Linganisha mbinu

	Ubunifu wa ABAB Unaojirekebisha	Uchanganuzi wa Mfululizo wa Wakati Uliokatizwa (ITS)	Muundo wa Msingi Mbalimbali
Nyanja	Muundo wa Majaribio	Uhitimisho wa Kisababishi	Muundo wa Majaribio
Familia	Process / pipeline	Regression model	Process / pipeline
Mwaka wa asili	1984 (foundational ABAB); adaptive extensions ~2000s–2010s	2002	1968
Mwanzilishi	Extended from Barlow & Hersen's ABAB reversal tradition; adaptive rules formalized in behavioral and clinical single-subject research (late 20th–early 21st century)	Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)	Donald M. Baer, Montrose M. Wolf, Todd R. Risley
Aina	Single-subject experimental design	Quasi-experimental segmented regression	Single-subject experimental design
Chanzo asilia	Barlow, D. H., & Hersen, M. (1984). Single Case Experimental Designs: Strategies for Studying Behavior Change (2nd ed.). Pergamon Press. ISBN: 978-0205143641	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.	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.
Majina mbadala	adaptive reversal design, adaptive single-subject ABAB, ABAB with adaptive phase-change rules, dynamic ABAB design	ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi	MBD, multiple-baseline single-case design, staggered baseline design, multiple-probe design
Zinazohusiana	2	5	4
Muhtasari	The Adaptive ABAB Design is a single-subject experimental methodology that extends the classic ABAB reversal design by incorporating data-driven, prospective decision rules to determine when to transition between baseline (A) and intervention (B) phases. Rather than fixing phase lengths in advance, the researcher uses pre-specified criteria — such as stability thresholds, slope targets, or effect-size benchmarks — to guide each phase change, improving both experimental control and clinical responsiveness.	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.	The multiple baseline design is a single-subject experimental design that demonstrates functional control by introducing an intervention at staggered time points across two or more baselines — typically across different behaviors, individuals, or settings. Because no withdrawal of treatment is required, it is especially suitable when the target behavior is irreversible or when removing an effective intervention would be unethical.