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 i...