New Zealand Statistical Association
2004 Conference
Submitted Talks

James Reilly
Department of Statistics, University of Auckland

Multiple Imputation and Complex Survey Data

Multiple imputation is a powerful and widely used method for handling missing data. Following imputation, analysis results for the imputed datasets can easily be combined to estimate sampling variances that include the effect of imputation. However, situations have been identified where the usual combining rules can overestimate these variances. More recently, variance underestimates have also been shown to occur. A new multiple imputation method based on estimating equations has been developed to address these concerns, although this method requires more information about the imputation model than just the analysis results from each imputed dataset. Furthermore, the new method only handles i.i.d. data, which means it would not be appropriate for many surveys. In this talk, this method is extended to accommodate complex sample designs, and is applied to two complex surveys with substantial amounts of missing data. Results will be compared with those from the traditional multiple imputation variance estimator, and the implications for survey practice will be discussed.