Compară metode

	Analiză conjoint robustă	Analiza Conjoint
Domeniu	Statistică	Design experimental
Familie	Latent structure	Hypothesis test
Anul apariției	1990s–2000s	1978
Autorul original	Adaptations developed by robust statistics researchers building on Green and Srinivasan's conjoint framework	Paul E. Green & V. Srinivasan
Tip	Preference decomposition / stated preference	Decomposition-based utility estimation
Sursa seminală	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.	Green, P.E. & Srinivasan, V. (1978). Conjoint analysis in consumer research: Issues and outlook. Journal of Consumer Research, 5(2), 103–123.
Denumiri alternative	robust CA, outlier-resistant conjoint analysis, robust stated preference analysis	CBC conjoint, choice-based conjoint, adaptive conjoint analysis, full-profile conjoint
Înrudite	4	6
Rezumat	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.