Compara mètodes

	Anàlisi conjunta	Model Logit Mixt	Simulació Monte Carlo
Camp	Disseny experimental	Econometria	Presa de decisions
Família	Hypothesis test	Regression model	MCDM
Any d'origen	1978	2000	1949
Autor original	Paul E. Green & V. Srinivasan	Daniel McFadden & Kenneth Train	Metropolis, N., Ulam, S.
Tipus	Decomposition-based utility estimation	Random-parameters discrete choice model	Robustness wrapper — Monte Carlo uncertainty propagation
Font seminal	Green, P.E. & Srinivasan, V. (1978). Conjoint analysis in consumer research: Issues and outlook. Journal of Consumer Research, 5(2), 103–123.	Train, K. E. (2009). Discrete Choice Methods with Simulation (2nd ed.). Cambridge University Press. ISBN: 978-0-521-74738-7	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
Àlies	CBC conjoint, choice-based conjoint, adaptive conjoint analysis, full-profile conjoint	Random Parameters Logit, Mixed Multinomial Logit, Error Components Logit, Karma Logit Modeli	—
Relacionats	6	3	0
Resum	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.	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.