The 11 types of survey questions and when to use each one

Imagine you’re launching an employee engagement survey. To get a true sense of how your  people feel at your company, you’ll need to ask a mix of question types.

For example, “How much do you agree with the statement: I feel empowered to try new things at work?” or “Finish the sentence: I feel the most engaged when…”. 

These two simple questions present you with diverse types of survey data. The first captures structured, quantitative data. The second provides unstructured, qualitative insight. Together, they reveal not just how people feel but why.

To get this kind of depth and clarity, it helps to understand which types of questions to use and when. In this article, we explore 11 different types of survey questions. For each one, we’ll cover when to use it, what to watch out for and examples.

Types of survey questions

1. Multiple choice questions
2. Rating scale questions
3. Likert scale questions
4. Ranking questions
5. Open-ended questions
6. Dichotomous (yes/no) questions
7. Matrix questions
8. Dropdown questions
9. Slider scale questions
10. Image choice questions
11. MaxDiff questions

Summary of survey question types

Here’s a brief overview of the 11 different types of survey questions covered throughout the article.

Question type	Best for	Common use cases
Multiple choice questions	Collecting structured data	Segmenting audience, assessing brand awareness
Rating scale questions	Measuring attitudes over time	CSAT, employee engagement
Likert scale questions	Measuring nuanced opinions	Brand perception, employee surveys
Ranking questions	Understanding priority	Feature prioritization, message testing
Open-ended questions	Capturing qualitative insights	Pain points, emotional responses
Dichotomous questions	Quick factual or binary data	Eligibility screening, behavioral confirmation
Matrix questions	Comparing multiple attributes consistently	Touchpoint evaluation, multi-product satisfaction
Dropdown questions	Handling long answer lists	Country, job title selection
Slider scale questions	Measuring continuous intensity	Price sensitivity, strength of preference
Image choice questions	Testing visual content	Logo, packaging or design feedback
MaxDiff questions	Prioritizing with clarity	Feature importance, value proposition testing

11 types of survey questions 

Survey data can be structured or unstructured, qualitative or quantitative, depending on the type of questions you ask. Keep this in mind when you create surveys, as each is analyzed differently and offers diverse insights. Let’s explore each question type in detail:

1. Multiple choice questions

Definition: 

A Multiple-choice question is a closed-ended format where a respondent selects one (or more) answer from a list of options. 

They are popular because they are easy for people to answer and for you to analyze. This format works especially well for gathering demographic survey questions such as age, income or location.

Best for:

• Collecting structured and quantitative data you can easily count and group into categories. 
• Personalising the user journey based on responses. For example, you ask about user goals in the welcome survey to then trigger a specific onboarding flow based on particular answers.
• Doing a quick quantitative analysis, like a pulse assessment of your target audience.

Use cases:

• Assessing brand awareness: Use these types of questions to see how familiar your prospects are with your brand. For example, ask survey respondents to mark all the brands in your market segment that they’re familiar with.
• Identifying purchase decision drivers: Ask multiple-choice questions to identify which factors most influence a customer’s buying decision, such as price, quality or reviews. 

Watch out for:

• Overlapping choices: Each option should stand on its own and measure one key aspect. For example, avoid this: “What’s the main reason you signed up for X?” “Option 1: To access survey templates. Option 2: To simplify survey creation. Option 3: To use templates and cut time.”
• Forgetting the ‘Other’ option: You may know your audience, but you should always include an “Other” option if applicable. This prevents respondents from dropping off before finishing the survey.

2. Rating scale questions

Definition: 

Rating scale questions collect structured and quantitative data and invite people to give only one answer by using a numerical scale (e.g., 1 to 5 or 1 to 10). These types of questions allow you to measure intensity, satisfaction or likelihood.

Best for:

• Measuring attitudes and sentiment over time, which makes them ideal for recurring surveys, like comparing employee satisfaction year-on-year. 
• Comparing satisfaction levels across different stages and touchpoints of the customer journey. For example, contrast customer satisfaction after purchase and after product delivery.

Use cases:

• Customer satisfaction (CSAT): Ask customers to rate their satisfaction levels on a scale of one to five or one to 10.
• Employee engagement: Gauge how your employees feel about different aspects of their job and the company. 

Watch out for:

• Cultural differences in scale interpretation: Explain what you mean by each endpoint to reduce misinterpretation. For instance, “Rate your satisfaction with these features from one to 10 (one being very dissatisfied; and 10, very satisfied). 
• Central tendency bias: It’s common to see respondents gravitate toward neutral answers, so take this into account when analyzing the data. Also, use even scales to avoid this bias in questions where you need respondents to take a stance.

 3. Likert scale questions

Likert scale questions invite respondents to answer by choosing an option on a rating scale. This is a type of rating scale, but with more nuance, as scales can go from four to eleven points. An odd-numbered scale includes a neutral middle option (such as “neither agree nor disagree”), while an even-numbered scale removes that midpoint and encourages respondents to take a definitive stance.

Best for:

• Measuring opinions, attitudes and perceptions by asking direct questions and offering structured statements as answers. For example, “Strongly disagree, disagree, agree, strongly agree.”
• Tracking changes in sentiment from one period to another. For example, through a net promoter score (NPS) survey

Use cases:

• Employee engagement surveys: Invite employees to rate their opinions regarding different aspects of the company by choosing an option on a scale.
• Brand perception tracking: Ask customers to rate how they feel about your brand and track the results over time.

Watch out for:

• Central tendency bias: Respondents sticking to the middle answer (We explained this in more detail in the previous section).
• Survey fatigue: Asking too many Likert-type questions with similar response options can cause fatigue and lead to respondents giving random answers. 

 4. Ranking questions

Definition: 

Ranking questions ask respondents to rank the answers in order of preference or importance. This helps you identify which choices matter most to your audience and how each one compares to the rest.

Best for:

• Understanding relative preferences or importance of certain factors. For example, asking people to rank the influence of certain factors when purchasing a product, including: Price, service, features, customer support effectiveness and reviews. 
• Prioritizing features or benefits according to the top-ranked answer and modifying your product or service offer to improve customer satisfaction.

Use cases:

• Product feature prioritization: For example, if your respondents rank one item highly in your feature request survey, you can use this information to modify your product roadmap.
• Marketing message testing: You can also use ranking questions to assess your audience’s relatability to certain messages for marketing campaigns, UX/web copy or crisis management statements.

Watch out for:

• Survey fatigue: Like in the previous section, asking customers to rank too many items may cause cognitive overload.
• Ranking fatigue skews results: A high cognitive load could lead to respondents answering without reading and hurting data reliability.

5. Open-ended questions

Include a screenshot of an open-ended question on the Attest platform 

Definition: 

Open-ended survey questions let people answer in their own words, rather than choosing from a list. They help to uncover detailed feedback and understand why someone gave a certain rating in a previous question or feels a particular way. While the responses take longer to analyze, they often reveal deeper insights.

Best for:

• Uncovering rich, qualitative insights by following up on previous answers by asking an open-ended question to understand “Why?”.
• Capturing verbatim customer language, as 50% of customers expect brands to mirror their speech. Reading the answers in their own words lets you see how they talk, so you can emulate it later.

Use cases:

• Identify customer pain points: Give your customers the chance to express themselves freely and see if there are any unexpected pain points that come to light.
• Emotional responses: Open-ended questions allow respondents to let you know exactly how they feel about a certain aspect. This makes the feedback more meaningful.

Watch out for:

• Low response rates if overused: Ask open-ended questions sparingly to avoid respondents from skipping them or dropping off, as these require more thought.
• Resource-intensive analysis at scale: Consider that analyzing qualitative data is time-consuming. So, rely on sentiment and text analysis AI-powered tools to simplify this step.

6. Dichotomous questions

Include a screenshot of a yes/no question on the Attest platform 

Definition: 

These are closed-ended questions that offer binary answers, e.g., yes/no, this/that. They’re often used to gather factual or binary feedback which allows researchers to classify or filter respondents based on a set of criteria.

Best for:

• Screening and qualification by asking a dichotomous question. For example: “Are you over 18 years old?”
• Collecting simple factual data without room for interpretation. For instance: “Do you own a car?”

Use cases:

• Eligibility screening: Determine early on whether or not a person is an eligible respondent, e.g., “Have you bought a smartphone online in the past 3 months?”
• Behavioral confirmation: Confirm certain actions or behaviors from the respondent, e.g., “Do you follow our Instagram account?”
• Quick quick binary opinions or product market fit checks: E.g., “Would you pay for an app that can do X?”

Watch out for:

• Oversimplification of nuanced topics: Complex attitudes, preferences or motivations may require scaled or open-ended formats to avoid misleading results.
• Limited actionable data: These questions are easy for respondents to answer and you to analyze, but they lack depth. Complement them with open-ended questions to assess certain topics.

7. Matrix questions 

Definition:

A matrix question groups related questions together in a table format, where respondents rate each item using the same scale(usually a Likert scale). Respondents need to read across the row and select an answer for each item.

Best for:

• Comparing attitudes across multiple variables. For example, ask how respondents feel about specific product features, service touchpoints or brand attributes. You can then compare their responses to spot patterns, preferences or pain points across each area.
• Measuring consistency and patterns across statements or experiences. This allows you to conduct a deep analysis and customer segmentation.

Use cases:

• Touchpoint evaluations: Measure how satisfied customers feel at each stage of their journey. This helps you identify opportunities, spot patterns and establish benchmarks across key interactions..
• Multi-product satisfaction studies: If you’re a company with different brands, services, or products, use matrix questions to quickly gauge how your customers feel about each of them.

Watch out for:

• Survey fatigue if you include too many rows: Long or repetitive matrices can overwhelm respondents, which may cause them to drop off or select the same answer across all items..
• Scale interpretation inconsistency: If the answer scale isn’t clearly defined or is too long, respondents may get confused or inconsistent in their answers which affects data quality.
• Poor mobile usability: Matrices can be hard to read on a mobile phone, especially if you’re asking long questions or have an extensive scale.

8. Dropdown questions  

Definition: 

Similar to multiple-choice survey questions, dropdown menus present respondents with a list of pre-written, mutually exclusive answers in a collapsed menu format. These are particularly helpful when there is a long list of answers. 

Best for:

• Simplifying long lists of mutually exclusive options (10+ answers), keeping the survey clean and organized.
• Conserving visual space, especially if people will be answering from a mobile phone.

Use cases:

• Country selection: Ask people where they’re from and allow them to choose from a long list of answers.
• Job title or industry classification: Allow people to share their professional information by simplifying the options. 

Watch out for:

• Harder to review answers visually: Dropdown questions work great for asking questions people already know the answer to (e.g., country of birth). It gets tricky when people need to read each of the answer options to make a decision.
• Accessibility issues on mobile devices: While dropdowns spare visual space, a long list of answers could have certain usability issues when opened from mobile devices, given the reduced screen space.

9. Slider scale questions

Definition: 

Slider scale questions invite respondents to answer a question by moving a slider along a range with a numbered or labeled scale (usually from 0 to 100). 

Best for:

• Measuring continuous variables and data beyond fixed categories, such as exact degrees of interest, satisfaction or likelihood.
• Gathering high-precision survey responses that offer more granularity than a traditional Likert or rating scale.

Use cases:

• Assess price sensitivity: Ask respondents how much they would pay for a product by using a sliding price scale. For example, $5 to $25.
• Measure intensity of preference: Understand how strongly someone feels about a feature, message or product by letting them rate it on a fluid scale.

Watch out for:

• Precision challenges on mobile: Choosing an answer on a big scale becomes harder on smaller screens.
• Confusion over scale endpoints: Even when both ends are clearly labeled, people may have trouble giving an exact answer on such a long scale.

10. Image choice questions

Definition: 

Picture choice survey questions prompt respondents with different visual answer options. Rather than choose from text labels, participants respond by clicking on the image that best represents their preference or opinion. 

Best for:

• Determining which visual design, layout or style resonates most with your audience. 
• Doing early concept validation of visual assets. For example, testing color palettes, logo styles and fonts.

Use cases:

• Packaging design feedback: Show multiple packaging options and ask which one feels more representative of your product.
• Assessing logo preference: Present different logo versions and assess which one aligns best with your brand’s positioning.

Watch out for:

• Image bias: If one image is sharper, better lit or more colorful, people may choose it for aesthetic reasons unrelated to the actual content or message. Make sure all your images follow the same standards to avoid this issue.
• Slow load times for images: Avoid using large files, as they may take too long to load, causes people to abandon the survey.

11. MaxDiff questions

Definition: 

MaxDiff (Maximum Difference Scaling) questions involve showing respondents a set of items and asking them to select the most and least important ones. This helps uncover not just what people prefer, but how strongly they feel about their preferences. 

Best for:

• Identifying what features, benefits or messages matter most for your target audience.
• Prioritizing options when everything seems important.
• Going beyond simple preference to understand the relative strength of said choice, not just the direction. In plain English: Find out what they want and how much they want it.

Use cases:

• Feature prioritization in product development: Determine which functionalities should be prioritized in roadmaps based on actual user value. For example: Which of these features should be prioritized and which shouldn’t?
• Value proposition testing: Evaluate which benefits resonate most with your audience to refine product positioning.

Watch out for:

• Repetitiveness if you overuse the same sets: When you ask respondents to evaluate too many sets can lead to survey fatigue.
• Complexity in analysis: MaxDiff questions require data to be modeled using specialized techniques like hierarchical Bayes or multinomial logit models which adds a layer of complexity.

Create more effective surveys with Attest 

Writing survey questions and choosing the right type for each topic is key to getting clear, actionable insights. Whether you’re measuring sentiment, assessing preferences or segmenting your audience, using the wrong type of question can lead to confusion, bias or irrelevant data.

To make it easier for you, check out our survey questionnaire templates for common use cases like customer satisfaction, employee engagement, market research, product testing and brand tracking. Explore all the templates here.