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| Pilot ABA Terv× | Pilot AB Design× | |
|---|---|---|
| Tudományterület | Kísérlettervezés | Kísérlettervezés |
| Módszercsalád | Process / pipeline | Process / pipeline |
| Keletkezés éve≠ | 1960s (ABA design); pilot adaptation in clinical and behavioral research from 1980s onward | 1960s (AB design); pilot framing formalized in practice by 1980s–1990s |
| Megalkotó≠ | Murray Sidman (ABA reversal logic); pilot study methodology broadly attributed to clinical trial traditions | Murray Sidman; Baer, Wolf & Risley (AB logic); pilot application emergent from single-subject research practice |
| Típus≠ | Single-subject experimental pilot design | Single-subject pilot experimental design |
| Alapmű≠ | Sidman, M. (1960). Tactics of Scientific Research: Evaluating Experimental Data in Psychology. Basic Books. link ↗ | Baer, D. M., Wolf, M. M., & Risley, T. R. (1968). Some current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis, 1(1), 91-97. DOI ↗ |
| Alternatív nevek | Pilot ABA reversal design, Pilot withdrawal design, Pilot single-subject reversal design, Feasibility ABA design | pilot AB phase design, preliminary AB design, exploratory AB single-case design, feasibility AB design |
| Kapcsolódó | 6 | 6 |
| Összefoglaló≠ | The Pilot ABA Design is a small-scale single-subject experiment that applies the ABA reversal structure — baseline, intervention, withdrawal — to test the feasibility, acceptability, and preliminary effect of an intervention before committing to a full-scale study. It provides early evidence of whether the treatment produces a detectable change and whether the reversal is ethically and practically achievable. | A pilot AB design applies the two-phase baseline-then-intervention structure of the AB single-subject design in an explicitly exploratory or feasibility mode — before committing to a more rigorous reversal or multiple-baseline study. The researcher collects repeated baseline (A) and intervention (B) data from one or a few individuals primarily to test measurement procedures, estimate effect size, verify data stability, and determine whether a stronger single-case design is warranted and feasible. |
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