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Unit 2: Classification of Data

notionally) in the groups or classes according to the unity of attributes that may subsist amongst Notes
a diversity of individuals.” The chief characteristics of any classification are:
1. The collected data are arranged into homogeneous groups.

2. The basis of classification is the similarity of characteristics or features inherent in the
collected data.
3. Classification of data signifies unity in diversity.

4. Classification of data may be actual or notional.
5. Classification of data may be according to certain measurable or non-measurable
characteristics or according to some combination of both.

Objectives of Classification

The main objectives of any classification are:

1. To present a mass of data in a condensed form.
2. To highlight the points of similarity and dissimilarity.
3. To bring out the relationship between variables.
4. To highlight the effect of one variable by eliminating the effect of others.

5. To facilitate comparison.
6. To prepare data for tabulation and analysis.

Requisites of a Good Classification

A good classification must possess the following features:
1. Unambiguous: The classification should not lead to any ambiguity or confusion.
2. Exhaustive: A classification is said to be exhaustive if there is no item that cannot be
allotted a class.
3. Mutually Exclusive: When a classification is mutually exclusive, each item of the data can
be placed only in one of the classes.
4. Flexibility: A good classification should be capable of being adjusted according to the
changed situations and conditions.

5. Stability: The principle of classification, once decided, should remain same throughout
the analysis, otherwise it will not be possible to get meaningful results. In the absence of
stability, the results of the same type of investigation at different time periods may not be
comparable.
6. Suitability: The classification should be suitable to the objective(s) of investigation.

7. Homogeneity: A classification is said to be homogeneous if similar items are placed in a
class.
8. Revealing: A classification is said to be revealing if it brings out essential features of the
collected data. This can be done by selecting a suitable number of classes. Making few
classes means over summarization while large number classes fail to reveal any pattern of
behaviour of the variable.

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