Sammenlign metoder
Gennemgå dine valgte metoder side om side; rækker, der afviger, er fremhævet.
| Conjoint Analyse× | Fuldt faktordesign× | |
|---|---|---|
| Fagområde | Forsøgsdesign | Forsøgsdesign |
| Familie | Hypothesis test | Hypothesis test |
| Oprindelsesår≠ | 1978 | 1926 |
| Ophavsperson≠ | Paul E. Green & V. Srinivasan | R. A. Fisher |
| Type≠ | Decomposition-based utility estimation | Parametric factorial experiment |
| Oprindelig kilde≠ | Green, P.E. & Srinivasan, V. (1978). Conjoint analysis in consumer research: Issues and outlook. Journal of Consumer Research, 5(2), 103–123. DOI ↗ | Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley. ISBN: 978-0471718130 |
| Aliasser≠ | 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) |
| Relaterede≠ | 6 | 5 |
| Resumé≠ | 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. |
| ScholarGateDatasæt ↗ |
|
|