Determination of sex from skeletal remains is performed using a number of methods developed by biological anthropology. They must be evaluated for consistency and for their performance in a forensic setting. Twenty skeletons of varied provenance had their sex determined by 15 existing methods of forensic anthropology (7 metric and 8 morphological). The methods were evaluated for their consistency in determination of sex. No single individual was identified as belonging to one sex exclusively. Ambiguous results were obtained by metric methods for fourteen individuals (70%) and by morphological methods for only five individuals (25%) (Chi-squared = 4.3, df = 1, P<0.05). Methods which use the size of bones as an indicator of sex perform poorly on skeletal remains of individuals of unknown provenance. Methods which combine morphologic and metric techniques, that is, geometric morphometric analysis, may result in greater levels of consistency.
Although the concept of race has been thoroughly criticised in biological anthropology, forensic anthropology still uses a number of methods to determine the 'race' of a skeleton. The methods must be evaluated to see how effective they are given large individual variation. This study used 20 cases of skeletons of varied provenance to test whether the nine published methods of 'race' determination, using a range of various approaches, were able to consistently identify the ethnic origin. No one individual was identified as belonging to just one 'major racial class', e.g. European, meaning that complete consistency across all nine methods was not observed. In 14 cases (70%), various methods identified the same individual as belonging to all three racial classes. This suggests that the existing methods for the determination of 'race' are compromised. The very concept of 'race' is inapplicable to variation that occurs between populations only in small ways and the methods are limited by the geographic population from which their discriminant functions or observations of morphological traits were derived. Methods of multivariate linear discriminant analysis, e.g. CRANID, are supposed to allocate an individual skull to a specific population rather than a 'major race'. In our analysis CRANID did not produce convincing allocations of individual skeletons to specific populations. The findings of this study show that great caution must be taken when attempting to ascertain the 'race' of a skeleton, as the outcome is not only dependent on which skeletal sites are available for assessment, but also the degree to which the unknown skeleton's population of origin has been investigated.
There are a number of methods of physical anthropology available to reconstruct living stature from skeletal remains. Some methods use dimensions of just a few bones, together with regression equations (mathematical, see Table 1: 1-7), while other methods require the whole skeleton and simply add the heights of specific skeletal components (anatomical, see Table 1: 8-11). This study investigates the consistency that mathematical and anatomical methods can provide in the determination of stature from skeletal remains. A significant difference was found between average heights of the same 20 individuals determined from seven mathematical and four anatomical methods (paired t-test, p < 0.001, df = 19). Mathematical methods provided taller height estimates than anatomical methods; the average difference was 47 mm. A repeated measures ANOVA indicated significant differences in the heights determined by all methods (p < 0.0001). Analysis of variance indicated significant differences in the heights determined by various mathematical methods (p < 0.03), whereas there were no significant differences in the heights amongst various anatomical methods (p < 0.77). When simple proportions of the length of the long bones to stature are used for reconstruction (see Table 1: 12), a bias is shown by mathematical methods to overestimate statures of short individuals and underestimate statures of taller individuals. To reduce this bias of linear regressions, we suggest that alternate methods, such as reduced major axis or organic correlation, should be employed (see Table 1: 13-15).
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