เปรียบเทียบวิธี
ดูวิธีที่เลือกเทียบกันแบบเคียงข้าง แถวที่ต่างกันจะถูกเน้นไว้
| การทดลองภาคสนามแบบอำพรางสองฝ่าย× | การทดลองภาคสนาม× | |
|---|---|---|
| สาขาวิชา | การออกแบบการทดลอง | การออกแบบการทดลอง |
| ตระกูล | Process / pipeline | Process / pipeline |
| ปีกำเนิด≠ | 1960s onward (field experiment tradition); double-blind controls applied from 1970s in social and policy field trials | 1920s–1930s (agriculture); 1990s–2000s (social sciences) |
| ผู้ริเริ่ม≠ | Fisher, R. A. (randomized field trials); double-blind practice traced to 19th-century clinical research, formalized for field settings by Campbell & Stanley (1963) | Formalized by R. A. Fisher (1935); systematized in social sciences by Harrison & List (2004) |
| ประเภท | Experimental design | Experimental design |
| แหล่งต้นตำรับ≠ | Gerber, A. S., & Green, D. P. (2012). Field Experiments: Design, Analysis, and Interpretation. W. W. Norton. ISBN: 978-0393979954 | Harrison, G. W., & List, J. A. (2004). Field experiments. Journal of Economic Literature, 42(4), 1009–1055. DOI ↗ |
| ชื่อเรียกอื่น | double-masked field trial, double-blind naturalistic experiment, blinded field study, DB field experiment | field trial, natural field experiment, randomized field experiment, field RCT |
| ที่เกี่ยวข้อง | 5 | 5 |
| สรุป≠ | A double-blind field experiment combines the high external validity of a real-world field setting with double-blind masking, in which neither the participants nor the personnel delivering the treatment know who has been assigned to the treatment or control condition. This design controls simultaneously for participant expectation effects and for experimenter/enumerator demand effects, making it one of the most rigorous tools available for causal inference outside the laboratory. | 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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