Discrete Choice Experiment
Also known as: DCE, Stated Choice Experiment, Stated-Preference Choice Experiment, Choice Experiment
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.
Key highlights
- Estimates the marginal effect of each attribute cleanly because attributes are varied independently by experimental design.
- Yields willingness-to-pay and other trade-off measures by taking ratios of utility coefficients, valuing even non-market goods.
- Grounded in random utility theory, with flexible extensions (mixed and nested logit) for heterogeneity and realistic substitution.
- Generates new data by design, so it can value attributes or products that do not yet exist in any market.
Intuition
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How it works
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When to use it
Use a discrete choice experiment when you need to estimate how attributes drive choice and to value attributes, especially non-market goods, in monetary or trade-off terms, by observing stated choices among designed alternatives. It is the method of choice in transport, health, and environmental economics for valuing things that have no market price, and in marketing for pricing and product decisions where it overlaps with choice-based conjoint. DCEs require that the relevant alternatives can be described by a manageable set of attributes and that respondents can make meaningful hypothetical choices, so they fit poorly when the good is hard to decompose into attributes, when the number of attributes overwhelms respondents, or when revealed-preference data are available and preferable. Because choices are hypothetical, results need validation against holdouts or real behavior to address potential hypothetical bias.
Strengths & limitations
- Estimates the marginal effect of each attribute cleanly because attributes are varied independently by experimental design.
- Yields willingness-to-pay and other trade-off measures by taking ratios of utility coefficients, valuing even non-market goods.
- Grounded in random utility theory, with flexible extensions (mixed and nested logit) for heterogeneity and realistic substitution.
- Generates new data by design, so it can value attributes or products that do not yet exist in any market.
- Relies on hypothetical choices, so stated preferences may diverge from real behavior (hypothetical bias).
- Basic conditional logit imposes independence of irrelevant alternatives, which can misstate substitution unless richer models are used.
- Estimates depend heavily on the attributes, levels, and ranges chosen, so design errors translate directly into biased valuations.
- Cognitive burden limits the number of attributes and choice tasks, constraining the complexity of the goods that can be studied.
Common pitfalls
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Applications
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Frequently asked
How is a discrete choice experiment different from choice-based conjoint?
They share the same statistical core, random utility theory and logit-family estimation from designed choice tasks, but differ in tradition and emphasis. 'Choice-based conjoint' is the marketing label, focused on product features, pricing, and market-share simulation, while 'discrete choice experiment' is the term in transport, health, and environmental economics, where the goal is often to value non-market goods and estimate willingness to pay for policy analysis. DCEs more frequently include a status-quo or opt-out alternative and emphasize marginal rates of substitution, whereas conjoint emphasizes market simulators. The underlying design and estimation, as set out by Louviere, Train, and Hensher, are essentially the same.
What does the independence of irrelevant alternatives mean and why does it matter?
The basic conditional logit model implies that the ratio of choice probabilities between two alternatives does not depend on what other alternatives are available, a property called independence of irrelevant alternatives (IIA). This can produce unrealistic substitution, the classic 'red bus / blue bus' problem, where adding a similar alternative steals share proportionally from all others rather than mostly from its close substitute. As Train and Hensher explain, mixed logit and nested logit relax IIA by allowing random coefficients or correlated nests, giving more realistic substitution patterns, which is why credible modern DCE analyses rarely stop at plain conditional logit.
How can a DCE estimate willingness to pay?
Because every attribute coefficient is a marginal utility measured on the same latent scale, dividing an attribute's coefficient by the price or cost coefficient converts utility into money: it gives the amount of money that delivers the same utility change as a one-unit change in the attribute. This marginal rate of substitution is the willingness to pay for that attribute. Hensher, Rose, and Greene treat these ratios as the central output of applied choice analysis. The figures are only as good as the design and the price range shown, however, and because choices are hypothetical they should be sanity-checked against holdouts or revealed-preference evidence.
Sources
- 1.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.
- 2.Hensher, D. A., Rose, J. M., & Greene, W. H. (2015). Applied Choice Analysis (2nd ed.). Cambridge: Cambridge University Press.ISBN 9781107465923
- 3.Train, K. E. (2009). Discrete Choice Methods with Simulation (2nd ed.). Cambridge: Cambridge University Press.ISBN 9780521766555
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Cite this page
ScholarGate. (2026, June 23). Discrete Choice Experiment. ScholarGate. https://scholargate.app/marketing-research/discrete-choice-experiment