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In analysis of principal components, eigenvalues represent the relative participation of each principal component in presenting the general variability of sampled material. The numerical value of a given eigenvalue is a direct indicator of the weight of a particular component in the general characteristics of the variability of a set of data. In practice, the distribution of the elements of the analyzed set in the space of the first three or four components allows one to present almost the complete diversity of the set.

Application of PCA in Taxonomy Research – Thrips (Insecta, Thysanoptera) as a Model Group