Best questions for citizen survey about disaster response satisfaction

Here are some of the best questions for a citizen survey about disaster response satisfaction, plus tips for crafting them. If you want to build a tailored survey in seconds, you can generate yours with Specific—AI does the heavy lifting for you.

Best open-ended questions for citizen survey about disaster response satisfaction

Open-ended questions let people speak in their own words, which helps you capture real experiences and deeper insights—especially when gathering feedback on nuanced topics like disaster response. Use these questions when you want rich, contextual stories rather than just stats.

1. Can you describe your experience with the disaster response efforts following the recent event?

2. What aspects of the disaster response did you find most helpful or effective?

3. Were there moments during the response when you felt unsupported? Please share examples.

4. How well were your immediate needs met during and after the disaster?

5. What suggestions do you have for making the disaster response more effective for you and your community?

6. Did you feel that information from authorities was timely and clear? Why or why not?

7. How did your local community play a role in your recovery?

8. What challenges did you face when seeking assistance or support?

9. How would you describe the fairness of how resources were distributed?

10. What advice would you share with other citizens preparing for a similar situation?

For context, studies have shown that trust and a sense of belonging deeply affect life satisfaction following disasters, as seen after the Wenchuan earthquake in China [3]. Open-ended questions help surface those stories and build better disaster plans.

Best single-select multiple-choice questions for citizen survey about disaster response satisfaction

Single-select multiple-choice questions are your go-to when you want quantitative, structured data—they’re easy for citizens to answer quickly, help you spot trends, and can open up follow-up conversations. Sometimes, picking is faster (and less mentally taxing) than composing detailed answers.

Question: How would you rate your overall satisfaction with the government's disaster response?

• Very satisfied

• Satisfied

• Neutral

• Dissatisfied

• Very dissatisfied

Question: Did you receive the assistance you needed during the disaster response?

• All the assistance I needed

• Some assistance, but not all

• No assistance

Question: Which organization was most helpful to you during the disaster response?

• Local government

• National government

• Community volunteers

• Aid organizations/NGOs

• Other

When to follow up with "why?" When a citizen responds with “dissatisfied,” always ask “Why?” to dig out actionable feedback. This gives you something to improve—maybe it was slow response or lack of info. The “why” is often more useful than the initial answer.

When and why to add the "Other" choice? Use “Other” when you’re not sure you know all the options, or people might have unique experiences—follow up afterward to let them describe what you missed. Unexpected insights show up here.

Should you use NPS for disaster response satisfaction surveys?

Net Promoter Score (NPS) frames satisfaction in a super-simple, highly comparable way: “How likely are you to recommend the local disaster response services to others?” Respondents answer on a scale from 0–10. NPS is widely used in feedback—with disaster response, it makes sense if you’re looking to benchmark trust and performance over time, or compare between communities and time periods. You can auto-generate an NPS survey for citizens on this topic.

It’s direct, relatable, and gives a quick health check, just like how government disaster assistance satisfaction was tracked in the Philippines—a recent survey showed a net satisfaction rating of 65%, with a clear, simple metric [1].

The power of follow-up questions

The secret weapon for any AI survey is dynamic follow-up questions. As covered in our guide on automatic AI follow-up questions, these let you dig deeper, in context, in real time. When someone says “I was dissatisfied,” the AI can ask, “Was it the timing, the resources, or something else?”—It’s an expert interview, at scale.

Having the AI run smart, conversational follow-ups saves huge amounts of time. Instead of chasing respondents with long email chains, you collect rich, multidimensional feedback in one take. The conversation feels organic—which is why it’s called a conversational survey.

• Citizen: "I didn't get what I needed during the response."

• AI follow-up: "Can you share more about what you needed or expected at that time?"

How many follow-ups to ask? Usually, 2–3 clarifying followups are enough. Specific lets you set when the AI should move on or keep probing, so you get just the insight you’re looking for—without repeating yourself or overwhelming your respondents.

This makes it a conversational survey: Using followups, your survey becomes a genuine, two-way exchange—not just a static form. Respondents feel heard, not interrogated. This approach leads to more thoughtful, authentic responses and naturally higher engagement.

AI survey response analysis: Don’t be afraid of analyzing unstructured answers. With AI survey response analysis, you can easily transform raw text responses into actionable, summarized insights—see our overview for analyzing survey responses.

Automated follow-ups are relatively new—try generating a survey and see how much richer your data can become.

How to prompt ChatGPT for strong citizen disaster survey questions

Using AI to help you brainstorm and structure survey questions isn’t just possible—it’s faster and more creative than traditional templates. Here’s a quick way to get started. Start simple, but ramp up the specificity as you go:

First prompt:

Suggest 10 open-ended questions for Citizen survey about Disaster Response Satisfaction.

The more context you provide, the better the output will be. For example:

We are a local government rebuilding after a major flood. Our goal is to understand both citizens' experiences with disaster response and what could be improved, especially in marginalized communities. Suggest 10 open-ended questions.

Then, add another layer:

Look at the questions and categorize them. Output categories with the questions under them.

Finally, dig into the areas that matter most to you:

Generate 10 questions for categories "Communication" and "Assistance received".

What is a conversational survey?

A conversational survey doesn’t just collect feedback—it engages people in a two-way chat that feels natural, like texting a neighbor who’s genuinely interested. Unlike traditional forms where you tick boxes or enter static text, conversational surveys powered by AI meet people where they are, make clarifying follow-ups, and adapt to what each person says.

Manual survey design is slow and rigid. AI survey generation, like with Specific's AI survey builder, is instant, context-aware, and can handle follow-ups on the fly, for every respondent.

Why use AI for citizen surveys? You get richer, more complete insights, higher completion rates, and more authentic stories from your community—plus you save time on creation and analysis. For an AI survey example, try prompting the Specific builder or check out our guide on creating citizen disaster response satisfaction surveys.

Specific is built for modern, conversational surveys—the experience is smooth, engaging, and lets you surface what matters most, for both survey creators and respondents. This sets it apart as a true authority in the space.

See this disaster response satisfaction survey example now

If you want data that’s contextual, actionable, and effortless to collect, see how a conversational disaster response satisfaction survey works in action—get powerful citizen insights and make faster, more informed decisions in your community.