方法对比

	Tau-U	Nonoverlap of All Pairs
领域	Social Work	Social Work
方法族	Process / pipeline	Process / pipeline
起源年份	2011	2009
提出者	Richard I. Parker, Kimberly J. Vannest, John L. Davis & Stephanie B. Sauber	Richard I. Parker & Kimberly J. Vannest
类型	Rank-based nonoverlap effect size that can correct for baseline trend	All-pairs nonoverlap effect size for single-case designs
开创性文献	Parker, R. I., Vannest, K. J., Davis, J. L., & Sauber, S. B. (2011). Combining nonoverlap and trend for single-case research: Tau-U. Behavior Therapy, 42(2), 284–299.	Parker, R. I., & Vannest, K. J. (2009). An improved effect size for single-case research: Nonoverlap of all pairs. Behavior Therapy, 40(4), 357–367.
别名	Tau-U Single-Case, Parker Tau-U, Kendall Tau Nonoverlap, Tau-U Effect Size	NAP, Nonoverlap of All Pairs (NAP), Parker-Vannest NAP, All-Pairs Nonoverlap
相关	4	4
摘要	Tau-U is a rank-based effect-size index for single-case research that combines the degree of nonoverlap between baseline and treatment phases with the trend within phases, and that can optionally subtract out any improving trend already present in the baseline. Developed by Richard Parker, Kimberly Vannest, and colleagues in 2011, it extends the Nonoverlap of All Pairs (NAP) statistic by adding a Kendall-style trend component, giving practitioners a single index that is robust to outliers, has a known sampling distribution for significance testing, and does not unfairly credit a treatment for change that the baseline was already heading toward.	Nonoverlap of All Pairs (NAP) is an effect-size index for single-case research that measures how completely a treatment phase separates from a baseline phase by examining every possible pairing of a baseline point with a treatment point. Introduced by Richard Parker and Kimberly Vannest in 2009 as an improvement on the Percentage of Nonoverlapping Data, NAP reports the proportion of those pairs in which the treatment point shows improvement, is mathematically equivalent to the area under a ROC curve and the Mann-Whitney statistic, and therefore carries a known sampling distribution that supports confidence intervals and significance testing.