Format

Send to

Choose Destination
Stat Methods Med Res. 2018 Apr;27(4):1128-1140. doi: 10.1177/0962280216654511. Epub 2016 Jun 22.

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

Author information

1
1 Netherlands Organization for Applied Scientific Research (TNO), Leiden, Netherlands.
2
2 EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, Netherlands.
3
3 VU University, Amsterdam, Netherlands.

Abstract

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 model to reliably estimate imputations. A solution is to use passive imputation or a parcel summary score that combine and consequently reduce the number of variables in the imputation model. The performance of these methods was evaluated in a simulation study and illustrated in an example. Passive imputation, which updated scale scores from imputed items, and parcel summary scores that use the average over available item scores were compared to using all items simultaneously, imputing total scores of scales and complete-case analysis. Scale scores and coefficient estimates from linear regression were compared to "true" parameters on bias and precision. Passive imputation and using parcel summaries showed smaller bias and more precision than imputing total scores and complete-case analyses. Passive imputation or using parcel summary scores are valid missing data solutions in studies that include many multi-item scales.

KEYWORDS:

Multiple imputation; item imputation; missing data; questionnaires; simulation study

PMID:
27334917
DOI:
10.1177/0962280216654511

Supplemental Content

Full text links

Icon for Atypon
Loading ...
Support Center