I have principal components based on correlation matrix. Statistica PCA method report outputs not only factor loadings that depend on components' variances (eigenvalues)

but also contributions ( for example,

PC1=Lambda_11*X1+Lambda_12*X_2+...

so the contribution of jth variable to ith PC is squared Lambda_ij and visa versa X1=Lambda_11*PC1+Lambda_21*PC2+...)

For example, I have V1 and PC1 correlation 0.32 and V1 and PC2 correlation 0.33. And the first contribution is 1.5%

and the second is 4.1%.

Which of them should I use to interpret my PC ? Loadings that depend on

components' variances or just contributions ?

but also contributions ( for example,

PC1=Lambda_11*X1+Lambda_12*X_2+...

so the contribution of jth variable to ith PC is squared Lambda_ij and visa versa X1=Lambda_11*PC1+Lambda_21*PC2+...)

For example, I have V1 and PC1 correlation 0.32 and V1 and PC2 correlation 0.33. And the first contribution is 1.5%

and the second is 4.1%.

Which of them should I use to interpret my PC ? Loadings that depend on

components' variances or just contributions ?

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