A Decision Tree for Nonmetric Sex Assessment from the Skull
Corresponding Author
Natalie R. Langley Ph.D.
Department of Anatomy, Mayo Clinic College of Medicine and Science, Scottsdale, AZ
Additional information and reprint requests:
Natalie R. Langley, Ph.D.
Department of Anatomy
Mayo Clinic College of Medicine and Science
13400 E. Shea Blvd.
Scottsdale, AZ 85259
E-mail: [email protected]
Search for more papers by this authorBeatrix Dudzik Ph.D.
Department of Anatomy, Lincoln Memorial University-DeBusk College of Osteopathic Medicine, Harrogate, TN
Search for more papers by this authorAlesia Cloutier M.S.
Lincoln Memorial University-DeBusk College of Osteopathic Medicine, Harrogate, TN
Search for more papers by this authorCorresponding Author
Natalie R. Langley Ph.D.
Department of Anatomy, Mayo Clinic College of Medicine and Science, Scottsdale, AZ
Additional information and reprint requests:
Natalie R. Langley, Ph.D.
Department of Anatomy
Mayo Clinic College of Medicine and Science
13400 E. Shea Blvd.
Scottsdale, AZ 85259
E-mail: [email protected]
Search for more papers by this authorBeatrix Dudzik Ph.D.
Department of Anatomy, Lincoln Memorial University-DeBusk College of Osteopathic Medicine, Harrogate, TN
Search for more papers by this authorAlesia Cloutier M.S.
Lincoln Memorial University-DeBusk College of Osteopathic Medicine, Harrogate, TN
Search for more papers by this authorAbstract
This study uses five well-documented cranial nonmetric traits (glabella, mastoid process, mental eminence, supraorbital margin, and nuchal crest) and one additional trait (zygomatic extension) to develop a validated decision tree for sex assessment. The decision tree was built and cross-validated on a sample of 293 U.S. White individuals from the William M. Bass Donated Skeletal Collection. Ordinal scores from the six traits were analyzed using the partition modeling option in JMP Pro 12. A holdout sample of 50 skulls was used to test the model. The most accurate decision tree includes three variables: glabella, zygomatic extension, and mastoid process. This decision tree yielded 93.5% accuracy on the training sample, 94% on the cross-validated sample, and 96% on a holdout validation sample. Linear weighted kappa statistics indicate acceptable agreement among observers for these variables. Mental eminence should be avoided, and definitions and figures should be referenced carefully to score nonmetric traits.
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