{"id":{"repo_id":"unlv","oai_identifier":"oai:oasis.library.unlv.edu:rtds-2642"},"canonical_url":"https://search.dev.ndltd.org/etd/unlv/oai:oasis.library.unlv.edu:rtds-2642","repository":{"repo_id":"unlv","name":"University of Nevada - Las Vegas","base_url":"https://oasis.library.unlv.edu/do/oai/"},"display":{"title":"Metricizing' non-metric craniofacial traits: Application of three dimensional geometric morphometric analysis to ancestral identification","abstract":"Non-metric traits are commonly used by anthropologists to distinguish between groups for such things as age, sex, and even ancestry. However, many non-metric traits cannot be measured with traditional osteological tools. While these traditional methods are useful, they do not take individual variation into account. This thesis uses geometric morphometric coordinate data to 'metricize' such traits and to evaluate their accuracy of correctly assigning unknown individuals to statistically defined groups; A 3-D digitizer was used to collect data from 39 craniofacial landmarks to capture the form of several phenotypic facial characteristics. The sample consisted of individuals from four different geographic locations (Precontact Peru, Historical China, Ancient Nubia, and Modern India). Principal Components Analysis confirmed that geographic groupings could be identified, while Procrustes Analysis and Thin-Plate Splines were used to assess the nature of form variation within and between groups. Discriminant functions calculated the probability of accurately assigning new individuals to their correct geographic group. The results indicate that phenotypic patterns, while variable, can be distinguished in certain populations and used successfully to predict group membership.","abstract_html":"Non-metric traits are commonly used by anthropologists to distinguish between groups for such things as age, sex, and even ancestry. However, many non-metric traits cannot be measured with traditional osteological tools. While these traditional methods are useful, they do not take individual variation into account. This thesis uses geometric morphometric coordinate data to 'metricize' such traits and to evaluate their accuracy of correctly assigning unknown individuals to statistically defined groups; A 3-D digitizer was used to collect data from 39 craniofacial landmarks to capture the form of several phenotypic facial characteristics. The sample consisted of individuals from four different geographic locations (Precontact Peru, Historical China, Ancient Nubia, and Modern India). Principal Components Analysis confirmed that geographic groupings could be identified, while Procrustes Analysis and Thin-Plate Splines were used to assess the nature of form variation within and between groups. Discriminant functions calculated the probability of accurately assigning new individuals to their correct geographic group. The results indicate that phenotypic patterns, while variable, can be distinguished in certain populations and used successfully to predict group membership.","abstract_has_math":false,"creators":["Decker, Summer Joy"],"institution":"University of Nevada, Las Vegas","degree_name":"Master of Arts (MA)","degree_level":"Thesis","degree_discipline":"Anthropology and Ethnic Studies","degree_department":null,"school":null,"contributors":["Jennifer L. Thompson","Bernardo T. Arriaza"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2004,"date_issued":"2004-01-01T08:00:00Z","date_published":"2004-01-01T08:00:00Z","updated_at":"2026-07-24T05:25:47Z","subjects":[],"languages":["English"],"rights":["IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/"],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["https://oasis.library.unlv.edu/rtds/1643"],"render_values":[{"text":"https://oasis.library.unlv.edu/rtds/1643","href":"https://oasis.library.unlv.edu/rtds/1643","code":true}]}]},"links":{"outbound_url":"https://doi.org/10.25669/mcfw-t3f5","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Jennifer L. Thompson","Bernardo T. 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This thesis uses geometric morphometric coordinate data to 'metricize' such traits and to evaluate their accuracy of correctly assigning unknown individuals to statistically defined groups; A 3-D digitizer was used to collect data from 39 craniofacial landmarks to capture the form of several phenotypic facial characteristics. The sample consisted of individuals from four different geographic locations (Precontact Peru, Historical China, Ancient Nubia, and Modern India). Principal Components Analysis confirmed that geographic groupings could be identified, while Procrustes Analysis and Thin-Plate Splines were used to assess the nature of form variation within and between groups. Discriminant functions calculated the probability of accurately assigning new individuals to their correct geographic group. The results indicate that phenotypic patterns, while variable, can be distinguished in certain populations and used successfully to predict group membership."]},{"key":"dc:format","label":"Dc Format","values":["pdf"]},{"key":"dc:title","label":"Title","values":["Metricizing' non-metric craniofacial traits: Application of three dimensional geometric morphometric analysis to ancestral identification"]}]}],"canonical_facts":{"dc:contributor":["Jennifer L. Thompson","Bernardo T. Arriaza"],"dc:creator":["Decker, Summer Joy"],"dc:description.abstract":["Non-metric traits are commonly used by anthropologists to distinguish between groups for such things as age, sex, and even ancestry. However, many non-metric traits cannot be measured with traditional osteological tools. While these traditional methods are useful, they do not take individual variation into account. This thesis uses geometric morphometric coordinate data to 'metricize' such traits and to evaluate their accuracy of correctly assigning unknown individuals to statistically defined groups; A 3-D digitizer was used to collect data from 39 craniofacial landmarks to capture the form of several phenotypic facial characteristics. The sample consisted of individuals from four different geographic locations (Precontact Peru, Historical China, Ancient Nubia, and Modern India). Principal Components Analysis confirmed that geographic groupings could be identified, while Procrustes Analysis and Thin-Plate Splines were used to assess the nature of form variation within and between groups. Discriminant functions calculated the probability of accurately assigning new individuals to their correct geographic group. 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