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| Szimulációval Támogatott Ok-Összehasonlító Kutatás× | MONTE-CARLO-SIMULATION× | |
|---|---|---|
| Tudományterület≠ | Kutatástervezés | Döntéshozatal |
| Módszercsalád≠ | Process / pipeline | MCDM |
| Keletkezés éve≠ | Late 20th–early 21st century (hybrid approach formalized ~1990s–2000s) | 1949 |
| Megalkotó≠ | Synthesized from causal-comparative tradition (Donald T. Campbell; Julian Stanley) and simulation methodology | Metropolis, N., Ulam, S. |
| Típus≠ | Hybrid observational-simulation design | Robustness wrapper — Monte Carlo uncertainty propagation |
| Alapmű≠ | Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2019). How to Design and Evaluate Research in Education (10th ed.). McGraw-Hill. ISBN: 978-1260087352 | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| Alternatív nevek≠ | simulation-augmented causal-comparative design, ex post facto simulation design, SA-CCR, causal-comparative with simulation validation | — |
| Kapcsolódó≠ | 4 | 0 |
| Összefoglaló≠ | Simulation-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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