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  • #46
    Originally posted by Arne Risa Hole View Post
    Dear Sarah,

    As demonstrated in the dcreate help file, in many applications a sensible way of creating an opt-out alternative is to set the attribute levels for the fixed alternative to the lowest level for the other alternatives and include an ASC for the opt out alternative. As Ric Scarpa points out above this is not always appropriate - sometimes you want the opt-out to have other attribute levels - but you cannot give it attribute levels that do not appear in the other alternatives, as such a design is not identified. If attribute level "1" only appears in the opt-out you cannot identify the difference between attribute level "1" and "2" (for example) - this difference will be captured by the ASC. How you present the opt-out to the respondents in your survey is a different matter - the utility of the opt-out is different from the utility of another alternative with all of the attributes set to the lowest level due to the ASC, so you do not need to present the opt-out alternative in those terms (it could for example simply be a tick-box saying "neither").

    I can't help more than this I'm afraid - good luck.

    Arne
    Thanks for your response Arne. If I understand you correctly, my opt-out would be fixed at the lowest levels of alt1 and alt2, but when I present it to respondents, I just need to include choice sets where Alt 1 and Alt 2 do not have the lowest level?

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    • #47
      Dear Arne,

      First, thank you very much for the very helpful dcreate module!

      I encouter a curious situation where two choice sets come out identical when I attempt to generate a specific design (similar to Example 3 in the help file, with the additional opt-out alternative with associated ASC).

      Here is my code:

      Code:
      matrix levmat = 3,2,2
      genfact, levels(levmat)
      matrix optout = J(1,3,1)
      matrix b = J(1,9,0)
      dcreate i.x1##i.x2 i.x1##i.x3, nalt(2) nset(30) fixedalt(optout) asc(3) bmat(b)

      When the algorithm converges (after 3 iterations), choice sets #5 and #7 are exactly the same. Is this normal/to be expected?

      Thanks again,

      Claudio

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      • #48
        Claudio

        Yes, with simple designs (few attributes/levels) with many choice sets this can happen - there is nothing in the dcreate code that explicitly prevents it. Note that in your case you can only construct 144 different choice sets, and some of them will not convey any information (for example a choice set with two identical alternatives), so will not be selected by dcreate.

        Arne

        Comment


        • #49
          Originally posted by Arne Risa Hole View Post
          Claudio

          Yes, with simple designs (few attributes/levels) with many choice sets this can happen - there is nothing in the dcreate code that explicitly prevents it. Note that in your case you can only construct 144 different choice sets, and some of them will not convey any information (for example a choice set with two identical alternatives), so will not be selected by dcreate.

          Arne
          Thank you Arne for your quick response!

          Too bad there is nothing in the dcreate code to prevent generating duplicate choice sets.

          I know that 66 unique 2-alternative choice sets can be constructed in my case (12*11/2). I admit that I haven't investigated how the modified Fedorov algorithm works, but I didn't expect to have duplicates using the dcreate module with fewer choice sets than this number.

          For your information, after playing a bit with the command by changing the number in nset to 24 and 36, and examining the results explicitly for choice sets containing the same two alternatives (either as alt=1 or alt=2 in the choice set), I obtained these results:

          - With nset(24): 4 sets were duplicated twice ; 2 sets were duplicated thrice.
          - With nset(36): 1 set was duplicated thrice ; 3 sets were duplicated 4 times ; and 2 sets were duplicated 5 times.

          If I understand correctly, this would mean that 8 (4*1 + 2*2) and 19 (1*2 + 3*3 + 2*4) choice sets, respectively, do not add any new information over the remaining unique sets in these generated designs, translating into 33% (8/24) and 53% (19/36) wasted choice sets (again, respectively).

          In such cases, would you recommend removing the duplicate choice sets or trying to generate an efficient design with fewer choice sets, or something else entirely?

          Thank you again,

          Claudio


          Comment


          • #50
            Claudio

            I personally don't really see the issue here - I would just generate a design with fewer choice sets. Both 24 and 36 are way more than you need, and the repeated choice sets reflect that.

            Arne

            Comment


            • #51
              Originally posted by Arne Risa Hole View Post
              Claudio

              I personally don't really see the issue here - I would just generate a design with fewer choice sets. Both 24 and 36 are way more than you need, and the repeated choice sets reflect that.

              Arne
              Thank you Arne for your helpful insights.

              Claudio

              Comment


              • #52
                Originally posted by Arne Risa Hole View Post
                Dear Mariya,

                I am glad to hear that you have found dcreate useful. A design with a higher D-efficiency is more efficient than a design for the same experiment with a lower D-efficiency, but the numbers themselves don't mean much as they depend on things like the number of attributes, levels etc. in your choice experiment.

                Arne
                Thany you for this explanation. Therefore how could I produce several designs for the same experiment to compare the D-efficiencies, would it be by changing the seed? How many should I compare?

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