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Interrupted Time Series i utdanningsforskning×Syntetisk kontrollmetode (SCM)×
FagfeltKausal inferensKausal inferens
FamilieRegression modelRegression model
Opprinnelsesår1979-20022003–2010
OpphavspersonShadish, Cook & Campbell (quasi-experimental design); Wagner et al. (segmented regression formalization)Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010)
TypeQuasi-experimental causal inferenceQuasi-experimental causal inference
Opprinnelig kildeShadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin. ISBN: 978-0395615560Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493-505. DOI ↗
AliasITS in education, educational ITS, segmented regression in education, policy interrupted time seriesSCM, synthetic control, synth estimator, Abadie-Diamond-Hainmueller method
Relaterte44
SammendragInterrupted 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.The Synthetic Control Method estimates the causal effect of a treatment or policy on a single treated unit by constructing a weighted combination of untreated units — the synthetic control — that closely resembles the treated unit before the intervention. The gap between the treated unit and its synthetic counterpart after the intervention is the estimated treatment effect.
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ScholarGateSammenlign metoder: Interrupted Time Series in Education Research · Synthetic Control Method. Hentet 2026-06-19 fra https://scholargate.app/no/compare