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| Μπεϋζιανή Ανάλυση Συζεύξεων× | Ανάλυση Conjoint× | |
|---|---|---|
| Πεδίο≠ | Στατιστική | Πειραματικός Σχεδιασμός |
| Οικογένεια≠ | Latent structure | Hypothesis test |
| Έτος προέλευσης≠ | 1995 | 1978 |
| Δημιουργός≠ | Allenby & Ginter (hierarchical Bayes formulation); conjoint roots in Luce & Tukey (1964) | Paul E. Green & V. Srinivasan |
| Τύπος≠ | Preference measurement / Bayesian hierarchical model | Decomposition-based utility estimation |
| Θεμελιώδης πηγή≠ | Allenby, G. M. & Ginter, J. L. (1995). Using extremes to design products and segment markets. Journal of Marketing Research, 32(4), 392–403. DOI ↗ | Green, P.E. & Srinivasan, V. (1978). Conjoint analysis in consumer research: Issues and outlook. Journal of Consumer Research, 5(2), 103–123. DOI ↗ |
| Εναλλακτικές ονομασίες≠ | Bayesian CA, hierarchical Bayes conjoint, HB conjoint, Bayesian preference modeling | CBC conjoint, choice-based conjoint, adaptive conjoint analysis, full-profile conjoint |
| Συναφείς | 6 | 6 |
| Σύνοψη≠ | Bayesian conjoint analysis estimates individual-level consumer preference weights for product attributes by combining conjoint choice tasks with a hierarchical Bayesian model. It yields part-worth utilities for each respondent rather than only group averages, enabling precise market simulation and segment discovery even from small per-person choice sets. | 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. |
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