Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Байесовский экс пост факто дизайн× | Причинно-сравнительное исследование× | |
|---|---|---|
| Область | Дизайн исследования | Дизайн исследования |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1964 (Kerlinger ex post facto); Bayesian integration from 1990s–2000s onward | 1964 |
| Автор метода≠ | Frederick N. Kerlinger (ex post facto framework); Bayesian extension draws on Laplace and modern Bayesian statistics | Fred N. Kerlinger |
| Тип≠ | Quantitative observational research design with Bayesian inference | Non-experimental quantitative research design |
| Основополагающий источник≠ | Kerlinger, F. N. (1973). Foundations of Behavioral Research (2nd ed.). Holt, Rinehart and Winston. link ↗ | Kerlinger, F. N. (1964). Foundations of Behavioral Research. Holt, Rinehart and Winston. link ↗ |
| Другие названия | Bayesian causal-comparative design, Bayesian after-the-fact design, Bayesian observational causal design, Bayesian retrospective causal study | ex post facto research, causal-comparative design, retrospective causal study, CCR |
| Связанные≠ | 5 | 3 |
| Сводка≠ | Bayesian ex post facto design investigates possible causal relationships among variables that have already occurred, without researcher manipulation of those variables, and quantifies uncertainty about those relationships using Bayesian statistical inference. The researcher selects groups that differ on an outcome or a presumed cause after the fact, then uses prior knowledge and observed data together — via Bayes' theorem — to estimate credible effect sizes, group differences, or predictors. | 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. |
| ScholarGateНабор данных ↗ |
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