This article will give you tips on how to analyze responses from an ex-cult member survey about mental health support needs using AI survey tools and best practices for survey analysis.
Choosing the right tools for analyzing survey responses
Let’s get straight to it: your approach to analyzing survey responses depends on whether the data is quantitative or qualitative.
Quantitative data: These are things like how many survey participants chose each response. You can crunch these numbers easily in Excel, Google Sheets, or any simple analytics dashboard. Tallying up choices is straightforward.
Qualitative data: Open-ended answers and follow-ups—where respondents describe their challenges, experiences, or needs—get messy fast. Reading through everything by hand? Forget it. This is where AI tools shine, enabling you to make sense of large volumes of raw text without burning days or weeks.
When you’re dealing with qualitative responses (which are almost always the most revealing part of a mental health support needs survey), you have two main options for tooling:
ChatGPT or similar GPT tool for AI analysis
Quick and flexible, but not built for complex survey data. You can copy exported survey responses right into ChatGPT and start chatting about them. It’s a great way to extract meaning from hundreds of open-ended responses in minutes. But honestly, this approach can get clunky—especially if your data needs filtering, or if you want to keep everything organized for team collaboration.
Manual effort required. You’ll need to prep your data, break long lists into manageable chunks (because of AI context size limits), and sometimes wrestle with inconsistent formatting. Great as a one-off, trickier for ongoing or in-depth analysis.
All-in-one tool like Specific
Purpose-built for survey data collection and AI analysis. Platforms like Specific combine AI-powered survey design, followup question logic, and in-depth AI response analysis into one seamless workflow.
Boosts data quality with follow-ups. As people fill out your conversational survey, the AI digs deeper with smart followup questions, drawing out richer, more specific responses. That extra context is pure gold when you’re analyzing mental health support needs among ex-cult members. You don’t need to manually set up probes—the AI handles it for you. Learn more about this in how automatic AI follow-up questions work.
One-click AI-powered insights. After collecting responses, the platform can instantly surface key themes, summarize long-form answers, and even let you chat with AI like you would in ChatGPT—but focused entirely on your survey data. You can filter, segment, and manage large data sets easily, skipping the spreadsheets and manual copy-paste purgatory.
If you want a deep dive into how this works, check out the AI survey response analysis feature.
Other market-leading tools like NVivo, MAXQDA, ATLAS.ti, Delve, or Looppanel also offer robust AI-powered approaches for qualitative data—automated coding, sentiment analysis, and visualization—although often with a steeper learning curve [1].
Useful prompts that you can use to analyze ex-cult member mental health support needs survey data
Let’s make your AI tool work smarter. Whether you’re using ChatGPT, Specific, or another platform, great prompts supercharge your analysis. Here’s how to get the most from your ex-cult member mental health support needs survey data:
Prompt for core ideas: This is my go-to for distilling the main themes out of large, messy answer sets. It’s the default prompt powering Specific’s own analysis, and it works surprisingly well in ChatGPT too:
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
Want your analysis to reflect your unique goals? AI always performs better when you prime it with context: For example—
We ran this survey among ex-cult members to understand unmet mental health support needs. We care most about actionable gaps and practical solutions for support groups and therapists. Summarize what matters.
Once you have core ideas, just ask: “Tell me more about XYZ (core idea)" to dig deeper into a theme and surface the nuances, stories, or examples tied to that topic.
Prompt for specific topic: To check if a certain topic or keyword came up—maybe “family estrangement” or “group therapy”—just ask: “Did anyone talk about XYZ?” You can also add “Include quotes” to pull direct evidence from the data.
Prompt for pain points and challenges: If you’re interested in the core struggles, run:
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: If you want to get a rich sense of the different types of ex-cult members needing support—maybe recent leavers versus long-term survivors—try:
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: To spot overall mood—are responses hopeful, negative, uncertain? 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 suggestions & ideas: To crowdsource improvement tips or fresh concepts:
Identify and list all suggestions, ideas, or requests provided by survey participants. Organize them by topic or frequency, and include direct quotes where relevant.
Prompt for unmet needs & opportunities: To highlight gaps you might miss otherwise:
Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.
Want to see the impact of smart questioning on survey outcomes? Check out best questions for ex-cult member mental health surveys or learn how to create your own ex-cult member mental health survey for hands-on guidance.
How Specific analyzes qualitative data based on question type
Open-ended questions (with or without follow-ups): Specific automatically summarizes all free-text answers and any related follow-up responses—you get a grouped summary and key insights without scanning hundreds of comments.
Multiple choice with follow-ups: Each option generates its own summary of the follow-up responses from those who selected it. Want to see why certain ex-cult members choose “peer support” over “professional counseling”? You get individualized narratives for each path.
NPS (Net Promoter Score) surveys: Specific gives you summaries for promoters, passives, and detractors separately. Each group’s feedback—collected in follow-up—gets its own dedicated insight. So you can address distinct needs, whether someone scored support as “10” or “3”.
Doing this in ChatGPT? You’ll need to filter and group responses manually, but it’s doable with enough effort and some spreadsheet wrangling. If you want a shortcut, see how the AI survey response analysis feature in Specific automates all this.
How to deal with AI context limits on large ex-cult member surveys
AI is powerful, but even the best tools have limits—specifically, a maximum “context size.” If your survey gets hundreds of detailed responses, you’ll quickly hit these boundaries.
Two smart strategies (built into Specific!) help you beat this bottleneck:
Filtering: Only send responses where people replied to specific questions or gave particular answers. Need feedback from ex-cult members who mentioned “isolation”? Filter your dataset before asking AI for insights—it’ll keep your request focused and within limits.
Cropping: Pick just the questions you need analyzed, and send only those to the AI model. This approach keeps the dataset efficient, and lets you focus on themes for one question at a time instead of everything at once.
If you want to learn how these work in practice, you’ll find useful info in the feature guide.
Collaborative features for analyzing ex-cult member survey responses
Running a survey like this is rarely a solo mission. Collaboration is key, yet organizing team efforts often turns nightmarish—shared exports, email loops, or everyone asking their own questions in ChatGPT. It’s chaos.
Analyze together, live, with chat. In Specific, you can analyze ex-cult member survey responses by chatting directly with AI, right inside a collaboration-friendly workspace—no downloads or emailing spreadsheets needed.
Multiple chats for focused collaboration. You can open separate analysis chats, each with its own filters and focus (say, “motivation for joining support groups” or “barriers to seeking therapy”). It shows who created each chat, so teamwork stays organized and nobody steps on anyone else’s insights.
Clear visibility into who said what. Every message in the AI analysis chat shows the sender’s avatar, keeping accountability and context clear—ideal for larger research teams or if you’re working with support practitioners and ex-cult advocacy organizations. This makes surfacing, discussing, and acting on respondent insights so much easier.
Curious about designing your survey to maximize collaboration? You might like the how-to guide on creating ex-cult member support needs surveys or, for total control, explore the AI survey generator with an ex-cult member preset.
Create your ex-cult member survey about mental health support needs now
Start collecting deeper insights and actionable themes by launching a conversational AI survey that empowers your analysis and accelerates your research—no technical setup required.