Comparer des méthodes

	Analyse conjointe	Plan d'expériences factoriel complet
Domaine	Plans d'expériences	Plans d'expériences
Famille	Hypothesis test	Hypothesis test
Année d'origine	1978	1926
Auteur d'origine	Paul E. Green & V. Srinivasan	R. A. Fisher
Type	Decomposition-based utility estimation	Parametric factorial experiment
Source fondatrice	Green, P.E. & Srinivasan, V. (1978). Conjoint analysis in consumer research: Issues and outlook. Journal of Consumer Research, 5(2), 103–123.	Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley. ISBN: 978-0471718130
Alias	CBC conjoint, choice-based conjoint, adaptive conjoint analysis, full-profile conjoint	factorial experiment, 2^k factorial, full factorial, Faktöriyel Deneme Deseni (Full Factorial, 2^k)
Apparentées	6	5
Résumé	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.	A full factorial design is a parametric experimental method in which every combination of factor levels is tested simultaneously, enabling the estimation of all main effects and all interaction effects in a single study. Rooted in R. A. Fisher's foundational work on designed experiments (1926) and systematically developed by Box, Hunter, and Hunter (2005) and Montgomery (2017), the 2^k form tests k two-level factors across 2^k experimental runs and is the benchmark against which all other factorial designs are measured.