方法对比
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| 联合分析× | 离散选择模拟× | |
|---|---|---|
| 领域≠ | 实验设计 | 仿真 |
| 方法族≠ | Hypothesis test | Process / pipeline |
| 起源年份≠ | 1978 | 1974 (McFadden's Nobel-cited logit); simulation extensions throughout 1990s–2000s |
| 提出者≠ | Paul E. Green & V. Srinivasan | Daniel McFadden (random utility theory); Kenneth Train (simulation methods) |
| 类型≠ | Decomposition-based utility estimation | Discrete choice modelling with Monte Carlo simulation |
| 开创性文献≠ | Green, P.E. & Srinivasan, V. (1978). Conjoint analysis in consumer research: Issues and outlook. Journal of Consumer Research, 5(2), 103–123. DOI ↗ | Train, K.E. (2009). Discrete Choice Methods with Simulation (2nd ed.). Cambridge University Press. DOI ↗ |
| 别名≠ | CBC conjoint, choice-based conjoint, adaptive conjoint analysis, full-profile conjoint | stated preference simulation, SP simulation, revealed preference modelling, Ayrık Seçim Simülasyonu (Stated Preference / SP Simulation) |
| 相关≠ | 6 | 5 |
| 摘要≠ | Conjoint analysis is a preference-measurement technique that decomposes overall product evaluations into the separate utility values — called part-worths — that respondents assign to each attribute level. Formalised by Green and Srinivasan in their seminal 1978 Journal of Consumer Research paper, the method has become the dominant tool in marketing research and product design for quantifying what buyers truly trade off when they choose between options. | Discrete choice simulation is a behavioural modelling method — grounded in random utility theory formalised by Daniel McFadden in the 1970s and extended to simulation-based estimation by Kenneth Train — that estimates how individuals choose among mutually exclusive alternatives and then uses those estimated preference parameters to forecast how choice shares would shift under hypothetical policy or market scenarios. It is the dominant quantitative tool in transport demand analysis, health economics, environmental valuation, and marketing research. |
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