Managing missing and erroneous data in nurse staffing surveys.

Abstract

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. To explore how missing and erroneous data were managed in a study that involved a cross-sectional survey of nurse staffing. 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. 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. Researchers should pilot surveys - even when using validated tools - to ensure participants interpret the questions as intended.