This article will give you tips on how to analyze responses from an ex-cult member survey about personal boundaries. You’ll get practical guidance to make sense of complex responses and uncover valuable insights using modern AI tools.
Choosing the right tools for ex-cult member survey analysis
The tools you choose for analyzing survey data depend on the kind of responses you receive. Here’s how you can approach different types of data:
Quantitative data: When you need to understand things like how many ex-cult members selected a specific option or gave a numerical rating, classic tools like Excel or Google Sheets do the trick. You can quickly chart patterns, run filters, and create summaries with built-in formulas.
Qualitative data: Responses to open-ended or follow-up questions—where ex-cult members describe experiences in their own words—are a different beast. Manually reading hundreds of responses is almost impossible, and meaningful themes easily get lost. That’s where AI-powered tools shine: they quickly summarize, group, and extract insights from this unstructured text.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Copy and paste workflow: If you’ve exported your survey’s qualitative responses, you can drop them into ChatGPT or another GPT-based tool and chat about what you find.
Drawbacks: It’s not that convenient—you need to format your data carefully, stay mindful of AI context limits, and prompts often need trial and error. There’s no direct visibility into who said what, and follow-up analysis gets clumsy without built-in filtering or chat context management.
All-in-one tool like Specific
Breezy from start to finish: Specific is designed for this use case. You launch your ex-cult member survey about personal boundaries as a chat-style interview, often using a survey preset.
Automatic follow-ups: As participants answer, Specific’s AI asks smart follow-up questions to get richer stories (learn more about how it works in automatic AI follow-up questions).
Seamless AI analysis: All responses are instantly summarized, aggregated by themes, and turned into clear insights. You can chat with the AI about your results, just like ChatGPT, but with better context management (see AI survey response analysis for a detailed overview). Filter by demographics, questions, or even follow-up replies—no spreadsheet wrangling.
Extra features: You can manage what information gets sent to the AI, keep your data organized, and collaborate across teams—all inside one platform. This is especially effective for nuanced, deeply personal survey data like responses from ex-cult members.
Other expert tools with AI capabilities are also worth considering, such as NVivo, MAXQDA, ATLAS.ti, Delve, and Looppanel, which support qualitative and mixed-methods analysis, automatic coding, and sentiment detection. These options offer structured workflows for in-depth research and are used internationally by social researchers and psychologists [1].
Useful prompts that you can use for AI survey analysis about personal boundaries
Once you’re using AI to analyze ex-cult member survey data, prompts are your superpower. The right prompt helps the AI uncover not just what was said, but why it matters. It’s all about getting to the heart of personal boundaries, trauma, and growth.
Prompt for core ideas: Use this to boil down large sets of qualitative responses to main topics and explanations. This is the default engine behind Specific’s summary feature, but you can copy it straight into ChatGPT or any other advanced GPT model:
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. Tell the AI about your survey’s audience and your goals. For example:
You are analyzing survey responses from ex-cult members about personal boundaries. The survey explores challenges and motivations around setting healthy boundaries after leaving a controlling environment. Extract the key themes, then summarize them in a list with the number of mentions.
Prompt for follow-up detail: After identifying a core idea or boundary issue, dig deeper by asking:
Tell me more about “difficulty saying no”—include examples or quotes from the data.
Prompt for specific topic validation: To check if anyone talked about a particular theme, ask:
Did anyone talk about trauma from group activities? Include quotes.
Prompt for pain points and challenges: This is especially useful in context of ex-cult members working on personal boundaries:
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: Helps you zero in on why people prioritize certain boundaries or what motivates them most:
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: To gauge how ex-cult members feel about their boundary progress:
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.
If you want more specialized prompts for your audience, you can always refer to best questions for ex-cult member personal boundaries surveys or explore ways to tweak your survey using the AI survey editor.
How Specific analyzes qualitative data by question type
Specific handles qualitative survey data by making summaries targeted to the question structure. Here’s how it works for each question type:
Open-ended questions (with or without follow-ups): You get a summary of all responses given to the question itself, plus any follow-up answers. This paints a big-picture perspective on core themes—ideal for nuanced topics like personal boundaries after cult experiences.
Choices with follow-ups: For multiple choice questions where the AI asked further questions based on participants’ picks, Specific groups and summarizes all follow-up responses for each option. You see not only what people picked, but their deeper reasoning.
NPS (Net Promoter Score): Specific splits feedback into categories—detractors, passives, and promoters—then makes a summary per group based on their follow-up comments. This is spot-on when you need to understand satisfaction and referral likelihood around recovery programs or support services for ex-cult members.
You can achieve similar insights using ChatGPT with good prompts and manual curation, but it requires more effort—copy-pasting, keeping track of which responses correspond to which question, and sometimes tracking user IDs.
Solving AI context size limits for large survey analysis
Modern AIs have context limits: if your survey collects hundreds or thousands of lengthy, open-ended responses, you’ll hit a wall where you can’t analyze everything at once. This is especially relevant for detailed ex-cult member surveys focused on personal boundaries, where participants often share long stories.
There are two main strategies to break through the context barrier (Specific bakes these features into the tool):
Filtering: Filter by which questions people replied to, or even by the choices they selected. Analyzing only those conversations drastically cuts the dataset to a manageable chunk for the AI.
Cropping: Instead of sending every question’s responses to the AI, pick just the ones you want to analyze. This lets you dig deep into, say, just the personal boundaries section, without the AI losing focus or timing out.
If you’re using plain GPT tools, you’ll need to pre-process and split your survey manually—often exporting just part of your data at a time.
If you need to create these segmentation and analysis layers for your survey about personal boundaries, consider using the advanced AI survey analysis features in Specific.
Collaborative features for analyzing ex-cult member survey responses
Collaboration on survey analysis can be a major headache, especially with sensitive and complex responses like those from ex-cult members discussing personal boundaries.
Analyze by simply chatting: In Specific, you and your team can dig into survey responses just by chatting with the AI—no export required. It’s like having a data analyst always available, but tailored for your own data and goals.
Multi-chat analysis: You can spin up multiple analysis chats, each with its own set of filters. Analyze one chat focused on "Boundary-setting challenges", another on "Recovery milestones". Each chat automatically shows who created it, so you know whose perspective or goal is front and center.
Clear team insights: In collaborative chats with the AI, you’ll see avatars and names next to each message. It feels natural to hand off insights, validate ideas together, or keep parallel work streams running without confusion.
This workflow makes a huge difference for research teams, therapists, or peer support groups analyzing boundary recovery progress. It’s much easier to keep track, stay aligned, and share actionable findings with your group or organization.
If you want to learn more about this multi-user workflow, check out how teams collaborate on survey response analysis in Specific.
Create your ex-cult member survey about personal boundaries now
Start gathering deep, actionable insights from ex-cult members by creating a survey that feels natural and yields richer answers—AI-powered analysis delivers better clarity and helps your team take meaningful action faster.