Preserving relationships between variables with MIVQUE based imputation for missing survey data

Item nonresponse is often dealt with through imputation. Marginal imputation, which consists of treating separately each variable requiring imputation, generally leads to biased estimators of parameters (e.g., coefficients of correlation) measuring relationships between variables. Shao and Wang (2002) proposed a joint imputation procedure and showed that it leads to asymptotically unbiased estimators of coefficients of correlation. In this paper, we propose a modification of the Shao–Wang procedure, where initial imputed values obtained using this method, are modified so as to satisfy calibration constraints, which corresponds to MIVQUE estimators of model parameters. When the bivariate distribution of the variables being imputed is symmetric or exhibits a low degree of asymmetry, the proposed procedure is shown to be significantly more efficient than the Shao–Wang procedure in terms of mean square error. Results from a simulation study supports our findings.