This article will give you tips on how to analyze responses from a Police Officer survey about Peer Support Programs using AI survey analysis tools.
Choosing the right tools for analyzing survey responses
The method and tools you use to analyze survey data really depend on how the data is structured. Here’s how I approach it:
Quantitative data: If your survey has simple counts—like how many officers chose a specific option or rated a program highly—Excel or Google Sheets handle the job easily. You can sort, filter, and graph the basics with standard spreadsheet features.
Qualitative data: When you’re dealing with open-ended answers, or those rich follow-up comments, things get trickier. If you try to scan them all by eye, details slip through the cracks, especially as volume grows. This is where AI tools shine, helping digest the flood of responses into clear insights.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
ChatGPT and similar AI models let you paste blocks of your exported survey data in, then start a conversation to summarize, theme, or otherwise break down what people are saying.
But, there are a few headaches:
Your data usually needs a lot of cleanup first. Formatting gets weird. There’s context size limits—if your survey has a lot of responses, you may have to chop them up into smaller, less meaningful chunks. And jumping back and forth between Excel, CSV files, and an AI chat adds unnecessary friction.
All-in-one tool like Specific
Specific is built exactly for this workflow. You can create surveys, collect responses, and instantly analyze them—all in one place. You get the benefits of AI survey analysis without the spreadsheet mess.
Here’s what stands out:
When you run a survey, the AI automatically asks follow-up questions, so you don’t just get superficial answers—you get the why, not just the what.
The AI-powered survey response analysis in Specific lets you chat directly with the data, summarize responses, pull out core themes, and turn feedback into action—all without copy-pasting or reformatting.
You have more control than with generic tools: filter, group, or manage what gets passed to AI for deeper analysis.
If you’re curious about building or refining a police officer peer support program survey from scratch, I’d also check out the best question ideas for peer support surveys or the step-by-step AI survey builder guide for police officer peer support programs.
Choosing a modern AI-based tool isn’t just about convenience. Peer support programs in law enforcement are growing in importance, helping to reduce mental health stigma and improve officer wellbeing—so using the best tools ensures your analysis of those programs is accurate and actionable. Research shows that nearly 90% of officers using peer support found it helpful in managing stress, with many reporting better job performance and home life as well. [2]
Useful prompts that you can use to analyze police officer survey responses about peer support programs
AI tools (like ChatGPT or Specific) work best when you guide them with clear prompts. Here are my favorites for unlocking insights from survey responses.
Prompt for core ideas: This is my go-to for surfacing the main ideas or themes in a big dataset. (It’s also the default prompt in Specific, but works in GPT tools 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
For the best results, always give the AI extra context about your survey: describe what your goals are, what you want to learn, and what the respondents are like. For example:
Here is a dataset of responses from police officers about peer support programs in their department. We want to understand what motivates officers to use peer support, where the biggest challenges are, and how these programs are impacting job satisfaction and wellbeing.
Follow up with prompts like: “Tell me more about XYZ (core idea)” or have the AI expand on the top themes that emerge.
Looking to dig into something specific? Use this direct question:
Prompt for specific topic:
“Did anyone talk about XYZ?” (For example: “Did anyone mention concerns about confidentiality?”)
Tip: You can add, “Include quotes,” to see actual officer comments.
You can get really granular if you want. Here are a few more that work great with this kind of survey:
Prompt for personas:
“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:
“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:
“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:
“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:
“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:
“Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.”
How Specific analyzes different types of survey questions
The way AI interprets qualitative data should match the kind of question you’ve asked. Here’s how it works in Specific (and you can apply the same logic with a bit more manual work in ChatGPT):
Open-ended questions, with or without follow-ups: The AI provides a comprehensive summary, breaking down both the initial responses and the extra detail gathered in follow-up exchanges. This is where program strengths, motivators, or cultural shifts often appear—essential for understanding sentiment beneath the surface.
Choice questions with follow-ups: Each selection has its own summary for the related follow-up responses. So, if officers select “Yes, I’ve used peer support,” you’ll see a dedicated breakdown of their reasoning and outcomes, followed by separate insight for the “No” responders.
NPS (Net Promoter Score) questions: The system automatically divides feedback into promoters, passives, and detractors. Each group’s comments are summarized separately, so you can immediately spot patterns among, say, those officers who actively recommend peer support versus those staying silent.
With a program as sensitive as peer support, separating and comparing these responses is key to designing improvements that actually matter. Evidence also supports that judgment-free support environments improve officers’ willingness to discuss their mental health, reducing stigma and building trust overall. [1]
If you want to experiment with these question formats, the Specific NPS survey generator for police officer peer support programs is a fast way to start.
Maximizing insights when faced with AI context limits
The biggest technical hurdle in analyzing survey data with AI is context size. If your survey gathered hundreds of stories, you’ll run into hard caps on how much data you can analyze in one go.
There are two main strategies to handle this (and Specific has these built in):
Filtering: Focus your analysis on just the conversations or responses that matter most—like only those officers who used peer support, or just those who mentioned a specific challenge. Narrowing the dataset both solves the context size issue and gives you more precise insights.
Cropping Questions: Instead of blasting the AI with entire dialog logs, select only the most important questions or topics. This keeps things efficient and means you’re not losing any analytical power, just avoiding irrelevant chatter.
If you’re using ChatGPT, you’ll need to manually segment your data. In Specific, it’s a simple selection step—and you can rerun analyses instantly with different slices of the data.
For peer support surveys, that means you can quickly zoom in on officers who reported using (or not using) these programs. Interestingly, in recent studies, 77.1% of officers hadn’t accessed peer support—usually because they felt no need for it. But when they did, the feedback was overwhelmingly positive. [2]
Collaborative features for analyzing police officer survey responses
Collaboration is often the bottleneck in getting real value from a peer support program survey. You run a survey, download all the data, and then—too often—it sits in someone’s inbox or the results remain siloed in a single spreadsheet.
With Specific, analysis is a team sport. You can chat with the AI to analyze the dataset, then share and discuss those chats right in the platform. Each chat appears with its creator’s avatar, making it simple to see which team member was driving which discovery. Filter settings stick to each chat, so one group can focus on “on-duty” experiences while another explores “off-duty” challenges in peer support.
Transparency is built in: You won’t lose track of who said what. Multiple analysis threads help keep your thinking organized and prevent duplication or missed insights.
Visual cues make a difference for busy law enforcement teams—you always know what stage of analysis each dataset is in, who’s responsible, and you can build a knowledge base over time.
If you’re interested in building a better survey workflow, check out the AI survey generator for police officer peer support programs, or use the broader AI survey maker for other internal programs.
Create your police officer survey about peer support programs now
Start gathering honest, actionable feedback from your law enforcement team today—gain deep insights into peer support program effectiveness, without the manual grind, and unlock the power of AI-driven analysis uniquely designed for real-world policing challenges.