Correction of Bias in Imputing Missing Values of Categorical Variables |
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| Authors: | Ruiguang Song Kathleen McDavid Harrison Debra L Hanson H Irene Hall |
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| Institution: | 1. Division of HIV/AIDS Prevention , Centers for Disease Control and Prevention , Atlanta , Georgia , USA rsong@cdc.gov;3. Division of HIV/AIDS Prevention , Centers for Disease Control and Prevention , Atlanta , Georgia , USA |
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| Abstract: | Markov Chain Monte Carlo (MCMC) is the most common method used in multiple imputation. However, it is not unbiased when it is applied to imputations of categorical variables. The literature has considered the problem for binary variables with only two levels. In this article, we consider more general situations. We not only evaluate the bias associated with the imputation of categorical variables using the MCMC method, but also introduce a method to correct the bias. A simulation study is conducted and an application is provided to demonstrate the advantages of using the correction factors proposed in this article. |
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| Keywords: | Categorical variable Multiple imputation Rounding bias |
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