Sex determination of human skeletal populations using latent profile analysis

Accurately estimating biological sex from the human skeleton can be especially difficult for fragmentary or incomplete remains often encountered in bioarchaeological contexts. Where typical anatomically dimorphic skeletal regions are incomplete or absent, observers often take their best guess to classify biological sex. Latent profile analysis (LPA) is a mixture modeling technique which uses observed continuous data to estimate unobserved categorical group membership using posterior probabilities. In this study, sex is the latent variable (male and female are the two latent classes), and the indicator variables used here were eight standard linear measurements (long bone lengths, diaphyseal and articular breadths, and circumferences). Mplus (Muthén and Muthén: Mplus user's guide, 6th ed. Los Angeles: Muthén & Muthén, 2010) was used to obtain maximum likelihood estimates for latent class membership from a known sample of individuals from the forensic data bank (FDB) (Jantz and Moore-Jansen: Database for forensic anthropology in the United States 1962-1991, Ann Arbor, MI: Interuniversity Consortium for Political and Social Research, 2000) (n = 1,831), yielding 87% of correct classification for sex. Then, a simulation extracted 5,000 different random samples of 206 complete cases each from the FDB (these cases also had known sex). We then artificially imposed patterns of missing data similar to that observed in a poorly preserved bioarchaeological sample from Medieval Asturias, Spain (n = 206), and ran LPA on each sample. This tested the efficacy of LPA under extreme conditions of poor preservation (missing data, 42%). The simulation yielded an average of 82% accuracy, indicating that LPA is robust to large amounts of missing data when analyzing incomplete skeletons.