[Limdep Nlogit List] About dominance in D-optimal design
teagle at tceagle.com
Wed Feb 17 04:06:40 AEDT 2021
Technically the dominated alternative is only required to make the design more d-optimal. If you are willing to sacrifice some d-optimality you can remove it. Having a dominant alternative in a task is not useful information if the alternative it dominates is never chosen and as such it is a waste of task/design space. The dominated and dominant alternative adds no useful information to the model estimation. I typically build the design such that domination does not occur, but that sacrifices d-optimality. A short cut approach is to modify by hand the dominate alternative so that is does not dominate any longer. I would only do that if you have no means to restrict the initial or final design to exclude such dominance.
From: Limdep <limdep-bounces at mailman.sydney.edu.au> On Behalf Of Lixian Qian
Sent: Monday, February 15, 2021 5:58 PM
To: limdep at limdep.itls.usyd.edu.au
Subject: [Limdep Nlogit List] About dominance in D-optimal design
Hello, DCM experts,
I am not sure whether this is the best place to ask a question about alternative dominance in DCM experiment design, but I hope you can give me some advices.
We have a D-optimal stated preference experiment design with 4 alternatives. We find in some scenarios, alternative B dominates alternative A.
We are not sure whether the scenarios with alternative dominance should be removed from the design. If removed, will the design still be optimal?
We had some pilot data based on the DCM experiment design. No matter whether we remove scenarios with alternative dominance, the choice model analysis based on NLogit does not show significant differences on key attributes. Does it mean it is acceptable for us to keep all scenarios in the formal data collection?
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