresponding eigenvalue.
For more information about principal components, see Principal Components in Multivariate Methods.