EconomicsCBSE Class 11 · Organisation of Data

Classification of Data and Discrete and Continuous Variables

Updated 17 October 2026 · Fact-checked

Raw data are unorganised figures as first collected. Classification groups them by time (chronological), place (spatial), attribute (qualitative) or size (quantitative). A variable is discrete if it takes only isolated values such as the number of children, and continuous if it can take any value in a range, such as height or income.

Raw data can be classified four ways; a discrete variable has gaps between counted values, while a continuous variable fills every point in a range.

Illustration drawn for QuestionBuddy.

Understand Classification of Data and Types of Variables

Raw data. Data in the form collected, unorganised and difficult to read; for example the marks of 40 students in the order the answer scripts were checked. Organisation arranges them to see their main features.

Classification is the process of grouping data into homogeneous classes. Objectives: to condense, to show similarities and differences, to prepare for tabulation and analysis.

Bases of classification.

Chronological (temporal): by time, e.g. yearly population from 2001 to 2021; also called a time series.
Spatial (geographical): by place, e.g. rainfall by state.
Qualitative: by an attribute that cannot be measured numerically, e.g. gender, religion, literacy; we only count how many have it.
Quantitative: by a measurable characteristic (variable), e.g. income, age, marks.

Variable. A characteristic that can take different values.

Discrete variable: takes isolated (counted) values, usually whole numbers: number of children, number of rooms, number of employees. Between 2 and 3 no value is possible.
Continuous variable: can take any value in a range, including fractions: height, weight, income, time, temperature.

Why it matters. A discrete variable is usually grouped as ungrouped values (0, 1, 2, 3) or classes; a continuous variable must be grouped into classes because individual values rarely repeat. Treatment of class limits depends on the type (exclusive classes are natural for continuous variables).

Examples. Number of vehicles at a signal: discrete. Weights of vehicles: continuous. Gender of customers: attribute (qualitative). Sales by month: chronological.

Frequency and frequency distribution (preview). The number of observations in a class is its frequency.

Table: four bases with an example each.

Classification of Data and Types of Variables: key facts

Bases: Chronological time
Spatial place
Qualitative attribute
Quantitative size; Four
Discrete: Isolated values (counted); Children, rooms
Continuous: Any value in a range (measured); Height, income
Time series: Chronological classification; Values over time

Chronological	time
Spatial	place
Qualitative	attribute
Quantitative	size

How to answer Classification of Data and Types of Variables questions

Questions give a data description and ask for the type of classification or variable.

1Ask: is it by time, place, attribute or size?
2Ask: is the variable counted (discrete) or measured (continuous)?
3Qualitative attributes are counted, not measured.
4Give a reason with the example.
5State why continuous data need classes.

Classification of Data and Types of Variables in real life

The idea, met in an everyday situation. Answer the question in your own words, then compare with the model answer. The one-line hook is what to remember.

Class survey

Suppose a class records for each student the number of siblings, the height in cm and whether they like cricket.

Name the type of each item.

Try it first, then show the solutionSolution

Number of siblings: discrete variable (counted).
Height: continuous variable (measured).
Liking cricket: qualitative attribute (counted yes or no).

Answer: Discrete, continuous, qualitative.

Memory hook: Count, measure, attribute.

Tricks to remember Classification of Data and Types of Variables

Short memory aids. Say each one aloud once, then use it on a question the same day.

TPAS
Time, Place, Attribute, Size.
Use it when: Bases
Count = Discrete, Measure = Continuous
Counted gaps; measured flows.
Use it when: Variables
Raw is raw
Raw data are unorganised.
Use it when: Raw data

Classification of Data and Types of Variables: solved examples

Ordered from board level to HOTS. Try each one before you open the solution.

Example 1: Type of variable · 2 marks · Very short answerBoard level

Classify as discrete or continuous: (a) number of cars in a family (b) weight of a person (c) monthly income (d) number of pages in a book.

Try it first, then show the solutionSolution

(a) discrete; (d) discrete (1 mark).
(b) continuous; (c) continuous (1 mark).

Answer: Discrete: a, d; continuous: b, c.

Example 2: Bases of classification · 2 marks · Very short answerBoard level

Name the basis of classification in: (a) literacy of men and women (b) yearly rainfall (c) rainfall by state (d) marks of students.

Try it first, then show the solutionSolution

(a) qualitative; (b) chronological (1 mark).
(c) spatial; (d) quantitative (1 mark).

Answer: Qualitative, chronological, spatial, quantitative.

Example 3: Objectives · 3 marks · Short answerHarder board level

State three objectives of classification and why raw data are difficult to read.

Try it first, then show the solutionSolution

To condense a large mass of data into a few classes (1 mark).
To bring out similarities and differences, and prepare for tabulation (1 mark).
Raw data are unorganised, so patterns cannot be seen (1 mark).

Answer: Condense, compare, prepare.

Example 4: Chronological vs spatial · 3 marks · Short answerHarder board level

Explain with examples the difference between chronological and spatial classification, and the use of each.

Try it first, then show the solutionSolution

Chronological groups by time, e.g. yearly production (1 mark).
Spatial groups by place, e.g. production by state (1 mark).
Chronological shows trend; spatial shows regional differences (1 mark).

Answer: Time series versus regional comparison.

Example 5: Is it continuous? · 5 marks · Long answerHOTS / competency

Marks out of 100 given to whole numbers, the height of a plant and the number of leaves on it are recorded. Classify each, explain the borderline case, and say how each would be grouped.

Try it first, then show the solutionSolution

Leaves: discrete (counted) (1 mark).
Height: continuous (measured) (1 mark).
Marks in whole numbers are counted in steps but can be treated as continuous when the underlying ability is continuous (1 mark).
Height must be grouped into classes such as 10-20 cm (1 mark).
Leaves can be listed as individual values or grouped (1 mark).

Answer: Leaves discrete; height continuous; marks borderline.

Where students lose marks in Classification of Data and Types of Variables

The slips examiners see most often, and what to write instead.

Mistake: Height as discrete
Whole-number reporting.
Wrong working
Height is discrete.
Right working
Height is continuous.
Do this: Measured.
Usually costs: 1 mark.
Mistake: Attribute as variable
Counting vs measuring.
Wrong working
Gender is a continuous variable.
Right working
Gender is a qualitative attribute.
Do this: Not numeric.
Usually costs: 1 mark.
Mistake: Time series as spatial
Basis confused.
Wrong working
Yearly data are spatial.
Right working
Yearly data are chronological.
Do this: Time.
Usually costs: 1 mark.

Exam tips

State the basis with a reason.
Count or measure: decide the variable type.
Give examples.
Mention time series for chronological data.

Practice questions on Classification of Data and Types of Variables

Write each answer on paper first. Open the hint only if you are stuck, and the answer only when you have finished.

Q1 · 1 mark · MCQBoard level
The time taken by an athlete to run 100 m is
(a) a discrete variable (b) a continuous variable (c) an attribute (d) a time series
Hint
Measured.
Answer
(b).
Q2 · 1 mark · Assertion-reasonHarder board level
Assertion (A): Gender is a qualitative attribute.
Reason (R): It cannot be measured numerically but can be counted.
(a) Both A and R are true and R is the correct explanation of A.
(b) Both A and R are true but R is not the correct explanation of A.
(c) A is true but R is false.
(d) A is false but R is true.
Hint
Attribute.
Answer
(a).
Q3 · 2 marks · Very short answerBoard level
Give two examples each of discrete and continuous variables.
Hint
Counted vs measured.
Answer
Discrete: number of children, number of rooms. Continuous: height, weight (also income or time).
Q4 · 4 marks · Case-basedHarder board level
Read and answer.
Suppose a table lists the population of a state for 2001, 2011 and 2021.
(a) Name the basis of classification. (1)
(b) What is such a series called? (1)
(c) Is population a discrete or continuous variable? (2)
Hint
Time.
Answer
(a) Chronological. (b) Time series. (c) Discrete, since people are counted in whole numbers.
Q5 · 3 marks · Short answerHOTS / competency
Suppose a researcher notes the daily temperature of a city for a month. Classify the data by basis and variable, and explain why grouping into classes is needed.
Hint
Continuous.
Answer
Chronological (daily), quantitative; temperature is continuous. Grouping is needed because exact values rarely repeat, so classes like 20-25 bring out the pattern.

Test your recall

Answer in your head or on paper first, then open the card. Pulling an answer out of memory makes it stick far better than re-reading.

1.Raw data?Show answer
Unorganised data.
2.Discrete example?Show answer
Number of children.
3.Continuous example?Show answer
Height.
4.Time series?Show answer
Chronological series.
5.Attribute?Show answer
Qualitative feature.
6.Spatial?Show answer
By place.

Classification of Data and Types of Variables: frequently asked questions

What is classification?

Grouping of data into classes.

What is a variable?

A characteristic that takes different values.

What is a discrete variable?

One with isolated, counted values.

What is a continuous variable?

One that can take any value in a range.

What is a qualitative classification?

Grouping by attribute.