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An important indicator of data quality is the fraction of missing data. Missing data (also called “item non-response”) means that for some reason data on particular items or questions are not available for analysis. In practice, many researchers tend to solve this problem by restricting the analysis to complete cases through “listwise” deletion of all cases with missing data on the variables of interest. However, this results in loss of information, and therefore estimates will be less efficient. Furthermore, there is the possibility of systematic differences between units that respond to a particular question and those that do not respond—that is, item nonresponse error. If this is the case, the basic assumptions necessary for analyzing only complete cases are not met, and the analysis results ...

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