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| パネルベースの後事的デザイン× | 事後調査デザイン× | |
|---|---|---|
| 分野 | 研究デザイン | 研究デザイン |
| 系統 | Process / pipeline | Process / pipeline |
| 提唱年≠ | 1950s–1970s (synthesized from ex post facto tradition and panel survey research) | 1960s (systematic codification); concept used in social science from early 20th century |
| 提唱者≠ | Developed from Kerlinger's ex post facto framework combined with panel survey methodology (Lazarsfeld, Kerlinger) | Formalized by Fred N. Kerlinger; foundational treatment by Donald T. Campbell and Julian C. Stanley |
| 種類≠ | Non-experimental longitudinal observational design | Non-experimental quantitative research design |
| 原典≠ | Kerlinger, F. N. (1986). Foundations of Behavioral Research (3rd ed.). Holt, Rinehart and Winston. ISBN: 978-0030417511 | Kerlinger, F. N. (1964). Foundations of Behavioral Research. Holt, Rinehart and Winston. link ↗ |
| 別名 | panel ex post facto study, longitudinal causal-comparative design, retrospective panel design, panel causal-comparative study | after-the-fact research, retrospective non-experimental design, causal-comparative design, EPF design |
| 関連≠ | 2 | 3 |
| 概要≠ | A panel-based ex post facto design tracks the same group of participants across multiple time points to examine how pre-existing differences in an independent variable — one the researcher did not manipulate — are associated with changes in an outcome over time. It merges the temporal depth of panel methodology with the causal-comparative logic of ex post facto research, enabling stronger causal inference than a single cross-sectional snapshot while remaining fully non-experimental. | 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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