Handling missing data in self-report measures
Research in Nursing and Health
Data Collection, Data Interpretation, Statistical, Deletion techniques, human, Humans, Imputation, information processing, Item level missingness, methodology, Missing data, Models, Statistical, nursing research, questionnaire, Questionnaires, review, Self-report measures, statistical analysis, statistical model, statistics, Variable level missingness
Self-report measures are extensively used in nursing research. Data derived from such reports can be compromised by the problem of missing data. To help ensure accurate parameter estimates and valid research results, the problem of missing data needs to be appropriately addressed. However, a review of nursing research literature revealed that issues such as the extent and pattern of missingness, and the approach used to handle missing data are seldom reported. The purpose of this article is to provide researchers with a conceptual overview of the issues associated with missing data, procedures used in determining the pattern of missingness, and techniques for handling missing data. The article also highlights the advantages and disadvantages of these techniques, and makes distinctions between data that are missing at the item versus variable levels. Missing data handling techniques addressed in this article include deletion approaches, mean substitution, regression-based imputation, hot-deck imputation, multiple imputation, and maximum likelihood imputation. © 2005 Wiley Periodicals, Inc.
Fox-Wasylyshyn, S. M. and El-Masri, M M.. (2005). Handling missing data in self-report measures. Research in Nursing and Health, 28 (6), 488-495.