Vertaile menetelmiä

	Bayesiläinen ex post facto -asetelma	Ex Post Facto -suunnittelu
Tieteenala	Tutkimusasetelma	Tutkimusasetelma
Menetelmäperhe	Process / pipeline	Process / pipeline
Syntyvuosi	1964 (Kerlinger ex post facto); Bayesian integration from 1990s–2000s onward	1960s (systematic codification); concept used in social science from early 20th century
Kehittäjä	Frederick N. Kerlinger (ex post facto framework); Bayesian extension draws on Laplace and modern Bayesian statistics	Formalized by Fred N. Kerlinger; foundational treatment by Donald T. Campbell and Julian C. Stanley
Tyyppi	Quantitative observational research design with Bayesian inference	Non-experimental quantitative research design
Alkuperäislähde	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.
Rinnakkaisnimet	Bayesian causal-comparative design, Bayesian after-the-fact design, Bayesian observational causal design, Bayesian retrospective causal study	after-the-fact research, retrospective non-experimental design, causal-comparative design, EPF design
Liittyvät	5	3
Tiivistelmä	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.	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.