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


The utility of latent class analysis to estimate sex for populations of human skeletal remains

NICHOLAS V. PASSALACQUA1, ZHEN ZHANG2 and STEVEN J. PIERCE2.

1Anthropology, Michigan State University, 2Center for Statistical Training & Consulting (CSTAT), Michigan State University

Friday 11:00-11:15, Parlors Add to calendar

This presentation will demonstrate the utility of latent class analysis (LCA) for estimating sex from large samples of human skeletal remains. Accurately estimating biological sex from the human skeleton can be especially difficult for fragmentary or incomplete remains often encountered in bioarchaeological contexts, what Konigsberg and Frankenberg (2007) refer to as "paleodemography under duress". Where typical anatomically dimorphic skeletal regions are incomplete or absent, observers often take their best-guess to classify biological sex. LCA estimates the probability of membership in each latent class from observed relationships between a set of indicator variables. 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 (Buikstra and Ubelaker 1997). Mplus (Muthen and Muthen 2010) was used to obtain maximum likelihood estimates for latent class membership from a known sample of individuals from the Forensic Databank (FDB) (Jantz and Moore-Jansen 2000) (n=1831), yielding 87% correct classification for sex. Then, a simulation extracted 5000 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 LCA on each sample. This tested the efficacy of LCA under extreme conditions of poor preservation (42% missing data). The simulation yielded an average of 82% accuracy, indicating that LCA is robust to large amounts of missing data when analyzing incomplete skeletons.

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