This article will give you tips on how to analyze responses/data from a Citizen survey about Vaccination Attitudes. We’ll dive into the nuts and bolts of using AI to analyze both quantitative and qualitative survey feedback, and how to turn Citizen Vaccination Attitudes data into actionable insights.
Choosing the right tools for analyzing Citizen Vaccination Attitudes survey responses
How you analyze survey responses depends a lot on their form and structure. The tools you’ll use will change depending on whether your data is quantitative or qualitative.
Quantitative data: Numeric data (answers to multiple choice or rating questions) is straightforward. You can use tools like Google Sheets or Excel to count responses, calculate percentages, or create charts that show, for instance, how many Citizens believe vaccines are important or how many consider them effective.
Qualitative data: Open-ended questions and follow-up responses present a bigger challenge. You can’t just read through hundreds of answers manually; you need AI tools to find patterns, summarize views, and extract themes from all that Citizen feedback.
There are two common approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Export and Chat: You can copy exported survey data into ChatGPT (or similar GPT-based tools) and then prompt the AI to analyze the text.
Caveat: While possible, this approach isn’t convenient. Formatting issues can crop up, and managing context—especially with lots of open-ended Vaccination Attitudes responses—quickly becomes tedious. You have to structure data, paste it in batches, and keep track of context. There’s no built-in way to organize or slice your Citizen data for deeper dives.
All-in-one tool like Specific
Purpose-built for survey analysis: Tools such as Specific are designed with survey workflows in mind. Specific allows you to both collect Citizen survey responses (including follow-ups) and instantly analyze them with AI—all in one interface.
Higher data quality: Because Specific uses AI to ask intelligent follow-up questions, you capture richer, more consistent data about Citizen attitudes—far beyond what a standard form could provide (read more about AI-powered follow-ups).
Instant AI insights: After collecting responses, Specific summarizes answers, finds recurring themes, and even highlights core ideas and their frequency. You simply chat with the AI about results—no spreadsheets, no hassle. You also get advanced controls over what data is analyzed or which questions are included in the AI context, making it possible to pull out nuanced insights about vaccination hesitancy, motivations, or beliefs among Citizens. Try it for yourself: See how to analyze survey responses using Specific’s AI chat.
Useful prompts that you can use for analyzing Citizen Vaccination Attitudes survey responses
Prompts drive the quality of your AI-powered analysis. Whether you’re using Specific, ChatGPT, or another GPT-based tool, the right prompts turn raw survey data into insights you can actually use. Here are field-tested prompts for Citizen Vaccination Attitudes surveys:
Prompt for core ideas: Use this to get a clear summary of big-picture themes (also used by Specific by default):
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
Tip: AI always performs better when you provide as much context as possible—describe your survey, what you want to learn, who responded, and your goals. For example:
The following responses are from a survey of 1,000 Citizens across several EU countries, conducted in 2023, about attitudes toward vaccination for COVID-19 and childhood diseases. Our goal is to understand hesitancy trends, underlying motivations, and possible information gaps.
Now, once you see your list of core ideas, you can dive deeper with variations like:
Prompt for follow-up exploration: Tell me more about [core idea]
Prompt for specific topics: Did anyone talk about [XYZ—vaccine safety, for example]? Include quotes.
Prompt for personas: Useful if you want to identify distinct views or clusters in Citizen demographics—age group, gender, or educational background influence vaccine attitudes [1,6]:
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 pain points and challenges: Zero in on vaccine hesitancy, misinformation, and barriers to access: 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 motivations & drivers: What actually pushes someone to get vaccinated? In the UK, 65% of previously hesitant adults got vaccinated mainly to help restrictions ease and life return to normal [4]. Use: From the survey conversations, extract the primary motivations, desires, or reasons participants express for their behaviors or choices. Group similar motivations together and provide supporting evidence from the data.
Prompt for sentiment analysis: If you want to know overall attitudes (positive/negative/neutral), use: 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: Especially important for policy makers or health workers trying to build better outreach: Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.
Don’t forget, you can create your own survey using an AI survey generator pre-loaded with Citizen vaccination attitude question presets, or browse how to create a custom survey from scratch with this guide.
How Specific analyzes qualitative data based on question type
Different questions require different analysis workflows. Here’s how Specific makes sense of qualitative survey data on attitudes toward vaccination:
Open-ended questions (with or without follow-ups): Specific provides an instant summary for all open-ended responses, grouping follow-up answers so you can see patterns in Citizen opinions—for example, contrasting those who believe vaccines are important with those who are hesitant.
Choices with follow-ups: Every answer choice (e.g., pro-vaccine, hesitant, anti-vaccine) gets its own qualitative summary for all related follow-up questions, so it’s easy to dig into why people selected each choice and what drove their opinions.
NPS questions: Each Net Promoter Score category (detractors, passives, promoters) receives a grouped summary of follow-up explanations—revealing, for example, that many promoters cite public health as their reason, while detractors mention concerns about vaccine safety or side effects.
You could replicate this workflow in ChatGPT, but you’d need to do a lot more manual work—copying and filtering exchanges, keeping track of which follow-ups relate to which choices, and manually separating your analysis by question type. Read more on AI analysis features in Specific for survey data.
Dealing with AI context size limits when analyzing survey responses
One big technical challenge with AI analysis—especially for larger Citizen surveys—is the context window limit of GPT models. If your survey returns hundreds or thousands of long-form answers about vaccination attitudes, you can’t feed them all into the AI at once.
There are two effective solutions (both handled natively in Specific):
Filtering: Only conversations matching your selected criteria are analyzed. For example, you can analyze just those Citizen responses mentioning hesitancy, a specific age group, or only people who answered a critical follow-up.
Cropping: Instead of analyzing entire conversations, you can send just a subset of questions (e.g., only open-ended or follow-up responses about vaccine safety) to the AI. This way, you avoid blowing through context limits and preserve analysis accuracy.
You can manage this filtering and cropping manually when using generic GPT tools, but it’s a headache. Specific makes it a breeze—letting you zoom in on subsets of data for focused analysis.
Collaborative features for analyzing Citizen survey responses
Survey analysis isn’t a solo sport: On large Citizen Vaccination Attitudes projects, teams often struggle to track threads, summarize results, and make sure everyone’s working from the same data. People duplicate spreadsheets, lose context, and waste time retreading each other’s ground.
In Specific, AI-driven analysis is built for teams: You chat with the AI right inside the survey platform—no exporting needed, just natural discussion with the data. Multiple chats can be created for any Citizen survey—each with custom filters (such as “only hesitant respondents” or “positive sentiment only”). It’s clear who started each chat and which focus it serves, so teams never get wires crossed.
Real-time visibility for teams: Every message shows the sender’s avatar, making it simple to follow the flow of insights across sales, research, or public health colleagues. You always know who’s probing what, which saves time and reduces duplicate work.
These collaborative features increase transparency and make it easy to track hypotheses or follow emerging ideas, which is especially important in rapidly changing topics like vaccination attitudes.
Create your Citizen survey about Vaccination Attitudes now
Unlock deeper insights and actionable data on Citizen vaccination attitudes with instant AI-powered survey analysis—capture richer responses and deliver results your team can use today.