Page 302 - DMGT404 RESEARCH_METHODOLOGY

Research Methodology

Notes
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%.

SPSS Command for Cluster Analysis

Stage 1
Enter the input data along with variable and value labels in an SPSS file.

1. Click on STATISTICS at the spss menu bar.
2. Click on CLASSIFY followed by HIERARCHICAL CLUSTER.
3. Dialogue box will appear select all the variables which are required to be used in cluster
analysis. This can be done by clicking on the right arrow to transfer them from the variable
list on the left.
4. Click on METHOD. The dialogue box will open. Choose "Between Groups Linkage" as the
CLUSTER METHOD.
5. Click CONTINUE to return to main dialogue box.
6. Click STATISTICS on the main dialogue box. Choose "Agglomeration schedule" so that it
will appear in the final output click CONTINUE.

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