Characteristics of an Ideal Classification
The
classification of data largely depends upon the nature, scope and purpose of
the investigation. Following are some of the characteristics of ideal
classification.
i. Classification should not be Ambiguous
There should not
be ambiguity in the classification, if so, it would kill the purpose of
classification.
By
classification, we aim at filtering down the ambiguity therefore if there is
any ambiguity, it would spoil the purpose. Various classes should be defined in
such a way that there should be no doubt or confusion in the mind of the reader.
For example, population census is divided in two classes i.e., literate and
illiterate. For people to understand what is meant by those two classes, we
must define clearly what we mean by literate and illiterate, so that by looking
at the population data, we can compare between literate and illiterate persons
in the population data. The classification of clear data without any ambiguity
will give us the clear picture of the situation.
ii. Classification should be Stable
An ideal
classification should have stability and it should not be subject to changes;
otherwise comparison would become difficult. It should be maintained that
definitions are used in strict sense and no changes are made unless it is
necessary. Because it will make very difficult the work of comparing the old
data with the new data.
iii. Classification should not be Rigid
A good
classification should be flexible and capable of giving room to new situations
and circumstances. By stability, the rigidity of classes is never meant. No
classification can be stable forever. With the passage of time and changes in
circumstances, some classes become obsolete and have to be dropped, while fresh
classes have to be adopted and added. Therefore, a good classification should
be such that, it may adjust to new changes and yet remain stable.
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