Abstract

Missing data are an inevitable reality in research. Nurse educators can promote proactive thinking about this topic to help avoid excessive missingness. The purpose of this article is to encourage nurses to view missing data as an accepted reality and to consider strategies for anticipating and minimizing missing data throughout the research process. The common causes of missing data and ways to minimize their occurrence are discussed, along with suggestions for adopting a statistical mindset about missing data. Rubin's framework for missingness as a random process, modern statistical methods for analyzing missing data, and recommendations for reporting also are discussed. Nurse educators and researchers should understand all aspects of missing data, including the types, occurrence, causes, potential problems, and strategies for preventing, handling, and reporting missing data. By doing so, the occurrence of missing data can be lessened, thereby minimizing various problems that can result. [J Nurs Educ. 2020;59(5):249-255.].

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.