J Am Assoc Nurse Pract. 2015 Apr;27(4):230-2. doi: 10.1002/2327-6924.12208. Epub 2015 Feb 11.

Missing data within a quantitative research study: How to assess it, treat it, and why you should care.

Journal of the American Association of Nurse Practitioners

William Bannon

PMID: 25676704 DOI: 10.1002/2327-6924.12208

Abstract

Missing data typically refer to the absence of one or more values within a study variable(s) contained in a dataset. The development is often the result of a study participant choosing not to provide a response to a survey item. In general, a greater number of missing values within a dataset reflects a greater challenge to the data analyst. However, if researchers are armed with just a few basic tools, they can quite effectively diagnose how serious the issue of missing data is within a dataset, as well as prescribe the most appropriate solution. Specifically, the keys to effectively assessing and treating missing data values within a dataset involve specifying how missing data will be defined in a study, assessing the amount of missing data, identifying the pattern of the missing data, and selecting the best way to treat the missing data values. I will touch on each of these processes and provide a brief illustration of how the validity of study findings are at great risk if missing data values are not treated effectively.

©2015 American Association of Nurse Practitioners.

MeSH terms

• Data Accuracy*
• Data Interpretation, Statistical*
• Humans
• Research Design / standards
• Research Report / standards
• Surveys and Questionnaires / standards
• Surveys and Questionnaires / statistics & numerical data

Publication Types

• Journal Article