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In this webinar, discover how Sopact Sense revolutionizes data collection and analysis.
Are surveys qualitative or quantitative? Complete guide with examples, questionnaire samples, and question templates for nonprofits.
A workforce development director spends six weeks designing a participant survey. She debates every question: ask "How many job interviews did you complete?" or "How did the program change how you think about employment?" When results arrive, she has 200 rows of numbers and 200 paragraphs she cannot analyze together. She chose both methods — and got neither to work.
A survey is neither inherently qualitative nor quantitative — it is a data collection method that produces both types of data depending on how questions are structured. A survey asking "On a scale of 1–5, how confident do you feel about your finances?" generates quantitative data. The same survey asking "Describe what financial confidence means to you" generates qualitative data. Most nonprofit and social sector surveys contain both, which is why the question does not have a single correct answer.
Tools like SurveyMonkey and Qualtrics treat these as separate outputs: you get charts for scale questions and a wall of exported text for open-ended ones. Neither platform closes the loop between the two. Sopact Sense is built on the premise that the number means nothing without the story, and the story means nothing without the number — so the platform analyzes both inside the same workflow.
The short answer for researchers: surveys are most commonly used as quantitative instruments, but they regularly incorporate qualitative questions. A purely qualitative survey is rare; a purely quantitative survey is common; a mixed-method survey is optimal for nonprofit impact measurement.
A qualitative survey is a data collection instrument designed primarily to capture open-ended responses, narratives, and participant experiences that cannot be reduced to numbers. Qualitative survey questions use open-ended formats — "What barriers prevented you from completing the program?" rather than "Rate your barriers on a scale of 1–5." The data requires thematic analysis, not statistical aggregation.
Qualitative surveys are used when organizations need to understand the why behind an outcome, not just the what. A nonprofit program evaluation that shows 60% of participants secured employment within 90 days needs qualitative data to explain what drove that result and what held back the other 40%. Survey tools that only tally responses cannot surface those answers.
Sopact's Intelligent Column analyzes qualitative survey responses at scale — extracting themes, sentiment, and patterns across hundreds of open-ended answers — work that previously required a research analyst and weeks of manual coding.
Strong qualitative survey questions are specific, behavioral, and tied to observable program outcomes. Below are examples organized by program type, structured for open-ended response:
Workforce development:
Youth programs:
Community development:
What distinguishes a good qualitative survey question from a poor one is specificity. "How did you feel?" produces thin, unusable data. "Describe a specific situation where you applied what you learned" produces actionable insight. For impact measurement and management, qualitative questions should always pair with a corresponding quantitative indicator so the story and the number can be analyzed together.
A complete qualitative survey questionnaire for a nonprofit typically includes 8–12 questions across four categories: background context (2–3 questions), program experience (3–4 questions), outcomes and changes (3–4 questions), and recommendations (1–2 questions). Questions should be sequenced to build respondent comfort before asking about outcomes.
A sample qualitative survey questionnaire for a six-month workforce program:
Section 1 — Context
Section 2 — Experience3. Which aspects of the program were most useful to your job search?4. What was missing or could have been stronger?5. Describe your relationship with your case manager or program staff.
Section 3 — Outcomes6. What specific skills or knowledge did you gain that you did not have before?7. How has your financial situation changed since completing the program?8. Describe the most significant way this program has affected your life.
Section 4 — Forward9. What advice would you give to participants who are just starting?10. Is there anything else you want us to know?
SurveyMonkey and Typeform can collect these responses. Neither can analyze them at scale or connect them to a participant's longitudinal record across programs. Sopact Sense does both — and links each qualitative response to the participant's quantitative outcomes tracked through grant reporting workflows.
A quantitative survey is a structured data collection instrument that produces numerical data suitable for statistical analysis. Quantitative surveys use closed-ended question formats: rating scales, yes/no questions, multiple choice, ranking, and numerical inputs. The data can be aggregated, compared across cohorts, and visualized in charts.
Quantitative surveys dominate the social sector because funders require reportable metrics. The limitation: quantitative surveys tell you that something changed — not how or why. The most common error organizations make is designing quantitative surveys that track activity (attendance, completions, referrals) rather than outcomes (confidence, skill gain, behavior change). Qualtrics and SurveyMonkey both enable this mistake; neither platform enforces outcome-aligned survey design.
Quantitative survey questions are structured to produce countable, comparable data. Well-designed questions use validated scales and anchor each response option with behavioral language.
Likert scale examples (1–5):
Binary / yes-no examples:
Numerical examples:
For survey design for nonprofits, every quantitative question should serve a specific metric in your theory of change — not simply document activity.
The decision between qualitative and quantitative survey approaches is not binary — it is contextual. The right question is: what decision will this data support?
Use quantitative surveys when you need to measure change across a cohort, compare performance across sites, report progress to funders, or benchmark against a target. Use qualitative surveys when you need to understand the mechanism of change, identify unexpected barriers, capture participant voice for storytelling, or diagnose why a quantitative metric is moving — or not moving. Use both when you need evidence that is both reportable and actionable, which describes virtually all social sector programs.
The dominant framing of "qualitative vs. quantitative survey" creates an artificial choice that serves neither researchers nor program operators. The False Binary is the assumption that an organization must pick a survey type rather than first asking what analysis it needs.
Most survey platforms reinforce The False Binary by design. Qualtrics routes numeric responses to dashboards and open-ended responses to a separate text export. SurveyMonkey does the same. The result is that qualitative and quantitative data are collected together but analyzed apart — creating what Sopact calls The Analysis Gap.
The Analysis Gap is the space between what your numbers show and what your participants are telling you. A program that shows 70% employment placement but receives qualitative responses describing burnout and inadequate support is missing critical intelligence hidden in that gap. Closing it requires a platform that treats both data types as a single intelligence system — not two separate exports.
A questionnaire is the instrument; the question design determines whether it produces qualitative or quantitative data. A questionnaire with Likert-scale items and multiple choice produces quantitative data. A questionnaire with open-ended narrative prompts produces qualitative data. Most questionnaires used in nonprofit program measurement include both — making them mixed-method instruments.
The terms "survey" and "questionnaire" are often used interchangeably. Technically, a questionnaire is the specific set of written questions; a survey is the broader process of administration, collection, and analysis. Both can be qualitative, quantitative, or mixed.
The Likert scale is quantitative. Although it captures participant perception — which feels qualitative — it produces ordered numerical data that can be aggregated, averaged, and compared statistically. A Likert-scale response of "4 out of 5" is a number that can be added to a dataset, tracked over time, and run through statistical analysis.
The common confusion arises because Likert scale questions measure attitudes, beliefs, and experiences. But the measurement instrument itself is quantitative. A 5-point scale that asks "How confident are you in your ability to manage debt?" produces a number — not a narrative. If you need to understand what that confidence means to a participant, you need an additional open-ended question paired alongside the scale. Sopact Sense pairs these automatically and correlates the score with the qualitative theme at the individual participant level.
A mixed-method survey pairs every open-ended question with a corresponding scaled question measuring the same construct. This design enables cross-analysis: you can compare whether participants who scored low on a confidence scale were also more likely to describe specific structural barriers in their qualitative responses.
Four steps for building a mixed-method survey:
Step 1 — Define outcomes first. List the changes your program is expected to produce before writing a single question. Each outcome needs at least one quantitative indicator and one qualitative prompt.
Step 2 — Write the quantitative question. Frame it as a statement with a 5-point agreement scale. "I have the skills I need to manage my finances independently." (1 = Strongly Disagree, 5 = Strongly Agree)
Step 3 — Write the paired qualitative prompt. Ask for the story behind the number. "In your own words, describe what financial independence means to you and how close you feel to achieving it."
Step 4 — Design for analysis, not just collection. Before finalizing questions, confirm your platform can connect the two. If you are exporting qualitative responses to a spreadsheet and coding them manually, the mixed-method design fails in practice.
Sopact Sense executes all four steps in a single workflow — and the same mixed-method logic underpins its application review software, which combines structured eligibility criteria with qualitative narrative assessment in one platform. Organizations using Sopact for both survey data collection and application review eliminate the reconciliation work that consumes most of a program team's analytical time.
A survey is neither inherently qualitative nor quantitative — it is a data collection method. The type of data produced depends on how questions are structured. Closed-ended questions (ratings, multiple choice, yes/no) produce quantitative data. Open-ended questions produce qualitative data. Most surveys include both, making them mixed-method instruments.
Surveys are most commonly used in quantitative research because they measure and compare outcomes across large populations. However, surveys support qualitative research when they include open-ended questions analyzed for themes rather than statistics. In impact measurement practice, surveys function best as mixed-method instruments that satisfy both funder reporting requirements and program improvement needs.
A quantitative survey uses structured, closed-ended questions to produce numerical data — counts, scales, and percentages. A qualitative survey uses open-ended questions to produce narrative data — descriptions, stories, and explanations. The difference is not the topic covered but how responses are structured and analyzed.
A qualitative survey questionnaire sample shows how open-ended questions are organized across sections: context, program experience, outcomes, and recommendations. A typical qualitative questionnaire includes 8–12 open-ended prompts sequenced to build respondent comfort before asking about outcomes and impact.
Strong qualitative survey questions are specific, behavioral, and tied to program outcomes. Examples: "Describe the most significant change in your professional skills since starting the program." "What specific obstacles did you face in your job search, and how did the program help you address them?" Avoid vague prompts like "How did you feel?" which produce thin, unusable data.
A questionnaire is the instrument used to collect data; the type of questions determines whether output is qualitative or quantitative. Questionnaires with Likert scales and multiple choice produce quantitative data. Questionnaires with open-ended prompts produce qualitative data. Most program evaluation questionnaires include both.
The Likert scale is quantitative. It measures perceptions and attitudes using ordered numerical options (1–5 or 1–7), producing data that can be aggregated and statistically analyzed. It is often confused with qualitative measurement because it captures subjective experience, but the output is numerical.
Yes. Mixed-method surveys pair closed-ended questions (quantitative) with open-ended questions (qualitative) to capture both the measurable outcome and the lived experience behind it. This is the recommended design for nonprofit program measurement because it produces data that is both reportable to funders and actionable for program staff.
A qualitative survey for program evaluation typically includes 8–12 questions. Fewer than 8 often miss critical context; more than 15 creates respondent fatigue in open-ended formats where each question requires written narrative response. Each question should serve a specific analytic purpose.
Common examples: "On a scale of 1–5, how confident do you feel about your ability to secure employment?" "Did you obtain employment within 90 days of program completion? Yes / No." "How many job interviews did you complete in the past 30 days?" Each question should map to a specific outcome metric in the program's theory of change.
Quantitative survey data is numerical: scores, counts, percentages, and rankings, analyzed through statistics and visualized in charts. Qualitative survey data is textual: narratives, descriptions, and themes, analyzed through coding and interpretation. Both data types are necessary for complete program insight — quantitative data satisfies funder reporting; qualitative data drives program improvement.
SurveyMonkey and Qualtrics collect data. Sopact Sense collects and analyzes it — automatically correlating qualitative themes with quantitative outcome scores, tracking individual participants across multiple surveys over time, and generating intelligence reports without manual data reconciliation. Organizations move from data collection to organizational learning in the same platform.