1. CONJOINT ANALYSIS July 2014 updated
Prepared by Michael Ling Page 1
QUANTITATIVE RESEARCH METHODS
SAMPLE OF
CONJOINT PROCEDURE
Prepared by
Michael Ling

2. 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

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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

4. 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).

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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

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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