Confronta i metodi

	Ricerca causale-comparativa trasversale	Design Ex Post Facto
Campo	Disegno della ricerca	Disegno della ricerca
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	1960s onward	1960s (systematic codification); concept used in social science from early 20th century
Ideatore	Donald T. Campbell and Julian C. Stanley (quasi-experimental foundations); refined in education research by various methodologists	Formalized by Fred N. Kerlinger; foundational treatment by Donald T. Campbell and Julian C. Stanley
Tipo	Non-experimental quantitative design	Non-experimental quantitative research design
Fonte seminale	Frankfort-Nachmias, C., & Nachmias, D. (2015). Research Methods in the Social Sciences (8th ed.). Worth Publishers. ISBN: 978-1429295154	Kerlinger, F. N. (1964). Foundations of Behavioral Research. Holt, Rinehart and Winston.
Alias	cross-sectional ex post facto design, single-wave causal-comparative study, cross-sectional group-comparison design, cross-sectional criterion-group study	after-the-fact research, retrospective non-experimental design, causal-comparative design, EPF design
Correlati	3	3
Sintesi	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.	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.