方法对比

	Adaptive Conjoint Analysis	Discrete Choice Experiment
领域	Marketing Research	Marketing Research
方法族	Process / pipeline	Regression model
起源年份	1987	1983
提出者	Richard M. Johnson (Sawtooth Software)	Jordan J. Louviere & George Woodworth; Daniel McFadden (random utility theory)
类型	Computer-adaptive conjoint combining self-explication and paired comparisons	Stated-preference experiment for estimating preferences and willingness to pay
开创性文献	Green, P. E., Krieger, A. M., & Agarwal, M. K. (1991). Adaptive Conjoint Analysis: Some Caveats and Suggestions. Journal of Marketing Research, 28(2), 215-222.	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.
别名	ACA, Adaptive Conjoint, Computer-Adaptive Conjoint, Self-Explicated and Paired-Comparison Conjoint	DCE, Stated Choice Experiment, Stated-Preference Choice Experiment, Choice Experiment
相关	4	4
摘要	Adaptive Conjoint Analysis (ACA) is a hybrid, computer-administered conjoint method that builds each respondent's part-worth utilities by combining a self-explicated priors stage with a sequence of adaptively chosen paired-comparison trade-offs. Developed by Richard Johnson at Sawtooth Software in the mid-1980s, ACA was designed to handle many more attributes than a respondent could realistically evaluate in full-profile or choice tasks. The interview first asks people to rate the desirability of attribute levels and the importance of attributes, then uses those answers to generate paired product comparisons that are roughly balanced in utility, which are the most informative trade-offs. Respondents indicate graded preference between each pair, and the program updates the utilities in real time, focusing later questions where uncertainty is greatest. Green, Krieger, and Agarwal's 1991 evaluation in the Journal of Marketing Research documented both ACA's strengths and important caveats about its self-explicated component and attribute-importance estimates. ACA produces individual-level utilities that can drive purchase-likelihood calibration and market simulation.	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.