choice-metrics.com

I am designing a labelled choice experiment to estimate WTP for more reliable freight transport along a particular corridor. There are three labelled alternatives:

• Normal Road Transport
  Normal Intermodal Transport
  High speed Intermodal Transport

There are no status quo alternatives. The different attributes are:

• Transport Cost
  Transport time (origin to destination)
  Travel time reliability (mean-variance method)

I want a D-efficient design. Should I add a constraint so that within each alternative, higher reliability of service and lower travel time is always accompanied by higher costs charged to the users?

You would usually only include constraints to make choice tasks more realistic. So if you think that the profiles would become too unrealistic, then you can certainly impose constraints. However, note that to be able to estimate choice models, one would need sufficient variation in the data and therefore imposing constraints could hinder model estimation if the constraints are too strict. But as long as you do not perfectly correlate attribute levels you should still be fine in most cases. If you generate an efficient design, and your D-error is finite, then you will be able to estimate the model.

You can also consider constraints to avoid dominant alternatives. Ngene does this automatically for unlabelled experiments, but with labels there is usually no issue with dominance. In your case, if you would use generic coefficients across your labelled alternatives, you could still apply dominance checks across the three alternatives in Ngene.

Michiel