Large-scale complex surveys typically contain a large number of variables measured on an even larger number of respondents. Missing data is a common problem in such surveys. Since usually most of the variables in a survey are categorical, multiple imputation requires robust methods for modelling...

Monitoring soil quality provides indispensable inputs for effective policy advice, but very few poorer countries can implement high-quality surveys on soil. We offer an alternative, low-cost imputation-based approach to generating various soil quality indicators. The estimation results validate...

Monitoring soil quality provides indispensable inputs for effective policy advice, but very few poorer countries can implement high-quality surveys on soil. We offer an alternative, low-cost imputation-based approach to generating various soil quality indicators. The estimation results validate...

Using data on annual individual labor income from three representative panel datasets (German SOEP, British BHPS, Australian HILDA) we investigate a) the selectivity of item non-response (INR) and b) the impact of imputation as a prominent post-survey means to cope with this type of measurement...

Using data on annual individual labor income from three representative panel datasets (German SOEP, British BHPS, Australian HILDA) we investigate a) the selectivity of item non-response (INR) and b) the impact of imputation as a prominent post-survey means to cope with this type of measurement...

Statistical Analysis in surveys is often facing missing data. As case-wise deletion and single imputation prove to have undesired properties, multiple imputation remains as a measure to handle this problem. In a longitudinal study, where for some missing values past or future data points might...