CONJOINT Analysis (July 2014 updated)
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CONJOINT Analysis (July 2014 updated)

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    CONJOINT Analysis (July 2014 updated) CONJOINT Analysis (July 2014 updated) Document Transcript

    • CONJOINT ANALYSIS July 2014 updated Prepared by Michael Ling Page 1 QUANTITATIVE RESEARCH METHODS SAMPLE OF CONJOINT PROCEDURE Prepared by Michael Ling
    • CONJOINT ANALYSIS July 2014 updated Prepared by Michael Ling Page 2 PART 1 The questionnaire is designed based on a 2^3 fractional factorial design to compare the main effects of four attributes – price, quality, gears, bike types – on consumer’s decision making. Interaction effects are not to be considered here. The design matrix for the questionnaire is as shown below. The respondents are asked to rank their purchase preferences amongst the eight scenarios on a 15-point Likert scale that ranges from “Extremely likely to buy” to “Extremely unlikely to buy”. Price Quality Gears Bike Type 1 $600 (+1) High (+1) Yes (+1) Sports (+1) 2 $400 (-1) High (+1) Yes (+1) Sports (+1) 3 $600 (+1) Low (-1) Yes (+1) Regular (-1) 4 $400 (-1) Low (-1) Yes (+1) Regular (-1) 5 $600 (+1) High (+1) No (-1) Regular (-1) 6 $400 (-1) High (+1) No (-1) Regular (-1) 7 $600 (+1) Low (-1) No (-1) Sports (+1) 8 $400 (-1) Low (-1) No (-1) Sports (+1) PART 2 The individual (Respondent #1) and the group responses of the experiment are listed in Table 1. The coding scheme used for the four independent categorical variables in the regression analysis is as shown below. Code Price Quality Gears Bike Type 1 $600 High Yes Sports -1 $400 Low No Regular Individual Responses
    • CONJOINT ANALYSIS July 2014 updated Prepared by Michael Ling Page 3 In the case of the individual, R2 is 1 because respondent #1 is the population (Table 2) and hence the p-values are not relevant. Price (p < .001), Quality (p < .001) and Gears (p < .001) are found to be statistically significant, whereas Bike Types is non-significant. The regression equation is Rating = 8 - 5.5 * Price + 1.0 * Quality + 5.0 * Gears where the regression coefficients of Price, Quality, Gears and Bike Type are -5.5, 1, 5 and 0 respectively (Table 3). The standardized coefficients of Price, Quality, Gears and Bike Types are -.980, .178, .089 and 0 respectively. The relative importance of the attributes can be found by comparing their t values and, in the individual case, Price is the most important attribute as it has the largest absolute t value, followed by Quality and Gears (Table 3). A review of the individual responses (Table 1) supports that Price is the most important attribute as the four highest preference ratings 15, 14, 13 and 12 are accorded to the low price scenarios 2, 4, 6 and 8 respectively. Consequently, the individual can be considered as a value buyer and his preference is in the order of (i) low price and (ii) high quality. Group Responses In the case of the group, R2 is .535 which indicates that the regression model accounts for 53.5 percent of the variance (Table 4). The adjusted R2 is .482. The statistically significant attributes are Price (p < .001) and Quality (p < .001) only. Gear and Bike Types are not statistically significant (alpha at 0.05 level). The regression equation is Rating = 8.425 – 2.375 * Price + 2.075 * Quality where the regression coefficients of Price and Quality are -2.375 and 2.075 respectively (Table 5). The p-values are used when inference needs to be made to a population from a sample. As stated earlier, the p-values have no relevance in the individual case. Comparing Individual and Group Effects
    • CONJOINT ANALYSIS July 2014 updated Prepared by Michael Ling Page 4 In the case of the group, the standard coefficients of Price and Quality are BetaPrice = - .532 and BetaQuality = .465 respectively. When compared against those in the individual case, BetaPrice = -.98 and BetaQuality = .178, Price is relatively more important to the individual than the group and Quality is relatively more important to the group than the individual. Price and Quality are found to be significant in the individual and the group (all at p < .001). Gears is found to be significant in the individual (p<.001) but not in the group. Bike Types is found to be non-significant in both the individual and the group. In the case of the individual, the regression equation is Rating = 8 - 5.5 * Price + 1.0 * Quality + 5.0 * Gears. The incremental change of utility is ($600-$400)/11 = $18.18/unit. As a result, the amount that the individual would pay for is as below:-  An extra unit of Quality is 2*1*$18.18 = $36.36.  An extra unit of Gears is 2*5*$18.18 =$181.8.  An extra unit of Bike Types is $0 (non-significant). In the case of the group, the regression equation is Rating = 8.425 – 2.375 * Price + 2.075 * Quality. The incremental change of utility is ($600-$400)/4.75 = $42.11/unit. As a result, the amount that the group would pay for is as below:-  An extra unit of Quality is 2*2.075*$42.11 = $174.74.  An extra unit of Gears is $0 (non-significant).  An extra unit of Gears is $0 (non-significant).
    • CONJOINT ANALYSIS July 2014 updated Prepared by Michael Ling Page 5 Appendix Table 1: Responses Respondent 1 (Individual) Respondent 2 Respondent 3 Respondent 4 Respondent 5 Q1 4.00 12.00 10.00 10.00 8.00 Q2 15.00 15.00 14.00 14.00 9.00 Q3 2.00 6.00 6.00 1.00 7.00 Q4 13.00 10.00 9.00 5.00 15.00 Q5 3.00 8.00 7.00 7.00 12.00 Q6 14.00 13.00 13.00 9.00 13.00 Q7 1.00 1.00 2.00 3.00 11.00 Q8 12.00 3.00 4.00 4.00 12.00 Table 2: Model Summary (Individual) Model R R Square Adjusted R Square Std. Error of the Estimate 1 1.000a 1.000 1.000 .00000 a. Predictors: (Constant), BType, Gear, Quality, Price Table 3: Coefficients (Individual)a Model Unstandardized Coefficients Standardized Coefficients t Sig.B Std. Error Beta 1 (Constant) 8.000 .000 5.937E8 .000 Price -5.500 .000 -.980 -4.082E8 .000 Quality 1.000 .000 .178 74211271.080 .000 Gear .500 .000 .089 37105635.540 .000 BType .000 .000 .000 .000 1.000 a. Dependent Variable: Responses
    • CONJOINT ANALYSIS July 2014 updated Prepared by Michael Ling Page 6 Table 4: Model Summary (Group) Model R R Square Adjusted R Square Std. Error of the Estimate 1 .732a .535 .482 3.25434 a. Predictors: (Constant), BType, Gear, Quality, Price Table 5: Coefficients (Group)a Model Unstandardized Coefficients Standardized Coefficients t Sig.B Std. Error Beta 1 (Constant) 8.425 .515 16.373 .000 Price -2.375 .515 -.532 -4.616 .000 Quality 2.075 .515 .465 4.033 .000 Gear .825 .515 .185 1.603 .118 BType -.225 .515 -.050 -.437 .665 a. Dependent Variable: Responses