EFFICIENT RANDOM IMPUTATION FOR MISSING DATA IN COMPLEX SURVEYS
 
                                        Randy R. Sitter
 
                                    Simon Fraser University
 
                                          Abstract 
 
         A simple adjusted random imputation method for handling item nonresponse in
         complex surveys is presented. This method eliminates the imputation variance
         of the estimator of a mean or total, and at the same time preserves the distribution
         of item values. Jackknife and bootstrap variance estimators that depend only on
         the reported values in the data file are also proposed. It is necessary to identify
         the respondent and imputed values in the data file as well as the imputation class.
         Simulation results on the performance of the proposed method in estimating a total,
         population variance and distribution function are also presented.
 
         (Joint work with J.N.K. Rao and Jiahua Chen)