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UGC NET PAPER 1 · UNIT 07 · MODULE 2: UNDERSTANDING DATA · TOPIC 1

Data: Meaning, Characteristics and Types

What good data looks like and how data is typed by form, source and time.

  • 6 sub-topics
  • 3 min read
  • Topic 1 of 11

What this topic covers

6 sub-topics, in the order to study them. Tap one to jump to it.

  1. §OverviewData are raw facts, figures, observations or measurements collected for reference or analysis.
  2. 1.1Characteristics of good dataData are useful for decision making only when they have these qualities:
    • Accuracy
    • Relevance
    • Timeliness
    • Completeness
    • Consistency
  3. 1.2Types of data by formQuantitative data are numerical and measurable, such as marks, income and age.
  4. 1.3Types of data by sourcePrimary data are collected first-hand by the researcher for the study at hand.
  5. 1.4Types of data by time dimensionThis classification is often tested through examples, so learn the example with each type.
  6. 1.5Other useful termsCovers Population (universe), Sample, Parameter, Statistic, Unit and variable and more.
    • Population (universe)
    • Sample
    • Parameter
    • Statistic
    • Unit and variable
  7. 1.6Worked examplesExample 1.
    • Example 1
    • Example 2
    • Example 3

Revision mode hides the full explanations and keeps each section's key terms, lists and tables. Use it from your second read onward.

§Overview#

Data are raw facts, figures, observations or measurements collected for reference or analysis. On their own they carry little meaning: the number "72" is data, but "72 per cent of students passed" is information. Information is data that has been processed, organised and given context. Knowledge is information combined with understanding and experience, and wisdom is the judgement to apply it well. This ladder (data, information, knowledge, wisdom) is a favourite one-line MCQ.

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1.1Characteristics of good data#

In brief

Data are useful for decision making only when they have these qualities:

  • Accuracy
  • Relevance
  • Timeliness
  • Completeness
  • Consistency
  • Reliability
  • Comparability

Data are useful for decision making only when they have these qualities:

  • Accuracy: free from error and close to the true value.

  • Relevance: related to the question being studied.

  • Timeliness: available when the decision is needed, and not out of date.

  • Completeness: no important cases or variables missing.

  • Consistency: the same definitions and units are used throughout.

  • Reliability: repeated collection gives similar results.

  • Comparability: can be compared across places and periods.

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1.2Types of data by form#

In brief

Quantitative data are numerical and measurable, such as marks, income and age.

Quantitative data are numerical and measurable, such as marks, income and age. Qualitative data are descriptive and non-numerical, such as opinion, gender, caste and colour. Topic 4 covers this split in detail.

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1.3Types of data by source#

In brief

Primary data are collected first-hand by the researcher for the study at hand.

Primary data are collected first-hand by the researcher for the study at hand. Secondary data were collected earlier by someone else for another purpose and are reused. Topic 2 covers this in detail.

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1.4Types of data by time dimension#

In brief

This classification is often tested through examples, so learn the example with each type.

This classification is often tested through examples, so learn the example with each type.

Type Meaning Example
Cross-sectional Many units observed at one point in time Literacy rate of all 28 states in 2024
Time series One unit observed over many time points India's GDP for every year from 2000 to 2024
Panel (longitudinal) The same units followed over time The same 500 households surveyed every year for 10 years

A quick way to remember: cross-section is a snapshot, time series is a film of one thing, and panel is a film of many things.

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1.5Other useful terms#

In brief

Covers Population (universe), Sample, Parameter, Statistic, Unit and variable and more.

  • Population (universe)
  • Sample
  • Parameter
  • Statistic
  • Unit and variable
  • Raw data versus array
  • Metadata

  • Population (universe): the entire group under study.

  • Sample: the part of the population actually studied.

  • Parameter: a numerical value describing a population (for example the true population mean).

  • Statistic: a numerical value describing a sample (for example the sample mean). Memory aid: Parameter goes with Population, Statistic goes with Sample.

  • Unit and variable: the unit is the object observed (a student); the variable is the characteristic measured (marks).

  • Raw data versus array: raw data are unordered; an array is the same data sorted in ascending or descending order.

  • Metadata: data about data, such as the source, date, definition and unit of a data set.

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1.6Worked examples#

In brief

Example 1.

  • Example 1
  • Example 2
  • Example 3

Example 1. A study records the monthly rainfall of Ahmedabad for 12 months of 2025. Which type of data is this? Answer: time series, because one place is observed over several time points.

Example 2. A survey records the monthly household income of 200 families in one city in a single week. Which type of data is this? Answer: cross-sectional, because many units are observed at one time.

Example 3. "The average height of all students in a university is 165 cm" is a parameter, and "the average height of 50 selected students is 163 cm" is a statistic.

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Test yourself on Data: Meaning, Characteristics and Types

Practise with free MCQs and answer keys, then return here to fix what you got wrong.

↑ Module 2: Understanding Data · All 12 topics in Data Interpretation · Unit overview and sample MCQs

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