방법 비교

	Standardized Mean Difference for Single-Case Designs	Single-Case Experimental Design
분야	Disability Studies	Disability Studies
계열	Regression model	Process / pipeline
기원 연도	2012	2013
창시자	Larry V. Hedges; James E. Pustejovsky; William R. Shadish	Thomas R. Kratochwill and the What Works Clearinghouse single-case design panel
유형	Design-comparable standardized effect size for single-case data	Within-subject experimental pipeline for evaluating interventions on individuals
원전	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.	Kratochwill, T. R., Hitchcock, J. H., Horner, R. H., Levin, J. R., Odom, S. L., Rindskopf, D. M., & Shadish, W. R. (2013). Single-case intervention research design standards. Remedial and Special Education, 34(1), 26-38.
별칭	Between-Case SMD, Single-Case d-Statistic, Hedges-Pustejovsky-Shadish d, SCD Standardized Mean Difference	Single-Subject Experimental Design, N-of-1 Experimental Design, Single-Case Research Design, SCED
관련	2	3
요약	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.	Single-case experimental design (SCED) is a family of rigorous within-subject experimental methodologies for evaluating whether an intervention causes change in an individual, widely used in rehabilitation, special education, and applied behavior analysis. Rather than averaging across a large sample, SCED measures a defined target behavior repeatedly across a baseline (A) phase and an intervention (B) phase, and infers a causal effect when the change is replicated at three or more different points in time within the same case. Internal validity is built into the design itself through systematic manipulation of the independent variable and repeated demonstrations of effect, not through a control group. The 2013 What Works Clearinghouse single-case design standards, formalized by Kratochwill and colleagues, codified what counts as a credible SCED, including requirements for systematic manipulation, at least three attempts to demonstrate an effect, and minimum data points per phase. SCED is the experimental backbone of evidence-based practice for individuals whose conditions, contexts, or low incidence make group designs impractical.