The 81st Annual Meeting of the American Association of Physical Anthropologists (2012)

Going head to head: FORDISC vs CRANID in the determination of ancestry from craniometric data


Human Evolutionary Studies Program and Department of Archaeology, Simon Fraser University

Thursday All day, Plaza Level Add to calendar

The computer programs FORDISC and CRANID are frequently used to determine the ancestry of human skeletal remains. Using forms of Discriminant Function Analysis, they compare cranial measurements of an unknown specimen to specimens in reference databases to identify the likely ancestry of the unknown. Both rely heavily on W.W. Howells’ database of archaeological specimens for their reference samples. When analyzing an unknown using Howells’ data, FORDISC allows up to 82 variables to be entered in any combination and can constrain its searches by sex and/or geographic region. In contrast, CRANID requires 29 specific variables and its searches cannot be limited by sex or region.

Several studies have tested FORDISC and challenged its accuracy, but CRANID has not been similarly tested. Nor have the two programs been systematically compared. With this in mind, the present study used 200 specimens from the Howells reference sample to compare how each program performs using the same 29 variables.

The results indicate that FORDISC and CRANID achieve similar success rates for ancestry determination using the same 29 variables. However, the number of correctly identified specimens was lower than previous tests of FORDISC had achieved using 56 variables. In addition, although FORDISC and CRANID use similar methods, ancestry determinations were not always consistent between the two. More problematically, neither of the programs’ recommended acceptance criteria separated correct and incorrect results unambiguously. Overall, both programs performed below expectations and should be used with caution when assigning ancestry to skeletal remains.

Research funded by SSHRC-CGS#766-2007-1077, the Canada Research Chairs Program, the Canada Foundation for Innovation, and Simon Fraser University.

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