Σύγκριση μεθόδων
Εξετάστε τις επιλεγμένες μεθόδους δίπλα-δίπλα· οι γραμμές που διαφέρουν επισημαίνονται.
| Σχεδιασμός Φυσικού Πειράματος Παραγοντικού Τύπου× | Πειραματικός Σχεδιασμός Παραγόντων× | |
|---|---|---|
| Πεδίο | Πειραματικός Σχεδιασμός | Πειραματικός Σχεδιασμός |
| Οικογένεια | Process / pipeline | Process / pipeline |
| Έτος προέλευσης≠ | 1920s (factorial origins, Fisher); natural experiment formalization 1990s–2000s; factorial natural experiment usage widespread 2000s–present | 1926–1935 |
| Δημιουργός≠ | Extension of natural experiment tradition (Dunning, Angrist & Pischke) combined with factorial design logic (Fisher) | Ronald A. Fisher |
| Τύπος≠ | Quasi-experimental research design | Quantitative experimental design |
| Θεμελιώδης πηγή≠ | Dunning, T. (2012). Natural Experiments in the Social Sciences: A Design-Based Approach. Cambridge University Press. ISBN: 978-1107698000 | Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd. link ↗ |
| Εναλλακτικές ονομασίες≠ | factorial quasi-experiment, multi-factor natural experiment, factorial exogenous variation design | factorial design, factorial ANOVA design, multi-factor experiment, crossed-factor design |
| Συναφείς≠ | 4 | 6 |
| Σύνοψη≠ | A factorial natural experiment exploits naturally occurring exogenous variation across two or more factors simultaneously, allowing researchers to estimate main effects and interactions without random assignment. Natural events, policy changes, or institutional rules create treatment conditions that approximate a factorial structure, enabling causal inference in observational settings where controlled experimentation is infeasible or unethical. | A factorial experiment is an experimental design in which two or more independent variables (factors) are manipulated simultaneously, and every combination of their levels is tested. Introduced by Ronald Fisher in the 1920s–1930s, it is the standard approach whenever a researcher needs to detect not only the main effect of each factor but also whether the effect of one factor depends on the level of another — the interaction effect. |
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