Title: Handling missing data in surveys—Concepts, approaches, and applications in pharmacy and health services research
Abstract: A recent review of missing data in pharmacy literature has highlighted that a low proportion of studies reported how missing data was handled. In this chapter, we discuss the concept of missing data in survey research, how missing data is classified, common techniques to account for missingness, and how to report on missing data. The chapter provides guidance to mitigate the occurrence of missing data through planning. Considerations include estimating expected missing data, intended versus unintended missing data, survey length, working with electronic surveys, choosing between standard and filtered form questions, forced responses, and straight-lining, as well as responses that can generate missingness such as "I don't know" and "Not Applicable (NA)." We introduce methods for analyzing data with missing values, such as deletion, imputation, and likelihood methods. The chapter provides a framework and flow chart for choosing the appropriate analysis method based on how much missing data is observed and the type of missingness. Special circumstances involving missing data have been discussed, such as in studies with repeated or single measures, factor analysis, or as part of data linkage. Finally, a checklist of questions is provided for researchers to guide the reporting of the missing data when conducting future research.
Publication Year: 2022
Publication Date: 2022-01-01
Language: en
Type: book-chapter
Indexed In: ['crossref']
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