Conjoint question: parameter estimates all levels

jayLee1988 — Tue, 01 Dec 2015 16:42:49 GMT

Dear. All members in this group.

i'm Jaewon Lee from South Korea.

first, i'm sorry because i'm not good at writing English.

i'm just beginner of SAS and statistics.

this summer i been Laos for survey.

Lao milk products market survey.

i collected 1,200 Laos normal customer in Local market.

i want to do one of the conjoint analyse that multinomial.

but result comes out something wrong.

if someone know the reason then please help me.

i will upload sas code and data set.

you can see example of data set below this writing.

if you need data then please send me email then i can share for you.

jaymail@snu.ac.kr

MY QUESTION IS WHEN I CALCULATE THE WILLINGNESS TO PAY.

IT COMES OUT NON-SENSE.

I WANT TO FIND THE REASON WHY PARAMETER VALUE IS SO LOW.

i used this code

proc mdc data=High;
model CHO = PR NTD LNB DNS LKR JPN / type=clogit nchoice=6;
id Q;
run;

and result is

The MDC Procedure

Conditional Logit Estimates

Parameter Estimates
Parameter DF Estimate Standard Error t Value Approx Pr > |t|
PR 1 0.0000414 0.0000219 1.89 0.0592
FAT 1 -0.0448 0.006516 -6.87 <.0001
CAL 1 0.001522 0.000961 1.58 0.1131
NTD 1 1.4351 0.1939 7.40 <.0001
LNB 1 0.4812 0.2189 2.20 0.0280
DNS 1 1.2436 0.1882 6.61 <.0001
LKR 1 0.2931 0.2101 1.39 0.1630
JPN 1 0.8761 0.1982 4.42 <.0001

PR is price.
FAT is fat
CAL is calories
NTD is one of milk brand in laos (Netherlands brand)
LNB is one of milk brand in laos (Laos brand * not existing but had before)
DNS is one of milk brand in laos (Denmark brand)
LKR is one of milk brand in laos (Korea brand * not existing but imagine)
JPN is one of milk brand in laos (Japan brand)

normally cost of one pack of milk is 2,000 kip to 5,000 kip. (kip is laos currency)

but according this result, if i want to calculate willingness to pay is 34,664kips. it's non sense.

when i calculate without FAT and Cal

The MDC Procedure

Conditional Logit Estimates

Parameter Estimates
Parameter DF Estimate Standard Error t Value Approx Pr > |t|
PR 1 0.0000291 0.0000214 1.36 0.1746
NTD 1 1.4304 0.1378 10.38 <.0001
LNB 1 0.5439 0.1551 3.51 0.0005
DNS 1 1.2242 0.1447 8.46 <.0001
LKR 1 0.1756 0.1614 1.09 0.2768
JPN 1 0.6821 0.1498 4.55 <.0001

this is example of data. i collected 1,200 answers in Vientiane, Pakse and Luangprabang Laos.

ID Q OPT CHO PR1 PR NTD LNB DNS LKR JPN NON FAT CAL SEX AGE KID EDU PRM INC
1 1 1 0 2 2000 1 0 0 0 0 0 0 110 2 2 1 1 3 6
1 1 2 0 4 4000 0 1 0 0 0 0 0 170 2 2 1 1 3 6
1 1 3 1 3 3000 0 0 1 0 0 0 8 110 2 2 1 1 3 6
1 1 4 0 3 3000 0 0 0 1 0 0 12 150 2 2 1 1 3 6
1 1 5 0 5 5000 0 0 0 0 1 0 12 110 2 2 1 1 3 6
1 1 6 0 0 0 0 0 0 0 0 1 0 0 2 2 1 1 3 6
1 2 1 0 4 4000 1 0 0 0 0 0 8 150 2 2 1 1 3 6
1 2 2 0 6 6000 0 1 0 0 0 0 0 150 2 2 1 1 3 6
1 2 3 1 5 5000 0 0 1 0 0 0 0 130 2 2 1 1 3 6
1 2 4 0 5 5000 0 0 0 1 0 0 15 150 2 2 1 1 3 6
1 2 5 0 2 2000 0 0 0 0 1 0 15 170 2 2 1 1 3 6
1 2 6 0 0 0 0 0 0 0 0 1 0 0 2 2 1 1 3 6
1 3 1 1 4 4000 1 0 0 0 0 0 15 190 2 2 1 1 3 6
1 3 2 0 6 6000 0 1 0 0 0 0 8 190 2 2 1 1 3 6
1 3 3 0 3 3000 0 0 1 0 0 0 4 170 2 2 1 1 3 6
1 3 4 0 3 3000 0 0 0 1 0 0 15 130 2 2 1 1 3 6
1 3 5 0 2 2000 0 0 0 0 1 0 12 190 2 2 1 1 3 6
1 3 6 0 0 0 0 0 0 0 0 1 0 0 2 2 1 1 3 6
1 4 1 0 4 4000 1 0 0 0 0 0 4 110 2 2 1 1 3 6
1 4 2 0 3 3000 0 1 0 0 0 0 0 190 2 2 1 1 3 6
1 4 3 1 4 4000 0 0 1 0 0 0 12 130 2 2 1 1 3 6
1 4 4 0 5 5000 0 0 0 1 0 0 4 190 2 2 1 1 3 6
1 4 5 0 6 6000 0 0 0 0 1 0 15 110 2 2 1 1 3 6
1 4 6 0 0 0 0 0 0 0 0 1 0 0 2 2 1 1 3 6
1 5 1 1 5 5000 1 0 0 0 0 0 8 170 2 2 1 1 3 6
1 5 2 0 2 2000 0 1 0 0 0 0 4 150 2 2 1 1 3 6
1 5 3 0 6 6000 0 0 1 0 0 0 4 130 2 2 1 1 3 6
1 5 4 0 6 6000 0 0 0 1 0 0 12 170 2 2 1 1 3 6
1 5 5 0 2 2000 0 0 0 0 1 0 8 130 2 2 1 1 3 6
1 5 6 0 0 0 0 0 0 0 0 1 0 0 2 2 1 1 3 6
1 6 1 0 2 2000 1 0 0 0 0 0 4 130 2 2 1 1 3 6
1 6 2 0 3 3000 0 1 0 0 0 0 15 190 2 2 1 1 3 6
1 6 3 1 6 6000 0 0 1 0 0 0 4 190 2 2 1 1 3 6
1 6 4 0 6 6000 0 0 0 1 0 0 0 130 2 2 1 1 3 6
1 6 5 0 3 3000 0 0 0 0 1 0 0 170 2 2 1 1 3 6
1 6 6 0 0 0 0 0 0 0 0 1 0 0 2 2 1 1 3 6
1 7 1 0 2 2000 1 0 0 0 0 0 8 190 2 2 1 1 3 6
1 7 2 0 5 5000 0 1 0 0 0 0 15 130 2 2 1 1 3 6
1 7 3 1 2 2000 0 0 1 0 0 0 12 170 2 2 1 1 3 6
1 7 4 0 2 2000 0 0 0 1 0 0 0 150 2 2 1 1 3 6
1 7 5 0 6 6000 0 0 0 0 1 0 15 150 2 2 1 1 3 6
1 7 6 0 0 0 0 0 0 0 0 1 0 0 2 2 1 1 3 6
1 8 1 1 5 5000 1 0 0 0 0 0 4 170 2 2 1 1 3 6
1 8 2 0 4 4000 0 1 0 0 0 0 8 130 2 2 1 1 3 6
1 8 3 0 4 4000 0 0 1 0 0 0 0 11 2 2 1 1 3 6
1 8 4 0 4 4000 0 0 0 1 0 0 15 170 2 2 1 1 3 6
1 8 5 0 3 3000 0 0 0 0 1 0 12 130 2 2 1 1 3 6
1 8 6 0 0 0 0 0 0 0 0 1 0 0 2 2 1 1 3 6
1 9 1 1 6 6000 1 0 0 0 0 0 8 170 2 2 1 1 3 6
1 9 2 0 5 5000 0 1 0 0 0 0 0 190 2 2 1 1 3 6
1 9 3 0 5 5000 0 0 1 0 0 0 12 150 2 2 1 1 3 6
1 9 4 0 2 2000 0 0 0 1 0 0 15 110 2 2 1 1 3 6
1 9 5 0 4 4000 0 0 0 0 1 0 4 150 2 2 1 1 3 6
1 9 6 0 0 0 0 0 0 0 0 1 0 0 2 2 1 1 3 6
1 10 1 0 3 3000 1 0 0 0 0 0 4 110 2 2 1 1 3 6
1 10 2 0 4 4000 0 1 0 0 0 0 12 190 2 2 1 1 3 6
1 10 3 1 3 3000 0 0 1 0 0 0 8 150 2 2 1 1 3 6
1 10 4 0 5 5000 0 0 0 1 0 0 8 110 2 2 1 1 3 6
1 10 5 0 6 6000 0 0 0 0 1 0 12 110 2 2 1 1 3 6
1 10 6 0 0 0 0 0 0 0 0 1 0 0 2 2 1 1 3 6
2 1 1 0 2 2000 1 0 0 0 0 0 0 110 2 3 1 1 3 5
2 1 2 0 4 4000 0 1 0 0 0 0 0 170 2 3 1 1 3 5
2 1 3 1 3 3000 0 0 1 0 0 0 8 110 2 3 1 1 3 5
2 1 4 0 3 3000 0 0 0 1 0 0 12 150 2 3 1 1 3 5
2 1 5 0 5 5000 0 0 0 0 1 0 12 110 2 3 1 1 3 5
2 1 6 0 0 0 0 0 0 0 0 1 0 0 2 3 1 1 3 5
2 2 1 0 4 4000 1 0 0 0 0 0 8 150 2 3 1 1 3 5
2 2 2 0 6 6000 0 1 0 0 0 0 0 150 2 3 1 1 3 5
2 2 3 1 5 5000 0 0 1 0 0 0 0 130 2 3 1 1 3 5
2 2 4 0 5 5000 0 0 0 1 0 0 15 150 2 3 1 1 3 5
2 2 5 0 2 2000 0 0 0 0 1 0 15 170 2 3 1 1 3 5
2 2 6 0 0 0 0 0 0 0 0 1 0 0 2 3 1 1 3 5
2 3 1 1 4 4000 1 0 0 0 0 0 15 190 2 3 1 1 3 5
2 3 2 0 6 6000 0 1 0 0 0 0 8 190 2 3 1 1 3 5
2 3 3 0 3 3000 0 0 1 0 0 0 4 170 2 3 1 1 3 5
2 3 4 0 3 3000 0 0 0 1 0 0 15 130 2 3 1 1 3 5
2 3 5 0 2 2000 0 0 0 0 1 0 12 190 2 3 1 1 3 5
2 3 6 0 0 0 0 0 0 0 0 1 0 0 2 3 1 1 3 5
2 4 1 0 4 4000 1 0 0 0 0 0 4 110 2 3 1 1 3 5
2 4 2 1 3 3000 0 1 0 0 0 0 0 190 2 3 1 1 3 5
2 4 3 0 4 4000 0 0 1 0 0 0 12 130 2 3 1 1 3 5
2 4 4 0 5 5000 0 0 0 1 0 0 4 190 2 3 1 1 3 5
2 4 5 0 6 6000 0 0 0 0 1 0 15 110 2 3 1 1 3 5
2 4 6 0 0 0 0 0 0 0 0 1 0 0 2 3 1 1 3 5
2 5 1 0 5 5000 1 0 0 0 0 0 8 170 2 3 1 1 3 5
2 5 2 0 2 2000 0 1 0 0 0 0 4 150 2 3 1 1 3 5
2 5 3 0 6 6000 0 0 1 0 0 0 4 130 2 3 1 1 3 5
2 5 4 1 6 6000 0 0 0 1 0 0 12 170 2 3 1 1 3 5
2 5 5 0 2 2000 0 0 0 0 1 0 8 130 2 3 1 1 3 5
2 5 6 0 0 0 0 0 0 0 0 1 0 0 2 3 1 1 3 5
2 6 1 0 2 2000 1 0 0 0 0 0 4 130 2 3 1 1 3 5
2 6 2 0 3 3000 0 1 0 0 0 0 15 190 2 3 1 1 3 5
2 6 3 1 6 6000 0 0 1 0 0 0 4 190 2 3 1 1 3 5
2 6 4 0 6 6000 0 0 0 1 0 0 0 130 2 3 1 1 3 5
2 6 5 0 3 3000 0 0 0 0 1 0 0 170 2 3 1 1 3 5
2 6 6 0 0 0 0 0 0 0 0 1 0 0 2 3 1 1 3 5
2 7 1 0 2 2000 1 0 0 0 0 0 8 190 2 3 1 1 3 5
2 7 2 0 5 5000 0 1 0 0 0 0 15 130 2 3 1 1 3 5
2 7 3 1 2 2000 0 0 1 0 0 0 12 170 2 3 1 1 3 5
2 7 4 0 2 2000 0 0 0 1 0 0 0 150 2 3 1 1 3 5
2 7 5 0 6 6000 0 0 0 0 1 0 15 150 2 3 1 1 3 5
2 7 6 0 0 0 0 0 0 0 0 1 0 0 2 3 1 1 3 5
2 8 1 1 5 5000 1 0 0 0 0 0 4 170 2 3 1 1 3 5
2 8 2 0 4 4000 0 1 0 0 0 0 8 130 2 3 1 1 3 5
2 8 3 0 4 4000 0 0 1 0 0 0 0 11 2 3 1 1 3 5
2 8 4 0 4 4000 0 0 0 1 0 0 15 170 2 3 1 1 3 5
2 8 5 0 3 3000 0 0 0 0 1 0 12 130 2 3 1 1 3 5
2 8 6 0 0 0 0 0 0 0 0 1 0 0 2 3 1 1 3 5
2 9 1 1 6 6000 1 0 0 0 0 0 8 170 2 3 1 1 3 5
2 9 2 0 5 5000 0 1 0 0 0 0 0 190 2 3 1 1 3 5
2 9 3 0 5 5000 0 0 1 0 0 0 12 150 2 3 1 1 3 5
2 9 4 0 2 2000 0 0 0 1 0 0 15 110 2 3 1 1 3 5
2 9 5 0 4 4000 0 0 0 0 1 0 4 150 2 3 1 1 3 5
2 9 6 0 0 0 0 0 0 0 0 1 0 0 2 3 1 1 3 5
2 10 1 0 3 3000 1 0 0 0 0 0 4 110 2 3 1 1 3 5
2 10 2 1 4 4000 0 1 0 0 0 0 12 190 2 3 1 1 3 5
2 10 3 0 3 3000 0 0 1 0 0 0 8 150 2 3 1 1 3 5
2 10 4 0 5 5000 0 0 0 1 0 0 8 110 2 3 1 1 3 5
2 10 5 0 6 6000 0 0 0 0 1 0 12 110 2 3 1 1 3 5
2 10 6 0 0 0 0 0 0 0 0 1 0 0 2 3 1 1 3 5
3 1 1 0 2 2000 1 0 0 0 0 0 0 110 2 4 1 1 3 3
3 1 2 1 4 4000 0 1 0 0 0 0 0 170 2 4 1 1 3 3
3 1 3 0 3 3000 0 0 1 0 0 0 8 110 2 4 1 1 3 3
3 1 4 0 3 3000 0 0 0 1 0 0 12 150 2 4 1 1 3 3
3 1 5 0 5 5000 0 0 0 0 1 0 12 110 2 4 1 1 3 3
3 1 6 0 0 0 0 0 0 0 0 1 0 0 2 4 1 1 3 3
3 2 1 0 4 4000 1 0 0 0 0 0 8 150 2 4 1 1 3 3
3 2 2 1 6 6000 0 1 0 0 0 0 0 150 2 4 1 1 3 3
3 2 3 0 5 5000 0 0 1 0 0 0 0 130 2 4 1 1 3 3
3 2 4 0 5 5000 0 0 0 1 0 0 15 150 2 4 1 1 3 3
3 2 5 0 2 2000 0 0 0 0 1 0 15 170 2 4 1 1 3 3
3 2 6 0 0 0 0 0 0 0 0 1 0 0 2 4 1 1 3 3
3 3 1 0 4 4000 1 0 0 0 0 0 15 190 2 4 1 1 3 3
3 3 2 1 6 6000 0 1 0 0 0 0 8 190 2 4 1 1 3 3
3 3 3 0 3 3000 0 0 1 0 0 0 4 170 2 4 1 1 3 3
3 3 4 0 3 3000 0 0 0 1 0 0 15 130 2 4 1 1 3 3
3 3 5 0 2 2000 0 0 0 0 1 0 12 190 2 4 1 1 3 3
3 3 6 0 0 0 0 0 0 0 0 1 0 0 2 4 1 1 3 3
3 4 1 0 4 4000 1 0 0 0 0 0 4 110 2 4 1 1 3 3
3 4 2 1 3 3000 0 1 0 0 0 0 0 190 2 4 1 1 3 3
3 4 3 0 4 4000 0 0 1 0 0 0 12 130 2 4 1 1 3 3
3 4 4 0 5 5000 0 0 0 1 0 0 4 190 2 4 1 1 3 3
3 4 5 0 6 6000 0 0 0 0 1 0 15 110 2 4 1 1 3 3
3 4 6 0 0 0 0 0 0 0 0 1 0 0 2 4 1 1 3 3
3 5 1 0 5 5000 1 0 0 0 0 0 8 170 2 4 1 1 3 3
3 5 2 1 2 2000 0 1 0 0 0 0 4 150 2 4 1 1 3 3
3 5 3 0 6 6000 0 0 1 0 0 0 4 130 2 4 1 1 3 3
3 5 4 0 6 6000 0 0 0 1 0 0 12 170 2 4 1 1 3 3
3 5 5 0 2 2000 0 0 0 0 1 0 8 130 2 4 1 1 3 3
3 5 6 0 0 0 0 0 0 0 0 1 0 0 2 4 1 1 3 3
3 6 1 0 2 2000 1 0 0 0 0 0 4 130 2 4 1 1 3 3
3 6 2 1 3 3000 0 1 0 0 0 0 15 190 2 4 1 1 3 3
3 6 3 0 6 6000 0 0 1 0 0 0 4 190 2 4 1 1 3 3
3 6 4 0 6 6000 0 0 0 1 0 0 0 130 2 4 1 1 3 3
3 6 5 0 3 3000 0 0 0 0 1 0 0 170 2 4 1 1 3 3
3 6 6 0 0 0 0 0 0 0 0 1 0 0 2 4 1 1 3 3
3 7 1 0 2 2000 1 0 0 0 0 0 8 190 2 4 1 1 3 3
3 7 2 1 5 5000 0 1 0 0 0 0 15 130 2 4 1 1 3 3
3 7 3 0 2 2000 0 0 1 0 0 0 12 170 2 4 1 1 3 3
3 7 4 0 2 2000 0 0 0 1 0 0 0 150 2 4 1 1 3 3
3 7 5 0 6 6000 0 0 0 0 1 0 15 150 2 4 1 1 3 3
3 7 6 0 0 0 0 0 0 0 0 1 0 0 2 4 1 1 3 3
3 8 1 0 5 5000 1 0 0 0 0 0 4 170 2 4 1 1 3 3
3 8 2 1 4 4000 0 1 0 0 0 0 8 130 2 4 1 1 3 3
3 8 3 0 4 4000 0 0 1 0 0 0 0 11 2 4 1 1 3 3
3 8 4 0 4 4000 0 0 0 1 0 0 15 170 2 4 1 1 3 3
3 8 5 0 3 3000 0 0 0 0 1 0 12 130 2 4 1 1 3 3
3 8 6 0 0 0 0 0 0 0 0 1 0 0 2 4 1 1 3 3
3 9 1 0 6 6000 1 0 0 0 0 0 8 170 2 4 1 1 3 3
3 9 2 1 5 5000 0 1 0 0 0 0 0 190 2 4 1 1 3 3
3 9 3 0 5 5000 0 0 1 0 0 0 12 150 2 4 1 1 3 3
3 9 4 0 2 2000 0 0 0 1 0 0 15 110 2 4 1 1 3 3
3 9 5 0 4 4000 0 0 0 0 1 0 4 150 2 4 1 1 3 3
3 9 6 0 0 0 0 0 0 0 0 1 0 0 2 4 1 1 3 3
3 10 1 1 3 3000 1 0 0 0 0 0 4 110 2 4 1 1 3 3
3 10 2 0 4 4000 0 1 0 0 0 0 12 190 2 4 1 1 3 3
3 10 3 0 3 3000 0 0 1 0 0 0 8 150 2 4 1 1 3 3
3 10 4 0 5 5000 0 0 0 1 0 0 8 110 2 4 1 1 3 3
3 10 5 0 6 6000 0 0 0 0 1 0 12 110 2 4 1 1 3 3
3 10 6 0 0 0 0 0 0 0 0 1 0 0 2 4 1 1 3 3

topic Conjoint question: parameter estimates all levels in Statistical Procedures

Conjoint question: parameter estimates all levels