Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Cēloņu salīdzinošā izpēte× | Dizains ex post facto× | |
|---|---|---|
| Nozare | Pētījuma dizains | Pētījuma dizains |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1964 | 1960s (systematic codification); concept used in social science from early 20th century |
| Autors≠ | Fred N. Kerlinger | Formalized by Fred N. Kerlinger; foundational treatment by Donald T. Campbell and Julian C. Stanley |
| Tips | Non-experimental quantitative research design | Non-experimental quantitative research design |
| Pirmavots | 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 ↗ |
| Citi nosaukumi | ex post facto research, causal-comparative design, retrospective causal study, CCR | after-the-fact research, retrospective non-experimental design, causal-comparative design, EPF design |
| Saistītās | 3 | 3 |
| Kopsavilkums≠ | 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. |
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