Date of Award

Spring 5-2022

Degree Type

Masters Thesis

Degree Name

Master of Arts (MA)


Social Science and Global Studies

Committee Chair

Dr. Marie Danforth

Committee Chair School

Social Science and Global Studies

Committee Member 2

Dr. Bridget Hayden

Committee Member 2 School

Social Science and Global Studies

Committee Member 3

Dr. Sharon Young

Committee Member 3 School

Social Science and Global Studies


Cranial measurements have been a cornerstone of physical anthropology since its formation as a discipline in the early 1900s. However, most other ancestry determination methods come with a significant epistemological issue: they differentiate individuals into discrete categories without accounting for the issue of admixture. Advances in data mining and analysis techniques can now be used to help resolve this issue through soft computing, also known as “fuzzy math”. This type of advanced computational math requires specialized knowledge in computer programming, statistics, and data analysis techniques unless one is using computer programs specially designed to run these analyses.

This project compiled a database from multiple open-source craniometrics data and utilized prepared packages within the R statistical environment to find a valid soft computing method for fuzzy ancestry determination that does not require extensive knowledge in computer programming or data mining. Exploration of database demographics notes an excess of White-identified individuals, and when tested, this demographic skew impacts the ability of the given package to return valid results. The package chosen was valid using the compiled database. Exploration of causes for the invalid results, including a significant White skew in the underlying database due to accessibility of metric databases, overfitting, and the inherent issues of admixture on craniometric research, are explored, and future directions discussed.