Vertaile menetelmiä

	Poikkileikkaava ex post facto -asetelma	Ex Post Facto -suunnittelu
Tieteenala	Tutkimusasetelma	Tutkimusasetelma
Menetelmäperhe	Process / pipeline	Process / pipeline
Syntyvuosi	1964–1973	1960s (systematic codification); concept used in social science from early 20th century
Kehittäjä	Fred N. Kerlinger (formalized ex post facto methodology)	Formalized by Fred N. Kerlinger; foundational treatment by Donald T. Campbell and Julian C. Stanley
Tyyppi	Non-experimental quantitative research design	Non-experimental quantitative research design
Alkuperäislähde	Kerlinger, F. N. (1973). Foundations of Behavioral Research (2nd ed.). Holt, Rinehart and Winston. ISBN: 978-0030862731	Kerlinger, F. N. (1964). Foundations of Behavioral Research. Holt, Rinehart and Winston.
Rinnakkaisnimet	cross-sectional causal-comparative design, retrospective cross-sectional design, after-the-fact cross-sectional study, cross-sectional EPF design	after-the-fact research, retrospective non-experimental design, causal-comparative design, EPF design
Liittyvät	4	3
Tiivistelmä	A cross-sectional ex post facto design investigates presumed causal relationships by comparing groups that already differ on a key characteristic — all measured at a single point in time. Because the independent variable (e.g., smoking history, prior educational attainment) has already occurred and cannot be manipulated, the researcher works backward from observed outcomes to infer probable antecedents. It is widely used in education, public health, and the social sciences when experimental control is ethically or practically impossible.	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.