Qualitative vs Quantitative: Which Do You Need? (Probably Both)

Numbers show scale, stories reveal meaning. Discover why the best evaluations combine both approaches, when to use each method, and how to integrate statistics with human stories for powerful impact evidence.

An evaluation team was presenting findings to a charity’s board. They showed impressive numbers: 72 percent of participants improved on the outcome measure. Employment rates were up 15 points year-on-year. Cost-effectiveness had improved.

The board sat back satisfied. “Excellent results,” the chair said.

Then a trustee asked: “But what’s actually different for people? Can you show us a real example of how someone’s life changed?”

The evaluator looked uncomfortable. “Well, the numbers show improvement, but we didn’t gather qualitative data. We focused on quantitative measures for rigour.”

The trustee leaned forward. “The numbers are interesting, but they don’t tell me whether we’re actually helping people or just moving them around. I want to know what’s different.”

This is a common tension. Quantitative data (numbers, statistics, scales) feels rigorous and objective. Qualitative data (stories, observations, descriptions) feels rich and meaningful but subjective.

Many organisations assume they have to choose. They pick numbers for credibility, or stories for depth. They don’t realise they need both.

Let me show you why qualitative and quantitative data serve different purposes, and why the most powerful evaluations combine them.

What’s the Difference?

Let’s start with clear definitions:

Quantitative data is numerical. It answers “How much?” or “How many?”

Examples:

65 percent of participants gained employment
Average anxiety score reduced from 18 to 10
340 people accessed the service
Cost per outcome achieved: £450

Quantitative data is about measurement and counting.

Qualitative data is descriptive. It answers “What?” and “How?” and “Why?”

Examples:

Participants report that mentoring relationships helped them believe employment was possible
Staff observe participants taking on leadership roles in group sessions
A young person describes how the programme changed how she talks about her future
Community members comment on increased activity and connection in the neighbourhood

Qualitative data is about understanding and meaning.

The key difference: Quantitative shows scale and pattern. Qualitative shows depth and texture.

You might have quantitative data showing 70 percent of people improved. Qualitative data tells you what that improvement looks like, how it happened, whether it’s sustainable, and what it means to people.

Why Organisations Often Choose One Over the Other

Why quantitative feels safer:

It seems objective (numbers don’t lie)
It’s easily comparable (this year vs last year; our service vs another)
It looks rigorous and professional
Funders understand numbers
It’s easy to report (a percentage is clear)
It’s often what funders ask for

Why organisations might skip quantitative:

It feels reductive (numbers miss important nuances)
It’s hard to get good quantitative data (requires consistent measurement)
Some outcomes resist quantification
It’s easy to game (manipulate numbers)
Staff worry it depersonalises their work

Why qualitative feels important:

It captures depth and meaning
It shows real impact in people’s words
It’s harder to game (harder to fake authentic stories)
It connects to why the work matters
It preserves people’s dignity and complexity
Beneficiaries often prefer sharing stories to filling out scales

Why organisations might skip qualitative:

It seems subjective and unrigorous
It’s time-consuming to collect and analyse
It’s harder to compare or aggregate
Funders sometimes doubt it as “real evidence”
It requires interpretation (which feels like opinion)
Smaller sample sizes (can’t interview everyone)

Both concerns are somewhat valid. But both types of data are also valuable.

What Each Type of Data Does Well

Quantitative data excels at:

Showing scale: How widespread is this outcome?
Showing change: Did things improve? By how much?
Showing comparability: How does this compare to last year or to other organisations?
Showing patterns: Are certain groups achieving more than others?
Making a credible case: Funders and commissioners trust numbers

Example: “Employment rates improved from 35 percent to 55 percent. This is a 57 percent improvement year-on-year.”

This clearly shows progress at scale.

Qualitative data excels at:

Showing how things work: What’s the mechanism behind outcomes?
Showing meaning: What does this outcome actually mean to people?
Showing context: What circumstances affect whether outcomes happen?
Showing unintended consequences: Did anything unexpected happen?
Building connection: Stories make impact real and human

Example: “A young person said, ‘Before this programme I didn’t think anyone would hire me. Now I’ve applied for three jobs and I’m going to interviews. I’m terrified but I believe I could actually get employed.'”

This shows what the 55 percent employment outcome actually means in human terms.

Why You Probably Need Both

Here’s where it gets important: the best evaluation uses both quantitative and qualitative data because they answer different questions.

Questions quantitative data answers:

How many people achieved this outcome?
Did the percentage improve year-on-year?
Do different groups achieve at different rates?
What’s the cost per outcome?
Is this sustainable at scale?

Questions qualitative data answers:

How did change happen for people?
What was the mechanism? What made the difference?
Does this outcome matter in people’s lived experience?
What barriers prevented some people from achieving it?
What unintended effects happened?
Would people choose this outcome if asked?

Notice: these aren’t the same questions. You can’t answer “How did change happen?” with a percentage. You can’t answer “What percentage achieved this?” with a story.

The combination:

Quantitative shows you 65 percent of participants gained employment (impressive number).

Qualitative shows you employment happened because people developed job-seeking skills, gained confidence through mentoring relationships, and accessed job leads through programme connections.

Together: You know it happened (quantitative) and you understand how and why it happened (qualitative). That’s powerful.

Quantitative shows you anxiety scores reduced for 70 percent of participants.

Qualitative shows you people describe managing difficult moments without panic, using techniques they learned, and feeling more able to cope.

Together: You know anxiety improved and what that looks like in practice.

Common Misconceptions

Misconception 1: Qualitative data is “nice to have” but quantitative is what matters

False. Qualitative data is essential to understanding whether your quantitative results mean what you think they mean.

A programme shows 80 percent employment. But qualitative data reveals people lasted an average of three weeks before quitting due to poor working conditions. That changes the meaning entirely.

Misconception 2: Quantitative data is objective and qualitative is biased

Not quite. Quantitative data can be gamed (by changing definitions, cherry-picking participants, manipulating scoring). Qualitative data can be biased (by selectively sharing stories, misinterpreting what people meant).

Both require rigour and honesty to be credible.

Misconception 3: You need lots of qualitative data to be credible

False. A small number of well-selected stories told faithfully can be more credible than cherry-picked anecdotes. Quality matters more than quantity.

Misconception 4: Funders only care about numbers

Some do. Many increasingly want to understand the “so what?” behind the numbers. They want to know the qualitative reality.

Presenting quantitative and qualitative together is often more persuasive than numbers alone.

How to Use Them Together Effectively

Approach 1: Use quantitative to show reach, qualitative to show depth

Quantitative: “We served 450 people, of whom 280 completed the programme.”

Qualitative: “Here are three examples of how people experienced the programme and what changed for them.”

Together: You’ve shown scale (450 people) and meaning (what change looks like).

Approach 2: Use quantitative to identify patterns, qualitative to understand them

Quantitative: “Employment outcomes are 75 percent for young people aged 16-21 but only 40 percent for people aged 35+.”

Qualitative: “Interviews reveal that younger participants face discrimination based on experience gaps, which mentoring can help overcome. Older participants face discrimination based on age, which mentoring can’t prevent. This explains the difference.”

Together: You’ve identified a pattern (quantitative) and explained it (qualitative).

Approach 3: Use qualitative to generate hypotheses, quantitative to test them

Qualitative: “In interviews, people describe confidence as the key shift. They say mentors believing in them made the difference.”

Quantitative: “We measured confidence before and after mentoring. It improved for 82 percent of people. Participants who reported strong mentor relationships had greater confidence gains than those with weaker relationships.”

Together: You’ve explored why outcomes happen and tested your hypothesis with data.

Approach 4: Use quantitative for accountability, qualitative for learning

Quantitative: “We achieved 65 percent of our employment target.”

Qualitative: “Interviews reveal barriers to employment: lack of childcare support, transport costs, and employer discrimination. These are more significant than skills deficits. This suggests we need to adjust our approach.”

Together: You’re accountable for outcomes (quantitative) and learning how to improve (qualitative).

Practical Guidance for Combining Data Types

If you’re currently using only one type and want to add the other:

Start with one simple qualitative addition to quantitative data:

You’re already measuring outcomes numerically. Add one question to your exit survey: “Can you describe a specific moment or change for you because of this programme?”

Collect 20-30 brief responses. You now have qualitative data showing what outcomes look like.

Or start with one simple quantitative addition to qualitative data:

You’re gathering stories. Add one simple measure: “On a scale of 1-10, how confident do you feel?” asked before and after.

You now have quantitative data showing the scale of confidence change that your stories describe.

Build it into normal practice:

Don’t add evaluation as separate work. Integrate it:

Outcome surveys can include both closed questions (quantitative) and open questions (qualitative)
Exit interviews can ask for both ratings and stories
Focus groups can yield both counts (“how many experienced X?”) and descriptions (“what did X look like?”)

Analyse together:

Don’t keep data types separate. Look at them together:

Where does quantitative data show progress? What do qualitative stories say about that progress?
Where do people report challenges? What does quantitative data show about those challenges?

What Good Combined Data Looks Like

Here’s an example of effectively combined quantitative and qualitative data:

Quantitative findings:

68 percent of participants improved on the outcome measure
Improvement was consistent across age groups (65-70 percent)
Participants who attended 8+ sessions showed greater improvement (78 percent) than those who attended fewer (52 percent)
Average improvement: 4.2 points on the 10-point scale

Qualitative findings:

Participants describe three key elements that supported improvement: the peer relationships in the group, learning specific strategies, and staff believing in them
Early drop-outs (before session 5) report not feeling welcomed or understanding how the programme was relevant
Participants sustaining attendance describe the group as “feeling like people get me” and “a place I can be honest”

Combined story:

The programme is effective for most people (68 percent improved), with sustained attendance being a key factor. Understanding what helps people stay engaged (welcome, relevance, peer connection) is important, as drop-outs happened early and appear linked to not feeling welcomed. The mechanism of change seems to involve multiple elements: peer support, practical strategies, and staff belief. Future improvement should focus on stronger onboarding to improve early engagement.

That tells a complete story: numbers showing scale and patterns, stories showing meaning and mechanism.

Common Pitfalls to Avoid

Pitfall 1: Using qualitative data to override quantitative findings

If quantitative data shows 60 percent improved but you have three compelling stories of people who didn’t, don’t conclude “actually, it didn’t work.” Recognise the stories as important but don’t use them to invalidate scale.

Both are true: most people improved (quantitative) and some didn’t, with real stories of struggle (qualitative).

Pitfall 2: Using quantitative data to dismiss qualitative concerns

If quantitative shows 75 percent improved but qualitative reveals the improvement was small and people don’t feel different, don’t dismiss the qualitative. It’s telling you something important about what the numbers mean.

Pitfall 3: Cherry-picking stories that confirm your hypothesis

If you’ve decided your programme works and only share stories of success, you’re not doing real evaluation. Share stories that tell the whole truth: successes and struggles, intended and unintended effects.

Pitfall 4: Quantifying what shouldn’t be quantified

Not everything needs a number. Some qualitative findings are more powerful left as descriptions: “Participants describe their relationships with each other as genuine friendships, which many say they’d never experienced before.”

Forcing “80 percent report friendship” might lose the meaning.

Reflection Questions

Before you move on, take a moment to consider:

What type of data are you currently gathering? Only quantitative, only qualitative, or both?

What questions could you answer with the other type that you can’t currently answer?

If you added one simple measure of the other type, what would you learn?

What would your impact story be if you combined quantitative and qualitative data effectively?

Are there places where quantitative and qualitative data tell conflicting stories? What would that tell you?

About This Series

This guide is part of a learning series on Measuring Social Impact for Charities and Social Enterprises. We’re here to make evaluation practical, accessible, and useful, not overwhelming.

Want to go deeper? Social Value Lab supports organisations to develop proportionate, practical approaches to measuring and communicating impact. We believe every organisation deserves to understand and communicate their value, regardless of size or budget.

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