Anthropology, Department of


First Advisor

William Belcher

Second Advisor

Megan Moore

Third Advisor

Brittany Walter

Date of this Version

Spring 4-23-2020

Document Type



A thesis presented to the faculty of the Graduate College at the University of Nebraska in partial fulfillment of requirements for the degree of Master of Arts

Major: Anthropology

Under the supervision of Professor William R. Blecher

Lincoln, Nebraska, April 13, 2020


Copyright 2020, Maxwell S. Rooney


Current methodologies in body mass estimation are lacking in accuracy when compared to the methods of sex, age, and ancestry estimation familiar to forensic anthropologists. For this reason, the practical application of body mass estimation remains underutilized, hindering the study of a potentially advantageous aspect of the biological profile.

This study highlights body mass estimation in a forensic context while taking the osteological paradox into account through the utilization of a unique population: the US military personnel killed on the USS Oklahoma during the Pearl Harbor attack on December 7, 1942. Because these individuals were similar in age (adults, age 18-43 years) and their deaths were catastrophic rather than attritional, it provides an opportunity to control for many variables that other populations cannot. Ruff’s (1991) methodology for estimating body mass was applied, utilizing measurements taken from anteroposterior radiographs of the proximal femur and the development of body mass estimation equations via simple and linear regression modeling. These data were cross-referenced to body mass data collected by the US military during the individual’s enlistment. The mean squared error of estimate yielded by Ruff’s (1991) equations on the sample population was 104.12 and 62.00 for regression involving femoral head breadth and shaft breath, respectively. This differs from the mean squared error, 81.85 and 59.99, yielded by the equations created for USS Oklahoma data. While these results are expected in sample-specific linear regression, the controlled attributes of the sample and the equations produced offer another opportunity through which we can further our understanding of body mass estimation.

Advisor: William Belcher