Sammenlign metoder

	Kryss-over naturlig eksperiment	Tidsrekkeanalyse med avbrudd (Interrupted Time Series, ITS)
Fagfelt	Forsøksdesign	Kausal inferens
Familie	Process / pipeline	Regression model
Opprinnelsesår	Crossover designs: mid-20th century; applied to natural experiments: 1990s–2000s	2002
Opphavsperson	Drawn from crossover trial methods (Jones & Kenward) and natural experiment tradition (Mill, 1843; Dunning, 2012)	Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)
Type	Quasi-experimental design	Quasi-experimental segmented regression
Opprinnelig kilde	Dunning, T. (2012). Natural Experiments in the Social Sciences: A Design-Based Approach. Cambridge University Press. ISBN: 978-1107698000	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.
Alias	within-unit natural experiment, reversal natural experiment, crossover quasi-experiment	ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi
Relaterte	5	5
Sammendrag	A crossover natural experiment exploits an externally imposed condition — a policy change, law, or environmental event — that exposes the same units (individuals, regions, firms) to both treatment and control states at different times. By observing each unit in multiple conditions, researchers use within-unit variation to estimate causal effects without researcher-controlled randomization, combining the internal validity advantage of crossover designs with the real-world relevance of natural experiments.	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.