multiple imputation

Passive imputation and parcel summaries are both valid to handle missing items in studies with many multi-item scales

© 2016, © The Author(s) 2016. Previous studies showed that missing data in multi-item scales can best be handled by multiple imputation of item scores. However, when many scales are used, the number of items will become too large for the imputation …

Methods for significance testing of categorical covariates in logistic regression models after multiple imputation: Power and applicability analysis

© 2017 The Author(s). Background: Multiple imputation is a recommended method to handle missing data. For significance testing after multiple imputation, Rubin's Rules (RR) are easily applied to pool parameter estimates. In a logistic regression …

Missing data in a multi-item instrument were best handled by multiple imputation at the item score level

Objectives Regardless of the proportion of missing values, complete-case analysis is most frequently applied, although advanced techniques such as multiple imputation (MI) are available. The objective of this study was to explore the performance of …

Don't Miss Out!

Missing data occurs in many empirical studies. It is vital for study results to handle the missing data correctly. The best solution to deal with missing data depends on the reasons for the occurrence of missing data and on the analysis that is planned. In the project a guide was developed to find the best way to deal with missing data in multi-item questionnaires. The website www.missingdata.nl also provides a lot of information about missing data and methodology.