{Reference Type}: Journal Article
{Title}: Split-sample reliability estimation in health care quality measurement: Once is not enough.
{Author}: Nieser KJ;Harris AHS;
{Journal}: Health Serv Res
{Volume}: 59
{Issue}: 4
{Year}: 2024 Aug 24
{Factor}: 3.734
{DOI}: 10.1111/1475-6773.14310
{Abstract}: <B>OBJECTIVE: </B>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.<BR><B>METHODS: </B>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.<BR><B>METHODS: </B>Simulation studies of split-sample reliability estimation were conducted under varying practical scenarios.<BR><B>METHODS: </B>All data were simulated using functions in R.<BR><B>RESULTS: </B>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.<BR><B>CONCLUSIONS: </B>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.