Qualitative and quantitative analysis reads numbers and narratives as one finding, not two reports. The definition, the divide, and how to combine them.
Qualitative and quantitative analysis is running both halves as one analysis: reading the open-text reason against a codebook and pairing it with the number on the same participant. Sopact calls the result the Connected Record: the rating and the open-text explaining it kept on one record under a persistent Contact ID, so the score and its reason are analyzed together instead of in two systems that never rejoin.
Teams describe the current workflow as stitching: pull the numbers from one tool, code the open-ends in another, then try to line them up by hand for a report. Each half works fine in a silo, but it does not work together, so the analysis is manual, slow, and hard to present as a defensible picture when a reviewer asks how a conclusion was reached.
Key takeaways
When the numbers and the open-text are captured in different systems, the analysis inherits the split. A cross-tab of rating by theme requires exporting both, matching IDs, and coding the text before anything can be compared, so the combined analysis is a project rather than a query and often skipped under time pressure.
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 by theme cross-tab is a query over the Connected Record. Compare the two families on qualitative vs quantitative, or see the discipline on mixed-methods data analysis.
A combined analysis usually means two toolchains: SPSS, Excel, or Qualtrics for the numbers, and NVivo, ATLAS.ti, MAXQDA, or Dedoose for the text. Each is capable on its half, and each keeps its results in its own file, so producing a single view of score by reason means an analyst is the integration layer, joining exports by hand every cycle.
The one test that separates them: ask the stack to show average score by theme, with the exact sentence behind each theme, on one record. A two-toolchain setup answers by exporting rows for you to join. Sopact answers from the Connected Record, because the open-text was read on arrival and tied to the number.
The move that changes a combined analysis is reading the open-text against a codebook as it lands, so score-by-theme, sentiment-by-segment, and driver analysis are queries rather than assemblies. Sopact drafts the themes from the respondent’s words, quotes the sentence, and keeps them tied to the numbers, so a human confirms a draft instead of hand-coding a corpus before any comparison starts.
Kept on the Connected Record, the analysis is longitudinal: score and reason across every wave on one persistent ID. Sopact reads on arrival, so a team sees a result shift and the sentence that explains it together, in time to respond rather than at the end of a coding cycle.
A two-toolchain setup analyzes the number and the reason apart and joins them by hand; the Connected Record reads open-text on arrival and analyzes score and reason as one. The difference is whether a cross-tab by theme is a query or a project.
| The question | Two toolchains | Connected Record |
|---|---|---|
| Analyze the numbers? | Yes: the stats tool | Yes, on the participant record |
| Analyze the open-text? | Yes: the coding tool | Yes, against a codebook on arrival |
| Cross-tab score by theme? | A manual join | Yes: a query over one record |
| Update as responses land? | No: a batch project | Yes: read on arrival |
See concrete scenarios on mixed-methods research examples, or the step-by-step on how to analyze survey data.
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 one analysis is to run it on your own responses. Export your ratings and the open-text on the same 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.
It is running both halves as one analysis: reading the open-text reason against a codebook and pairing it with the number on the same participant. Sopact keeps the result on the Connected Record, so the score and its reason are analyzed together rather than in two systems that never rejoin.
Because the open-text is read against a codebook and tied to the same persistent ID as the numbers, a score-by-theme cross-tab is a query over the Connected Record rather than a manual join of two exports.
Those tools analyze the number and the reason in separate files, so a combined view is an analyst’s hand-join. Sopact is record-centric: it reads the open-text on arrival and keeps score and reason on one Connected Record, so the combined analysis is a query.
Yes. Sopact reads each open-text answer against the codebook the moment it lands, so score-by-theme and driver views update as responses land rather than after a batch coding project on the Connected Record.
Yes. Every theme quotes the sentence behind it and stays tied to the number, so a reviewer can trace a conclusion to the respondent’s own words. Sopact keeps this on the Connected Record, which is what makes the analysis defensible.
Not for the combined view. Sopact reads open text against a codebook and ties it to the number, so a team gets score and reason on one Connected Record instead of moving data between two toolchains.
No. Sopact drafts themes and driver analysis from the respondent’s 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 score and reason on one persistent ID, so a participant’s trajectory across waves is one analysis. The Connected Record survives each cycle, which is what makes a longitudinal combined view possible.
Next: measure outcomes with both on qualitative and quantitative measurements, or see the discipline on mixed-methods data analysis.
