Jackknife variance estimation under imputation : an empirical investigation / by J. G. Kovar and E. J. Chen. : CS11-620/93-7E-PDF

"Imputation is a common technique employed by survey-taking organizations in order to address the problem of nonresponse. While in most of the cases the resulting completed data sets provide good estimates of means and totals, the corresponding variances are often grossly underestimated. A number of methods to remedy this problem exists, but most of them depend on the sampling design and the imputation method. On the other hand, the multiple imputation technique is data storage and time intensive. Recently, Rao and Shao (1992) have proposed a unified jackknife approach to variance estimation of imputed data sets. The present paper explores this technique empirically, using a real population of businesses, under a simple random sampling design and a uniform nonresponse mechanism. Extensions to stratified multistage sample designs are considered and comparisons to the multiple imputation technique are presented. Finally, the performance of the proposed variance estimator under non-uniform response mechanisms is briefly investigated"--Abstract.

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