This article will give you tips on how to analyze responses from an Ex-Cult Member survey about housing stability. If you’re running a survey like this, you’ll want to get practical insights you can use, fast.
Picking tools for analyzing survey responses—quantitative vs qualitative data
The best approach—and the right tools—depend on what kind of data your ex-cult member survey has collected. Let’s zoom into both:
Quantitative data: If you’re counting how many respondents chose specific options (multiple choice, scales, NPS), then classic tools like Excel or Google Sheets will do the trick. Tally up responses, use built-in charts, and surface obvious trends instantly.
Qualitative data: If your survey has open-ended questions (or rich, AI-driven follow-ups), you’re going to hit a wall trying to read through everything manually. This is where AI-powered tools really shine—they digest massive volumes of text and give you patterns and themes that would take a human researcher days to uncover.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
One option: Export your survey’s open-ended responses as text, then paste them into ChatGPT or another GPT-based tool. Start a conversation—ask questions like “What are the most common themes?” or “Show me specific verbatim quotes about stability.”
This method works—but it has limits. Handling large volumes of unstructured data in this way is tricky; context size is limited, formatting can be a pain, and keeping track of versions is a headache. You won’t get native filtering, and building summary insights often takes many rounds of prompting.
Still, this can be a solid option if you’re just dipping your toes in or have only a handful of interviews to analyze.
All-in-one tool like Specific
Purpose-built AI platforms like Specific take out the grunt work entirely. These tools not only collect responses with modern, chat-style surveys, but also perform AI-driven analysis instantly. Here’s how it works with Specific:
When you build your survey, Specific’s AI runs follow-up questions automatically—digging deeper into each answer and getting to the “why” behind housing challenges. See an overview of the automatic follow-up questions feature for more on how this works.
Once results are in, Specific lets you chat directly with the AI about your survey data. Ask for summaries, get lists of top themes, identify pain points, or filter down to particular groups in seconds—no spreadsheets required.
Customizable control: You decide which AI context is used. You can filter by answer, respondent, or apply follow-up logic—so you get razor-sharp insights. Read more on how this works in the AI survey response analysis overview.
Other AI tools also exist for deep-dive qualitative analysis, such as NVivo, MAXQDA, Delve, Atlas.ti, and Looppanel. Each brings its own strengths, for example, automatic coding, sentiment analysis, and theme identification for responses—even from audio or video interviews. Depending on your workflow, one may fit your needs better than another. [1]
Useful prompts that you can use to analyze ex-cult member housing stability survey data
Knowing the right AI prompts can save you hours and help you extract the most important stories from your data. Here are some proven ones for analyzing ex-cult member surveys on housing stability:
Prompt for core ideas: This is the go-to prompt for surfacing themes from massive response sets. It’s built right into Specific, but you can use it anywhere (including ChatGPT):
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 works best with context. Before starting, provide details about your survey, who your ex-cult members are, what you care about, and any background that could guide the analysis. For example, you might say:
This survey asks former cult members about their current housing stability, reasons for instability, and what resources could help them. We’re especially interested in understanding systemic barriers and personal struggles. Please focus on themes related to barriers, support systems, and pathways to stability.
Prompt for digging deeper into a specific topic: After you get the “core ideas,” ask follow-up questions to learn more. Try:
"Tell me more about housing discrimination and how it impacts ex-cult members."
Prompt for identifying if a topic was mentioned: Use this to spot-check or validate if certain issues came up:
"Did anyone talk about eviction or risk of homelessness? Include quotes."
Prompt for personas: Useful for clustering respondents based on life situation, strengths, or needs:
"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 what’s hard for your respondents:
"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: Pinpoint what gives people hope—and what keeps them striving:
"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: This is a quick way to get a sense of “mood”—are your ex-cult members hopeful, discouraged, angry, or something else?
"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 create your own survey for ex-cult member housing topics? Try the AI-powered survey generator for ex-cult member housing stability or read this deep dive on picking the best questions for your housing stability survey.
How Specific breaks down different question types for analysis
Specific’s analysis engine adapts based on the question type:
Open-ended questions (with or without follow-ups): The AI summarizes every response, then provides an aggregate summary across all answers—including those extra layers of detail mined by automatic follow-up questions. You get a bird’s-eye view and real human stories—fast.
Choices with follow-ups: For every answer choice, you get a theme summary specific to responses given after that choice. Example: If someone selected “unable to pay rent,” you’ll see a dedicated summary just for the follow-up replies tied to that option.
NPS (Net Promoter Score): Specific splits out summary analysis by group—promoters, passives, detractors. You see trends, motivators, and blockers for each segment. Ideal for finding signals you’d otherwise miss. You can explore this hands-on in the NPS survey builder for ex-cult member housing stability.
You can replicate this in ChatGPT, but you’ll need extra steps—segment the data by hand, keep track of context limits, and do heavy copy-pasting if you want to slice responses by choice or NPS group.
How to work with large volumes of responses and AI context limits
Every AI model—including GPT, ChatGPT, or analysis engines used by NVivo, MAXQDA, or Specific—has “context” size limits. If your survey received hundreds of ex-cult member responses about housing, it may exceed what the AI can process at once.
The best solutions, natively available in Specific, are:
Filtering: Drill down to just the subset of conversations you care about. Filter by who answered a certain question, picked a certain choice, or provided key details in their follow-up. The AI analyzes only what’s relevant—and you never hit the context wall.
Cropping questions: Select only the questions you want analyzed. Maybe it’s the “biggest barriers” open-ended prompt, or the follow-up after “unstable housing.” Cropping keeps the data tight and speeds up the analysis engine.
Other advanced tools like NVivo, MAXQDA, Delve, Atlas.ti, and Looppanel employ similar strategies, letting you filter, code, and segment qualitative data to fit your context and make the most of your AI quota. [2] [3]
Collaborative features for analyzing ex-cult member survey responses
When tackling surveys about housing stability for ex-cult members, you don’t want analysis to become a bottleneck as teams email spreadsheets and summary docs back and forth.
In Specific, collaboration is built-in at every stage. Anyone on your team can launch an analysis chat with the AI—just like chatting with a colleague. No need to share raw files or wait for “the analyst” to find time. Each chat can have its own filters (e.g., only members from certain regions, or respondents mentioning eviction risk), and you always see the creator—so credit is clear and teammates can build on each other’s work.
Multiple simultaneous chats. Need a focused thread on financial challenges, another on emotional barriers, or one for demographic slices? Spin up parallel analysis chats, all with their own filters and purposes. Every message shows the user’s avatar—no more guessing who asked what.
Team-friendly data control. You can see what’s been filtered, saved, or cropped per chat. Get instant context and never worry who last changed something. This structure takes you from “everyone’s emailing their own analysis spreadsheet” to having a single workspace built for clarity and speed.
Create your ex-cult member survey about housing stability now
Build your survey and get actionable insights instantly with AI-powered follow-ups and collaborative analysis tailored for complex topics like housing stability—no more manual busywork, just answers that matter.