Imputation methods for mixed datasets in bioarchaeology

Bibliographic Data

ID5254527
AuthorsJ Ryan-Despraz (0000-0002-0794-4317, University of Bern), A Wissler (0000-0001-7129-5342, McMaster University)
Year2024
Volume16
Issue11
Pages187-187
Publication date2024-11-01
Open AccessYes
TypeARTICLE
VenueArchaeological and Anthropological Sciences (JOURNAL)
Journal identifiersISSN: 1866-9557 • E-ISSN: 1866-9565
PublisherSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1007/s12520-024-02078-2
PMID39450370
OpenAlexW4403701698
LanguageEN
Citations received1
References cited57

Missing data is a prevalent problem in bioarchaeological research and imputation could provide a promising solution. This work simulated missingness on a control dataset (481 samples × 41 variables) in order to explore imputation methods for mixed data (qualitative and quantitative data). The tested methods included Random Forest (RF), PCA/MCA, factorial analysis for mixed data (FAMD), hotdeck, predictive mean matching (PMM), random samples from observed values (RSOV), and a multi-method (MM) approach for the three missingness mechanisms (MCAR, MAR, and MNAR) at levels of 5%, 10%, 20%, 30%, and 40% missingness. This study also compared single imputation with an adapted multiple imputation method derived from the R package "mice". The results showed that the adapted multiple imputation technique always outperformed single imputation for the same method. The best performing methods were most often RF and MM, and other commonly successful methods were PCA/MCA and PMM multiple imputation. Across all criteria, the amount of missingness was the most important parameter for imputation accuracy. While this study found that some imputation methods performed better than others for the control dataset, each imputation method has advantages and disadvantages. Imputation remains a promising solution for datasets containing missingness; however when making a decision it is essential to consider dataset structure and research goals

Data mining · Imputation (statistics · Missing data · Statistics · Computer Science · Data Analysis with R · Mathematics · Statistical Methods and Bayesian Inference · Statistical Methods and Inference