방법 비교

	Tau-U	Visual Analysis of Single-Case Data
분야	Social Work	Social Work
계열	Process / pipeline	Process / pipeline
기원 연도	2011	2010
창시자	Richard I. Parker, Kimberly J. Vannest, John L. Davis & Stephanie B. Sauber	Applied behavior analysis tradition; codified by Kratochwill et al. (What Works Clearinghouse)
유형	Rank-based nonoverlap effect size that can correct for baseline trend	Structured graphical judgment of intervention effect in single-case time-series data
원전	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.	Kratochwill, T. R., Hitchcock, J., Horner, R. H., Levin, J. R., Odom, S. L., Rindskopf, D. M., & Shadish, W. R. (2010). Single-Case Designs Technical Documentation. What Works Clearinghouse, U.S. Department of Education.
별칭	Tau-U Single-Case, Parker Tau-U, Kendall Tau Nonoverlap, Tau-U Effect Size	Visual Inspection of Single-Case Data, Single-Case Visual Analysis, Graphical Analysis of Single-Subject Data, Visual Analysis of Time-Series Graphs
관련	4	4
요약	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.	Visual analysis is the primary method for judging whether an intervention produced an effect in single-case and single-system designs: the data are plotted as a time series across baseline and intervention phases and read systematically for changes in level, trend, variability, immediacy of effect, overlap between phases, and consistency across similar phases. Rooted in applied behavior analysis and codified by the What Works Clearinghouse single-case standards, it treats the graph itself as the evidence and reserves the label 'effect' for changes that are clear, replicated within the design, and unlikely to reflect ordinary fluctuation.