Comparar métodos

	Investigación Causal-Comparativa Asistida por Simulación	Simulación de Monte Carlo
Campo	Diseño de investigación	Toma de decisiones
Familia	Process / pipeline	MCDM
Año de origen	Late 20th–early 21st century (hybrid approach formalized ~1990s–2000s)	1949
Autor original	Synthesized from causal-comparative tradition (Donald T. Campbell; Julian Stanley) and simulation methodology	Metropolis, N., Ulam, S.
Tipo	Hybrid observational-simulation design	Robustness wrapper — Monte Carlo uncertainty propagation
Fuente seminal	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
Alias	simulation-augmented causal-comparative design, ex post facto simulation design, SA-CCR, causal-comparative with simulation validation	—
Relacionados	4	0
Resumen	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.