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UGC NET PAPER 1 · UNIT 07 · MODULE 2: UNDERSTANDING DATA · TOPIC 4

Quantitative and Qualitative Data

The comparison table, subtypes, converting between the two and mixed-methods research.

  • 6 sub-topics
  • 3 min read
  • Topic 4 of 11

What this topic covers

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

  1. §OverviewQuantitative data answer the question "how much or how many?" and are expressed in numbers.
  2. 4.1ComparisonBasis Quantitative data Qualitative data Nature Numerical and measurable Descriptive and categorical Question answered How much, how many, how often Why, how, what kind…
  3. 4.2SubtypesQuantitative data can be discrete (counts such as number of students) or continuous (measurements such as height).
    • Quantitative data
    • Qualitative data
  4. 4.3Converting between the twoQualitative data can be quantified by coding, for example male = 1 and female = 2, or by counting how often a theme appears.
  5. 4.4Mixed-methods researchMixed-methods research combines both kinds of data in one study.
  6. 4.5Strengths and limitationsQuantitative strengths: precise, comparable, easy to generalise and to test statistically. Limitations: may miss context and meaning. Qualitative strengths: rich detail…
  7. 4.6Worked examplesExample 1.
    • Example 1
    • Example 2
    • Example 3
    • Example 4

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§Overview#

Quantitative data answer the question "how much or how many?" and are expressed in numbers. Qualitative data answer "what kind?", "why?" or "how?" and are expressed in words, categories, images or descriptions. Research in education and social science uses both, and UGC NET often tests the differences, examples and matching tools.

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4.1Comparison#

In brief

Basis Quantitative data Qualitative data Nature Numerical and measurable Descriptive and categorical Question answered How much, how many, how often Why, how, what kind…

Basis Quantitative data Qualitative data
Nature Numerical and measurable Descriptive and categorical
Question answered How much, how many, how often Why, how, what kind
Examples Marks, income, age, number of children Opinion, attitude, gender, colour, religion
Collection tools Structured questionnaire, tests, scales, experiments Interviews, observation, focus groups, case studies, open-ended questions
Analysis Statistics: mean, correlation, regression, t-test Content analysis, thematic analysis, narrative analysis, grounded theory
Sample size Usually large Usually small
Approach Objective, can be generalised Subjective, in-depth, context bound
Reasoning Deductive (tests a hypothesis) Inductive (builds a theory)
Presentation Tables, graphs, charts Quotations, themes, descriptions, concept maps

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4.2Subtypes#

In brief

Quantitative data can be discrete (counts such as number of students) or continuous (measurements such as height).

  • Quantitative data
  • Qualitative data

Quantitative data can be discrete (counts such as number of students) or continuous (measurements such as height). By measurement scale they are interval or ratio.

Qualitative data can be nominal (categories with no order, such as gender or state of residence) or ordinal (categories with an order, such as satisfaction levels from low to high). Binary data (yes or no) are a special nominal case.

A useful trick: a Likert scale response such as "strongly agree" to "strongly disagree" is ordinal. If it is coded 1 to 5 and averaged, it is being treated as quantitative, which is a debated practice.

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4.3Converting between the two#

In brief

Qualitative data can be quantified by coding, for example male = 1 and female = 2, or by counting how often a theme appears.

Qualitative data can be quantified by coding, for example male = 1 and female = 2, or by counting how often a theme appears. This does not make the variable quantitative in meaning: the codes are labels only. Quantitative data can be turned into categories by grouping, for example marks into grades A, B and C.

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4.4Mixed-methods research#

In brief

Mixed-methods research combines both kinds of data in one study.

Mixed-methods research combines both kinds of data in one study. A common design uses a survey (quantitative) to find patterns, followed by interviews (qualitative) to explain them. Triangulation means checking a finding using more than one method, source or data type to improve validity.

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4.5Strengths and limitations#

In brief

Quantitative strengths: precise, comparable, easy to generalise and to test statistically. Limitations: may miss context and meaning. Qualitative strengths: rich detail…

  • Quantitative strengths: precise, comparable, easy to generalise and to test statistically. Limitations: may miss context and meaning.

  • Qualitative strengths: rich detail, flexible, explores new topics. Limitations: time-consuming, subjective, hard to generalise.

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4.6Worked examples#

In brief

Example 1.

  • Example 1
  • Example 2
  • Example 3
  • Example 4

Example 1. Which is quantitative: "customer feedback comments", "daily sales in rupees", "favourite colour" or "type of school"? Answer: daily sales in rupees.

Example 2. A researcher interviews 15 teachers about why they resist technology and groups the answers into themes. Which type of data and reasoning is this? Answer: qualitative data with inductive reasoning.

Example 3. The number of books issued each day is which type of data? Answer: quantitative and discrete.

Example 4. Grades A, B, C, D are which kind of data? Answer: qualitative and ordinal.

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Test yourself on Quantitative and Qualitative Data

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

Last updated: . Notes are a revision aid; check official UGC sources for any rule or scheme that changes over time. Spotted a mistake? Write to netlawverse@gmail.com.