方法对比
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| 单盲分数阶实验× | 双盲分数析因实验× | |
|---|---|---|
| 领域 | 实验设计 | 实验设计 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1940s–1950s (fractional factorial foundations); blinding conventions formalised through 1960s–1980s | 1960s onward (combination widely used in pharmaceutical and food science research) |
| 提出者≠ | Fractional factorial theory: R. L. Plackett & J. P. Burman (1946); single-blinding practice codified in clinical trial methodology (20th century) | Fractional factorial: Box & Hunter (1961); double-blind convention: clinical trial methodology (mid-20th century) |
| 类型≠ | Controlled experimental design | Controlled experimental design with blinding and factor-space reduction |
| 开创性文献 | Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley-Interscience. ISBN: 978-0471718130 | Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley-Interscience. ISBN: 978-0471718130 |
| 别名 | single-masked fractional factorial, single-blind FFD, partially blinded fractional factorial, single-blind 2^(k-p) design | double-blind FFE, blinded fractional factorial design, double-blind FFD, masked fractional factorial experiment |
| 相关≠ | 5 | 3 |
| 摘要≠ | A single-blind fractional factorial experiment studies multiple factors simultaneously by testing only a strategically chosen subset — a fraction — of all possible factor-level combinations, while keeping participants unaware of which treatment condition they receive. This design yields substantial information about main effects and selected interactions at a fraction of the cost of a full factorial experiment, with single-blinding reducing participant-side response bias. | A double-blind fractional factorial experiment combines two powerful methodological protections: fractional factorial design, which tests a carefully chosen subset of all possible factor combinations to achieve efficiency, and double-blind administration, which prevents both participants and assessors from knowing which treatment combination has been applied. The result is an experiment that is both resource-efficient and protected against expectation and assessment bias. |
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