Missing data in bioarchaeology II: A test of ordinal and continuous data imputation.
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 advanc...
| Publicado en: | American Journal of Biological Anthropology Vol. 179; no. 3; pp. 349 - 365 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Wiley-Blackwell
Nov2022
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=159724822&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 159724822 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 26927691 MYY3 jtl: American Journal of Biological Anthropology issn: 26927691 maglogo: N pubinfo: dt: Nov2022 vid: 179 iid: 3 pid: 480 pub: Wiley-Blackwell artinfo: ui: 159724822 10.1002/ajpa.24614 ppf: 349 ppct: 16 formats: tig: atl: Missing data in bioarchaeology II: A test of ordinal and continuous data imputation. aug: au: Wissler, Amanda Blevins, Kelly E. Buikstra, Jane E. affil: Department of Anthropology, University of South Carolina, Columbia South Carolina,, USA Archaeology Department, Durham University, Durham, UK Center for Bioarchaeological Research, School of Human Evolution and Social Change, Arizona State University, Tempe Arizona,, USA su: Physical anthropology Missing data (Statistics) Multiple imputation (Statistics) Archaeological human remains Random forest algorithms Statistical power analysis Paleopathology sug: subj: Physical anthropology Missing data (Statistics) Multiple imputation (Statistics) Archaeological human remains Random forest algorithms Statistical power analysis Paleopathology keyword: bioarchaeology imputation missing data paleopathology bioarchaeology imputation missing data paleopathology ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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