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| Single-Case Design in Education× | Analyse de séries chronologiques interrompues (ITS)× | |
|---|---|---|
| Domaine≠ | Education | Inférence causale |
| Famille≠ | Process / pipeline | Regression model |
| Année d'origine≠ | 2013 | 2002 |
| Auteur d'origine≠ | Applied behavior analysis and special education (Baer, Wolf & Risley; Horner; Kratochwill) | Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial) |
| Type≠ | Experimental design establishing intervention effects within individual cases via repeated measurement | Quasi-experimental segmented regression |
| Source fondatrice≠ | 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. DOI ↗ | Bernal, J. L., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: a tutorial. International Journal of Epidemiology, 46(1), 348-355. DOI ↗ |
| Alias≠ | Single-Subject Design, Single-Case Experimental Design, SCED, N-of-1 Educational Design | ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi |
| Apparentées≠ | 4 | 5 |
| Résumé≠ | Single-case experimental designs establish whether an intervention causes a change in behavior or learning by intensively studying individual cases over time rather than comparing groups. Each case serves as its own control: an outcome is measured repeatedly during a baseline phase and again under intervention, and the effect is demonstrated by replicating the change across phases or across cases. Central to special education and applied behavior analysis, and recognized by the What Works Clearinghouse and Horner and colleagues' standards, single-case design offers rigorous causal evidence when group experiments are impractical. | Interrupted Time Series analysis is a quasi-experimental design that estimates the effect of a single, well-dated intervention by comparing the trajectory of an outcome before and after it occurs. Formalised as segmented regression by Wagner and colleagues (2002) and popularised as a public-health evaluation tutorial by Bernal, Cummins and Gasparrini (2017), it separates the intervention's impact into a change in level and a change in slope. |
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