This article will give you tips on how to analyze responses from a Citizen survey about Accessibility For People With Disabilities using AI-driven survey analysis tools.
Choosing the right tools for survey response analysis
How you approach analysis begins with understanding the structure of your data. The right tools depend on whether your survey responses are mostly quantitative or qualitative.
Quantitative data: For questions like, “How accessible do you find local government buildings?” with numeric or multiple-choice answers, you’re in luck. Classic tools like Excel or Google Sheets will let you count, graph, and segment these answers quickly.
Qualitative data: But what about open-ended questions or conversational survey responses? Reading through hundreds of comments is impossible—and patterns can easily be missed. You’ll want AI-powered tools to summarize, categorize, and distill meaning from all this nuance.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Copying exported data into ChatGPT: You can manually paste your survey export into a GPT tool and start “chatting” with the AI about your data. The benefit? Powerful text analysis—if you craft the right prompts (more on that soon). However, this method quickly gets inconvenient, especially as datasets grow. There’s no built-in structure, and keeping track of follow-ups or filtering by demographics gets messy fast.
All-in-one tool like Specific
Purpose-built for survey data: Specific is designed exactly for this kind of work. It doesn’t just collect responses—it asks AI-powered follow-up questions along the way, so your data is already higher quality and more contextual. See how automatic AI follow-ups work here.
Instant insight, no busywork: Once your survey is finished, Specific’s AI survey response analysis instantly summarizes large batches of qualitative feedback, finds key themes, and highlights actionable patterns—no spreadsheet wrangling required. You can chat directly with the AI about the findings, filter by NPS categories, or drill into any demographic segment you care about. See details at AI survey response analysis.
More control, less confusion: You can manage what gets sent to the AI, set filters, and save key chat threads about different slices of your results. This flexibility is hard to match with generic AI tools.
If you’re interested in creating a Citizen survey specifically about accessibility for people with disabilities, check out this AI survey generator with tailored questions or learn about the best practices in designing meaningful survey questions.
Useful prompts that you can use for Citizen Accessibility For People With Disabilities survey analysis
AI shines brightest when you tell it exactly what you want. Here are some practical prompts that work for both ChatGPT and platforms like Specific, helping you uncover real insights from your survey data.
Prompt for core ideas: This is my go-to when I want themes and clear takeaways—the backbone of good qualitative analysis. Paste your responses and use the following:
Your task is to extract core ideas in bold (4-5 words per core idea) + up to 2 sentence long explainer.
Output requirements:
- Avoid unnecessary details
- Specify how many people mentioned specific core idea (use numbers, not words), most mentioned on top
- no suggestions
- no indications
Example output:
1. **Core idea text:** explainer text
2. **Core idea text:** explainer text
3. **Core idea text:** explainer text
More context = More useful outputs: Give the AI specifics about your survey, the kind of Citizen you surveyed, and your goals. For example:
I'm analyzing a Citizen survey designed to uncover barriers in public transportation accessibility for people with disabilities. The respondents are adults from diverse regions. Please focus on both physical infrastructure and digital information barriers.
Prompt for follow-ups about a theme: Once you have core ideas, dig deeper on top themes. Ask:
“Tell me more about public building access”
Prompt for quick validation: To check if anyone mentioned a specific pain point:
“Did anyone talk about assistive technology? Include quotes.”
Prompt for pain points and challenges: Spot major obstacles by prompting:
Analyze the survey responses and list the most common pain points, frustrations, or challenges mentioned. Summarize each, and note any patterns or frequency of occurrence.
Prompt for personas: Build composite profiles using:
Based on the survey responses, identify and describe a list of distinct personas—similar to how “personas” are used in product management. For each persona, summarize their key characteristics, motivations, goals, and any relevant quotes or patterns observed in the conversations.
Prompt for sentiment analysis: Understand tone and emotion trends by using:
Assess the overall sentiment expressed in the survey responses (e.g., positive, negative, neutral). Highlight key phrases or feedback that contribute to each sentiment category.
Prompt for unmet needs & opportunities: Use:
“Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.”
Remember, you can combine these prompts to dig as deep as you need, especially since 63% of websites are still inaccessible to people with disabilities, creating a large pool of diverse and nuanced feedback that AI can help you organize efficiently [1]. For a more detailed guide on building effective questions, see best practices for Citizen accessibility survey questions.
How Specific analyzes qualitative survey data by question type
The value of your analysis jumps when you look at how your questions are structured. Here’s how Specific—drawing on years of experience with AI survey analysis—breaks down different types of survey questions:
Open-ended questions (with or without follow-ups): Specific gives you an AI-generated summary of the main ideas for every open-ended question, as well as any follow-up questions linked to them. You get direct insight into barriers citizens face, such as difficulty accessing public buildings or digital services.
Choices with follow-ups: For multiple choice or single-select questions (for example: “Which public service is least accessible to you?”), each answer choice gets its own summary of all follow-up responses related to that choice.
NPS (Net Promoter Score): Respondents are categorized into detractors, passives, or promoters, and you receive a summary of follow-up feedback for each category—unlocking rich qualitative data about what drives positive or negative perceptions.
You can achieve similar results with ChatGPT, but you’ll spend far more time filtering, pasting, and organizing your data. Tools like Specific’s AI survey response analyzer simply remove friction from the process.
Given that the employment rate for persons with disabilities is nearly 20 percentage points lower than the general population [2], rapid, actionable insights from your survey can help policymakers or advocates act or iterate their strategies faster.
Check out the Specific NPS survey template for Citizen accessibility for an example.
How to tackle challenges with AI context limits
Even the best AIs have a limitation—context size. If your survey response set is too long, you risk losing valuable data in the analysis phase. Here’s how to stay efficient:
Filtering: Analyze only the conversations where users replied to selected questions or gave specific answers. This approach lets you target the most relevant data and stay inside AI’s context constraints.
Cropping: Choose only specific questions to send to the AI for analysis. Not only does this make the process more focused, but it also allows you to process larger samples. Specific offers these filtering and cropping options out of the box, giving you full control when you need to go deep or stay broad.
For more details on how AI manages each context efficiently, read this guide to AI-powered survey analysis.
Collaborative features for analyzing Citizen survey responses
Collaboration on Citizen accessibility survey analysis often gets bogged down by messy spreadsheets, endless email threads, and uncertainty over who said what. Specific was built to cut the noise and foster real teamwork.
Analyze data by chatting: Anyone on your team—policy makers, researchers, or advocates—can analyze data by having a direct conversation with the AI, dramatically improving speed and clarity.
Multiple chat threads: Each analysis or “thread” can have its own set of filters applied (such as demographic group, region, or survey question), and each chat visibly displays who started it. This makes handoff and review simple.
Sender visibility in collaborative AI chat: In collaborative AI chats, you can see precise attribution—avatars and names show who made each comment. This feature is a game-changer when comparing observations, building consensus, or reviewing findings during accessibility policy development.
Learn more about these collaborative analysis features in Specific’s AI survey analyzer or see how to generate your own accessibility survey.
Create your Citizen survey about Accessibility For People With Disabilities now
Start collecting richer accessibility insights and make data-driven decisions faster with frictionless, AI-powered survey collaboration—just create your own survey for Citizen accessibility research today.