Table of Contents
ISRN Nursing
Volume 2011 (2011), Article ID 752320, 4 pages
Research Article

Additional Support for Simple Imputation of Missing Quality of Life Data in Nursing Research

1Clinical Research Centre, Kingston General Hospital, Kingston, ON, Canada K7L 2V7
2Department of Community Health and Epidemiology, Queen's University, Kingston, ON, Canada K7L 2V7
3School of Nursing, Faculty of Health Sciences, Queen's University, Kingston, ON, Canada K7L 2V7
4Practice and Research in Nursing (PRN) Group, Queen's University, Kingston, ON, Canada K7L 2V7
5Department of Anesthesiology and Perioperative Medicine, Queen's University, Kingston, ON, Canada K7L 2V7

Received 5 August 2011; Accepted 5 September 2011

Academic Editors: V. Lohne and H. S. Shin

Copyright © 2011 Wilma M. Hopman et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Background. Missing data are a significant problem in health-related quality of life (HRQOL) research. We evaluated two imputation approaches: missing data estimation (MDE) and assignment of mean score (AMS). Methods. HRQOL data were collected using the Medical Outcomes Trust SF-12. Missing data were estimated using both approaches, summary statistics were produced for both, and results were compared using intraclass correlations (ICC). Results. Missing data were imputed for 21 participants. Mean values were similar, with ICC > . 9 9 within both the Physical Component Summary and the Mental Component Summary when comparing the two methodologies. When imputed data were added into the full study sample, mean scores were identical regardless of methodology. Conclusion. Results support the use of a practical and simple imputation strategy of replacing missing values with the mean of the sample in cross-sectional studies when less than half of the required items of the SF-12 components are missing.