So sánh phương pháp

	MaxDiff / Best-Worst Scaling	Discrete Choice Experiment
Lĩnh vực	Marketing Research	Marketing Research
Họ	Regression model	Regression model
Năm ra đời	2005	1983
Người khởi xướng	Jordan J. Louviere; A. A. J. Marley & Jordan Louviere	Jordan J. Louviere & George Woodworth; Daniel McFadden (random utility theory)
Loại	Best-worst choice task for scaling relative importance of items	Stated-preference experiment for estimating preferences and willingness to pay
Công trình gốc	Louviere, J. J., Flynn, T. N., & Marley, A. A. J. (2015). Best-Worst Scaling: Theory, Methods and Applications. Cambridge: Cambridge University Press.	Louviere, J. J., & Woodworth, G. (1983). Design and Analysis of Simulated Consumer Choice or Allocation Experiments: An Approach Based on Aggregate Data. Journal of Marketing Research, 20(4), 350-367.
Tên gọi khác	MaxDiff, Best-Worst Scaling, BWS, Maximum Difference Scaling	DCE, Stated Choice Experiment, Stated-Preference Choice Experiment, Choice Experiment
Liên quan	4	4
Tóm tắt	MaxDiff, also known as best-worst scaling (BWS), measures the relative importance or preference of a set of items by repeatedly asking respondents to identify the best (most important or most preferred) and worst (least) item within small subsets. Introduced by Jordan Louviere and formalized by Marley and Louviere's 2005 probabilistic models, the method exploits the fact that people are far better at picking extremes than at rating many items on a scale. Each best-worst judgment reveals the maximum-difference pair in a set, and across many balanced subsets the choices pin down a single interval scale of item utilities. Because every respondent is forced to make trade-offs, MaxDiff sidesteps the scale-use bias and lack of discrimination that plague rating grids, where respondents often call everything important. Item scores can be computed by simple best-minus-worst counts or, more rigorously, by fitting a multinomial logit choice model, with hierarchical Bayes yielding individual-level, probability-scaled importances. The result is a clear, discriminating ranking of items that supports prioritization, segmentation, and feature selection.	A discrete choice experiment (DCE) is a stated-preference method in which respondents repeatedly choose their preferred option from sets of alternatives described by systematically varied attributes, allowing the analyst to estimate how each attribute drives choice. Grounded in McFadden's random utility theory and operationalized for designed experiments by Louviere and Woodworth in 1983, the DCE treats each choice as the selection of the alternative with the highest latent utility and recovers the utility coefficients from observed choices. Because attributes are varied independently by experimental design, the method isolates the marginal effect of each attribute, including price, and yields marginal rates of substitution such as willingness to pay. DCEs are analyzed with multinomial (conditional) logit and, increasingly, with mixed and nested logit models that relax restrictive assumptions and capture preference heterogeneity. The approach is essentially the same machinery as choice-based conjoint but is the standard term in transport, health, and environmental economics, where it is used to value non-market goods. Its rigor and flexibility have made it a dominant stated-preference technique across the social sciences.