Survey Question Types and Examples, Plus Writing Tips

Introduction to Survey Questions

Understanding the variety of survey question types available is pivotal for any market researcher looking to harness the full potential of their data collection. 

Survey questions are the backbone of market research, providing valuable insights that can drive business strategies and customer understanding. Crafting effective survey questions is not just a matter of asking what one wants to know—instead, it involves careful consideration of how each question is structured to best engage respondents and yield meaningful, actionable insights.

Survey questions come in various formats, each serving unique purposes and providing different types of data. From multiple choice questions to more complex grid or ranking formats, the type of question used can significantly influence the quality and type of information collected. 

This article provides an overview of the different survey question types, offering insights into their utility, considerations for their use, and real-world examples to illustrate their application.

All of the examples in this blog can be found in this Discover survey: Question Type Demonstration. By clicking the link, a copy of the survey will be added to your Discover projects.

Types of Survey Questions

Multiple-choice Questions

Multiple-choice questions are among the most common survey question types used in market research. They provide respondents with a list of predefined answers, allowing for quick, structured responses that are easy to analyze. This format is particularly useful for gathering data on preferences, behaviors, or demographic characteristics. 

There are typically different formats for multiple-choice questions including single-select (i.e., radio buttons), multi-select (i.e., checkboxes), and a dropdown menu. Examples of each of these formats are given below.

Multiple-choice questions are often reported using descriptive statistics in tables or charts and cross-tabulations (i.e., crosstabs).

Considerations:

• Clarity is key: Ensure that the question is unambiguous, and the answer choices are clear, mutually exclusive, and collectively exhaustive to avoid confusion and ensure accurate data. Using options like “Other” with text entry or an exclusive “None of the above” option can help respondents answer questions accurately if none of the predefined choices apply to them. See the multi-select example below for an example of a text-entry option and an exclusive option. 
• Avoid bias: Avoid leading or biased options that could skew the results. Include an "Other" text-entry option to capture data that may not fit within the predefined answers. 
• Utilize piping and survey logic and double-check question settings: Multiple-choice questions typically allow you to randomize responses, make a question required or optional, and even set display logic on certain answer options or the question itself. Multi-select questions can require a certain number or range of selections. You can also use “piping” and skip logic to make the survey adapt to respondent choices. Make sure all questions settings match your intentions for format and survey flow. 

Examples of multiple-choice questions:

Single-select multiple-choice question

Multi-select multiple-choice question

Dropdown multiple-choice question

Numeric (Number Entry) Questions

Numeric entry questions ask respondents to provide a number as their answer, making them ideal for collecting quantifiable data such as quantities, frequencies, or measurements. This type of question facilitates precise data analysis by producing numerical responses that are straightforward to compile and compare.

Considerations:

• Clear context: Specify the units or scale to use, if applicable, to ensure uniformity in the responses.
• Range validation: Implement checks to prevent unrealistic values, enhancing data reliability.
• Necessity: Sometimes to answer a research question, a range or bucket or values will suffice. Consider if a single-select question with ranges such as “Below $50,000” and “$50,000 to $99,000” is more appropriate than asking for a specific value.

Numeric Question Example:

Open-ended (Text-Entry) Questions

Open-ended questions allow respondents to answer in their own words, providing richer, more detailed insights. These questions are invaluable when exploring areas that require more nuanced understanding, such as opinions or experiences. Open-ends can also be great follow-up questions if a researcher wants to know why a respondent made a certain choice in a previous question.

Considerations:

• Use sparingly: These questions can be more taxing for respondents and more challenging to analyze due to the variability in responses.
• Clear prompts: Ensure questions are specific enough to guide the respondent but open enough to allow for personalized answers.
• Plan the analysis: Large quantities of text data can be time consuming to analyze manually. Plan for how data from open end questions will be utilized, visualized, and if a specific analytic technique will be required

Examples:

Multi-line open-end question

Single-line open-end question following up a selection of “Brand A”

Grid (Matrix) Questions

Grid or matrix questions let respondents evaluate multiple items using the same set of scales. This format is efficient for assessing a series of related attributes or behaviors. Grid questions can involve a series of single-select, multi-select, or numeric rows. There’s also a specialized grid question type called bipolar grid that allows for questions formats like semantic differentials. There are examples of each of these types of grids below.

Considerations:

• Simplicity and readability: Avoid overly complex grid questions that can lead to survey fatigue or not fit on a screen very well. Limit the number of items in the scale (i.e., the number of columns), and if there are too many row items, consider splitting the question into multiple grid questions on separate pages, or consider using dynamic lists to limit how many rows are displayed to respondents. 
• Plan the analysis:Generate some test data and confirm the data is formatted as desired. How will the data be analyzed? If the purpose of the grid is to rate the importance of or preference for a list of items, would a MaxDiff exercise be more appropriate?

Examples of grid (matrix) question types:

Single-select grid question

Multi-select grid question

Numeric grid question

Bipolar grid question

Ranking Questions

Ranking questions require respondents to order a set of items according to their preference, importance, or any other relevant criterion. This type of question is useful for identifying priorities or preferences among a list of options.

Considerations:

• Keep list short: Provide a manageable number of items to rank to avoid respondent fatigue, or rank a subset of the items instead of the entire list. For ranking a larger number of items, consider a MaxDiff exercise instead of a ranking question.
• Clear instructions: Make sure to specify whether '1' indicates the highest preference or the lowest to avoid confusion.

Examples of ranking questions:

 

Constant Sum Questions

Constant sum questions require respondents to distribute a given number of points, usually adding up to 100, across various attributes to signify quantity, or relative importance/significance.

Considerations:

• Clear instructions: Explicitly state how many points to allocate and what these points represent.
• Balance: Use this type of question when it is critical to understand the relative importance between different items.

Example constant sum question type:

Choice Modeling Questions

Choice modeling, including techniques like conjoint analysis and MaxDiff, helps identify the attributes most valued by respondents through comparative preference analysis. This approach is particularly useful for simulating real-world decision-making scenarios.

Considerations:

• Complexity: These are sophisticated techniques that may require more detailed explanations and respondent education. You can learn more on our website about conjoint analysis, and more about MaxDiff.
• Sample size: Larger sample sizes are generally needed to ensure reliable and valid results. Learn more about sample size rules of thumb for choice-based conjoint exercises.

Examples of choice modeling question types:

Choice-based conjoint exercise

MaxDiff exercise

Tips for Writing Good Survey Questions

Common Mistakes When Writing Survey Questions

Writing effective survey questions is crucial for gathering reliable and actionable data. However, even experienced researchers can fall into traps that compromise the quality of the data collected.

Avoiding Common Pitfalls:

• Leading Questions: Avoid wording that suggests a particular answer. For example, "Don't you agree that our product is the best?"
• Double-Barreled Questions: Ensure questions are focused on a single topic. For example, avoid asking, "How satisfied are you with our price and quality?"
• Biased Wording: Use neutral language to prevent skewing responses. Avoid terms with positive or negative connotations.
• Assumptive Questions: Do not presume certain behaviors or states. For instance, "How often do you use our service?" presumes the respondent is a user.
• Hypothetical Questions: Be wary of asking respondents to predict future behavior or hypothetical scenarios, which can lead to speculative answers. For example, “If you were planning on purchasing a vehicle ten years from now, what features would be most important to you?”

Strategies for Improving Survey Question Quality

Crafting quality survey questions is an art that requires careful consideration and strategy.

Key Strategies:

• Define Objectives: Clearly define what you want to learn from each question to ensure it aligns with your overall survey goals.
• Consider Other Data:Incorporate past research, existing data, qualitative research, and stakeholder or industry-expert opinion into survey design
• Include 'Prefer Not to Answer': Provide this option to respect respondent privacy and increase the likelihood of completing the survey. Think about which questions are truly necessary to meet objectives.
• Comprehensive Answer Choices: Ensure all plausible responses are covered, which helps in collecting more accurate data. Using exclusive options and text-entry options can help all respondents find an accurate answer choice.
• Clarity and Specificity: Avoid ambiguity by using precise language. Ensure that every respondent interprets the question in the same way. Sometimes images or tooltips can help provide clarify and specificity.

Enhancing Survey Design and Response Rates

The survey's design, including its structure and the way questions are presented, can significantly affect the data quality and response rates.

• Test Everything: Test all technical aspects of a survey and test the respondent experience. It is also often a good idea to “soft launch” a survey by sending it to a smaller list of recipients before launching to the entire list of potential respondents.
• Use Screener Questions and Quotas:Use screener questions to make sure the right people are taking the survey and use quotas to ensure a mix of people that represents the population of interest.
• Funnel Technique: Start with broad questions and gradually narrow down to more specific ones, helping to ease respondents into the questions. Similarly, see if there are ways of breaking up a complex question into smaller, simpler questions to reduce the chance of a respondent feeling overwhelmed.
• Keep Surveys Short and Relevant: Respect respondents' time. Shorter surveys generally have higher completion rates. Using skip and display logic can also help reduce survey length and keep the survey elements relevant for respondents.
• Answerable Questions: Avoid asking respondents to recall detailed information from the distant past, which can lead to inaccuracies.

By adhering to these best practices, researchers can significantly enhance the quality of their survey questions and the reliability of the data they collect.

Conclusion

Crafting effective survey question types is essential for gathering accurate and actionable data. Each question format—whether multiple choice, Likert scales, ranking, or choice modeling—serves a unique purpose in capturing respondent insights. Avoiding common pitfalls like leading questions, biased wording, and unclear answer choices ensures data integrity.

By applying best practices such as defining objectives, structuring surveys effectively, and keeping questions clear and relevant, researchers can improve response rates and data quality. Thoughtfully designed surveys lead to deeper consumer insights, helping businesses make informed, data-driven decisions.