Attribute removal in efficient design

[rushdi98]

Dear Prof. Bliemer,

I hope you are doing well.

I am working on a study that extends a previous study by introducing nudging into the choice tasks. I have access to prior parameter estimates from the earlier study. However, one of the six attributes was found to be statistically insignificant.

I would like to ask whether it would be methodologically appropriate to remove this attribute from the experimental design and still generate an efficient design using the modified Federov algorithm based on the remaining attributes and priors.

I would greatly appreciate your advice on whether excluding the insignificant attribute would be reasonable in this context, or whether it should still be retained for theoretical or design considerations.

Thank you very much.

Kind regards,
Rushdi

[Michiel Bliemer]

In most cases it will be fine to remove the insignificant attribute and use the remaining attributes and priors to generate an efficient design.

The only case where you need to be careful is when the coefficient of the removed attribute is quite large, despite being non-significant (i.e., it has a very large standard error). Then removing the attribute would influence the choice probabilities. But if the coefficient is such that the attribute has only a minor contribution to utility, then it should be fine.

Michiel

[rushdi98]

Thank you Prof. Bliemer.

I have another question regarding the priors. Do you recommend accounting for attribute non-attendance when generating priors for an efficient design? For example, would it be preferable to estimate an ECLC model on the pilot data, derive ANA-adjusted utility coefficients, and use those as design priors, or is it generally better to use the coefficients obtained directly from a standard logit model?

Best,
Rushdi