missing data

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 …

Analyzing Incomplete Item Scores in Longitudinal Data by Including Item Score Information as Auxiliary Variables

Copyright © Taylor & Francis Group, LLC. The aim of this study is to investigate a novel method for dealing with incomplete scale scores in longitudinal data that result from missing item responses. This method includes item information as auxiliary …

Including auxiliary item information in longitudinal data analyses improved handling missing questionnaire outcome data

© 2015 Elsevier Inc. All rights reserved. Objectives Previous studies show that missing values in multi-item questionnaires can best be handled at item score level. The aim of this study was to demonstrate two novel methods for dealing with …

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.