Summarizing benchmarks in the national database of nursing quality indicators using bootstrap confidence intervals.
When summarizing the benchmarks for nursing quality indicators with confidence intervals around the means, bounds too high or too low are sometimes found due to small sample size or violation of the normality assumption. Transforming the data or truncating the confidence intervals at realistic values can solve the problem of out of range values. However, truncation does not improve upon the non-normality of the data, and transformations are not always successful in normalizing the data. The percentile bootstrap has the advantage of providing realistic bounds while not relying upon the assumption of normality and may provide a convenient way of obtaining appropriate confidence intervals around the mean for nursing quality indicators.
Gajewski
Byron
B
Department of Hearing and Speech, School of Nursing and School of Allied Health, Center for Biostatistics and Bioinformatics, The University of Kansas Medical Center (MS 4043), Kansas City, KS, USA.
Hall
Matthew
M
Dunton
Nancy
N
D019985:Benchmarking / Q000706:statistics & numerical data*; D016001:Confidence Intervals; D019992:Databases as Topic; D006801:Humans; D009738:Nursing Service, Hospital / Q000592:standards* / Q000706:statistics & numerical data; D019984:Quality Indicators, Health Care / Q000706:statistics & numerical data*; D018709:Statistics, Nonparametric; D014481:United States