Imputation procedures for categorical data: their effects on the goodness-of-fit chi-square statistic |
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Authors: | Phyllis A Gimotty Morton B Brown |
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Institution: | 1. Michigan Cancer Foundation , Detroit, MI 48201;2. Department of Biostatistics , University of Michigan , Ann Arbor, MI 48109 |
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Abstract: | An imputation procedure is a procedure by which each missing value in a data set is replaced (imputed) by an observed value using a predetermined resampling procedure. The distribution of a statistic computed from a data set consisting of observed and imputed values, called a completed data set, is affecwd by the imputation procedure used. In a Monte Carlo experiment, three imputation procedures are compared with respect to the empirical behavior of the goodness-of- fit chi-square statistic computed from a completed data set. The results show that each imputation procedure affects the distribution of the goodness-of-fit chi-square statistic in 3. different manner. However, when the empirical behavior of the goodness-of-fit chi-square statistic is compared u, its appropriate asymptotic distribution, there are no substantial differences between these imputation procedures. |
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Keywords: | missing data categorical data resampling plans |
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