TABLE 6

Eigenvalues and variance explained by principle componentsa

• a Shown are eigenvalues of the correlation matrix. Eigenvalue is a measure of variance in the data along a particular principal component (PC). Proportion of variance describes the percentage of variability explained by every principal component. As seen in the table, the first two PCs have eigenvalues of more than 1, and they account for 80.57% of variance. ADCC scores are derived from PC1 scores, which have an eigenvalue of 5.424 and cumulative variance of 67.81%.