Confronta i metodi

	Disegno Ex Post Facto Cross-Sectional	Ricerca causale-comparativa trasversale
Campo	Disegno della ricerca	Disegno della ricerca
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	1964–1973	1960s onward
Ideatore	Fred N. Kerlinger (formalized ex post facto methodology)	Donald T. Campbell and Julian C. Stanley (quasi-experimental foundations); refined in education research by various methodologists
Tipo	Non-experimental quantitative research design	Non-experimental quantitative design
Fonte seminale	Kerlinger, F. N. (1973). Foundations of Behavioral Research (2nd ed.). Holt, Rinehart and Winston. ISBN: 978-0030862731	Frankfort-Nachmias, C., & Nachmias, D. (2015). Research Methods in the Social Sciences (8th ed.). Worth Publishers. ISBN: 978-1429295154
Alias	cross-sectional causal-comparative design, retrospective cross-sectional design, after-the-fact cross-sectional study, cross-sectional EPF design	cross-sectional ex post facto design, single-wave causal-comparative study, cross-sectional group-comparison design, cross-sectional criterion-group study
Correlati	4	3
Sintesi	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.	Cross-sectional causal-comparative research compares two or more pre-existing groups — defined by a characteristic or experience that has already occurred — on one or more outcome variables, with all data collected at a single point in time. Because the presumed cause (group membership) precedes measurement but cannot be manipulated, the design sits between purely descriptive and truly experimental work. It is widely used in education, psychology, and social sciences when randomization is impossible or unethical.