I have a student version of the survey engine for doing my master’s thesis, so I can only use the orthogonal design for a choice experiment. When I tested my survey with my friends, they got identical alternatives and dominant ones. Those will not be good while modelling the data. I couldn’t edit the design matrix as well. Is there any way to remove those issues? I am attaching a screenshot for reference.
Greeshma, This entirely expected with an orthgonal design. Orthogonal designs have no prior knowledge to avoid dominance. In addition as you have generic alternatives, attributes with fewer levels than alternatives. will naturally repeat.
So here’s the reality.
For most practical applications it doesnt matter, there will be enough colour in the data and linear independence to guarantee models will converge. And generally these models will be useful for most commercial applications.
It is not enough to perform some test and be concerned. If you are, then you need to read the published literature, understand it and then apply a design appropriate for your study’s aims and the properties you desire in your final model. I suggest you start with Street&Burgess to understand orthogonal designs.
Then read Bliemer & Rose to get a grip in D-Efficient design which control for dominance. Perhaps then look at Baysean designs as well.
Once that is done there are many options. You can use published designs or generate your own or use Ngene to help you get there. But I woukd strongly caution against generating designs without understanding their application if peer-review is of a concern. Good Luck!