Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Diseño Longitudinal Ex Post Facto× | Diseño Ex Post Facto× | |
|---|---|---|
| Campo | Diseño de investigación | Diseño de investigación |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | 1964–1986 (Kerlinger 1964 first edition; Campbell & Stanley 1966) | 1960s (systematic codification); concept used in social science from early 20th century |
| Autor original≠ | Fred N. Kerlinger (systematized); Donald T. Campbell & Julian C. Stanley (quasi-experimental framework) | 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. (1986). Foundations of Behavioral Research (3rd ed.). Holt, Rinehart and Winston. ISBN: 978-0030417498 | Kerlinger, F. N. (1964). Foundations of Behavioral Research. Holt, Rinehart and Winston. link ↗ |
| Alias | longitudinal causal-comparative design, longitudinal after-the-fact design, longitudinal retrospective design, LEPF design | after-the-fact research, retrospective non-experimental design, causal-comparative design, EPF design |
| Relacionados≠ | 5 | 3 |
| Resumen≠ | A longitudinal ex post facto design combines the time-depth of longitudinal research with the retrospective logic of ex post facto inquiry. Participants are grouped by a naturally occurring characteristic or past event — not randomly assigned — and then observed or measured at multiple points over time. The goal is to trace how pre-existing differences between groups unfold or predict outcomes across an extended period, without the researcher ever manipulating the independent variable. | 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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