विधियों की तुलना करें

	Standardized Effect Size for Single-Case Research	Tau-U
क्षेत्र	Social Work	Social Work
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	2012	2011
प्रवर्तक	Larry V. Hedges, James E. Pustejovsky & William R. Shadish	Richard I. Parker, Kimberly J. Vannest, John L. Davis & Stephanie B. Sauber
प्रकार	Standardized mean-difference effect size comparable to between-groups d	Rank-based nonoverlap effect size that can correct for baseline trend
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., Davis, J. L., & Sauber, S. B. (2011). Combining nonoverlap and trend for single-case research: Tau-U. Behavior Therapy, 42(2), 284–299.
उपनाम	Single-Case d, Within-Case Standardized Mean Difference, Design-Comparable Effect Size, Single-Case Standardized Mean Difference	Tau-U Single-Case, Parker Tau-U, Kendall Tau Nonoverlap, Tau-U Effect Size
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.	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.