Abstract:
While postcraniometric sex estimation has shown promising results in North American (NA) samples, methods and standards for sex estimation in South Africa (SA) are restricted by incomplete samples and a lack of robust statistical techniques.
The purpose of this study was to evaluate accuracies of sex estimation in the postcrania of modern South Africans using multivariate statistics and to compare pattern expression of sexual dimorphism in black, white and coloured groups.
The study included analysing the skeletons of a total of 360 SA black, white and coloured individuals and the data of 240 NA black and white individuals (equal sex and ancestry). Sympercents expressed sexual dimorphism and where compared in the three SA groups and with the NA individuals. The creation of different bone models and a variety of multivariate models revealed the potential of multivariate techniques. Comparisons of linear discriminant analysis (LDA), flexible discriminant analysis (FDA) and logistic regression indicated which model provided the greatest discriminatory power between sex and sex-ancestry groups in SA.
Among the SA groups coloureds were the most sexually dimorphic; however, overall NA individual showed the greatest differences between the sexes. Multivariate classification accuracies using bone models (various measurements from individual bones) ranged between 75% and 91%, whereas classification accuracies using multivariate subsets (combinations of measurements from different bones) ranged from 85% to 98%. When classifying into sex and ancestry, a multivariate subset using eight measurements achieved classification accuracies of up to 80%. Overall FDA achieved the best results, whereas logistic regression achieved the lowest results for both bone models and multivariate subsets.
Postcranial bones achieve comparable classification accuracies to the pelvis and higher accuracies than metric or morphological techniques using the cranium in SA. Large differences in sexual dimorphism between NA and SA warrant the creation of population-specific standards and custom databases for SA.
