The determination of ancestry is an important part of an individual's identification when creating a biological profile. This thesis scrutinizes postcranial variation using over 65 osteometric sorting measurements in an attempt to identify those measurements that display the most significant differences among Koreans, Africans, and Europeans. Data was collected from four American skeletal collections and one South Korean skeletal collection for a total sample population of 306 individuals: 24 of Korean ancestry, 66 of African ancestry, and 216 of European ancestry. In an effort to minimize the number of measurements needed for ancestral assessment, stepwise discriminant analysis was performed for measurements of each skeletal region and region combinations. Initial findings highly misclassified Africans, so the results of this study were separated into two parts: Koreans from Africans/Europeans and Africans from Europeans. A majority of the functions developed in the first part of the analysis resulted in cross-validated classifications of 80% and greater for Koreans and 77% or greater for Africans/Europeans with the highest classifying function for both ancestral groups being composed of upper limb measurements. Most of the discriminant functions from the second part of the analysis correctly differentiated Africans with 70% or greater accuracy and Europeans with 72% or greater accuracy with the highest classifying function for both groups consisting of pelvis, lower limb, and foot measurements. These functions indicate that ancestry can be determined successfully from postcranial elements; that certain skeletal regions are better indicators of ancestry than others; and that osteological remains do not need to be complete to develop an informative biological profile.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd-5678 |
| Date | 01 January 2012 |
| Creators | Okrutny, Elizabeth Carol |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations |
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