Σύγκριση μεθόδων

	Σχεδιασμός Πολλαπλής Βασικής Γραμμής με Πραγματιστικές Προσαρμογές	Ανάλυση Διακοπτόμενης Χρονοσειράς (ITS)
Πεδίο	Πειραματικός Σχεδιασμός	Αιτιακή Συμπερασματολογία
Οικογένεια	Process / pipeline	Regression model
Έτος προέλευσης	1968 (original MBD); pragmatic adaptation formalized in 2000s–2010s	2002
Δημιουργός	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)
Τύπος	Single-case experimental design variant	Quasi-experimental segmented regression
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	PMBD, pragmatic MBD, real-world multiple baseline design, flexible multiple baseline design	ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi
Συναφείς	3	5
Σύνοψη	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.