Hello Choice Metrics team,
I am new to choice modelling and I am attempting to do a labelled experiment with 2 transport alternatives with 7 attributes.
I have just collected a pilot sample consisting of 200 respondents. Each participant was presented with 5 choice questions from an orthogonal design.
The output from MNL model using the pilot data is as follows. Some attributes aren’t significant.
Estimated parameters with approximate standard errors from BHHH matrix:
Estimate BHHH se BHH t-ratio (0)
asc_opt1 0.000000 NA NA
asc_opt2 -0.002910 0.769784 0.003781
b1_d 0.428036 0.193494 2.212141
b1_s 0.000000 NA NA
b1_c -0.312258 0.198598 -1.572313
b1_n -0.299048 0.201146 -1.486726
b2_j -0.019784 0.009211 -2.147976
b3_wk -0.060960 0.013719 -4.443643
b4_wt 0.009319 0.024181 0.385376
b5_f -0.141534 0.049345 -2.868238
b7_j -0.033064 0.018807 -1.758096
b8_f -0.057674 0.049403 -1.167416
b9_p -0.105266 0.013544 -7.772286
b6_e 0.257088 0.070776 3.632405
Final LL: -624.1295
Is it still possible to use Baysian priors to create a choice set from the above results for the full survey as many of the priors are very close to zero?
design
;alts = opt1, opt2
;block = 9
? efficient design
;eff = (mnl, d, mean)
;alg = swap
;rows = 36
;bdraws = sobol(200)
;model:
U(opt1) = b1.dummy[0.42|-0.31|-0.29] * x1[0,2,3,1]
+ b2[(n,-0.02,0.01)] * x2[30,40,50]
+ b3[(n,-0.06,0.01)] * x3[10,20]
+ b4[0] * x4[1,5,10]
+ b5[(n,-0.1,0.05)] * x5[2,3,4,5,6]
+ b6[(n,0.26,0.07)] * x7[0,1]
+ asc1[(n,-0.002,0.77)]
/
U(opt2) = b7[(n,-0.03,0.019)] * x2_2[30,35,40]
+ b8[(n,-0.06,0.05)] * x5_2[1,2,3,4,5]
+ b9[(n,-0.11,0.01)] * x6_2[0,5,10,15]
+ b6 * x7