Classification of Data

After collecting data, it needs to be classified. Classified data is easier to understand. Lets discuss the meaning, purpose, and methods of data classification.

Meaning of Classification of Data

The first task of a researcher is to collect data. Data collection is done through various sources and methods. The data collected initially is disorganized. Such data is called raw data. Raw data is not considered suitable for analysis. 

Therefore, such raw data needs to be classified based on different criteria. The process of separating the collected data into different groups or classes based on specific characteristics is called data classification. 

For example, collecting information on 100 students who have completed grade 10 and then separating them into groups or classes based on geographical area, age group, gender, ethnicity, or the grade they obtained in the examination falls under data classification.

Purpose of Data Classification

Data collected for the first time is scattered in a disorganized manner. It is difficult to draw accurate conclusions from this data. Therefore, the collected data needs to be classified. Data classification is mainly done for the following purposes:

(a) To Present Data in a Concise Form: Data classification is used to present data in a concise form by removing unnecessary information from the large, disorganized raw data. This makes the data easier to understand, and the main characteristics of the data can also be easily identified.

(b) To Compare Data: Data classification is done in various ways. Classification helps in comparing different types of data. For example, students studying in a class can be classified based on gender, separating them into groups of male and female students. 

After classification, the academic achievement levels of male and female students can be compared. Thus, classification is used to compare data in two or more groups.

(c) To Study Interrelationships: Data can be classified based on two or more characteristics to study the relationship between these characteristics. For example, students who have completed grade 10 can be classified based on their gender and the optional subjects they study to study the interrelationship between gender and optional subjects.

(d) To Simplify Other Statistical Work: Data classification helps in performing other statistical tasks such as tabulation, graphical and diagrammatic presentation, data analysis, and interpretation by classifying disorganized data according to their common characteristics. It becomes easier to calculate measures like mean, median, mode, etc., from classified data.

Methods of Data Classification

Data can be classified in various ways. The basis for data classification mainly depends on the nature of the data and the objective of the research. Generally, data is classified based on geographical, chronological, qualitative, and quantitative bases.

(a) Geographical Classification: According to this method, the collected data is divided into different groups based on specific regions or locations. 

Examples of geographical classification include separating data collected from across Nepal into Himalayan region, Hilly region, and Terai region, classifying data based on provinces, or classifying data based on districts. 

In geographical classification, data is presented in order based on the alphabetical order of the locations, the size of the data, or the importance of the data.

(b) Chronological Classification: According to this method, data is classified based on time, such as year, month, week, day, etc. In this classification, data is presented in chronological order. This type of classification is used in economics to show production, income, expenditure, etc., at different times.

(c) Qualitative Classification: If data that cannot be numerically expressed is classified based on qualitative aspects, such classification is called qualitative or descriptive classification. Qualitative aspects include gender, character, color, literacy, occupation, beauty, etc. 

In this type of classification, all units of data are classified based on whether they possess a certain quality or not. For example, classifying a certain population based on how many people are literate or illiterate is qualitative classification.

(d) Quantitative Classification: If data is classified after being numerically expressed, such classification is called quantitative classification. For example, classification based on age, height, weight, price, production, income, expenditure, sales, profit, etc., is quantitative classification.