Sexing skulls using discriminant function analysis of visually assessed traits

The accuracy of sex determinations based on visual assessments of the mental eminence, orbital margin, glabellar area, nuchal area, and mastoid process was tested on a series of 304 skulls of known age and sex from people of European American, African American, and English ancestry as well as on an ancient Native American sample of 156 individuals whose sex could be reliably determined based on pelvic morphology. Ordinal scores of these sexually dimorphic traits were used to compute sex determination discriminant functions. Linear, kth-nearest-neighbor, logistic, and quadratic discriminant analysis models were evaluated based on their capacity to minimize both misclassifications and sex biases in classification errors. Logistic regression discriminant analysis produced the best results: a logistic model containing all five cranial trait scores correctly classified 88% of the modern skulls with a negligible sex bias of 0.1%. Adding age at death, birth year, and population affinity to the model did not appreciably improve its performance. For the ancient Native American sample, the best logistic regression model assigned the correct pelvic sex to 78% of the individuals with a sex bias of only 0.2%. Similar cranial trait frequency distributions were found in same-sex comparisons of the modern African American, European American, and English samples. The sexual dimorphism of these modern people contrasts markedly with that of the ancient Native Americans. Because of such population differences, discriminant functions like those presented in this paper should be used with caution on populations other than those for which they were developed.