Sammenlign metoder

	Afbrudt tidsserieanalyse (ITS)	Multipel baseline-design
Fagområde	Kausal inferens	Forsøgsdesign
Familie	Regression model	Process / pipeline
Oprindelsesår	2002	1968
Ophavsperson	Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)	Donald M. Baer, Montrose M. Wolf, Todd R. Risley
Type	Quasi-experimental segmented regression	Single-subject experimental design
Oprindelig kilde	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.
Aliasser	ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi	MBD, multiple-baseline single-case design, staggered baseline design, multiple-probe design
Relaterede	5	4
Resumé	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.