Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Diseño transversal ex post facto× | Diseño Ex Post Facto× | |
|---|---|---|
| Campo | Diseño de investigación | Diseño de investigación |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | 1964–1973 | 1960s (systematic codification); concept used in social science from early 20th century |
| Autor original≠ | Fred N. Kerlinger (formalized ex post facto methodology) | Formalized by Fred N. Kerlinger; foundational treatment by Donald T. Campbell and Julian C. Stanley |
| Tipo | Non-experimental quantitative research design | Non-experimental quantitative research design |
| Fuente seminal≠ | 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. link ↗ |
| Alias | 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 |
| Relacionados≠ | 4 | 3 |
| Resumen≠ | 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. |
| ScholarGateConjunto de datos ↗ |
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