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A practical comparison of single and multiple imputation methods to handle complex missing data in air quality datasets

Authors
Journal
Chemometrics and Intelligent Laboratory Systems
0169-7439
Publisher
Elsevier
Publication Date
Volume
134
Identifiers
DOI: 10.1016/j.chemolab.2014.02.007
Keywords
  • Missing Data
  • Single Imputation
  • Expectation–Maximization
  • Multiple Imputation
  • Air Quality

Abstract

Abstract Datasets with missing data ratios ranging from 24% to 4%, corresponding to three air quality monitoring studies, were used to ascertain whether major differences occur when five currently used imputation methods are applied (four single imputation methods and a multiple imputation one). Unrotated and Varimax-rotated factor analyses performed on the imputed datasets were compared. All methods performed similarly, although multiple imputation yielded more disperse imputed values. Main differences occurred when a variable with missing values correlated poorly to the other features and when a variable had relevant loadings in several unrotated factors, which sometimes changed the order of the rotated factors.

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