Split‐sample reliability estimation in health care quality measurement: Once is not enough
研究发现单次分样本信度估计对数据随机分割很敏感，尤其在样本量小或变异性低时；建议对多次分割的估计值取平均以获得更稳定的结果。
OBJECTIVE: To examine the sensitivity of split-sample reliability estimates to the random split of the data and propose alternative methods for improving the stability of the split-sample method. DATA SOURCES AND STUDY SETTING: Data were simulated to reflect a variety of real-world quality measure distributions and scenarios. There is no date range to report as the data are simulated. STUDY DESIGN: Simulation studies of split-sample reliability estimation were conducted under varying practical scenarios. DATA COLLECTION/EXTRACTION METHODS: All data were simulated using functions in R. PRINCIPAL FINDINGS: Single split-sample reliability estimates can be very dependent on the random split of the data, especially in low sample size and low variability settings. Averaging split-sample estimates over many splits of the data can yield a more stable reliability estimate. CONCLUSIONS: Measure developers and evaluators using the split-sample reliability method should average a series of reliability estimates calculated from many resamples of the data without replacement to obtain a more stable reliability estimate.