Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Konjunktā analīze× | Multinomiālā loģistiskā regresija× | |
|---|---|---|
| Nozare≠ | Eksperimentu plānošana | Ekonometrija |
| Saime≠ | Hypothesis test | Regression model |
| Izcelsmes gads≠ | 1978 | 1974 |
| Autors≠ | Paul E. Green & V. Srinivasan | McFadden |
| Tips≠ | Decomposition-based utility estimation | Multinomial logistic regression |
| Pirmavots≠ | Green, P.E. & Srinivasan, V. (1978). Conjoint analysis in consumer research: Issues and outlook. Journal of Consumer Research, 5(2), 103–123. DOI ↗ | McFadden, D. (1974). Conditional Logit Analysis of Qualitative Choice Behavior. In P. Zarembka (Ed.), Frontiers in Econometrics (pp. 105-142). Academic Press. ISBN: 978-0127761503 |
| Citi nosaukumi≠ | CBC conjoint, choice-based conjoint, adaptive conjoint analysis, full-profile conjoint | multinomial logistic regression, polytomous logistic regression, softmax regression, Çok Kategorili Lojistik Regresyon |
| Saistītās≠ | 6 | 5 |
| Kopsavilkums≠ | 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. | Multinomial logistic regression is a maximum-likelihood method for a nominal (unordered) dependent variable with more than two categories. Building on McFadden's 1974 treatment of qualitative choice, it gives each category its own set of coefficients relative to a reference category. |
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