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| Каузално-сравнително изследване× | Експостфакто дизайн× | |
|---|---|---|
| Област | Дизайн на изследването | Дизайн на изследването |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 1964 | 1960s (systematic codification); concept used in social science from early 20th century |
| Създател≠ | Fred N. Kerlinger | Formalized by Fred N. Kerlinger; foundational treatment by Donald T. Campbell and Julian C. Stanley |
| Тип | Non-experimental quantitative research design | Non-experimental quantitative research design |
| Основополагащ източник | Kerlinger, F. N. (1964). Foundations of Behavioral Research. Holt, Rinehart and Winston. link ↗ | Kerlinger, F. N. (1964). Foundations of Behavioral Research. Holt, Rinehart and Winston. link ↗ |
| Други названия | ex post facto research, causal-comparative design, retrospective causal study, CCR | after-the-fact research, retrospective non-experimental design, causal-comparative design, EPF design |
| Свързани | 3 | 3 |
| Резюме≠ | Causal-comparative research is a non-experimental quantitative design in which the researcher compares two or more groups that already differ on an independent variable — one that was not manipulated — to investigate possible causes or consequences of that difference. Because group membership is pre-existing rather than randomly assigned, the design can suggest causal relationships but cannot establish them with the certainty of a true experiment. It is widely used in education, psychology, and social sciences when experimental manipulation is impractical 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. |
| ScholarGateНабор от данни ↗ |
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