Usporedite metode

	Adaptivni ABA nacrt – Adaptivni ABA eksperimentalni nacrt s jednim ispitanikom	Adaptivni AB dizajn
Područje	Eksperimentalni dizajn	Eksperimentalni dizajn
Obitelj	Process / pipeline	Process / pipeline
Godina nastanka	1968 (ABA foundation); adaptive extensions formalized ~2010–2020	1968 (AB foundation); 2000s (adaptive extensions)
Tvorac	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)
Vrsta	Single-subject experimental design with adaptive phase rules	Single-subject experimental design with adaptive phase-change rules
Temeljni izvor	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.	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.
Drugi nazivi	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
Srodne	6	6
Sažetak	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.