Experiment Design
How to design a statistically efficient choice experiment - attributes, levels, D-efficiency, blocking, and advanced design methods.
Knowledge Base -> Experiment Design
SurveyEngine, 09.07.2026
General methodology
Blocking and versions in DCE experimental designs - A full D-efficient design may require 20 or more choice sets - too many for any single respondent. Blocking divides the design across respondents without losing efficiency.
D-efficiency - what makes a good experimental design? - D-efficiency measures how precisely a design estimates preference parameters. Most researchers use it without understanding what drives it.
Using Ngene for advanced DCE design - NGENE is required for advanced DCE designs - Bayesian priors, complex constraints, generic-specific parameters, and designs SurveyEngine's built-in generator does not support.
Using Ngene-Lite for DCE designs - You can now create experiment designs directly in SurveyEngine using Ngene™ Lite. This is preferred choice of professional researchers who need D-Efficient designs and the ability to add priors and control dominance.
Health research
Adding consistency checks to a patient preference DCE - Consistency checks detect respondents who are not reading the choice sets. They are a standard data quality measure in health preference research.
Dominant alternative design in health preference DCEs - A dominant alternative is one that is strictly better on every attribute. Including one by accident destroys the information value of every choice set it appears in.
Labelled vs unlabelled designs in health DCEs - Labelled and unlabelled DCEs measure different things. The choice between them has direct consequences for what the estimates can and cannot say.
Transport research
Best-worst scaling in transport research - Best-worst scaling produces relative importance rankings across a large attribute set with less respondent burden than a DCE. It does not produce WTP estimates.
Pivot designs in transport mode choice research - Generic SP attribute levels feel unrealistic to respondents making real travel decisions with specific costs and times. Pivot designs anchor levels to the respondent's actual journey.
Environmental research
Opt-out design in environmental valuation choice experiments - The opt-out alternative is contested in environmental DCEs. Excluding it biases WTP upward; including it requires careful handling of protest responses.
Choosing the payment vehicle in environmental DCEs - The payment vehicle determines what respondents think they are actually agreeing to pay. It affects WTP estimates as much as the environmental attributes do.