Types of Classification of Data
Once data is collected, the next important step is to group it meaningfully. Classification can be done in different ways depending on the type of data and the purpose of the study. Generally, data is classified into the following types:
Qualitative Classification (Descriptive)
This type of classification is based on qualities, attributes, or characteristics that cannot be measured numerically.
Examples of such attributes:
- Gender (Male, Female, Other)
- Religion (Hindu, Muslim, Christian, etc.)
- Marital Status (Single, Married, Divorced)
- Literacy (Literate, Illiterate)
- Employment Status (Employed, Unemployed)
⭐ Explanation
Qualitative classification divides data based on non-numerical traits. These traits can only be described, not measured. The categories may be words, labels, or classes representing the attribute.
This type is very useful in social sciences, behavioural studies, human resource data, and census reports.
Quantitative Classification (Numerical)
Here, classification is based on numerical characteristics, meaning the data can be measured and expressed in numbers.
Examples:
- Income groups (₹0–10,000; ₹10,000–20,000, etc.)
- Age groups (0–9, 10–19, 20–29, etc.)
- Marks scored (0–30, 31–60, 61–90, etc.)
- Population by height/weight
⭐ Explanation
Quantitative classification helps when we want to analyze numerical variation. It reveals patterns like high-income vs low-income groups, young vs old age groups, etc.
This is one of the most commonly used forms in statistics.
Temporal Classification (Based on Time)
This classification arranges data according to time periods.
Examples:
- Annual sales (2015, 2016, 2017...)
- Population growth every decade
- Monthly rainfall (January, February, …)
- Daily temperature
- Hourly traffic count
⭐ Explanation
Temporal classification is essential for time-series analysis. It helps observe how a variable changes over time — increasing, decreasing, seasonal patterns, trends, cycles, etc.
This is widely used in economics, weather analysis, financial markets, business reports, and performance comparison.
Spatial Classification (Based on Area)
Spatial classification groups data based on location or place.
Examples:
- Population state-wise (Odisha, Bihar, Kerala…)
- Literacy rate district-wise
- Sales region-wise (East, West, North, South)
- Crop production country-wise
⭐ Explanation
Spatial classification is used when we want to compare or analyze geographical differences. It helps identify regional patterns, inequalities, development levels, or geographic trends.
This is commonly used in census reports, marketing surveys, agricultural data, and regional economic studies.
Simple & Manifold Classification
⭐ a) Simple Classification
Here, data is classified based on only one characteristic.
Example:
Explanation: Simple classification is used when the study focuses on a single factor. It is easy to prepare and interpret.
⭐ b) Manifold (Multiple) Classification
Here, data is classified on the basis of two or more characteristics simultaneously.
Example:
-
Classifying students based on:
- Gender (Male/Female)
- And then on marks (Above 60%, Below 60%)
Explanation: Manifold classification gives a multi-dimensional view of data. It is very useful in deeper analysis where multiple factors influence results, e.g., socio-economic surveys or business research.
Statistics • Variables
Variables in Statistics – Meaning, Types & Role in Classification
In statistics, the word variable refers to anything that can change, vary, or take on different values from one person, object, or situation to another.
A variable is the opposite of a constant. A constant remains the same everywhere, but a variable changes with time, place, person, or condition.
Simple Understanding of Variables
If you collect data about people, their:
- Age
- Income
- Height
- Marks
- Opinions
All these values will differ for different individuals. Hence, they are called variables.
Why are variables important?
Variables form the foundation of statistical analysis. Without variables, there is nothing to measure, compare, classify, or study. They help us understand patterns, identify trends, and make decisions.
Classification of Variables (Highly Descriptive)
Variables can be classified in different ways depending on their nature and how they are measured. The major classifications are given below.
Qualitative Variables (Attributes)
These variables describe qualities or characteristics that cannot be measured numerically.
Examples:
- Gender
- Religion
- Nationality
- Marital status
- Eye color
These variables answer questions like “What type?” and “Which group?”. Since they are descriptive in nature, they fall under qualitative classification.
Quantitative Variables (Numerical Variables)
These variables can be measured and expressed in numbers. Their values represent quantity or magnitude.
Examples:
- Age
- Income
- Height
- Weight
- Number of family members
Quantitative variables answer questions like “How much?” and “How many?”. These variables form the basis of quantitative classification.
Continuous Variables
Continuous variables are a subtype of quantitative variables. They can take any value—whole numbers, decimals, or fractions—within a given range.
Examples:
- Height (for example, 168.5 cm, 170.2 cm)
- Weight (for example, 55.1 kg, 55.9 kg)
- Temperature
- Time
Continuous variables have an infinite number of possible values. Even between two numbers, infinitely many values can exist.
Discrete Variables
Discrete variables are another subtype of quantitative variables. They can take only whole numbers, not decimals.
Examples:
- Number of children
- Number of students in a class
- Marks out of 100 (if given as whole numbers)
Discrete variables result from counting, not measuring. They move from one whole number to the next (1, 2, 3, 4) without intermediate decimal values.
Dependent and Independent Variables
These variables show cause–effect relationships.
Independent Variable
The factor that influences or causes change.
Example: Hours studied.
Dependent Variable
The factor that changes because of the independent variable.
Example: Marks scored.
In simple terms, independent variables cause, and dependent variables respond.
Classification of Variables Under Quantitative Classification
When we apply quantitative classification, we specifically use quantitative variables, which can be further grouped into:
Used when the variable can take only whole number values.
Examples:
- Number of customers visiting per day
- Number of books sold
- Number of accidents recorded
How it is classified
Data is grouped into discrete classes such as:
- 0–5 customers
- 6–10 customers
- 11–15 customers
Each class represents countable units.
Used when the variable can take any value within a range, including decimals.
Examples:
- Weight
- Height
- Price
- Income
How it is classified
Data is grouped into continuous class intervals such as:
- ₹0–10,000
- ₹10,001–20,000
- ₹20,001–30,000
- 150–155 cm
- 155–160 cm
Intervals cover every possible value within the range, ensuring exhaustiveness.
How Variables Fit Into Quantitative Classification
Quantitative classification works only with numerical variables. It arranges them into class intervals so that:
- Comparison becomes easy
- Trends become visible
- The data becomes analysis-friendly
For example, a dataset of student marks (a quantitative variable) can be classified into:
- 0–20
- 21–40
- 41–60
- 61–80
- 81–100
This helps identify performance levels clearly.
Conclusion: A variable is anything that can vary or change. Variables are classified into qualitative and quantitative, and further into continuous, discrete, dependent, and independent.
In quantitative classification, only numerical variables are used, and they are arranged into discrete or continuous classes depending on the nature of data.