Objectives: Previous research has shown that while missing data are common in bioarchaeological studies, they are seldom handled using statistically rigorous methods. The primary objective of this article is to evaluate the ability of imputation to manage missing data and encourage the use of advanced statistical methods in bioarchaeology and paleopathology. An overview of missing data management in biological anthropology is provided, followed by a test of imputation and deletion methods for handling missing data.Materials and Methods: Missing data were simulated on complete datasets of ordinal (n = 287) and continuous (n = 369) bioarchaeological data. Missing values were imputed using five imputation methods (mean, predictive mean matching, random forest, expectation maximization, and stochastic regression) and the success of each at obtaining the parameters of the original dataset compared with pairwise and listwise deletion.Results: In all instances, listwise deletion was least successful at approximating the original parameters. Imputation of continuous data was more effective than ordinal data. Overall, no one method performed best and the amount of missing data proved a stronger predictor of imputation success.Discussion: These findings support the use of imputation methods over deletion for handling missing bioarchaeological and paleopathology data, especially when the data are continuous. Whereas deletion methods reduce sample size, imputation maintains sample size, improving statistical power and preventing bias from being introduced into the dataset.

Objectives: Missing data are a frequent and unavoidable challenge in bioarchaeological research, yet researchers seldom make explicit statements about the bias and inferential limitations that missing data introduce into their studies. There are no guidelines for best practices for the treatment or reporting of missing data. As an initial step in taking stock and exploring approaches to missing data in bioarchaeology, this study reviews bioarchaeological publications to identify methods currently in use for addressing this significant problem.Materials and Methods: Over 950 bioarchaeology articles (2011-2020) from four major anthropology journals were surveyed, searching for the terms "missing," "absent," "unobserv," "replace," and "imputat." The 267 articles so identified were categorized into one of nine bioarchaeological subtopics and scored according to a set of six broad approaches for handling missing data.Results: Results indicate that bioarchaeologists handle missing data in a variety of ways. Methods such as antimere substitution, listwise deletion and pairwise deletion are widely used. Subject subtopics favor different techniques for handling missing values. Bioarchaeological articles categorized as archaeology, pathology, and trauma used basic missing data approaches, while those such as biodistance and morphology more often employed advanced statistics. Despite the ubiquity of missing data, considerations of how they introduce bias were uncommon and standards for reporting were inconsistent.Conclusions: These findings highlight areas in which bioarchaeologists can improve techniques for handling and reporting missing data. Greater attention to these shortcomings will increase the statistical rigor of the field.