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| Conjoint-Analyse× | Monte-Carlo-Simulation× | |
|---|---|---|
| Fachgebiet≠ | Versuchsplanung | Entscheidungsfindung |
| Familie≠ | Hypothesis test | MCDM |
| Entstehungsjahr≠ | 1978 | 1949 |
| Urheber≠ | Paul E. Green & V. Srinivasan | Metropolis, N., Ulam, S. |
| Typ≠ | Decomposition-based utility estimation | Robustness wrapper — Monte Carlo uncertainty propagation |
| Wegweisende Quelle≠ | Green, P.E. & Srinivasan, V. (1978). Conjoint analysis in consumer research: Issues and outlook. Journal of Consumer Research, 5(2), 103–123. DOI ↗ | Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association DOI ↗ |
| Aliasnamen≠ | CBC conjoint, choice-based conjoint, adaptive conjoint analysis, full-profile conjoint | — |
| Verwandt≠ | 6 | 0 |
| Zusammenfassung≠ | 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. | MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result. |
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