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Simuleringsassistert kausalt-komparativ forskning×Monte Carlo-simulering×
FagfeltForskningsdesignBeslutningstaking
FamilieProcess / pipelineMCDM
OpprinnelsesårLate 20th–early 21st century (hybrid approach formalized ~1990s–2000s)1949
OpphavspersonSynthesized from causal-comparative tradition (Donald T. Campbell; Julian Stanley) and simulation methodologyMetropolis, N., Ulam, S.
TypeHybrid observational-simulation designRobustness wrapper — Monte Carlo uncertainty propagation
Opprinnelig kildeFraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2019). How to Design and Evaluate Research in Education (10th ed.). McGraw-Hill. ISBN: 978-1260087352Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗
Aliassimulation-augmented causal-comparative design, ex post facto simulation design, SA-CCR, causal-comparative with simulation validation
Relaterte40
SammendragSimulation-assisted causal-comparative research is a hybrid observational design that combines the ex post facto logic of causal-comparative studies — comparing groups that differ on a naturally occurring variable — with computational simulation to strengthen causal inference, test counterfactuals, and assess the robustness of observed group differences. By augmenting real-world comparisons with simulated scenarios, researchers can explore causal mechanisms that cannot be manipulated experimentally.MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
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ScholarGateSammenlign metoder: Simulation-assisted causal-comparative research · MONTE-CARLO-SIMULATION. Hentet 2026-06-18 fra https://scholargate.app/no/compare