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Research Methodology - Dr. Krishan K. Pandey

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146 <strong>Research</strong> <strong>Methodology</strong><br />

some attribute, say C with which attributes A and B are associated (but in reality there is no association<br />

between A and B). Such association may also be the result of the fact that the attributes A and B<br />

might not have been properly defined or might not have been correctly recorded. <strong>Research</strong>er must<br />

remain alert and must not conclude association between A and B when in fact there is no such<br />

association in reality.<br />

In order to judge the significance of association between two attributes, we make use of Chisquare<br />

test * by finding the value of Chi-square ( χ2 ) and using Chi-square distribution the value of<br />

χ2 can be worked out as under:<br />

χ 2<br />

2<br />

dOij −Eiji<br />

=∑<br />

i = 1, 2, 3 …<br />

E<br />

where j = 1, 2, 3 …<br />

O ij = observed frequencies<br />

E ij = expected frequencies.<br />

Association between two attributes in case of manifold classification and the resulting contingency<br />

table can be studied as explained below:<br />

We can have manifold classification of the two attributes in which case each of the two attributes<br />

are first observed and then each one is classified into two or more subclasses, resulting into what is<br />

called as contingency table. The following is an example of 4 × 4 contingency table with two attributes<br />

A and B, each one of which has been further classified into four sub-categories.<br />

Table 7.2: 4 × 4 Contingency Table<br />

Attribute A<br />

ij<br />

A 1 A 2 A 3 A 4 Total<br />

B 1 (A 1 B 1 ) (A 2 B 1 ) (A 3 B 1 ) (A 4 B 1 ) (B 1 )<br />

Attribute B B 2 (A 1 B 2 ) (A 2 B 2 ) (A 3 B 2 ) (A 4 B 2 ) (B 2 )<br />

B 3 (A 1 B 3 ) (A 2 B 3 ) (A 3 B 3 ) (A 4 B 3 ) (B 3 )<br />

B 4 (A 1 B 4 ) (A 2 B 4 ) (A 3 B 4 ) (A 4 B 4 ) (B 4 )<br />

Total (A 1 ) (A 2 ) (A 3 ) (A 4 ) N<br />

Association can be studied in a contingency table through Yule’s coefficient of association as<br />

stated above, but for this purpose we have to reduce the contingency table into 2 × 2 table by<br />

combining some classes. For instance, if we combine (A 1 ) + (A 2 ) to form (A) and (A 3 ) + (A 4 ) to form<br />

(a) and similarly if we combine (B 1 ) + (B 2 ) to form (B) and (B 3 ) + (B 4 ) to form (b) in the above<br />

contingency table, then we can write the table in the form of a 2 × 2 table as shown in Table 4.3<br />

* See Chapter “Chi-square test” for all details.

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