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Przeglądaj wybrane metody obok siebie; wiersze, które się różnią, są wyróżnione.
| Analiza conjoint× | Mixed Logit× | |
|---|---|---|
| Dziedzina≠ | Planowanie eksperymentów | Ekonometria |
| Rodzina≠ | Hypothesis test | Regression model |
| Rok powstania≠ | 1978 | 2000 |
| Twórca≠ | Paul E. Green & V. Srinivasan | Daniel McFadden & Kenneth Train |
| Typ≠ | Decomposition-based utility estimation | Random-parameters discrete choice model |
| Źródło pierwotne≠ | 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. ISBN: 978-0-521-74738-7 |
| Inne nazwy≠ | CBC conjoint, choice-based conjoint, adaptive conjoint analysis, full-profile conjoint | Random Parameters Logit, Mixed Multinomial Logit, Error Components Logit, Karma Logit Modeli |
| Pokrewne≠ | 6 | 3 |
| Podsumowanie≠ | 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. | The Mixed Logit model, introduced formally by McFadden and Train (2000) and elaborated in Train (2009), is a flexible discrete choice framework that allows preference parameters to vary randomly across decision-makers. By integrating standard logit probabilities over a mixing distribution of coefficients, it overcomes the restrictive independence of irrelevant alternatives (IIA) property and accommodates unobserved taste heterogeneity, panel data correlation, and complex substitution patterns across alternatives. |
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