I.G. Shirobokov. On the application of the principal component analysis in cranial metric studies

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Shirobokov Ivan Grigor'evich. Peter the Great Museum of Anthropology and Ethnography (the Kunstkamera) RAS (St. Petersburg, Russian Federation).

E-mail: ivansmith @ bk.ru

 ORCID: 0000-0002-3555-7509

 

Abstract

Principal component analysis (PC) is often used by anthropologists to assess the homogeneity of the cranial samples, but so far there is no common approach to the interpretation of its results. Two reference groups included 900 male skulls were formed to try to set such standards. Random samples of different sizes were extracted from the model groups; the data on 14 craniometric traits for these groups were subjected to the principal component analysis. The consistent patterns of the average value, variability of the loadings of the first two PCs and the variability of eigenvalues were revealed. The first two PCs can be described as the factor of the total size and the factor of the facial skeleton form. In large samples, the first component has high correlations with the geometric means of all linear metric characters while the second component is related the variability of the negative correlated transverse and longitudinal cranial traits. Correlation analysis revealed that the second PC can interchange with the third and even fourth PC due to random differences in the eigenvalues of the PCs. The results of the experiment in which the samples randomly combined from skulls of Finns and Sami, demonstrated that the principal component analysis can not be effectively used for detecting the hidden structure of sample if researchers have no independent information on external (non-cranial metric) traits meaningful for differentiation of the sample.

 

Key words: skull morphology, sampling problem, research bias, principal component analysis.

 

DOI: 10.31250/2658-3828-2020-1-121-138

 

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