Managing missing and erroneous data in nurse staffing surveys

被引：5

作者：

Al-Ghraiybah, Tamer ^{[1
]}

Sim, Jenny ^{[2
]}

Fernandez, Ritin ^{[3
]}

Lago, Luise ^{[4
]}

机构：

[1] Univ Wollongong, Sch Nursing Midwifery & Indigenous Hlth, Wollongong, NSW, Australia

[2] Univ Newcastle, Sch Nursing & Midwifery, Gosford, NSW, Australia

[3] Univ Wollongong, Sch Nursing, Nursing, Wollongong, NSW, Australia

[4] Univ Wollongong, Ctr Hlth Res Illawarra Shoalhaven Populat, Wollongong, NSW, Australia

来源：

NURSE RESEARCHER | 2023年 / 31卷 / 02期

关键词：

data analysis; qualitative research; quantitative research; research; safe staffing; staffing levels; workforce; workforce planning; MULTIPLE IMPUTATION; SINGLE;

D O I：

10.7748/nr.2023.e1878

中图分类号：

R47 [护理学];

学科分类号：

1011 ;

摘要：

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.

引用

页码：19 / 27

页数：9

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