পদ্ধতির তুলনা করুন

	রোবাস্ট কনজয়েন্ট অ্যানালাইসিস	কনজয়েন্ট অ্যানালাইসিস
ক্ষেত্র	পরিসংখ্যান	পরীক্ষামূলক নকশা
পরিবার	Latent structure	Hypothesis test
উদ্ভবের বছর	1990s–2000s	1978
প্রবর্তক	Adaptations developed by robust statistics researchers building on Green and Srinivasan's conjoint framework	Paul E. Green & V. Srinivasan
ধরন	Preference decomposition / stated preference	Decomposition-based utility estimation
মৌলিক উৎস	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.
অপর নাম	robust CA, outlier-resistant conjoint analysis, robust stated preference analysis	CBC conjoint, choice-based conjoint, adaptive conjoint analysis, full-profile conjoint
সম্পর্কিত	4	6
সারসংক্ষেপ	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.