Porównaj metody

	Nonoverlap of All Pairs	Tau-U
Dziedzina	Social Work	Social Work
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	2009	2011
Twórca	Richard I. Parker & Kimberly J. Vannest	Richard I. Parker, Kimberly J. Vannest, John L. Davis & Stephanie B. Sauber
Typ	All-pairs nonoverlap effect size for single-case designs	Rank-based nonoverlap effect size that can correct for baseline trend
Źródło pierwotne	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.	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.
Inne nazwy	NAP, Nonoverlap of All Pairs (NAP), Parker-Vannest NAP, All-Pairs Nonoverlap	Tau-U Single-Case, Parker Tau-U, Kendall Tau Nonoverlap, Tau-U Effect Size
Pokrewne	4	4
Podsumowanie	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.	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.