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FOCUS AREAS

Conjoint Analysis: Tire Pressure

Input Data Sets

Conjoint analysis requires preference and attribute variables. Each row of the data set corresponds to one of the products being evaluated. Each column corresponds to either an attribute level of the products or a preference score for the products. In this example, the preference variables are the ranks from the seven respondents, and the attribute variables are the four factors. The data set TIRES is created with the following DATA step. 

   data tires;
      input brand $ charges price mileage rank1-rank7;
      avgrank=sum(of rank1-rank7)/7;
      datalines;
   TireMax    7.50 45.00 40000 17 17  7 14 13 15 14
   TireMax    0.00 75.00 40000 18 15 17 17 17 18 17
   TireMax    0.00 60.00 60000 15 10  9  9  9  8  1
   TireMax    7.50 75.00 60000 16 11 15 12 14 16  6 
   TireMax    0.00 45.00 80000 13  3  1  1  2  3  9 
   TireMax    7.50 60.00 80000 14  4  8  4  5  9  8 
   GoodTreads 0.00 60.00 40000 11 12 13 15 16 12 15 
   GoodTreads 7.50 75.00 40000 12 14 18 18 18 17 18 
   GoodTreads 0.00 45.00 60000  9  5  4  7  4  4  3 
   GoodTreads 7.50 60.00 60000 10  7 10 10 11  6  2 
   GoodTreads 7.50 45.00 80000  7  2  2  2  3  2 10 
   GoodTreads 0.00 75.00 80000  8  6 12  5  6  7 11 
   RollsAhead 0.00 45.00 40000  5 16  6 13 12 14 13 
   RollsAhead 7.50 60.00 40000  6 13 11 16 15 13 16 
   RollsAhead 7.50 45.00 60000  3  8  5  8  7  5  4 
   RollsAhead 0.00 75.00 60000  4  9 14 11 10 11  5 
   RollsAhead 0.00 60.00 80000  1  1  3  3  1  1  7 
   RollsAhead 7.50 75.00 80000  2 18 16  6  8 10 12 
   ;

MRA Example: Tire Pressure Variable Selection

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