The basic concept of multiple imputation is straightforward and easy to understand, but the application to real data imposes many implementation problems. To define useful imputation models for a dataset that consists of categorical and of continuous variables with distributions that are...

Questions on income in surveys are prone to two sources of errors that can cause bias if not addressed adequately at the analysis stage. On the one hand, income is considered sensitive information and response rates on income questions generally tend to be lower than response rates for other...

Applications of multiple imputation have long outgrown the traditional context of dealing with item nonresponse in cross-sectional datasets. Nowadays multiple imputation is also applied to impute missing values in hierarchical datasets, address confidentiality concerns, combine data from...

Applications of multiple imputation have long outgrown the traditional context of dealing with item nonresponse in cross-sectional datasets. Nowadays multiple imputation is also applied to impute missing values in hierarchical datasets, address confidentiality concerns, combine data from...

Questions on income in surveys are prone to two sources of errors that can cause bias if not addressed adequately at the analysis stage. On the one hand, income is considered sensitive information and response rates on income questions generally tend to be lower than response rates for other...

Multiple imputation is widely accepted as the method of choice to address item-nonresponse in surveys. However, research on imputation strategies for the hierarchical structures that are typically found in the data in educational contexts is still limited. While a multilevel imputation model...