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Objectives and Characteristics of Classification of Data

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Data can not always be found in an organised manner. Therefore, an analyst or investigator has to properly organise the collected data for a better analysis of information and to reach the desired results. One of the most important methods of organising such data is known as the classification of data. Under this method, the raw information is converted into different statistical series in a way that provides better and more meaningful results. 

Classification of Data

For performing statistical analysis, various kinds of data are gathered by the investigator or analyst. The information gathered is usually in raw form, which is difficult to analyze. To make the analysis meaningful and easy, the raw data is converted or classified into different categories based on their characteristics. This grouping of data into different categories or classes with similar or homogeneous characteristics is known as the Classification of Data. Each division or class of the gathered data is known as a Class. The different basis of classification of statistical information are Geographical, Chronological, Qualitative (Simple and Manifold) and Quantitative or Numerical. 

For example, if an investigator wants to determine the poverty level of a state, he/she can do so by gathering the information of people of that state, and then classifying them on the basis of their income, education, etc. 

According to Conner, “Classification is the process of arranging things (either actually or notionally) in groups or classes according to their resemblances and affinities, and gives expression to the unity of attributes that may exist amongst a diversity of individuals.

Objectives of Classification of Data

1. Brief and Simple

Raw data gathered by the investigator cannot provide him/her with meaningful and effective results. Therefore it is essential to convert the raw material into different categories for which classification of data is used. The basic motive of the classification of data is to present the raw data collected by the investigator or analyst into different categories in a way that is brief and simple. Proper classification of data makes the data analysis more convenient. 

2. Utility

For the purpose of investigation, an analyst collects information from different sources and then classifies the data into different categories. Classification of data distinguishes the collected diverse set of data by bringing out similar or homogeneous information together, thus enhancing its utility.

3. Distinctiveness

It is not easy to form results from raw data gathered in one place in a heterogeneous manner. Therefore, it is essential to classify the given data into different categories. Classification of data aims at providing the analyst with obvious differences in the given set of data more distinctly. 

4. Comparability

It is not possible to compare two sets of data in raw form. Classification of data helps an investigator in comparing the given two sets of data and estimating results. For example, if we say the number of firms producing laptops in different locations of Kerala and Punjab is 30 and 25, respectively. It is easier to compare this information instead of raw data consisting of the names of every industry in Kerala and Punjab producing different goods. 

5. Scientific Arrangement

Classification of the raw data according to their similar characteristics helps in facilitating the proper arrangement of the collected data in a scientific manner. The scientific arrangement of data increases the reliability of data. 

6. Attractive and Effective

Classification makes the collected raw data effective and attractive. A lot can be understood just by looking at the data if it is properly presented and classified. 

Characteristics of a Good Classification 

1. Comprehensiveness

To get better results, it is essential to classify the collected raw data in a comprehensive manner. It means that every item of the collected data should get into some class, category, or group, and no item or data should be left behind. 

2. Clarity

The investigator collecting and analyzing data should classify the raw information into different classes clearly. Clear placement of items into different categories or classes means that there should not be even a single item whose placement brings out confusion in the mind of the reader and the investigator. 

3. Homogeneity

For effective results in the investigation, it is essential to distinguish the collected raw data into different homogeneous groups. In other words, the classification of data based on similar characteristics helps in easy and better analysis of data. 

4. Suitability

A classified set of data can provide essential information and required results only when the composition of the classes suits the objective of the investigation. For example, for determining the literacy rate of a country, gathering information and classifying it on the basis of people’s income and expenditure does not make any sense. The collected data must be classified as educated and uneducated. 

5. Stability

There should be stability in the basis of the classification of data. It means that the basis of the classification of data should not change with every investigation. In other words, a specific kind of investigation should be performed on the basis of the same set of classifications. 

6. Elastic

The classification of collected data should be elastic. Elasticity in data means that if the investigator wants to change something in classification because of any change in the purpose or objective of the investigation, he/she should be able to do so. 

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Last Updated : 06 Apr, 2023
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