This article provides a procedure for choosing appropriate imputation methods of incomplete bioarcheological or forensic skeleton specimen data, with an explanation of the authors’ research methodology and procedure development process.
It is not uncommon for biological anthropologists to analyze incomplete bioarcheological or forensic skeleton specimens. As many quantitative multivariate analyses cannot handle incomplete data, missing data imputation or estimation is a common preprocessing practice for such data. Using William W. Howells' Craniometric Data Set and the Goldman Osteometric Data Set, the authors evaluated the performance of multiple popular statistical methods for imputing missing metric measurements. Results indicated that multiple imputation methods outperformed single imputation methods, such as Bayesian principal component analysis (BPCA). Multiple imputation with Bayesian linear regression implemented in the R package norm2, the Expectation–Maximization (EM) with Bootstrapping algorithm implemented in Amelia, and the Predictive Mean Matching (PMM) method and several of the derivative linear regression models implemented in mice, performed well regarding accuracy, robustness, and speed. Based on the findings of this study, the authors suggest a practical procedure for choosing appropriate imputation methods. Publisher Abstract Provided