Classification of Data: Types and Uses

The data collected, also referred to as raw or ungrouped, is always unorganized and must be arranged and represented in a meaningful and easily understandable format to allow further statistical analysis. As a result, a researcher must compress a large amount of data into a more intelligible and assimilable form. Tabulation is associated with the systematic organization and display of categorized data, whereas classification of data is associated with grouping into different classes based on specific qualities. 

Forms of representation of data

We have three types of representation of data. 

Textual or Descriptive presentation– The data is described in the text. This type of display is more appropriate when the amount of data isn’t too vast.

Tabular presentation– Data is displayed horizontally and vertically in a tabular representation. The most significant benefit of tabulation is it arranges data for statistical analysis and decision-making.

Tabulation

Tabulation is the procedure of summarising grouped or classified data in a table to be readily understood, and a researcher can quickly find the information they need. A table is a column-by-column and row-by-row arrangement of categorized data. As a result, a statistical table allows a researcher to show a large amount of data in a thorough and ordered manner. It allows for easier comparison and frequently shows patterns in data that would otherwise go undetected. In reality, classification and tabulation of data are not two separate procedures. They complement each other. Data are categorized before tabulation and then shown in distinct rows and columns of a table. As a result, classification and tabulation of data are successive stages.

A table has the following components.

• Table Number 
• Title 
• Captions or Column Headings 
• Stubs or Row Headings 
• Body of the Table
• Unit of Measurement
• Source
• Note

The classification and tabulation is of four types. Those are:

Types of Classification of data in statistics

Below is the list of four types of classification of data:

• Geographical Classification- We categorize the data according to geographical region or location in this sort of classification.
• Qualitative Classification- In this sort of categorization, data is categorized based on similar features or qualities such as sex, literacy, religion, job, etc. Such characteristics cannot be quantified using a scale. When one attribute, which is dichotomous, is classified, two classes are established, one having the attribute and the other not having the feature.
• Chronological Classification- The collected data is presented chronologically according to the sequence of time stated in weeks, months, years, etc. The information is organized in chronological order.
• Quantitative Classification- Quantitative Classification is the classification of data based on measurable attributes such as height, weight, etc.

Quantitative classification of data in statistics has two types. They are-

• Discrete frequency distribution

• Continuous frequency distribution

This kind of classification of data has two elements: variable and frequency. 

• Variable

The term “variable” refers to a feature that changes in magnitude or amount. Depending on the context, a variable might be continuous or discrete. 

Depending on the context, a variable might be continuous or discrete.  Variable 

It can only take specific real number values (integers). 

• Continuous Variable 

Within a specific interval, t can have any numerical value.

• Frequency

The number of times every variable is repeated is referred to as frequency.

• Frequency Distribution 

The term “frequency distribution” refers to the classification of data based on a measurable characteristic such as pricing, weight, height, or earnings. 

Diagrammatic presentation– Compared to tabular or textual displays, this enables the quickest explanation of the real issue to be described by data. The abstract thoughts in numbers are efficiently translated into a more tangible and easily understood form when data is presented in a diagrammatic format. Although diagrams are less precise, they are far more effective at expressing facts than tables.

Conclusion

Raw Statistical data is practically impossible to comprehend, so correct presentation is required. When data is provided in an easy-to-understand format, the reader may gain information in a shorter time, and statistical analysis becomes more accessible. Fundamental data is straightforward to identify and retrieve with a well-planned data analysis system. This is especially relevant in legal discovery, risk management, and compliance. This is why the classification of data is vital to analyze and decide crucial matters.