পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| কনজয়েন্ট অ্যানালাইসিস× | মিশ্র লজিট মডেল× | |
|---|---|---|
| ক্ষেত্র≠ | পরীক্ষামূলক নকশা | অর্থমিতি |
| পরিবার≠ | Hypothesis test | Regression model |
| উদ্ভবের বছর≠ | 1978 | 2000 |
| প্রবর্তক≠ | Paul E. Green & V. Srinivasan | Daniel McFadden & Kenneth Train |
| ধরন≠ | Decomposition-based utility estimation | Random-parameters discrete choice model |
| মৌলিক উৎস≠ | Green, P.E. & Srinivasan, V. (1978). Conjoint analysis in consumer research: Issues and outlook. Journal of Consumer Research, 5(2), 103–123. DOI ↗ | Train, K. E. (2009). Discrete Choice Methods with Simulation (2nd ed.). Cambridge University Press. ISBN: 978-0-521-74738-7 |
| অপর নাম≠ | CBC conjoint, choice-based conjoint, adaptive conjoint analysis, full-profile conjoint | Random Parameters Logit, Mixed Multinomial Logit, Error Components Logit, Karma Logit Modeli |
| সম্পর্কিত≠ | 6 | 3 |
| সারসংক্ষেপ≠ | 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. |
| ScholarGateডেটাসেট ↗ |
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