Handling missing data in self-report measures

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...

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Bibliographic Details
Published in:Research in nursing & health 2005-12, Vol.28 (6), p.488-495
Main Authors: Fox-Wasylyshyn, Susan M., El-Masri, Maher M.
Format: Article
Language:English
Subjects:
Biological and medical sciences
Chromosome aberrations
Data collection
Data Collection - methods
Data Interpretation, Statistical
deletion techniques
Humans
imputation
item level missingness
Medical genetics
Medical research
Medical sciences
Miscellaneous
missing data
Models, Statistical
Nursing
Nursing Research - statistics & numerical data
Public health. Hygiene
Public health. Hygiene-occupational medicine
Research methodology
Self evaluation
self-report measures
Statistical analysis
Surveys and Questionnaires
variable level missingness
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Summary: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. Res Nurs Health 28:488–495, 2005
ISSN:0160-6891
1098-240X
DOI:10.1002/nur.20100