Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Muundo wa Adaptive ABA× | Muundo wa AB Adaptive× | |
|---|---|---|
| Nyanja | Muundo wa Majaribio | Muundo wa Majaribio |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 1968 (ABA foundation); adaptive extensions formalized ~2010–2020 | 1968 (AB foundation); 2000s (adaptive extensions) |
| Mwanzilishi≠ | Baer, Wolf & Risley (ABA baseline); adaptive decision-rule extensions developed in single-case methodology literature (Kratochwill & Levin, 2010s) | Baer, Wolf & Risley (AB foundation); Kratochwill & Levin (adaptive single-case extensions) |
| Aina≠ | Single-subject experimental design with adaptive phase rules | Single-subject experimental design with adaptive phase-change rules |
| Chanzo asilia≠ | 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 ↗ | 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 ↗ |
| Majina mbadala | adaptive withdrawal design, adaptive ABA withdrawal design, data-driven ABA design, adaptive single-case ABA | adaptive single-case AB design, data-driven AB design, adaptive baseline-intervention design, adaptive AB phase design |
| Zinazohusiana | 6 | 6 |
| Muhtasari≠ | The Adaptive ABA Design is a single-subject experimental framework that follows the classic three-phase ABA withdrawal structure — baseline (A1), intervention (B), and return-to-baseline (A2) — while embedding prospective decision rules that allow researchers or clinicians to extend, shorten, or otherwise modify each phase in response to observed data patterns rather than following a fixed schedule. This adaptive layer makes the design responsive to individual participant trajectories while preserving experimental control. | The adaptive AB design is a single-subject experimental design that retains the two-phase baseline-then-intervention structure of the classic AB design but replaces fixed session-count rules with pre-specified data-driven criteria — such as stability thresholds or trend benchmarks — that determine when to transition between phases. This adaptive logic allows the phase boundary to move in response to the individual participant's actual performance trajectory rather than a predetermined schedule. |
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