Σύγκριση μεθόδων
Εξετάστε τις επιλεγμένες μεθόδους δίπλα-δίπλα· οι γραμμές που διαφέρουν επισημαίνονται.
| Πειραματική Μελέτη Πεδίου με Μονή Τύφλωση× | Πειραματική Μελέτη Πεδίου× | |
|---|---|---|
| Πεδίο | Πειραματικός Σχεδιασμός | Πειραματικός Σχεδιασμός |
| Οικογένεια | Process / pipeline | Process / pipeline |
| Έτος προέλευσης≠ | Mid-20th century (blinding conventions formalised 1940s–1960s) | 1920s–1930s (agriculture); 1990s–2000s (social sciences) |
| Δημιουργός≠ | Established practice in experimental social science and clinical research; codified by Campbell & Stanley (1963) and Shadish, Cook & Campbell (2002) | Formalized by R. A. Fisher (1935); systematized in social sciences by Harrison & List (2004) |
| Τύπος≠ | Controlled field experiment with partial blinding | Experimental design |
| Θεμελιώδης πηγή≠ | Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin. ISBN: 978-0395615560 | Harrison, G. W., & List, J. A. (2004). Field experiments. Journal of Economic Literature, 42(4), 1009–1055. DOI ↗ |
| Εναλλακτικές ονομασίες≠ | single-masked field experiment, field experiment with single blinding, single-blind natural-setting trial | field trial, natural field experiment, randomized field experiment, field RCT |
| Συναφείς≠ | 6 | 5 |
| Σύνοψη≠ | A single-blind field experiment combines real-world experimental conditions with partial blinding: either participants or outcome assessors — but not both — are kept unaware of treatment assignment. This design reduces demand characteristics or observer bias while preserving ecological validity, making it a practical middle ground when full double-blinding is logistically infeasible in naturalistic settings. | A field experiment applies the logic of a randomized controlled trial in a naturally occurring, real-world environment rather than an artificial laboratory. Participants are randomly assigned to treatment and control conditions while going about everyday activities, allowing researchers to estimate causal effects with high internal validity while preserving a level of ecological realism that laboratory settings cannot offer. The design is especially prominent in economics, public health, political science, and development research. |
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