For Research Analysts ·
What you'll accomplish
What currently takes 4-8 hours of manual reading and tagging becomes 45-90 minutes of AI-assisted work. This guide walks you through the two-step approach: develop your theme list first, then code all responses against it in batches through ChatGPT.
What you'll need
Before going to ChatGPT, get your data in order.
What you should see: A numbered list of clean verbatim responses in a spreadsheet. Troubleshooting: If you have over 500 responses, split them into batches of 75-100 rows. This prevents the AI from truncating output.
Start a fresh ChatGPT conversation. Copy your first 50-75 responses and paste them with this prompt.
Here are open-ended responses to the survey question: "[paste your exact question text]"
[paste 50-75 verbatims here]
Please:
1. Identify 6-8 recurring themes across all responses
2. For each theme, give it a short label (2-4 words) and a 1-sentence definition
3. Note which 3 responses best illustrate each theme
Output only the theme list for now. We will code responses in the next step.
What you should see: A numbered theme list with definitions and example quotes. Troubleshooting: If themes are too broad (e.g., "Negative Experience"), ask: "Break theme 3 into more specific subcategories."
In the same conversation (so the AI remembers your theme list), send this:
Now using the theme list above, code each of the following responses.
For each response, assign it to 1-2 of the themes from our list.
Output as a table: Row # | Response (first 8 words) | Theme 1 | Theme 2
Responses to code:
[paste rows 1-75 with their row numbers]
What you should see: A table with each response coded to 1-2 theme labels from your approved list.
Continue in the same ChatGPT conversation:
Troubleshooting: If the AI starts inventing new theme names, say: "Stay with the original 8 themes we defined. Do not create new categories."
After all responses are coded:
What you should see: A summary table ready to paste into your report.
Theme generation:
Here are [N] responses to "[question text]". Identify 6-8 recurring themes. For each: short label (2-4 words), 1-sentence definition, best illustrating quote. Output theme list only.
Batch coding:
Using the theme list above, code each response below to 1-2 themes. Output as table: Row # | First 8 words | Theme 1 | Theme 2. Responses: [paste batch]
Frequency summary:
Based on all coding so far, give me a frequency table: Theme | Count | % of Total | 2 best quotes per theme.
Theme refinement:
Theme [X] seems too broad. Break it into 2-3 more specific subcategories based on the responses we've seen.