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Imputation methods for mixed datasets in bioarchaeology.

Jessica Ryan-Despraz1, Amanda Wissler2

  • 1Department of Physical Anthropology, University of Bern, Bern, Switzerland.

Archaeological and Anthropological Sciences
|October 25, 2024
PubMed
Summary
This summary is machine-generated.

Multiple imputation techniques, particularly Random Forest (RF) and multi-method (MM) approaches, show superior performance for handling missing data in bioarchaeological mixed datasets. The extent of missingness significantly impacts imputation accuracy.

Keywords:
BioarchaeologyImputationMissing dataMixed data

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Area of Science:

  • Bioarchaeology
  • Data Science
  • Statistical Modeling

Background:

  • Missing data is a significant challenge in bioarchaeological research, potentially compromising the integrity of analyses.
  • Imputation methods offer a potential solution for addressing missing values in mixed-type datasets (qualitative and quantitative).

Purpose of the Study:

  • To evaluate the performance of various imputation methods for mixed data in bioarchaeology.
  • To compare single imputation techniques against an adapted multiple imputation approach.

Main Methods:

  • Simulated missingness (5-40%) on a control dataset (481 samples x 41 variables).
  • Tested methods: Random Forest (RF), PCA/MCA, FAMD, hotdeck, predictive mean matching (PMM), RSOV, and a multi-method (MM) approach.
  • Compared single imputation with an adapted multiple imputation method (R package 'mice').
  • Evaluated performance across three missingness mechanisms (MCAR, MAR, MNAR).

Main Results:

  • Adapted multiple imputation consistently outperformed single imputation across tested methods.
  • Random Forest (RF) and multi-method (MM) approaches demonstrated the best performance.
  • PCA/MCA and PMM multiple imputation also showed strong results.
  • The amount of missing data was the most critical factor influencing imputation accuracy.

Conclusions:

  • Multiple imputation methods, especially RF and MM, are effective for handling missing data in bioarchaeological mixed datasets.
  • While specific methods excel, the choice of imputation technique should consider dataset characteristics and research objectives.
  • Imputation is a valuable tool for bioarchaeological research, but careful consideration of its limitations is essential.