Qualitative Analysis
Analyze open-ended responses and text feedback at scale
Showing 1–12 of 12 articles
How Text Analytics Works: From Open-Ends to Categories
A thousand open-ended responses is a research finding waiting to happen and a coding project nobody wants to start. Text Analytics uses AI to draft categories and classify responses against them - with you reviewing and steering every step. Here's how the workflow actually runs.
Writing Open-Ended Questions That Produce Classifiable Answers
How you word an open-ended question shapes how much there is to actually classify. A few practical wording habits make a real difference to how useful your Text Analytics results end up being.
Turning Category Results Into Quotes and Examples for a Report
A category frequency chart tells a stakeholder how often a theme came up. A well-chosen quote tells them what it actually felt like. Here's how to pull supporting quotes from your Text Analytics results without cherry-picking.
Reading a Category Frequency Chart Without Overinterpreting It
A bar chart of category counts looks precise. The responses behind it are messier than the chart lets on. Here's how to read a Text Analytics frequency chart responsibly.
Qualitative Data Quality: What Makes a Response Hard to Classify
Some open-ended responses resist classification no matter how good your categories are. Knowing what makes a response genuinely hard to code helps you tell a category problem from a data problem.
How Many Categories Is Too Many?
A category set can be too broad or too granular, and both are real problems. Here's how to tell when your Text Analytics category set has drifted too far in either direction.
From Raw Verbatims to a Report: The Full Text Analytics Workflow
Generating categories and running classification are two steps in a longer process. Here's the full path from a pile of open-ended responses to a chart in a finished report.
Comparing Category Sets Across Two Survey Waves
Running the same open-ended question wave after wave is only useful if the categories stay comparable. Here's how to keep a tracking study's Text Analytics results aligned across waves instead of drifting apart.
Coding Consistency: Why Re-Generating Categories Can Produce a Different Set Each Time
Run AI Auto-Generate Categories twice on the same question and you won't necessarily get the same category set back. That's expected, not a bug - and it matters most for tracking studies.
Choosing Between AI-Generated and Manually Built Categories
You're not required to use AI-generated categories in Text Analytics - building your own from scratch is just as supported. Here's how to decide which starting point fits your project.
Editing and Refining Categories in Text Analytics
An AI-generated category set is a first draft, not a final answer. Here's how to rename, merge, split, and correct categories in Text Analytics before you commit to classifying every response against them.
How Opionate Automated Thematic Analysis While Preserving Research Rigor
Learn how we automated thematic analysis, saving 99% execution time while preserving Braun & Clarke's research methodology through mandatory human validation.