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Managing missing and erroneous data in nurse staffing surveys.
Tamer Al-Ghraiybah1, Jenny Sim2, Ritin Fernandez3
1School of Nursing Midwifery and Indigenous Health, University of Wollongong, Wollongong, NSW, Australia.
Nurse Researcher
|March 30, 2023
Summary
This study explored managing missing and erroneous data in nurse staffing surveys. Effective data management and clear survey questions are crucial for reducing bias and improving research reproducibility.
Area of Science:
- Nursing Research
- Health Services Research
Background:
- Data analysis in research is often complicated by missing or erroneous data.
- While various data management methods exist, their effectiveness in cross-sectional nurse staffing surveys is not well-understood.
Purpose of the Study:
- To examine the strategies employed for managing missing and erroneous data within a cross-sectional survey of nurse staffing.
- To assess the impact of data management techniques on survey results.
Main Methods:
- The study utilized a cross-sectional survey to collect self-reported data from nurses on the registered nurse to patient ratio.
- Specific techniques for handling missing and erroneous data were detailed and applied.
- Survey results were analyzed both before and after data imputation.
Main Results:
- The study presents the findings of the nurse staffing survey, comparing outcomes before and after the implementation of data cleaning and imputation methods.
- The impact of different data management techniques on the final estimates was evaluated.
Conclusions:
- Transparent and effective management of missing data is essential for minimizing bias and enhancing the reproducibility of research findings.
- Nurse researchers require a solid understanding of available methods for handling missing and erroneous data.
- Ensuring clarity in survey questions and piloting surveys are critical steps to guarantee consistent participant interpretation and reliable data collection.
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