Choosing Attributes and Levels for a Discrete Choice Experiment
The decision before the study runs
Before a conjoint or discrete choice study can go into the field, someone has to decide what it actually tests: the attributes (price, features, packaging, messaging, positioning, and similar variables) and the levels within each that respondents will see and trade off against one another. This is the second step in defining an experiment, ahead of standing up the market simulation and running the controlled comparisons.
Get this step wrong in either direction and the study's answer is compromised. Leave out an attribute that actually drives the choice, and the experiment answers a different question than the one that matters. Include too many attributes, or combine levels into unrealistic packages nobody would encounter in the real world, and respondents disengage or answer inconsistently, degrading every estimate the study produces downstream.
What is an attribute in a discrete choice experiment?
An attribute is any dimension of the offer that could plausibly change a customer's decision. For a product, that might be price, a feature, packaging format, or a messaging claim. For a service or policy proposal, it might be coverage terms, delivery timing, or a benefit's wording. Each attribute needs multiple levels: the concrete variations respondents will actually see and compare, such as three price points or two versions of a feature.
The discipline here is narrowing, not listing everything that could conceivably matter: a well-specified attribute list holds the small set of variables that plausibly change the outcome.
How to narrow the list
Three checks separate a usable attribute list from an overloaded one:
- Does it plausibly drive the decision? If removing the attribute wouldn't change how a customer chooses, it's a candidate for exclusion rather than inclusion.
- Are the levels realistic in combination? Attributes tested together should reflect offers a customer could actually encounter, not synthetic combinations that never occur in the market.
- Is the list short enough to hold respondent attention? Every additional attribute adds cognitive load; a list that tries to cover every possible variable produces noisier answers about all of them, not more complete ones.
The ISPOR Conjoint Analysis Experimental Design Good Research Practices Task Force report sets out this same tradeoff as a core design constraint: attribute and level selection has to balance realism and relevance against respondent burden, because an overloaded design degrades the statistical properties of every estimate that comes out of it.
How does attribute selection fit into the experiment workflow?
Subconscious's discrete choice experiments use McFadden-style discrete choice and Mixed Logit estimation to recover the causal effect of each attribute and level on the choice a customer makes. That estimation is only as good as the attribute list feeding it: the statistical methods task force report documents how the analysis stage depends on decisions made at the design stage.
Subconscious's published validation record covers this modeling layer directly: our best configuration reaches 87% of the measured human ceiling on one study: 0.832 rank correlation against the published human result, where two independent samples of real humans reach 0.959. Across all 43 studies that pass design filters, drawn from a corpus of roughly 300 replicated studies across 9 domains, the mean is 0.73. See the causal fidelity paper. That figure describes replication across the corpus, not how accurately any single team selects its own attributes, which remains a judgment call shaped by domain and decision.
What are the limitations of attribute selection?
Attribute selection is a judgment call, not a mechanical procedure. Domain knowledge about what influences a customer's decision has to come from somewhere, whether prior research, stakeholder interviews, or a pilot round, before the list locks. No amount of downstream estimation sophistication corrects for a missing attribute upstream, and because levels must reflect realistic combinations, the same attribute list rarely transfers unchanged from one product or market to another.
Next step
Once the attribute list and levels are locked, the next step is standing up the market simulation and running the experiment. Teams that need to confirm a result against real people can move from a simulated study to real-human validation without changing the underlying causal question. Review worked examples, or look at the underlying research behind the estimation methods. Teams ready to scope a study can start with a demo.