方法对比
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| 稳健联合分析× | 联合分析× | |
|---|---|---|
| 领域≠ | 统计学 | 实验设计 |
| 方法族≠ | Latent structure | Hypothesis test |
| 起源年份≠ | 1990s–2000s | 1978 |
| 提出者≠ | Adaptations developed by robust statistics researchers building on Green and Srinivasan's conjoint framework | Paul E. Green & V. Srinivasan |
| 类型≠ | Preference decomposition / stated preference | Decomposition-based utility estimation |
| 开创性文献≠ | Croux, C., Filzmoser, P., & Oliveira, M. R. (2007). Algorithms for Projection-Pursuit Robust Principal Component Analysis. Chemometrics and Intelligent Laboratory Systems, 87(2), 218–225. DOI ↗ | Green, P.E. & Srinivasan, V. (1978). Conjoint analysis in consumer research: Issues and outlook. Journal of Consumer Research, 5(2), 103–123. DOI ↗ |
| 别名≠ | robust CA, outlier-resistant conjoint analysis, robust stated preference analysis | CBC conjoint, choice-based conjoint, adaptive conjoint analysis, full-profile conjoint |
| 相关≠ | 4 | 6 |
| 摘要≠ | Robust conjoint analysis decomposes respondent preferences for multi-attribute products or services into part-worth utilities while guarding against the distorting influence of outlying ratings or unusual respondents. It adapts classical conjoint estimation with robust regression or robust aggregation techniques so that conclusions about attribute importance remain trustworthy even when a minority of evaluations deviate markedly from the majority. | 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. |
| ScholarGate数据集 ↗ |
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