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Qualitative vs quantitative data comes down to words versus numbers in surveys. Quantitative data measures how many, how much, or how often something occurs. Qualitative data describes why, how, or what happened behind a pattern. Most effective surveys collect both types together in one instrument.
Key Takeaways
- Quantitative data comes from closed-ended questions with predefined answer options.
- Qualitative data comes from open-ended questions answered in respondents’ own words.
- Quantitative analysis uses statistics; qualitative analysis uses themes and categories.
- Using both types together produces richer, more actionable survey results.
- Qualitative and quantitative methods each answer different research questions specifically.
- CRM-native survey tools can analyze both data types on the same record.
What Is Quantitative Data in Surveys?
Quantitative data is any survey response that produces a number or count. It comes from closed-ended questions where respondents pick from predefined answer options. Rating scales, multiple choice, and yes/no questions all generate quantitative data.
Quantitative data examples in surveys
- A CSAT score of 4 out of 5 after a support interaction.
- 78% of respondents selecting “satisfied” or “very satisfied” on a scale.
- An NPS rating of 9 out of 10 from a quarterly survey.
- 42% of customers choosing “price” as their primary purchase factor.
Quantitative survey questions work best for measuring trends and tracking changes. They produce data that is easy to chart, compare, and statistically analyze.
What Is Qualitative Data in Surveys?
Qualitative data is any survey response expressed in words, not numbers. It comes from open-ended questions where respondents write their own answers. Free-text fields, comment boxes, and follow-up prompts all generate qualitative data.
Qualitative data examples in surveys
- “The onboarding process was confusing because nobody explained step three.”
- “I switched because your competitor offered a faster support response time.”
- “The product itself is excellent but the billing process needs improvement.”
- “Our team stopped using the reporting feature after the last update broke it.”
What Is the Difference Between Qualitative and Quantitative Data?
Qualitative and quantitative data differ in format, collection method, and analysis approach.
| Factor | Quantitative data | Qualitative data |
| Format | Numbers, counts, percentages | Words, descriptions, narratives |
| Question type | Closed-ended, predefined options | Open-ended, free-text response |
| What it answers | How many, how much, how often | Why, how, what happened |
| Analysis method | Statistical analysis, charts, averages | Thematic analysis, coding, categorization |
| Output | Scores, percentages, trend lines | Themes, patterns, representative quotes |
| Subjectivity | Objective, standardized across respondents | Subjective, unique per respondent |
Types of Survey Data: Where Each Type Comes From
Survey data types map directly to the question format used.
Quantitative question types:
- Likert scale (strongly disagree to strongly agree)
- Star or numeric rating (1 to 5 or 1 to 10)
- Multiple choice with single or multiple selection allowed
- Ranking or ordering questions with predefined items
- Yes/no or binary choice questions
Qualitative question types:
- Open-ended text boxes for free-form written answers
- “Please explain your answer” follow-up prompts after a rating
- “What would you change?” or “describe your experience” fields
- Comment fields at the end of a survey section
SurveyVista’s guide on how to write qualitative survey questions covers the design principles behind each type. It explains how to frame open-ended questions that produce actionable feedback.
Quantitative vs Qualitative Survey Questions: Side by Side
The same topic can be measured with either type of question. The choice depends on whether you need a number or a reason.
| Topic | Quantitative survey question | Qualitative survey question |
| Product satisfaction | How satisfied are you? (1 to 5 scale) | What do you like most about this product? |
| Support quality | How easy was it to resolve your issue? (1 to 5) | Describe your support experience in your own words. |
| Purchase decision | Which factor influenced your purchase most? (options) | What made you choose us over other options? |
| Employee engagement | I feel valued at work. (agree/disagree scale) | What would make this a better place to work? |
| Event feedback | How would you rate this event overall? (1 to 5) | What would you change about the event? |
Common Mistakes When Combining Both Data Types
Asking too many open-ended questions in one survey. Including three to five qualitative questions per survey prevents respondent fatigue effectively. More than that, and completion rates drop sharply in most programs.
Ignoring qualitative data in the analysis phase. Teams often chart quantitative scores and skip the open-ended comments entirely. The comments contain the explanation that makes the score actionable for follow-up.
Not pairing a qualitative question with its quantitative counterpart. A rating question followed by “please explain your answer” produces paired data. That pairing is what connects the number to the reason behind it.
Treating quantitative and qualitative data as two separate reports. When both data types land on the same CRM record, one report shows everything. A split across two tools produces two disconnected views instead.
Quantitative vs Qualitative Research in Survey Design
Quantitative vs qualitative research describes the broader methodology, not just question types. Survey research methods fall into one or both categories depending on design. Quantitative research tests a hypothesis using numerical data from a large sample. Qualitative research explores a topic using detailed responses from a smaller group.
In survey design, this distinction shapes three specific decisions:
Sample size. Quantitative research needs enough respondents for statistical significance. Qualitative research works with smaller groups but needs richer responses.
Question balance. A quantitative survey runs mostly closed-ended questions with limited open-ended fields. A qualitative survey inverts that ratio using mostly open-ended prompts.
Analysis approach. Quantitative analysis runs statistical operations and produces trend charts. Qualitative analysis codes responses into themes and surfaces representative quotes.
AAPOR’s best practices for survey research cover both qualitative and quantitative methods. Their guidance on pretesting specifically recommends qualitative methods before fielding. AAPOR’s 2026 Survey Practice special issue focuses entirely on mixed methods research. Using qualitative and quantitative methods together produces stronger survey research results.
How to Analyze Survey Data: Both Types Together
Most survey programs collect both data types simultaneously. The analysis challenge is connecting them together into one view.
Quantitative analysis steps:
- Calculate response distributions and averages per question asked.
- Segment results by customer tier, region, or demographic group.
- Track scores over time to identify trend direction and magnitude.
- Compare results against benchmarks or prior survey period data.
Qualitative analysis steps:
- Read through open-ended responses and tag recurring themes identified.
- Group tagged responses into categories that represent shared concerns.
- Count theme frequency to identify which issues appear most often.
- Pull representative quotes that illustrate each theme for stakeholder reporting.
Connecting the two:
- Pair a low quantitative score with the qualitative comment explaining why.
- Segment qualitative themes by the quantitative score that accompanied each response.
- Use sentiment analysis to classify open-ended text at scale automatically.
SurveyVista’s customer feedback questionnaire guide covers designing surveys with both types. The survey reporting page shows how both data types render in dashboards.
Why CRM-Native Survey Tools Handle Both Types Better
Survey data becomes most useful when it sits next to customer records. A CSAT score of 2 means more alongside the account’s renewal date. An open-ended complaint means more alongside the support case it followed.
Native Salesforce survey tools keep both data types on the same record. Quantitative scores land as fields that reports and dashboards can filter. Qualitative responses land as text that sentiment analysis can classify.
An external tool holding responses in a separate database breaks that link. A native tool like SurveyVista keeps it intact from submission.
Explore SurveyVista’s survey analysis software for both data types →
Frequently Asked Questions
Can qualitative data be converted into quantitative data?
Yes, through a process called coding. Researchers group open-ended responses into recurring themes. They count how often each theme appears. That frequency count becomes chartable, statistical data.
Which type of survey data is more prone to bias?
Both carry bias risks, but differently. Quantitative data risks selection bias from unrepresentative samples. Qualitative data risks researcher bias during interpretation. Respondents may also answer to please the researcher.
Do qualitative and quantitative data need different survey tools?
Not necessarily, if the platform supports both natively. Many standalone tools handle one type well. They treat the other as an afterthought, splitting data across separate systems.
How long should a survey take when it mixes both data types?
Aim for five to seven minutes total completion time. Adding several open-ended questions to a short survey often doubles completion time. Response rates typically drop as a result.
Should qualitative and quantitative findings be reported separately or together?
Together, whenever possible. A report pairing a declining score with customer comments works best. Stakeholders see both the trend and the reason in one view.
