Classification of Data & Objectives | Types, Purpose & Examples

Raw data is the information we collect from surveys, observations, or everyday records. This data is unorganized and usually just a long list of values or details. Because it’s not sorted, it can be hard to understand or use for analysis. When we arrange raw data into groups or categories, we call it grouped data. Grouping makes the data easier to read and study. We can organize it based on time, place, or specific intervals. Grouped data helps us find patterns, while raw data, by itself, is not very helpful for drawing clear conclusions.

What is Classification of Data?

Classification of data in statistics is the process of organizing data into homogeneous or comparable groups as per their general characteristics. The collected data, also identified as raw data or ungrouped data, is always in an unorganized form and requires to be organized and displayed in a meaningful and readily understandable form in order to help further statistical analysis. Quantitative figures are acknowledged as data and statistics is the art that deals with the:

• Selection of data
• Classification of data
• Portrayal of data
• Review of data
• Presentation of data

Maths Notes Free PDFs

Topic	PDF Link
Class 12 Maths Important Topics Free Notes PDF	Download PDF
Class 10, 11 Mathematics Study Notes	Download PDF
Most Asked Maths Questions in Exams	Download PDF
Increasing and Decreasing Function in Maths	Download PDF

The arrangement or classification of data helps users in comparison as well as analysis. For example, the population of a country can be classified according to gender, age, educational qualification, job profile and more. There are basically two types of data:

Primary data: The data which is gathered by original observation or analysis or count falls under primary data.

Secondary data: The data which are collected from the studies of others is termed secondary data. For example, data collected by a person is the primary sort of data for him; also this is secondary data for all others.

Some of the primary objectives regarding the classification of data are:

• It compresses the volume of data in an easily understandable form such that the similarities and variations can be instantly recognized.
• It reduces unnecessary details.
• It promotes comparison and highlights the important aspects of data.
•  It enables an individual to point out the important aspects of the data at a glance and help draw inferences.
• It helps in the statistical processing of the materials gathered.
• It gives importance to the prominent data accumulated while classifying the optional elements.

Bases of Classification of Data

Classification of data is the process of arranging data in an orderly manner. Now let us learn the types of data classification in statistics. The four important bases of classification are discussed below:

1. Qualitative Base
2. Quantitative Base
3. Geographical Base
4. Chronological Base

Geographical Classification of data

The classification of data according to location is what classification is called a geographical classification of data. In other words, we can say that in the geographical classification of data, we classify data according to geographical area or region.

For instance, if we perform the data regarding the production of sugarcane, pulses or cotton, in view of the four central regions in India, this would be recognized as a geographical classification of data as shown below:

It is also recognized as ‘spatial classification of data’.

Chronological Classification of data

In chronological classification of data, data are classified on the basis of time of existence, such as years, months, weeks, days, etc. In such a type of classification, data are arranged either in ascending or descending order with reference to time such as years, quarters, months, weeks, days etc. Chronological data classification is also known as temporal classification of data.

Qualitative Classification of data

Classification of data according to characteristics and attributes is called qualitative classification of data. In such a classification of data; data are categorized based on some attributes or quality such as gender, honesty, hair colour, literacy, intelligence, religion, etc.

For such a method of classification of data in statistics the attribute under study cannot be measured and can only be discovered whether it is present or missing in the sections of study. Qualitative data are classified into the following two types:

• Simple classification of data
• Manifold classification of data

Simple Classification of data

In such a type of qualitative classification of data, we qualify data precisely into two groups. For example, if the student population is to be analyzed concerning one attribute, say educational qualification, then we can classify them into two groups namely educated and uneducated. Similarly, they can also be classified into elementary-level education or higher education.

Such a type of classification where two by two groups are developed is termed simple or twofold or dichotomous classification of data. Here two classes are created, one holding the attribute and the other not holding the attribute.

Manifold Classification of data

Apart from forming only two groups, if we further divide the data based on some additional attributes within those attributes, it is identified as a manifold classification. This implies that when we organize data into two groups according to an attribute furthermore the two groups are divided into an additional two according to the added attribute. As a result, there can be various levels of classification of data with more than just two classes.

For example, the same classification of the student population can be done as male population and female population, and further, the male and female populations are classified into educated and uneducated which is further classified into basic level education and higher-level education.

Quantitative Classification of data

Quantitative classification of data refers to variables of quantities that can be either estimated or operated on. This implies in contrast to qualitative classification, quantitative classification of data enables the numerical distribution of data into classes.

A quantitative type of variable can be calculated, measured and/or operated with; it presents particular information on a numerical scale. From a sample of items, quantitative variables relate to the weight of the article, its temperature, its volume, height, income, results of students, or any type of computation or numerical value.

In such types of classification in statistics as we saw data is classified with a separate range of values, hence this represents the change in the value of an event over time or across different domains. Therefore, quantitative classification is also recognized as classification by variables. The quantitative data are of two types: discrete and continuous.

Discrete quantitative data points to variables that can be counted and possess a finite fixed amount. On the other hand, data that can be measured and have any value is counted under continuous quantitative data.

Discrete quantitative data example:

• The number of learners in a class.
• The total amount of data saved on a pen drive.

Continuous quantitative data example:

• The temperature of every day in summer.
• The weight of every character on a train.

Learn about Tabulation

Important Points on Classification of Data

So far we mention the types of classification of data and learn them one by one along with the importance of classification in statistics. Let us learn some important as well as miscellaneous points regarding the topic.

• The term variable means to alter or change. Therefore, variable means the property that varies differs, or shifts from individual to individual, time to time, location to location, etc.
• There are two types of variables: discrete variables and continuous variables.
• Frequency relates to the number of times every variable is repeated and frequency distribution points to data analyzed based on some variable that can be measured such as rates, weight, length, payments etc.
• A table is a methodical arrangement of statistical data within columns and rows. Rows denote the horizontal arrangements whereas the columns denote the vertical ones.
• If we classify the data concerning a single characteristic, then this is recognized as a one-way classification.
• When we classify the data concerning two characteristics at a time then we are performing a two-way classification.
• If we consider more than two characteristics at a particular instance to classify presented data then we are dealing with multi-way classification.
• There are four levels of measurement of statistical data. Starting from lowest to highest, the four levels of statistical data are nominal, ordinal, interval and ratio.

Properties of Classification of Data

1. Systematic Arrangement
   Data is grouped and arranged logically into different classes or categories for easy interpretation.
2. Homogeneity Within Classes
   All the data in a particular class share similar characteristics. For example, a class may include only people aged 20–30.
3. Mutual Exclusiveness
   Each data item fits into one and only one class. No overlap is allowed between classes.
4. Exhaustiveness
   All data must be included in one of the classes. No data should be left unclassified.
5. Uniformity
   The same basis or criteria are used for dividing the data into classes throughout the classification process.
6. Clarity
   Classes should be clearly defined so that there is no confusion in assigning data.
7. Flexibility
   The classification system should be adaptable to suit the purpose of the study or analysis.

Applications of Classification of Data

1. Easy Analysis
   Classification helps break large data into smaller groups, making it easier to study and understand.
2. Quick Decision-Making
   When data is grouped well, it's easier for businesses, schools, or the government to make smart decisions.
3. Efficient Data Handling
   Organizing data into classes helps manage and store it more effectively, especially when the volume is large.
4. Useful in Research
   Researchers use classified data to find trends, patterns, and relationships between variables.
5. Better Presentation
   Data in tables, graphs, or charts is easier to present and understand when it is properly classified.
6. Helpful in Surveys
   In survey reports, classification helps to group people based on age, income, gender, etc., for better study.
7. Supports Planning and Policy Making
   Governments use classified data for planning roads, schools, healthcare, and other public services.
8. Medical Records
   Hospitals classify patient data by age, disease, and treatment, helping doctors give better care.

We hope that the above article on Classification of Data is helpful for your understanding and exam preparations. Stay tuned to the Testbook App for more updates on related topics from Mathematics, and various such subjects. Also, reach out to the test series available to examine your knowledge regarding several exams.

If you are checking Classification of Data article, also check related maths articles:
Data Collection Instrument	Categorical Data
Data Collection Methods	Bias in Statistics
Types of Statistics	Data Interpretation

Important Links
NEET Exam
NEET Previous Year Question Papers	NEET Mock Test	NEET Syllabus
CUET Exam
CUET Previous Year Question Papers	CUET Mock Test	CUET Syllabus
JEE Main Exam
JEE Main Previous Year Question Papers	JEE Main Mock Test	JEE Main Syllabus
JEE Advanced Exam
JEE Advanced Previous Year Question Papers	JEE Advanced Mock Test	JEE Advanced Syllabus