방법 비교

	Celeration Line Analysis	Tau-U
분야	Social Work	Social Work
계열	Process / pipeline	Process / pipeline
기원 연도	1972	2011
창시자	Owen R. White & the precision-teaching tradition; codified for social work by Bloom, Fischer & Orme	Richard I. Parker, Kimberly J. Vannest, John L. Davis & Stephanie B. Sauber
유형	Trend-line procedure for projecting baseline trend into the intervention phase	Rank-based nonoverlap effect size that can correct for baseline trend
원전	Kazdin, A. E. (2011). Single-Case Research Designs: Methods for Clinical and Applied Settings (2nd ed.). Oxford University Press. ISBN: 9780195341881	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.
별칭	Celeration Line, Split-Middle Method, Trend Line Analysis (Single-Case), Celeration Approach	Tau-U Single-Case, Parker Tau-U, Kendall Tau Nonoverlap, Tau-U Effect Size
관련	4	4
요약	Celeration line analysis is a single-case method that fits a trend line to the baseline phase, projects that line forward into the intervention phase, and judges effect by how many intervention data points fall on the improvement side of the projected trend. Built on Owen White's split-middle technique from precision teaching and codified for social-work practice by Bloom, Fischer, and Orme, it directly addresses a weakness of level-only comparisons: it asks whether the client improved beyond the trajectory the baseline was already on, and pairs the count with a simple binomial test for statistical decision-making.	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.