방법 비교

	Randomization Test for Single-Case Designs	Standardized Mean Difference for Single-Case Designs
분야	Disability Studies	Disability Studies
계열	Process / pipeline	Regression model
기원 연도	1980	2012
창시자	Eugene S. Edgington; Patrick Onghena	Larry V. Hedges; James E. Pustejovsky; William R. Shadish
유형	Permutation-based statistical inference for single-case designs	Design-comparable standardized effect size for single-case data
원전	Edgington, E. S. (1980). Validity of Randomization Tests for One-Subject Experiments. Journal of Educational Statistics, 5(3), 235-251.	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.
별칭	Single-Case Randomization Test, Edgington Randomization Test, Permutation Test for Single-Subject Designs, Single-Case Permutation Inference	Between-Case SMD, Single-Case d-Statistic, Hedges-Pustejovsky-Shadish d, SCD Standardized Mean Difference
관련	2	2
요약	The randomization test for single-case experimental designs is a permutation-based procedure that yields a valid statistical p-value for an intervention effect in a single participant, provided that some experimentally controllable feature of the design — typically the moment the intervention begins or the order in which conditions are presented — was randomly determined before data were collected. Eugene Edgington showed in 1980 that this design-embedded randomization is what licenses inference: because the random assignment is the source of the test's probability statements, the procedure draws valid conclusions without assuming that the data are normally distributed or serially independent, two assumptions that single-case time-series data routinely violate. Edgington and Onghena's monograph established the modern framework, in which the observed test statistic is referred to the distribution of statistics generated by every admissible re-assignment of the data. In disability research, where interventions are often delivered to one person at a time and group designs are impractical, the randomization test provides a defensible significance test that complements visual analysis.	The between-case standardized mean difference is an effect-size measure that puts the result of a single-case experiment on the same numerical scale as Cohen's d from a conventional between-groups study, so that single-case and group findings can be combined in the same meta-analysis. Developed by Larry Hedges, James Pustejovsky, and William Shadish in 2012, it solves a long-standing problem: the many ad hoc nonoverlap indices used in single-case research (PND, PAND, IRD, Tau-U) are not comparable in scale to the standardized mean differences that dominate the broader evidence-synthesis literature. Their estimator models the single-case data with a hierarchical model that separates within-case variation from between-case variation, then standardizes the estimated treatment effect by the total standard deviation — the same denominator a between-subjects d would use. A 2013 extension specialized the estimator to multiple-baseline designs across individuals. The result is a design-comparable effect size with a known variance, suitable for disability and special-education research where single-case studies are abundant.