CONJOINT Analysis (July 2014 updated)

CONJOINT ANALYSIS July 2014 updated

Prepared by Michael Ling Page 1

QUANTITATIVE RESEARCH METHODS

SAMPLE OF

CONJOINT PROCEDURE

Prepared by

Michael Ling



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



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



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).



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



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

Education

CONJOINT Analysis (July 2014 updated)