Methoden vergelijken

	Onderbroken tijdreeksanalyse in onderwijsonderzoek	Difference-in-Differences (DiD)
Vakgebied	Causale inferentie	Econometrie
Familie	Regression model	Regression model
Jaar van ontstaan	1979-2002	1994
Grondlegger	Shadish, Cook & Campbell (quasi-experimental design); Wagner et al. (segmented regression formalization)	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Type	Quasi-experimental causal inference	Causal inference / panel regression
Oorspronkelijke bron	Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin. ISBN: 978-0395615560	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Aliassen	ITS in education, educational ITS, segmented regression in education, policy interrupted time series	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Verwant	4	5
Samenvatting	Interrupted time series (ITS) analysis is a quasi-experimental design that estimates the causal effect of an education policy or intervention by examining whether an outcome trend changes abruptly at the point of implementation. Applied to education, it is used to evaluate reforms, curriculum changes, testing policies, and school interventions using routinely collected longitudinal data without a randomised control group.	Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes.