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. / Dissertation (MSc)--University of Pretoria, 2015. / Anatomy / MSc / Unrestricted

The main goal of a forensic anthropological analysis of unidentified human remains is to establish an accurate biological profile. The largest obstacle in the creation or validation of techniques specific for subadults is the lack of large, modern samples. Techniques created for subadults were mainly derived from antiquated North American or European samples and thus inapplicable to a modern South African population as the techniques lack diversity and ignore the secular trends in modern children. This research provides accurate and reliable methods to estimate age and sex of South African subadults aged birth to 12 years from long bone lengths and breadths, as no appropriate techniques exist. Standard postcraniometric variables (n = 18) were collected from six long bones on 1380 (males = 804, females = 506) Lodox Statscan-generated radiographic images housed at the Forensic Pathology Service, Salt River and the Red Cross War Memorial Children’s Hospital in Cape Town, South Africa. Measurement definitions were derived from and/or follow studies in fetal and subadult osteology and longitudinal growth studies. Radiographic images were generated between 2007 and 2012, thus the majority of children (70%) were born after 2000 and thus reflect the modern population. Because basis splines and multivariate adaptive regression splines (MARS) are nonparametric the 95% prediction intervals associated with each age at death model were calculated with cross-validation. Numerous classification methods were employed namely linear, quadratic, and flexible discriminant analysis, logistic regression, naïve Bayes, and random forests to identify the method that consistently yielded the lowest error rates. Because some of the multivariate subsets demonstrated small sample sizes, the classification accuracies were bootstrapped to validate results. Both univariate and multivariate models were employed in the age and sex estimation analyses. Standard errors for the age estimation models were smaller in most of the multivariate models with the exception of the univariate humerus, femur, and tibia diaphyseal lengths. Univariate models provide narrower age estimates at the younger ages but the multivariate models provide narrower age estimates at the older ages. Diaphyseal lengths did not demonstrate any significant sex differences at any age, but diaphyseal breadths demonstrated significant sex differences throughout the majority of the ages. Classification methods utilizing multivariate subsets achieved the highest accuracies, which offer practical applicability in forensic anthropology (81% to 90%). Whereas logistic regression yielded the highest classification accuracies for univariate models, FDA yielded the highest classification accuracies for multivariate models. This study is the first to successfully estimate subadult age and sex using an extensive number of measurements, univariate and multivariate models, and robust statistical analyses. The success of the current study is directly related to the large, modern sample size, which ultimately captured a wider range of human variation than previously collected for subadult diaphyseal dimensions. / Thesis (PhD)--University of Pretoria, 2013. / gm2014 / Anatomy / unrestricted