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	Disegno Bayesiano Ex Post Facto	Ricerca Causal-Comparativa
Campo	Disegno della ricerca	Disegno della ricerca
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	1964 (Kerlinger ex post facto); Bayesian integration from 1990s–2000s onward	1964
Ideatore	Frederick N. Kerlinger (ex post facto framework); Bayesian extension draws on Laplace and modern Bayesian statistics	Fred N. Kerlinger
Tipo	Quantitative observational research design with Bayesian inference	Non-experimental quantitative research design
Fonte seminale	Kerlinger, F. N. (1973). Foundations of Behavioral Research (2nd ed.). Holt, Rinehart and Winston.	Kerlinger, F. N. (1964). Foundations of Behavioral Research. Holt, Rinehart and Winston.
Alias	Bayesian causal-comparative design, Bayesian after-the-fact design, Bayesian observational causal design, Bayesian retrospective causal study	ex post facto research, causal-comparative design, retrospective causal study, CCR
Correlati	5	3
Sintesi	Bayesian ex post facto design investigates possible causal relationships among variables that have already occurred, without researcher manipulation of those variables, and quantifies uncertainty about those relationships using Bayesian statistical inference. The researcher selects groups that differ on an outcome or a presumed cause after the fact, then uses prior knowledge and observed data together — via Bayes' theorem — to estimate credible effect sizes, group differences, or predictors.	Causal-comparative research is a non-experimental quantitative design in which the researcher compares two or more groups that already differ on an independent variable — one that was not manipulated — to investigate possible causes or consequences of that difference. Because group membership is pre-existing rather than randomly assigned, the design can suggest causal relationships but cannot establish them with the certainty of a true experiment. It is widely used in education, psychology, and social sciences when experimental manipulation is impractical or unethical.