Missing data in bioarchaeology II: A test of ordinal and continuous data imputation. Issue 3 (12th September 2022)

• Record Type: Journal Article
• Authors: Wissler, Amanda; Blevins, Kelly E.; Buikstra, Jane E.
• Is Part Of: American journal of biological anthropology. Volume 179:Issue 3(2022)
• Page Start: 349
• Page End: 364
• Publication Date: 2022-09-12
• Subjects: bioarchaeology -- imputation -- missing data -- paleopathology; Physical anthropology -- Periodicals; 599.9
• DOI: 10.1002/ajpa.24614
• Languages: English
• ISSNs: 2692-7691

Abstract

Abstract: 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.