BUSINESS RESEARCH METHODS ASSIGNMENT 1 Submitted by : Amol Bankar (10BM60008) Anuradha Chakraborty ( 10BM60014) Mayank Mohan (10BM60048) Niloy Ghosh (10BM60054)
BUSINESS RESEARCH METHODS
ASSIGNMENT 1
Submitted by :
Amol Bankar (10BM60008)
Anuradha Chakraborty ( 10BM60014)
Mayank Mohan (10BM60048)
Niloy Ghosh (10BM60054)
Conjoint Analysis
Conjoint analysis is a statistical technique used in market research to determine how people
value different features that make up an individual product or service. The objective of
conjoint analysis is to determine what combination of a limited number of attributes is most
influential on respondent choice or decision making. A controlled set of potential products or
services is shown to respondents and by analyzing how they make preferences between these
products, the implicit valuation of the individual elements making up the product or service
can be determined.
In the carpet cleaner example, we are evaluating 5 attributes and observing which attributes
are more valuable to the consumer. The following 18 combinations are presented to the
consumer along with 4 holdout cases which are used to test our analysis.
Card List
Card ID package design brand name price
Good
Housekeeping
seal
money-back
guarantee
1 1 A* Glory $1.39 yes no
2 2 B* K2R $1.19 no no
3 3 B* Glory $1.39 no yes
4 4 C* Glory $1.59 no no
5 5 C* Bissell $1.39 no no
6 6 A* Bissell $1.39 no no
7 7 B* Bissell $1.59 yes no
8 8 A* K2R $1.59 no yes
9 9 C* K2R $1.39 no no
10 10 C* Glory $1.19 no yes
11 11 C* K2R $1.59 yes no
12 12 B* Glory $1.59 no no
13 13 C* Bissell $1.19 yes yes
14 14 A* Glory $1.19 yes no
15 15 B* K2R $1.39 yes yes
16 16 A* K2R $1.19 no no
17 17 A* Bissell $1.59 no yes
18 18 B* Bissell $1.19 no no
19a 19 A* Bissell $1.59 yes no
20a 20 C* K2R $1.19 yes no
21a 21 A* Glory $1.59 no no
22a 22 A* Bissell $1.19 no no
a. Holdout
The 5 attributes that are tested and their apparent relation to the value of the product are given
as:
Model Description
N of Levels
Relation to Ranks
or Scores
package 3 Discrete
brand 3 Discrete
price 3 Linear (less)
seal 2 Linear (more)
money 2 Linear (more)
All factors are orthogonal.
The response of 10 individuals is taken and the part worth of the various factors is
determined as:
Utilities
Utility Estimate Std. Error
package A* -2.233 .192
B* 1.867 .192
C* .367 .192
brand K2R .367 .192
Glory -.350 .192
Bissell -.017 .192
price $1.19 -6.595 .988
$1.39 -7.703 1.154
$1.59 -8.811 1.320
seal no 2.000 .287
yes 4.000 .575
money no 1.250 .287
yes 2.500 .575
(Constant) 12.870 1.282
The regression coefficients for the linear factors come out to be:
Coefficients
B Coefficient
Estimate
price -5.542
seal 2.000
money 1.250
The percentage importance of each factor is determined as:
Importance Values
package 35.635
brand 14.911
price 29.410
seal 11.172
money 8.872
Averaged Importance
Score
Thus, we can see that package and price are the two most important factors for the consumer
while a money-back guarantee has the least importance. The producer may now concentrate
on the appropriate factors so as to provide maximum value to the consumers.
The validity of the analysis may be determined by the correlation between observed and
estimated preferences as shown by the following statistics.
Correlationsa
Value Sig.
Pearson's R .982 .000
Kendall's tau .892 .000
Kendall's tau for Holdouts .667 .087
a. Correlations between observed and estimated
preferences
CLUSTER ANALYSISCLUSTER ANALYSISCLUSTER ANALYSISCLUSTER ANALYSIS
Cluster Analysis can be of two types:
A. Hierarchical Cluster Analysis: It helps in revealing natural groupings. Therefore, here, the
no. of clusters is automatically found out by the software itself.
B. Non-hierarchical : Here, the required fixed no. of clusters is given.
Here, we tried both the analyses on the data file dmddata.sav.
Hierarchical Cluster Analysis
1. MODEL SUMMARY
2. THE VARIABLES:
A. Categorical Variables:
• Income
• Marriage
• Gender
B. Continuous Variables:
• Age
• Children
• Year of Residence
K-Means Cluster Analysis
1. MODEL SUMMARY
3. THE VARIABLES:
.
A. Categorical Variables:
• Income
• Married
• Response to previous survey
B. Continuous Variables:
• Age
• Children
Factor Analysis
The analysis given below was done for data sample file car_sales.sav in PASW 18.0. Following variables were considered for
1) Price in thousands
2) Engine size
3) Horsepower
4) Width
5) Wheelbase
6) Length
7) Fuel capacity
8) Fuel efficiency
The eigen values used for extraction were greater than 1 using principal components analysis. The rotation method used was varimax ( Kaiser Normalization) and regression analysis method was made for relating variables. The Scree Plot for the same is given below where the components with steeper slopes are
recognized as important factor while making a decision. In the plot given below, the slope is steeper for components 1 and 2 and these account for almost 88 % of variance of other factors.
Total Variance Explained
Component Initial Eigenvalues Extraction Sums of Squared
Loadings
Rotation Sums of Squared
Loadings
Total % of
Variance
Cumulative
%
Total % of
Variance
Cumulative
%
Total % of
Variance
Cumulative
%
1 4.987 62.341 62.341 4.987 62.341 62.341 3.386 42.329 42.329
2 1.506 18.822 81.163 1.506 18.822 81.163 3.107 38.834 81.163
3 .584 7.303 88.466
4 .332 4.155 92.621
5 .241 3.019 95.640
6 .150 1.880 97.520
7 .127 1.589 99.109
8 .071 .891 100.000
Extraction Method: Principal Component Analysis.
The rotation component matrix is shown below :
Rotated Component Matrix
Component
1 2
Price in thousands -.008 .925
Engine size .497 .776
Horsepower .228 .920
Width .782 .385
Wheelbase .935 .052
Length .895 .121
Fuel capacity .707 .480
Fuel efficiency -.546 -.637
The above matrix shows the correlation of the factors with principal components. The above
matrix shows that wheelbase, horsepower and price in thousands are the main factors when
decision for choosing a new car is made.