Comparer des méthodes

	Standardized Effect Size for Single-Case Research	Tau-U
Domaine	Social Work	Social Work
Famille	Process / pipeline	Process / pipeline
Année d'origine	2012	2011
Auteur d'origine	Larry V. Hedges, James E. Pustejovsky & William R. Shadish	Richard I. Parker, Kimberly J. Vannest, John L. Davis & Stephanie B. Sauber
Type	Standardized mean-difference effect size comparable to between-groups d	Rank-based nonoverlap effect size that can correct for baseline trend
Source fondatrice	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.
Alias	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
Apparentées	3	4
Résumé	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.