This article will give you tips on how to analyze responses from an ex-cult member survey about belief changes. If you want deep, actionable insights from your data, AI can transform how you approach survey analysis.
Selecting the right tools for analyzing ex-cult member belief change survey responses
The approach and tooling for analyzing survey responses really depend on the format and structure of your data.
Quantitative data: If your responses are things like “how many ex-cult members selected a specific belief change,” then simple tools like Excel or Google Sheets work well. You just run some quick pivots, see counts and percentages, and visualize results.
Qualitative data: For open-ended responses, or nuanced follow-ups about why beliefs changed, you need more firepower. Hundreds of stories or long-form answers can't just be read through manually—you'll want AI tools trained to surface themes, summarize, and extract the most from those personal narratives.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Copy and chat: You can export text responses and paste them into ChatGPT or a similar GPT platform. Then ask questions or give prompts to summarize and discover patterns.
Downside: Doing it this way is awkward for bigger data sets. You have to break up your responses, keep track of what you sent, and manually manage privacy or filtering. There’s no structure or built-in sense of the survey's logic (like grouping by choices, follow-ups, or tracking who said what).
All-in-one tool like Specific
Built for survey analysis: Specific is built for this exact use case: running detailed surveys, especially with complex open-ended follow-up questions, and then instantly analyzing the results with AI. It combines data collection with interpretation.
Increases response quality: When surveys run in Specific, the AI automatically asks relevant, real-time followup questions. That means you get deep, nuanced responses (not just surface-level answers), capturing the emotions and meaning behind belief changes. See it in action in this article on automatic AI follow-up questions.
Instant, actionable insight: The AI survey response analysis feature in Specific does the heavy lifting: summarizing open-ended narratives, surfacing consistent themes, organizing everything by question or answer, and making it all explorable by chatting directly with the results—similar to ChatGPT, but purpose-built for survey data.
Full control and transparency: You can manage what data is sent to the AI engine, filter responses, and collaborate with teammates. For tackling belief change interviews with a lot of nuance, structure and chat-like ease, it’s a major step up from “copy-paste and hope for the best.”
Other reputable AI tools for analyzing open-ended survey responses include NVivo, MAXQDA, Delve, Atlas.ti, and Looppanel. These solutions offer features such as automatic coding, sentiment analysis, visualization, and real-time collaboration, making them suitable for in-depth qualitative research. For example, NVivo and MAXQDA can handle complex data queries, visualize themes, and process various data types, while Delve and Looppanel streamline coding and note-taking for teams [1][2][3].
Useful prompts that you can use to analyze belief change data from ex-cult member surveys
Prompts are your secret weapon when working with AI tools—whether it’s ChatGPT, Specific, or another platform. Ask better questions, get better insights.
Prompt for core ideas: Use this to get a ranked summary of the main belief change themes people mentioned.
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
AI always performs better if you give it more context about the survey, the situation, your goals, and any special instructions. For example, you could add:
This survey collected stories from ex-cult members about the specific beliefs they left behind, why they changed, and what helped or hindered that process. Please highlight themes, especially around emotional triggers, support systems, and critical moments of doubt.
Dive deeper: If the AI surfaces a “core idea” you want to explore, ask: “Tell me more about [core idea].” This lets the AI surface all the angles or variations for that theme.
Check for specific topics: To see if anyone mentioned a particular factor, use: “Did anyone talk about [name the belief, event, or barrier]?” You can add “Include quotes” for juicy verbatim examples.
Prompt for personas: Uncover subgroups—for example, people whose belief changes happened suddenly versus over years, or those who left alone versus as a group:
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: Use this to surface barriers and setbacks in transitioning belief systems:
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 and drivers: Understand the core reasons people left certain beliefs behind:
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: Capture the emotional undercurrent:
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.
Want to see more example questions or prompts? Check out this list of best questions for ex-cult member survey about belief changes.
How Specific analyzes qualitative data based on question types
Open-ended questions with or without followups: Specific generates a full summary for all responses. If your belief change question had follow-up probes (like “What changed your mind?” or “How did you feel?”), those get rolled up into detailed summaries under each core question, making it easy to see both main themes and detailed context.
Choices with followups: If your survey asks, “Which of these beliefs did you used to hold?” and collects an essay after each selection, Specific will auto-generate summaries for the responses linked to each individual choice. You’ll see variations in experience for each type of belief.
NPS (Net Promoter Score): If you used an NPS format (“How likely are you to recommend leaving this group?”), Specific will create summaries for each segment—detractors, passives, promoters—and pool feedback from their follow-ups. This helps reveal what makes some ex-cult members more positive or hesitant.
You can do the same with ChatGPT, it's just a lot more clicking around and copy-pasting to group responses.
For deeper details and live examples, the AI survey response analysis page explains how this works for every survey type.
How to deal with AI context size limits when analyzing big belief change surveys
Context size limits: All AI models (GPT included) have a technical cap on how much text they can process at once. If you have hundreds of lengthy responses, you might hit this ceiling; not all answers will fit in a single chat or API call.
Specific solves this challenge automatically—but you can use these strategies in any advanced platform:
Filtering: Instead of dumping all conversations in at once, filter your survey dataset for just the people (or just the question) you care about. For example, filter down to ex-cult members who mentioned “doubt,” or those who selected particular belief categories. The AI reviews that focused slice, not the whole pie.
Cropping: Another option is to select just certain questions to analyze. For example, you can choose “Only analyze responses to the trigger question about why beliefs changed.” This way, you stay within context limits—and the insights are directly relevant.
For more, read about context management and response analysis on our features page.
Collaborative features for analyzing ex-cult member survey responses
Anyone who has ever analyzed ex-cult member belief changes knows: collaboration matters. Trying to juggle comments in spreadsheets, track who did what, and merge insights is painful.
Chat-driven teamwork: In Specific, analyzing survey data is as easy as chatting with the AI. Every team member can open their own analysis chat and ask unique questions—exploring data from new angles without stepping on each other's toes.
Multiple chats, clear ownership: Each chat has its own filter set and displays the creator’s name, so you always know whose insights you’re reading. No more duplicated work or confusion over what’s been reviewed.
Real user visibility: When collaborating, you’ll see avatars on each message, making it clearer who asked what, and letting you build on each other's findings in real time. This makes collaborative research feel more like a productive Slack thread—focused, transparent, and efficient.
Want to start drafting your own survey? Visit our ex-cult member belief changes survey generator to get started—prompted specifically for this topic. Or see a more open-ended AI survey builder for any topic.
Create your ex-cult member survey about belief changes now
Don't miss your chance to uncover authentic stories and key belief patterns—Specific turns complex ex-cult member feedback into clear, actionable insight, so you can make evidence-based decisions faster.