Порівняння методів

	Прагматична конструкція з множинними базовими лініями	Аналіз перерваних часових рядів (ITS)
Галузь	Планування експерименту	Причинно-наслідковий висновок
Родина	Process / pipeline	Regression model
Рік появи	1968 (original MBD); pragmatic adaptation formalized in 2000s–2010s	2002
Автор методу	Adapted from Baer, Wolf & Risley (1968); pragmatic variant developed within single-case methodology community	Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)
Тип	Single-case experimental design variant	Quasi-experimental segmented regression
Основоположне джерело	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.	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.
Інші назви	PMBD, pragmatic MBD, real-world multiple baseline design, flexible multiple baseline design	ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi
Пов'язані	3	5
Підсумок	The Pragmatic Multiple Baseline Design is a single-case experimental design that staggers intervention introduction across multiple participants, settings, or behaviors in real-world conditions where strict experimental control is impractical. By relaxing some idealized constraints — such as perfectly stable baselines or rigid staggering timelines — it preserves the core logic of the multiple baseline while accommodating clinical, educational, or community realities. It is especially valued when withholding treatment for ethical reasons is untenable and when practitioners need evidence from naturalistic settings.	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.