Qualitative vs Quantitative Data: What’s the Difference?

Understanding the difference between qualitative and quantitative data is essential for PhD students and early-career researchers. These two data types shape how research questions are framed, how data is collected, and how findings are analyzed.

If you are working on a dissertation, journal article, or research proposal, choosing the right type of data is not just a methodological decision. It directly affects the quality and credibility of your research.

In this guide, we will clearly explain what qualitative and quantitative data are, how they differ, and when each approach is most appropriate. The discussion is especially relevant for researchers working with qualitative data analysis.

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Qualitative data is non-numerical data that focuses on meaning, experience, and interpretation rather than measurement.

Instead of asking “how many” or “how much,” qualitative data helps researchers understand how people experience a phenomenon and why they interpret it in certain ways.

Common examples of qualitative data include:

• Interview transcripts
• Focus group discussions
• Field notes from observations
• Open-ended survey responses
• Documents, texts, and archival materials

If your data consists primarily of words, narratives, or descriptions, you are working with qualitative data.

Qualitative data is especially valuable when:

• Context and nuance are important
• The topic is complex or underexplored
• Human experience is central to the research question

Qualitative research is often used in social sciences, education, health research, and critical studies, where understanding meaning and perspective is essential.

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Consider a study on doctoral supervision.

A qualitative approach might involve conducting in-depth interviews with PhD students and asking questions such as:

“How would you describe your experience working with your supervisor?”

Participants may discuss mentorship, power dynamics, emotional support, or institutional pressure. These interview transcripts form the qualitative dataset.

Rather than producing a numerical score, qualitative data provides rich, contextual insight into how doctoral students experience supervision.

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Qualitative data analysis is an interpretive process focused on identifying patterns and meaning within textual or visual data.

Researchers typically:

1. Read the data multiple times
2. Apply codes to segments of text
3. Identify recurring themes or concepts
4. Develop interpretations grounded in participants’ perspectives

The outcome is not a statistic but an explanation, conceptual framework, or theory.

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Quantitative data is numerical data that can be counted, measured, and statistically analyzed.

It answers questions such as:

• How many people experience this?
• How often does it occur?
• Is there a difference between groups?

Examples of quantitative data include:

• Survey ratings and Likert scales
• Test scores and exam results
• Frequencies and percentages
• Experimental measurements

Quantitative data is commonly used to test hypotheses, identify patterns at scale, and generalize findings to larger populations.

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Using the doctoral supervision example again, a quantitative approach might involve distributing a survey to 200 PhD students.

Participants are asked to rate their satisfaction with supervision on a scale from one to five.

The results are analyzed statistically to calculate averages, compare departments, or test relationships between variables.

These numbers tell researchers what is happening at scale, but they do not explain why students feel the way they do.

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Qualitative Data	Quantitative Data
Non-numerical	Numerical
Focuses on meaning	Focuses on measurement
Small, in-depth samples	Large, representative samples
Interpretive analysis	Statistical analysis
Explores “how” and “why”	Answers “how many” and “how much”

Both approaches are valuable and often complementary.

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Qualitative Data Collection Methods

Qualitative data is typically collected using flexible, open-ended methods such as:

• Interviews
• Focus groups
• Observations
• Open-response surveys

These methods allow participants to express their perspectives in their own words.

Quantitative data is collected using structured and standardized tools, including:

• Closed-ended surveys
• Experiments
• Tests and assessments
• Existing numerical datasets

The goal is consistency, comparability, and statistical reliability.

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Qualitative data is most appropriate when:

• You are exploring a new or complex topic
• You want to understand lived experience
• Context and interpretation are central

If your research question begins with “How do people experience…” or “How do people make sense of…”, qualitative methods are often the best choice.

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Quantitative data is more suitable when:

• You are testing hypotheses
• You need measurable variables
• Generalization to a larger population matters

Quantitative methods are especially useful for identifying trends, comparing groups, and measuring outcomes.

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Many PhD projects use mixed methods, combining qualitative and quantitative data in a single study.

For example:

• Qualitative interviews may be used to generate hypotheses
• Quantitative surveys may then test those hypotheses at scale

Mixed methods research offers both depth and breadth, but it requires careful design and methodological clarity.

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Qualitative and quantitative data serve different purposes in research.

• Qualitative data provides depth, meaning, and context
• Quantitative data provides scale, measurement, and comparison

Strong research does not choose sides. It chooses methods that align with the research question.

If your work focuses on qualitative data analysis, do not feel pressured to reduce everything to numbers. Meaning matters, and qualitative insights can be theoretically powerful and methodologically rigorous.