Qualitative vs quantitative data explained: the difference, examples, a side-by-side comparison, and when to use each kind of data.
Quantitative data is the number a respondent gives; qualitative data is the open-text reason behind it. The two answer different questions, and treating them as rivals loses the point. Sopact keeps both on the Connected Record: the rating and the open-text explaining it on one participant record under a persistent Contact ID, so a score and its reason are read together rather than in two systems that never rejoin.
Program teams describe the split as a story they cannot tell: “we can’t tell a cohesive story across all of it,” because the numbers live in one silo and the reasons in another. A dashboard shows a rating fell without saying why, and the open-text that would explain it sits in a document nobody reads until the analysis cycle drags on for months.
Key takeaways
The rivalry framing is really a data-model problem. Quantitative answers go into a survey platform or a stats package, and qualitative goes into a document or a coding tool, so the story and the score are captured apart and stitched back together by hand, if at all. A low rating and the sentence that explains it end up in different files with no shared key.
Sopact is record-centric: the open-text is read on arrival against a codebook and tied to the same persistent ID as the numbers, so a rating and its reason live on one Connected Record. See the numbers side on quantitative data collection methods, or the combined discipline on mixed-methods data analysis.
The quantitative side runs on SPSS, Excel, Qualtrics, SurveyMonkey, and Google Forms; the qualitative side runs on NVivo, ATLAS.ti, MAXQDA, and Dedoose. Each is strong at its own half, and each was built for one half, so a team that needs both ends up moving data between them and reconciling a number with a reason across two exports.
The one test that settles the rivalry: ask the stack to show one rating with the exact sentence the same respondent wrote to explain it, on one record. A single-side tool answers by asking you to join two files. Sopact answers from the Connected Record, because the open-text was read on arrival and tied to the number.
Use quantitative when you need scale and comparison, and qualitative when you need the reason and the nuance a number cannot hold. The practical answer for most programs is both: the number sets the direction and the open-text explains it, which is why an outcome is more defensible when the score carries its reason. Sopact reads the open-text against a codebook so the reason is structured, not anecdotal.
Kept on the Connected Record, the pairing is longitudinal: a participant’s scores and the reasons behind them across every wave on one persistent ID. Sopact reads on arrival, so a team sees not only that a rating moved but the sentence that says why, in time to respond.
A single-side tool captures either the number or the reason and leaves the other in a separate system; the Connected Record reads open-text on arrival and ties it to the number on one record. The difference is whether a score and its reason are one record or two exports.
| The question | Two systems | Connected Record |
|---|---|---|
| Capture the number? | Yes: the quant tool | Yes, on the participant record |
| Capture the reason? | Yes: the qual tool | Yes, read against a codebook |
| Read them together? | No: a manual join | Yes: one persistent ID |
| See why a score moved? | Later, if reconciled | Yes: the sentence, on arrival |
Run both as one analysis on mixed-methods data analysis, or see the numbers side on quantitative data collection methods.
A dropping score is worth understanding while you can still respond to it, not in a report written after the program ends. The value of the open-text behind a number is highest the moment it lands, when the reason for a low rating can still change what happens next. That is the premise of the Loop, Sopact’s method for continuous intelligence: collect clean at the source, with the number and the open-text explaining it on one participant record; analyze on arrival, reading each open-text answer against a codebook the moment it lands and tying it to the number; improve in time, so the reason behind a dropping score surfaces while you can still act.
The Loop is also what makes a mixed-methods finding defensible: every theme traces back to the exact sentence a respondent wrote and the number that respondent also gave, the standard detailed in Loop traceability, so a conclusion rests on the Connected Record rather than a hand-coded spreadsheet no one can re-check.
One method, three moves that never stop
Then the next wave reads a little sharper. Read the method: the Loop methodology →
The fastest way to see the pairing is to run it on your own responses. Export a set of ratings with the open-text the same respondents wrote, on their IDs, then paste the prompts below into Sopact Sense’s Assistant, or reason through them with your team. The arrow above each links the Academy walkthrough with the expected output and tips.
Academy walkthrough → Analyze open-ended responses
Here is a batch of open-ended survey responses with each respondent's ID and their rating: [ATTACH]. Read each open-text answer against our codebook as it lands, tag the themes, quote the exact sentence behind each theme, and keep every answer tied to the number the same respondent gave, so I can read the reason next to the score on one record instead of in two separate exports.
Academy walkthrough → Connect the number and the reason
Here is our quantitative data and the open-ended responses on the same participant IDs: [ATTACH]. For each rating, pull the open-text the same respondent wrote that explains it, quote the sentence, and show the number and the reason on one record, so a low score carries the reason a respondent gave rather than sitting in a column with no explanation.
Academy walkthrough → Clean open-ended responses
Here is a raw export of open-ended responses on their participant IDs: [ATTACH]. Flag blanks, duplicates, and off-topic answers, normalize the text so it is analyzable, and keep each cleaned answer tied to its ID and the number that respondent gave, so the open-text is ready to read against a codebook on arrival rather than after a month of hand-cleaning.
Academy walkthrough → Find the drivers behind a score
Here are ratings and the open-ended responses on the same IDs: [ATTACH]. Read the sentiment in each answer, identify the drivers behind the rating with the sentence quoted, and tie each driver to the number, so I can see what is pushing a score up or down from the respondent's own words rather than guessing behind the average.
Each walkthrough is short and practical: what to do, the prompt to run, the output to expect, and the tips that keep it reliable.
Watch: reading open-text against a codebook on arrival and keeping every answer tied to the number on one Connected Record.
Quantitative is the number a respondent gives; qualitative is the open-text reason behind it. Sopact keeps both on the Connected Record — the rating and the open-text explaining it on one participant record under a persistent Contact ID — so a score and its reason are read together.
Neither; they answer different questions. A number tells you what changed and the open-text tells you why, and a defensible finding needs both. Sopact reads the open-text against a codebook and ties it to the number on the Connected Record, so mixed methods is an AND on one record.
Because most stacks capture the number in a survey or stats tool and the open-text in a document or coding tool, so the story and the score never reconnect. Sopact is record-centric and ties the open-text to the same persistent ID as the numbers on the Connected Record.
Yes. Because the rating and the open-text explaining it sit on one Connected Record, a rating that fell carries the sentence the respondent wrote to explain it, read against a codebook on arrival rather than reconstructed months later.
Not for the pairing. Sopact reads open text against a codebook and keeps it tied to the number, so a team gets both halves on one Connected Record instead of moving data between a quant tool and a coding tool.
It reads each open-text answer against a codebook, so the reason becomes a repeatable theme with the sentence quoted, tied to the number. Sopact keeps this on the Connected Record, so the reason is analyzable rather than a stack of quotes.
No. Sopact drafts the themes from the respondent’s own words with the sentence quoted; a human confirms or overrides them, human-in-the-loop. The Connected Record records what was read and from which answer.
Sopact keeps every score and reason on one persistent ID, so a participant’s trajectory is read as a whole. The Connected Record survives each wave, which is what makes a longitudinal view of both halves possible.
Next: run both as one analysis on qualitative and quantitative analysis, or read a whole survey on survey analysis.
