विधियों की तुलना करें

	Standardized Effect Size for Single-Case Research	Nonoverlap of All Pairs
क्षेत्र	Social Work	Social Work
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	2012	2009
प्रवर्तक	Larry V. Hedges, James E. Pustejovsky & William R. Shadish	Richard I. Parker & Kimberly J. Vannest
प्रकार	Standardized mean-difference effect size comparable to between-groups d	All-pairs nonoverlap effect size for single-case designs
मौलिक स्रोत	Hedges, L. V., Pustejovsky, J. E., & Shadish, W. R. (2012). A standardized mean difference effect size for single case designs. Research Synthesis Methods, 3(3), 224–239.	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.
उपनाम	Single-Case d, Within-Case Standardized Mean Difference, Design-Comparable Effect Size, Single-Case Standardized Mean Difference	NAP, Nonoverlap of All Pairs (NAP), Parker-Vannest NAP, All-Pairs Nonoverlap
संबंधित	3	4
सारांश	A standardized effect size for single-case research expresses the difference between treatment and baseline phases in standard-deviation units so that it can be placed on the same scale as the familiar between-groups Cohen's d and combined across studies in a meta-analysis. The design-comparable estimator of Hedges, Pustejovsky, and Shadish (2012) explicitly models within-case and between-case variation and applies a small-sample correction, addressing the long-standing problem that nonoverlap indices and naive single-case d statistics are not comparable to the effect sizes used in group-design research.	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.