### Labelled design - How to proceed?

Posted:

**Wed Jul 26, 2017 2:58 am**Dear Professor Bliemer and Ngene users,

I am dealing with a labelled design where there are two agri-food products and an opt-out alternative. I have no idea of any priors from previous researches but I have up to some extent a deep knowledge of this agri-food market. Nonetheless, I have some concerns regarding the best strategy to follow.

1) What is the best approach? Using priors from my own knowledge or employing priors quite close to 0 for the pre-test?

2) From the code below and using my own priors I am getting a relatively high D-error (0.30). I am aware that if I increase the number of rows I could improve the efficiency of the design. In this regard, as I am going to use real products (one-litre bottles of olive oil) to be relabelled I cannot increase the number or rows due to logistic issues. Thus, I am keeping the rows up to a limit of 36 (48 at the most). Is there anything I can do to improve the D-error?

3) On the other hand, I am considering the constants into the design using as priors the current market shares of both categories of olive oil (0.6 y 0.4). Do you find this a suitable approach?

4) Likewise, to estimate the priors of both utility functions (two categories of olive oil) I have distributed around 100 units of utility between the attributes for each alternative (categories of olive oil). Would it be a suitable approach? Or it would be better to distribute the 100 units of utility between the whole set of attributes belonged to both alternatives?

5) Having a look at the choice probabilities of the alternatives I have noticed that there are small differences which is in correspondence with the relatively high D-error. Any suggestion to improve the choice probabilities?

6) On the other hand, as I am going to modify the information displayed into the labels, it is likely that the choices are polarized towards one alternative (the one of best quality once the information is more accessible and understandable by the consumers) so the trade-off between alternatives could be not very informative. Is there any solution to this phenomenon when it happens?

7) At the beginning I thought in using a Bayesian design with uniform distributions around the priors but I think the results would be the same as using a non-Bayesian design... Different would be if I were able to set, for example, normal distributions but as I said I have no idea about the priors except my own knowledge.

8) Lastly, something that I understand it is the same but I would like to confirm… I have used this two syntax indistinctly, is there any difference?

+b4.dummy[0.15]*X4[0,1]

+b4[0.15] *X4[1,0]

Thank you so much in advance to the Ngene community

Happy summer time!

MAC

-----------

Design

;alts=A, B, C

;rows=36

;block=6

;eff = (mnl,d)

;con

;model:

U(A) = b1[0.40]

+b2.dummy[0.35]*X2[0,1]

+b3.dummy[0.15|0.10]*X3[0,1,2]

+b4.dummy[0.15]*X4[0,1]

+b5[-0.15]*X5[0,1,2,3]

/

U(B) = b6[0.60]

+b7.dummy[0.15]*X7[0,1]

+b8.dummy[0.3]*X8[0,1]

+b9.dummy[0.15]*X9[0,1]

+b10[-0.2]*X10[0,1,2,3]$

------

MNL probabilities

Choice situation a b c

1 0.396936 0.368255 0.23481

2 0.37167 0.465451 0.162879

3 0.439486 0.342272 0.218242

4 0.354555 0.401763 0.243682

5 0.363922 0.373135 0.262944

6 0.491655 0.298204 0.210141

7 0.381454 0.36285 0.255696

8 0.395766 0.332228 0.272006

9 0.490629 0.328879 0.180492

10 0.411351 0.345312 0.243337

11 0.271531 0.494761 0.233709

12 0.361376 0.335264 0.30336

13 0.432637 0.391466 0.175897

14 0.256683 0.571258 0.17206

15 0.41642 0.376792 0.206788

16 0.343901 0.303492 0.352607

17 0.309095 0.396885 0.29402

18 0.273873 0.537896 0.18823

...

I am dealing with a labelled design where there are two agri-food products and an opt-out alternative. I have no idea of any priors from previous researches but I have up to some extent a deep knowledge of this agri-food market. Nonetheless, I have some concerns regarding the best strategy to follow.

1) What is the best approach? Using priors from my own knowledge or employing priors quite close to 0 for the pre-test?

2) From the code below and using my own priors I am getting a relatively high D-error (0.30). I am aware that if I increase the number of rows I could improve the efficiency of the design. In this regard, as I am going to use real products (one-litre bottles of olive oil) to be relabelled I cannot increase the number or rows due to logistic issues. Thus, I am keeping the rows up to a limit of 36 (48 at the most). Is there anything I can do to improve the D-error?

3) On the other hand, I am considering the constants into the design using as priors the current market shares of both categories of olive oil (0.6 y 0.4). Do you find this a suitable approach?

4) Likewise, to estimate the priors of both utility functions (two categories of olive oil) I have distributed around 100 units of utility between the attributes for each alternative (categories of olive oil). Would it be a suitable approach? Or it would be better to distribute the 100 units of utility between the whole set of attributes belonged to both alternatives?

5) Having a look at the choice probabilities of the alternatives I have noticed that there are small differences which is in correspondence with the relatively high D-error. Any suggestion to improve the choice probabilities?

6) On the other hand, as I am going to modify the information displayed into the labels, it is likely that the choices are polarized towards one alternative (the one of best quality once the information is more accessible and understandable by the consumers) so the trade-off between alternatives could be not very informative. Is there any solution to this phenomenon when it happens?

7) At the beginning I thought in using a Bayesian design with uniform distributions around the priors but I think the results would be the same as using a non-Bayesian design... Different would be if I were able to set, for example, normal distributions but as I said I have no idea about the priors except my own knowledge.

8) Lastly, something that I understand it is the same but I would like to confirm… I have used this two syntax indistinctly, is there any difference?

+b4.dummy[0.15]*X4[0,1]

+b4[0.15] *X4[1,0]

Thank you so much in advance to the Ngene community

Happy summer time!

MAC

-----------

Design

;alts=A, B, C

;rows=36

;block=6

;eff = (mnl,d)

;con

;model:

U(A) = b1[0.40]

+b2.dummy[0.35]*X2[0,1]

+b3.dummy[0.15|0.10]*X3[0,1,2]

+b4.dummy[0.15]*X4[0,1]

+b5[-0.15]*X5[0,1,2,3]

/

U(B) = b6[0.60]

+b7.dummy[0.15]*X7[0,1]

+b8.dummy[0.3]*X8[0,1]

+b9.dummy[0.15]*X9[0,1]

+b10[-0.2]*X10[0,1,2,3]$

------

MNL probabilities

Choice situation a b c

1 0.396936 0.368255 0.23481

2 0.37167 0.465451 0.162879

3 0.439486 0.342272 0.218242

4 0.354555 0.401763 0.243682

5 0.363922 0.373135 0.262944

6 0.491655 0.298204 0.210141

7 0.381454 0.36285 0.255696

8 0.395766 0.332228 0.272006

9 0.490629 0.328879 0.180492

10 0.411351 0.345312 0.243337

11 0.271531 0.494761 0.233709

12 0.361376 0.335264 0.30336

13 0.432637 0.391466 0.175897

14 0.256683 0.571258 0.17206

15 0.41642 0.376792 0.206788

16 0.343901 0.303492 0.352607

17 0.309095 0.396885 0.29402

18 0.273873 0.537896 0.18823

...