Page 266 - DMGT209_QUANTITATIVE_TECHNIQUES_II

Unit 14: Multivariate Analysis

Notes
Table

Attribute 1 2 3 4 5 6 7
Brand – A 1 0 0 1 0 0 1
Brand – B 0 0 1 1 1 0 0

One measure of simple matching S is given by:


a d
S =
a b c d



Where a = No. of attributes possessed by brands A and B
b = No. of attributes possessed by brand A but not by brand B
c = No. of attributes possessed by brand B but not by brand A
d = No. of attributes not possessed by both brands.
1 2 3

Substituting, we get S =   0.43

1 2 2 2 7


A and B’s association is to be the extent of 43%.
It is now clear that object A possess attributes 1, 4, and 7 while object B possess the attributes 3,
4 and 5. A glance at the above table will indicate that objects A and B are similar in respect of 2
(0 & 0), 6 (0 & 0) and 4 (1 & 1). In respect of other attributes, there is no similarity between A and
B. Now we can arrive at a simple matching measure by (a) counting up the total number of
matches – either 0, 0 or 1, (b) dividing this number by the total number of attributes.
Symbolically SAB = M / N
SAB = Similarity between A and B
M = Number of attributes held in common (0 or 1)
N = Total number of attributes
SAB = 3 / 7 = 0.43
i.e., A & B are similar to the extent of 43%.

Self Assessment

Fill in the blanks:
6. In a typical correspondence analysis, a cross-tabulation table of frequencies is first
.......................
7. ....................... Analysis is a technique used for classifying objects into groups.
8. The ....................... application of cluster analysis is in customer segmentation and estimation
of segment sizes.

14.4 Conjoint Analysis

Conjoint analysis is concerned with the measurement of the joint effect of two or more attributes
that are important from the customers’ point of view. In a situation where the company would
like to know the most desirable attributes or their combination for a new product or service, the
use of conjoint analysis is most appropriate.

LOVELY PROFESSIONAL UNIVERSITY 261