Doctoral Dissertations

Date of Award


Degree Type


Degree Name

Doctor of Philosophy



Major Professor

Lyle W. Konigsberg

Committee Members

Richard L. Jantz, Andrew Kramer, Murray K. Marks, David Etnier


Estimating the biological profile for an unknown individual is a crucial part of forensic anthropology, bioarchaeology and paleodemography. The current research deals with one aspect of the biological profile: estimation of skeletal age-at-death. Several methods are available to estimate skeletal age-at-death, but most involve placing a skeletal element into a phase category. This type of phase-oriented age estimation, in addition to improper statistical methodology, leads to several problems: 1) observer subjectivity; 2) large age ranges and open-ended intervals; 3) stages that overlap one another; 4) aging bias; 5) age mimicry; and 6) taphonomic problems. Solutions to these methodological and statistical problems were offered by utilizing two dental metric features, translucency of the root and periodontal recession, and applying appropriate statistical analysis. Three skeletal collections, The Robert J. Terry Anatomical Collection, The Baraybar Forensic Biosample Collection, and the Lauchheim Medieval Cemetery Collection, dental remains were analyzed. Single-rooted teeth were analyzed following Lamendin et al’s (1992) method. The data were analyzed in the R statistical package using Bayesian analysis and inverse calibration. Age-at-death estimates for the Baraybar sample were generated by two inverse calibration methods and Bayesian analysis. The three age estimates were compared to highlight inherent problems with the inverse calibration methods.

The results showed that the Bayesian analysis reduced severity of several of the problems associated with adult skeletal age-at-death estimations. The Bayesian age-at-death estimates produced a lower overall mean error and higher correlation with actual age as compared to the inverse calibration methods for the Baraybar Forensic Biosample Collection. In addition, the Bayesian approach reduced aging bias, age mimicry, and the age ranges associated with the most probable age. The Baraybar Forensic Biosample Collection was used as a reference sample for the Lauchheim sample. Age-at-death estimates were also generated for this sample employing the two inverse calibration methods and Bayesian analysis.

This research lead to the conclusions that periodontal recession cannot be used as a univariate age indicator, due to its low correlation with chronological age. On the contrary, apical translucency yielded a high correlation with chronological age and was concluded to be an important age indicator. The Bayesian approach offered the most appropriate statistical analysis for the estimation of age-at-death with the current samples.

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