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# Types of Classification

(i) Classification by Space or Spatial Classification (iii) Classification by Attribute or Qualitative Classification and (iv) Classification by Size or Quantitative Classification.

Types of Classification

The raw data can be classified in various ways depending on the nature of data. The general types of classification are: (i) Classification by Time or Chronological Classification

(i) Classification by Space or Spatial Classification (iii) Classification by Attribute or Qualitative Classification and (iv) Classification by Size or Quantitative Classification. Each of these types is now described.

## Classification by Time or Chronological Classification

The method of classifying data according to time component is known as classification by time or chronological classification. In this type of classification, the groups or classes are arranged either in the ascending order or in the descending order with reference to time such as years, quarters, months, weeks, days, etc. Illustrations for statistical data to be classified under this type are listed below:

·           Number of new schools established in Tamil Nadu during 1995 – 2015

·           Pass percentage of students in SSLC Board Examinations over a period of past 5 years

·           Index of market prices in stock exchanges arranged day-wise

·           Month-wise salary particulars of employees in an industry

·           Particulars of outpatients in a Primary Health Centre presented day-wise.

### Example 3.1

The classification of data relating to the price of 10 gms of gold in India during 2001 - 2012 is given in Table 3.1

### Example 3.2

The classification of data relating to the population of India from 1961 to 2011 is provided in Table 3.2:

## Classification by Space (Spatial) or Geographical Classification

The method of classifying data with reference to geographical location such as countries, states, cities, districts, etc., is called classification by space or spatial classification. It is also termed as geographical classification. The following are some examples:

·           Number of school students in rural and urban areas in a State

·           Region-wise literacy rate in a state

·           State-wise crop production in India

·           Country-wise growth rate in South East Asia

### Example 3.3

The classification of data relating to number of schools and types of schools in 7 major cities of Tamil Nadu as per the Annual Budget Report 2012 – 2013 is given in Table 3.3

### Example 3.4

Average yield of rice (Kg/hec) during 2014-15 as per the records of Directorate of Economics and Statistics, Ministry of Agriculture and Farmers Welfare, Government of India, in five states in India is given in Table 3.4

## Classification by Attributes or Qualitative classification

The method of classifying statistical data on the basis of attribute is said to be classification by attributes or qualitative classification. Examples of attributes include nationality, religion, gender, marital status, literacy and so on.

Classification according to attributes is of two kinds: simple classification and manifold classification.

In simple classification the raw data are classified by a single attribute. All those units in which a particular characteristic is present are placed in one group and others are placed in another group. The classification of individuals according to literacy, gender, economic status would come under simple classification.

In manifold classification, two or more attributes are considered simultaneously. When more attributes are involved, the data would be classified into several classes and subclasses depending on the number of attributes. For example, population in a country can be classified in terms of gender as male and female. These two sub-classes may be further classified in terms of literacy as literate and illiterate.

While classifying the data according to attributes, it is essential to ensure that the attributes involved have to be defined without ambiguity. For example, while classifying income groups, the investigator has to define carefully the different non-overlapping income groups.

### Example 3.5

The classification of students studying in a school according to gender is given in Table 3. 5

## Classification by Size or Quantitative Classification

When the characteristics are measured on numerical scale, they may be classified on the basis of their magnitude. Such a classification is known as classification by size or quantitative classification. For example data relating to the characteristics such as height, weight, age, income, marks of students, production and consumption, etc., which are quantitative in nature, come under this category.

### Example 3.6

The classification of data relating to nutritive values of three items measured per 100 grams is provided in Table 3.6

In the classification of data by size, data may also be classified deriving number of classes based on the range of observations and assigning number of observations lying in each class. The following is another example for classification by size.

### Example 3.7

The classification of 55 students according to their marks is given in Table 3.7

### Rules for Classification of Data

There are certain rules to be followed for classifying the data which are given below.

·           The classes must be exhaustive, i.e., it should be possible to include each of the data points in one or the other group or class.

·           The classes must be mutually exclusive, i.e., there should not be any overlapping.

·           It must be ensured that number of classes should be neither too large or nor too small. Generally, the number of classes may be fixed between 4 and 15.

·           The magnitude or width of all the classes should be equal in the entire classification.

·           The system of open end classes may be avoided.

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