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

• Five imputation methods were tested on three real datasets with missing data.
• Varimax rotation was used to compare results from a practical viewpoint.
• Multiple imputation yielded more scattered values under certain circumstances.
• Rotated factors change their order when variables influence several components.
• Expectation–maximization and iterative use of scores and loadings performed best.

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.