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| 単一事例研究のための因子型ABAデザイン× | 要因単一被験者実験計画× | |
|---|---|---|
| 分野 | 実験計画法 | 実験計画法 |
| 系統 | Process / pipeline | Process / pipeline |
| 提唱年≠ | 1968 (ABA base); factorial extensions developed through 1980s–2000s | 1970s–1980s |
| 提唱者≠ | Derived from ABA reversal design (Baer, Wolf & Risley, 1968) extended with factorial manipulation principles | Applied behavior analysis tradition; systematized in Barlow & Hersen (1984) and Kazdin (1982) |
| 種類≠ | Single-case experimental design with factorial treatment structure | Experimental single-subject design with multiple independent variables |
| 原典≠ | Kratochwill, T. R., & Levin, J. R. (Eds.). (2010). Single-Case Intervention Research: Methodological and Statistical Advances. American Psychological Association. ISBN: 978-1433807909 | Kazdin, A. E. (2011). Single-Case Research Designs: Methods for Clinical and Applied Settings (2nd ed.). Oxford University Press. ISBN: 978-0195341881 |
| 別名 | Factorial reversal design, Multi-factor ABA design, Factorial withdrawal design, SCED factorial ABA | factorial SCED, factorial single-case design, factorial N-of-1 design, factorial within-subject experimental design |
| 関連 | 6 | 6 |
| 概要≠ | The Factorial ABA design embeds a factorial treatment structure within the ABA reversal framework. Rather than testing a single treatment against baseline, the researcher systematically varies two or more independent variables (factors) across treatment phases, using the ABA withdrawal logic to establish experimental control. This makes it possible to examine main effects and interactions among treatment components within a single-case or small-N experimental context. | A factorial single-subject experimental design applies the logic of factorial experiments — manipulating two or more independent variables simultaneously to study main effects and interactions — within a single-subject (N=1 or small N) repeated-measures framework. Instead of comparing groups, the same individual serves as their own control across systematically varied conditions, enabling fine-grained analysis of how multiple treatment components combine to influence behavior or clinical outcomes. |
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