Porovnat metody

	Simulací podpořený ex post facto design	Ex Post Facto Design
Obor	Design výzkumu	Design výzkumu
Rodina	Process / pipeline	Process / pipeline
Rok vzniku	Ex post facto: 1964; simulation-assisted hybrid: 1990s–2000s	1960s (systematic codification); concept used in social science from early 20th century
Tvůrce	Kerlinger, F. N. (ex post facto basis); simulation integration drawn from computational social science (Axelrod, Epstein, 1990s)	Formalized by Fred N. Kerlinger; foundational treatment by Donald T. Campbell and Julian C. Stanley
Typ	Non-experimental observational design with computational augmentation	Non-experimental quantitative research design
Původní zdroj	Kerlinger, F. N. (1964). Foundations of Behavioral Research. Holt, Rinehart and Winston.	Kerlinger, F. N. (1964). Foundations of Behavioral Research. Holt, Rinehart and Winston.
Další názvy	simulation-enhanced causal-comparative design, ex post facto with simulation, retrospective simulation design, SAEPF design	after-the-fact research, retrospective non-experimental design, causal-comparative design, EPF design
Příbuzné	4	3
Shrnutí	Simulation-assisted ex post facto design is a non-experimental observational approach in which the researcher examines already-occurred events or conditions using existing records and then supplements the empirical analysis with computational simulation to approximate counterfactual scenarios that cannot be observed in reality. The design retains the retrospective, naturalistic character of classic ex post facto research while leveraging agent-based, Monte Carlo, or system-dynamics simulation to address the inherent confound limitations of purely archival work.	Ex post facto design is a non-experimental quantitative research approach in which the researcher investigates a phenomenon after it has already occurred, examining pre-existing differences between groups to explore potential causal or associative relationships. Because the independent variable cannot be manipulated — it happened in the past — the design relies on careful group selection, retrospective data collection, and statistical controls to approximate causal inference without experimental intervention.