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- W2801091908 abstract "Missing data are commonly encountered in self-reported measurements and questionnaires. It is crucial to treat missing values using appropriate method to avoid bias and reduction of power. Various types of imputation methods exist, but it is not always clear which method is preferred for imputation of data with non-normal variables. In this paper, we compared four imputation methods: mean imputation, quantile imputation, multiple imputation, and quantile regression multiple imputation (QRMI), using both simulated and real data investigating factors affecting self-efficacy in breast cancer survivors. The results displayed an advantage of using multiple imputation, especially QRMI when data are not normal." @default.
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- W2801091908 date "2018-05-08" @default.
- W2801091908 modified "2023-09-24" @default.
- W2801091908 title "Comparisons of imputation methods with application to assess factors associated with self efficacy of physical activity in breast cancer survivors" @default.
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- W2801091908 doi "https://doi.org/10.1080/03610918.2018.1458132" @default.
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