Tamer Al-Ghraiybah PhD student, School of Nursing Midwifery and Indigenous Health, University of Wollongong, Wollongong, NSW, Australia
Jenny Sim Associate professor, School of Nursing and Midwifery, University of Newcastle, Gosford, NSW, Australia
Ritin Fernandez Honorary professor of nursing, School of Nursing, University of Wollongong, Wollongong, NSW, Australia
Luise Lago Senior research fellow, Centre for Health Research Illawarra Shoalhaven Population, University of Wollongong, Wollongong, NSW, Australia
Why you should read this article:

• To guide you in dealing with missing and erroneous data in cross-sectional nurse staffing surveys

• To understand that methods of managing missing and erroneous survey data are important for transparent and replicable research

• To ensure surveys are piloted and contain unambiguous questions so that participants understand them

Background Analysis can be problematic in research when data are missing or erroneous. Various methods are available for managing missing and erroneous data, but little is known about which are the best to use when conducting cross-sectional surveys of nurse staffing.

Aim To explore how missing and erroneous data were managed in a study that involved a cross-sectional survey of nurse staffing.

Discussion The article describes a study that used a cross-sectional survey to estimate the ratio of registered nurses to patients, using self-reported data by nurses. It details the techniques used in the study to manage missing and erroneous data and presents the results of the survey before and after the treatment of missing data.

Conclusion Managing missing data effectively and reporting procedures transparently reduces the possibility of bias in a study’s results and increases its reproducibility. Nurse researchers need to understand the methods available to handle missing and erroneous data. Surveys must contain unambiguous questions, as every participant should have the same understanding of a question’s meaning.

Implication for practice Researchers should pilot surveys – even when using validated tools – to ensure participants interpret the questions as intended.

Nurse Researcher. 31, 2, 19-27. doi: 10.7748/nr.2023.e1878

Correspondence

tag940@uowmail.edu.au

Peer review

This article has been subject to external double-blind peer review and checked for plagiarism using automated software

Conflict of interest

None declared

Permission

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