I'm working on an experimental design for my thesis, which aims to understand the substitution of tobacco-related products. For this, I consider a labeled design with an opt-out option. The atributtes and levels are the following:
For the pilot study I am producing a D-efficient design with ngene and the following is the code:
- Code: Select all
; alts= ecigdisp*, ecigrech*, cig*, alt4
; rows= 18
; block= 2
; eff= (mnl,d)
; alg= mfederov
; require:
cig.externality=2, cig.hide=1 , cig.harm=1
;model:
U(ecigdisp) = price[-0.000001]* price_disp[0.025,0.050,0.100,0.160] ? $ Price of disposable cigarette
+ flavour.dummy[0.0001|0.00001]* flavour[0,1,2] ? Flavour: traditional=0, mint=1, fruits=2
+ harm.dummy[-0.0001]* harm[0,1] ? Self-harm: less harmful 0(base), equally harmful=1
+ ext.dummy[-0.001|-0.00001|-0.00001]* externality[0,1,2,3] ? Harm to others: no=0(base), moderate=1, high=2, unkown=3
+ hide.dummy[-0.00001]* hide[0,1] ? Hide smoke: easy to hide=0(base), hard to hide=1
+ int1* price_disp * flavour ? Interaction term between price_disp & flavour
/
U(ecigrech)= price[-0.000001* price_rech[0.035,0.045,0.055,0.080] ? $ Price of rechargable cigarette
+ flavour.dummy[0.0001|0.00001]* flavour ? Flavour: traditional=0, mint=1, fruits=2
+ harm.dummy[-0.0001]* harm ? Self-harm: less harmful than traditional cig=0(base), equally harmful=1
+ ext.dummy[-0.001|-0.00001|-0.00001]* externality ? Harm to others: no harm=0(base), moderate=1, high=2, unkown=3
+ hide.dummy[-0.00001]* hide ? Hide smoke: easy to hide=0(base), hard to hide=1
+ int1* price_rech * flavour ? Interaction term between price_rech & flavour
/
U(cig)= price[-0.000001]* price_cig [0.025,0.05,0.200,0.350] ? $ Price of traditional tocabbo package
+ flavour.dummy[0.0001|0.00001]* flavour ? Flavour: traditional=0, mint=1, fruits=2
+ harm.dummy[-0.0001]* harm ? Self-harm - always harmful (harmful=1)
+ ext[-0.001|-0.00001|-0.00001]* externality ? Harm to others - always harmful
+ hide.dummy[-0.00001]* hide ? Hide smoke - always hard to hide
/
U(alt4)= b0
$
I left the code running for up to two hours, the D-error is 0.941978 and the A-error is 2.78576
Question 1: Since for the alternative cig (i.e. traditional cigarette) there is only one level for the attributes "ext", "hide" and "harm", is the way I have written it in the code correct?
Question 2: This is a design for an experiment. This would be the design I follow for the pilot study. Should I now consider a Bayesian D-efficient design, even though I have no priors? or should I continue with the D-efficient design as presented here?
Question 3: The program returns the following warning: "One or more attributes will not have level balance with the number of rows specified: ecigdisp.price_disp, ecigdisp.externality, ecigrech.price_rech, ecigrech.externality, cig.price_cig, cig.externality". In fact, with two of these attributes we obtain only two levels. What should be the rule for determining the number of rows? I am currently using Rose and Bliemer (2022) equation 3
I am also a bit concerned about the size of the D-error.
Any additional recomendations to improve the design would be really appreciated
Thank you so much