DCE with status quo and dominant alternatives

[AnzeJap]

Dear JvB,

I hope you will get this message Reading this thread it seems we have similar issues on how to include SQ into an efficient design, where few attributes are nominal variables (effects coded), however I am only at early stages of the research (field of forestry). First, I would appreciate very much if you could tell me how the design below worked? Me and my colleague are trying to create a design with six attributes where four are continuous variables and two are nominal:

ATR levels codes
atr1 7, 20, 34 1,2,3 (7 is a reference value)
atr2 0%, 5%, 15% 1,2,3 (0% is a reference value)
atr3 none, owner, other 0,1,2 (effects coded, 0 is a reference value)
atr4 .3%, 5%, 20% 1,2,3 (.3 is a reference value)
atr5 none, record, monitoring 0,1,2 (effects coded, 0 is a reference value)
atr6 0, 150, 300, 450, 600, 750, 900 EUR 1,2,3,4,5,6,7 (0 is a reference value)

Reference values are in SQ alternative, however reference values of all except of atr6 can also populate additional two non-SQ alternatives. This is where I see most resemblance between your and our case. (I hope I understood your research design properly).

We are planning to do a pilot study (based on sequential fractional factorial design), from where priors would hopefully be collected. Those are to be fed into a Bayesian effective design. The code we are constructing builds also on your correspondence with Michiel B. on this forum.

In addition to see how your case worked out I am especially concerned on how to implement nominal attributes in SQ alternative, as there will be no prior parameter estimate for the reference values. It is possible to estimate (n-1) parameters only for non-reference attribute levels. If I understand correctly you dealt this with adding the 'require' restriction. Am I right? How can Ngene calculate choice probabilities for SQ alternative as you do not have priors for reference values of dummy coded attributes?

I hope my question make sense ... and thank you very much for your reply.
Anže

[Michiel Bliemer]

Hi Anze,

JvB will hopefully respond, but below is my response.

Your model syntax would like something like this (you would still need to add prior values):

Code: Select all

;alg = mfederov
;require:
sq.atr1 = 7,
sq.atr2 = 0,
sq.atr3 = 0,
sq.atr4 = 0.3,
sq.atr5 = 0,
sq.atr6 = 0
;model:
U(alt1) = b1              * atr1[7,20,34]
        + b2              * atr2[0,5,15]
        + b3.effects[0|0] * atr3[1,2,0]     ? 0 = none, 1 = owner, 2 = other
        + b4              * atr4[0.3,5,20]
        + b5.effects[0|0] * atr5[1,2,0]     ? 0 = none, 1 = record, 2 = monitoring
        + b6              * atr6[0,150,300,450,600,750,900]
        /
U(alt2) = b1 * atr1
        + b2 * atr2
        + b3 * atr3
        + b4 * atr4
        + b5 * atr5
        + b6 * atr6
        /
U(sq)   = b1 * atr1
        + b2 * atr2
        + b3 * atr3
        + b4 * atr4
        + b5 * atr5
        + b6 * atr6

Note that the SQ alternative uses the exact same parameters as the other alternatives, so the priors for alt1 and alt2 are also used for sq.

If you have other questions for me, please create a separate thread.

Michiel

[AnzeJap]

Dear Michiel,

first, thank you very much for responding. I have no messages from JvB. I am not sure I am creating a new thread by this message as I am simply posting a reply to the last post. Please tell me if I am doing this wrong.

I have looked at the code you suggested, and a have a few questions if I may.

1. there is no constant term in the SQ alternative. I may want to add this so that I will be able to estimate the potential aversion/preferences for status-quo?
2. the reference value of atr. 6 (=0) can only occur in the SQ alternative so that the code needs to be slightly changed. I did so, do you think this is ok? (I see resemblance here with the JvB's design in related post)

;alg = mfederov
;require:
sq.atr1 = 7,
sq.atr2 = 0,
sq.atr3 = 0,
sq.atr4 = 0.3,
sq.atr5 = 0,
sq.atr6 = 0
;model:
U(alt1) = b1 * atr1[7,20,34]
+ b2 * atr2[0,5,15]
+ b3.effects[0|0] * atr3[1,2,0] ? 0 = none, 1 = owner, 2 = other
+ b4 * atr4[0.3,5,20]
+ b5.effects[0|0] * atr5[1,2,0] ? 0 = none, 1 = record, 2 = monitoring
+ b6 * atr6[150,300,450,600,750,900]
/
U(alt2) = b1 * atr1
+ b2 * atr2
+ b3 * atr3
+ b4 * atr4
+ b5 * atr5
+ b6 * atr6
/
U(sq) = b0
+ b1 * atr1
+ b2 * atr2
+ b3 * atr3
+ b4 * atr4
+ b5 * atr5
+ b6 * atr6
$

3. the min. number of rows needed for this design. I am a bit confused here as reading your conversation with JvB you were mentioning both no. of rows and no. of choice tasks (sets). The output of Negene are choice tasks (in rows), each having three alternatives, even though the code specifies 'rows'. If I understood correctly you distinguished between both. In some of my previous work I have used these two forms to calculate the min. no.:
- min. n. of choice sets (S): A1+1+(L-1)*A2+1, where A1 is no. of linear effects parameters, A2 no. of non-linear effects parameters (effects coded), and L no. of levels for non-linear effect (Hensher et al. 2005), (in my case (4+1)+(3-1)*2+1=10) and
- min. n. of choice sets (S): K/(J-1) (in my case 8/(2-1)=8).

So this is the number of choice sets? (or am I wrong).

4. I plan to to a pilot survey to estimate the priors. I will use a simple sequential orthogonal fractional factorial design as we have no general idea on priors. (I saw that JvB used an efficient design with estimates from his/her judgement.) I was wondering how is it possible to estimate utilities for SQ alt. and then probabilities as I will have no coefficient estimates for the reference values of effects coded attributes. I best case using a MNL on a pilot data you can estimate N-1 coefficients for those effects, which leaves the reference level with no estimate. Or am I wrong.

Thank you very much again. Your advice comes very precious!
Anže

[AnzeJap]

Hi Anze,

JvB will hopefully respond, but below is my response.

Your model syntax would like something like this (you would still need to add prior values):

Code: Select all

;alg = mfederov
;require:
sq.atr1 = 7,
sq.atr2 = 0,
sq.atr3 = 0,
sq.atr4 = 0.3,
sq.atr5 = 0,
sq.atr6 = 0
;model:
U(alt1) = b1              * atr1[7,20,34]
        + b2              * atr2[0,5,15]
        + b3.effects[0|0] * atr3[1,2,0]     ? 0 = none, 1 = owner, 2 = other
        + b4              * atr4[0.3,5,20]
        + b5.effects[0|0] * atr5[1,2,0]     ? 0 = none, 1 = record, 2 = monitoring
        + b6              * atr6[0,150,300,450,600,750,900]
        /
U(alt2) = b1 * atr1
        + b2 * atr2
        + b3 * atr3
        + b4 * atr4
        + b5 * atr5
        + b6 * atr6
        /
U(sq)   = b1 * atr1
        + b2 * atr2
        + b3 * atr3
        + b4 * atr4
        + b5 * atr5
        + b6 * atr6

Note that the SQ alternative uses the exact same parameters as the other alternatives, so the priors for alt1 and alt2 are also used for sq.

If you have other questions for me, please create a separate thread.

Michiel

Dear Michiel,

first, thank you very much for responding. I have no messages from JvB. I am not sure I am creating a new thread by this message as I am simply posting a reply to the last post. Please tell me if I am doing this wrong.

I have looked at the code you suggested, and a have a few questions if I may.

1. there is no constant term in the SQ alternative. I may want to add this so that I will be able to estimate the potential aversion/preferences for status-quo?
2. the reference value of atr. 6 (=0) can only occur in the SQ alternative so that the code needs to be slightly changed. I did so, do you think this is ok? (I see resemblance here with the JvB's design in related post)

;alg = mfederov
;require:
sq.atr1 = 7,
sq.atr2 = 0,
sq.atr3 = 0,
sq.atr4 = 0.3,
sq.atr5 = 0,
sq.atr6 = 0
;model:
U(alt1) = b1 * atr1[7,20,34]
+ b2 * atr2[0,5,15]
+ b3.effects[0|0] * atr3[1,2,0] ? 0 = none, 1 = owner, 2 = other
+ b4 * atr4[0.3,5,20]
+ b5.effects[0|0] * atr5[1,2,0] ? 0 = none, 1 = record, 2 = monitoring
+ b6 * atr6[150,300,450,600,750,900]
/
U(alt2) = b1 * atr1
+ b2 * atr2
+ b3 * atr3
+ b4 * atr4
+ b5 * atr5
+ b6 * atr6
/
U(sq) = b0
+ b1 * atr1
+ b2 * atr2
+ b3 * atr3
+ b4 * atr4
+ b5 * atr5
+ b6 * atr6
$

3. the min. number of rows needed for this design. I am a bit confused here as reading your conversation with JvB you were mentioning both no. of rows and no. of choice tasks (sets). The output of Negene are choice tasks (in rows), each having three alternatives, even though the code specifies 'rows'. If I understood correctly you distinguished between both. In some of my previous work I have used these two forms to calculate the min. no.:
- min. n. of choice sets (S): A1+1+(L-1)*A2+1, where A1 is no. of linear effects parameters, A2 no. of non-linear effects parameters (effects coded), and L no. of levels for non-linear effect (Hensher et al. 2005), (in my case (4+1)+(3-1)*2+1=10) and
- min. n. of choice sets (S): K/(J-1) (in my case 8/(2-1)=8).

So this is the number of choice sets? (or am I wrong).

4. I plan to to a pilot survey to estimate the priors. I will use a simple sequential orthogonal fractional factorial design as we have no general idea on priors. (I saw that JvB used an efficient design with estimates from his/her judgement.) I was wondering how is it possible to estimate utilities for SQ alt. and then probabilities as I will have no coefficient estimates for the reference values of effects coded attributes. I best case using a MNL on a pilot data you can estimate N-1 coefficients for those effects, which leaves the reference level with no estimate. Or am I wrong.

Thank you very much again. Your advice comes very precious!
Anže

[Michiel Bliemer]

Anze, please create a separate thread by clicking on New topic at the top of the forum page. I will respond once you have created a new thread to avoid that this topic and thread becomes very long and messy.

Michiel

[AnzeJap]

Michiel,

I did so. It is named "Efficient design with SQ alt. having dummy variables".