Compara mètodes

	Nonoverlap of All Pairs	Visual Analysis of Single-Case Data
Camp	Social Work	Social Work
Família	Process / pipeline	Process / pipeline
Any d'origen	2009	2010
Autor original	Richard I. Parker & Kimberly J. Vannest	Applied behavior analysis tradition; codified by Kratochwill et al. (What Works Clearinghouse)
Tipus	All-pairs nonoverlap effect size for single-case designs	Structured graphical judgment of intervention effect in single-case time-series data
Font seminal	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.	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.
Àlies	NAP, Nonoverlap of All Pairs (NAP), Parker-Vannest NAP, All-Pairs Nonoverlap	Visual Inspection of Single-Case Data, Single-Case Visual Analysis, Graphical Analysis of Single-Subject Data, Visual Analysis of Time-Series Graphs
Relacionats	4	4
Resum	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.	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.