Multiple choice questions are elemental to survey writing. They’re versatile, intuitive, and they yield clean data that’s easy for you to analyze.
Since they provide a fixed list of answer options, they give you structured survey responses and make it easier for your respondents to complete the survey.
However, the data you get back are limited to the choices you provide.
That means that if your answer options aren’t comprehensive, you risk bias in your results.
While it may be the question type that’s most straightforward, understanding the different types of multiple choice question and their uses is more nuanced.
Question types: Single answer vs. multiple answer
One of the basic differences between types of multiple choice questions is whether to let respondents choose multiple response options or just one.
Single-answer questions, the most common type, ask respondents to pick just one choice from a predetermined list.
This format really shines in binary questions, questions with ratings, or nominal scales. Someone can either agree or disagree with a statement, but he cannot do both; he can be at one point on a 10-point Net Promoter Score℠ (NPS) scale, but not several— he can be a 7, but not a 7 and a 9.
Single-answer multiple choice questions are also effective when you’re asking respondents to pick their favorite or least-favorite option from a predetermined list, or when asking them to select the option that comes closest to their own opinion.
Multiple-answer questions have a slightly different purpose.
If a single-answer multiple choice question asks “What is your favorite pizza topping?” a multiple-answer multiple choice question might ask “Which of the following pizza toppings do you like?” Here, respondents can check off all the choices that apply to them instead of being forced to pick just one.
It’s up to you, writing the survey, to decide which form of the question is more relevant.
Variations of the standard multiple choice question
Once you’ve determined whether you want your multiple choice question to have a single answer or multiple, you can decide whether you’d like to use one of the many variations of multiple choice question.
For example, Adding an “other” answer option or comment field can solve a common drawback of using a multiple choice question. When you give your respondents a fixed list of answer options, you’re forcing them to select only from the options you’ve provided, which can bias your results.
What if you don’t provide someone with the answer option he really wants to give? He may end up choosing an answer that he doesn’t agree with from your list of choices, which can affect the integrity of your results.
One way to address this problem is to provide several answer choices but still give respondents the option to write in their own custom response. When you’re writing your survey, simply check the box “Add an ‘Other’ Answer Option or Comment Field” and your respondents will be able to do exactly that.
You’ll be able to view the write-in comments in the Analyze tool, but you’ll have to do some extra work on your own to separate out each write-in response. Keep in mind that respondents should see the “other” option as a last resort. If too many people write in their own responses, it will weaken the comparisons you can make between your main answer choices.
Rating scale questions display a scale of any range—from 0 to 10, 1 to 5, 0 to 100, etc.—and ask respondents to select the numerical point on the scale that represents their response best.
In order for respondents to understand a rating scale, you must make explicit the relationship between the numbers on the scale and the concepts they measure, either in the question or on the rating scale itself.
The examples above are different ways of writing the Net Promoter Score, “the likeliness to recommend” metric that’s used on anything from market research surveys to customer satisfaction and employee engagement surveys.
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A Likert scale or Likert-type scale is probably the most used rating scale. The traditional Likert scale asks respondents to pinpoint how much they agree or disagree with a statement. Likert scales are perfect for measuring respondents’ attitudes or behaviors, particularly when they relate to sensitive subjects. Here’s an example:
You can vary your response options to make the focus of your question more or less granular. For example:
Alternative multiple choice question formats
Sometimes you may want to ask several questions in a row that each have the same response options.
For example, consider a series of Agree/Disagree questions, or a series of rating questions asking your respondents to pick the number from 1 to 10 that indicates how likely they are to recommend a product to a friend (much like the NPS).
If this is the case for your survey, consider using a matrix. While matrix questions simplify the question content, very large matrices can be burdensome for respondents, especially on mobile devices. Respondents might abandon surveys that are difficult to complete, which can impact your completion rates.
If your matrix is so large that respondents will have to continuously scroll right or down, you should break up your questions or reduce the number of answer choices you provide so that your survey is easier to complete.
Instead of displaying all of the answer choices beneath the question, the dropdown question gives respondents a scrollable list to select their answer from.
Dropdown questions work best for questions that have a long list of brief answer choices, such as asking a respondent for his home state or birth year.
They should be used sparingly. For most multiple choice questions, having all choices visible at the same time will give respondents context as they are answering the question.
Ranking questions let your respondents choose the order of answer choices that best fit their opinions.
For example, asking respondents to rank their top five pizza toppings tells you not just whether someone likes pepperoni, but how much in relation to the other flavors available.
If you want to capture more information than you can from simple multiple choice questions, then a ranking question might be best for you.
Ranking questions are more difficult to analyze than regular multiple choice questions.
They give you a sense of whether a respondent likes one answer choice more than another, but they don’t tell you how much more. So unless you are specifically interested in respondents’ preferences at the individual level and not just on average, ranking questions might add more complication to your survey than needed.
The benefits of multiple choice questions
Multiple choice questions are the most common question type used on SurveyMonkey. It’s not just because they’re the question type most people consider to be the “standard.” They also have specific advantages that other question types don’t.
They’re simple. It’s much easier to click a button than to type in a response. And the more accessible you make your survey the more completed responses you’ll get.
Oftentimes you’ll want to explicitly ask respondents to choose from two or more options: Do you agree or disagree? Yes or no? Do you think we should be doing more, less, or that we’re doing the right amount? In these cases, it’s best to provide the choices for your respondents to choose from.
They give response options concrete definitions. Often you’ll write survey questions with an idea of how respondents might answer. In situations like this, multiple choice is usually the best question type.
Let’s say you are asking people to pick which political candidate they’ll vote for. What if there are 20 candidates to choose from? If some respondents already know who they are going to vote for—great! They’ll have an easy time answering this question no matter what.
For most people, though, having the response options available in front of them will be a big help. It helps you, too. Sorting through answers to open-ended questions can take a lot of work, but analyzing data from closed-ended questions is easy.
It will make your life a lot easier when you can analyze your survey results without having to weed out responses with spelling mistakes or write-ins that aren’t serious. That way, Mickey Mouse will never appear on your shortlist for the next president.
They help guide your respondents. One benefit of multiple choice options is that they give your respondents context for how they should answer. Providing response options in a multiple choice format indicates how specific or general you want the respondents’ answers to be.
For example, does someone need to report his exact birthdate (January 3, 1975), or just the year (1975)?
Response options can also subtly nudge your respondents to provide more details than they would on their own. Think of a Likert scale. The more response options you provide, the more they are able to quantify how much they agree or disagree (i.e. “Agree,” “Agree strongly,” or “Agree Somewhat”).
Think carefully about your multiple choice questions before you send out your survey because the answer options you provide will determine the ways you can use your results. For example, providing an “other” answer option may be convenient, but it might make it more difficult to analyze your data.
They look better on mobile devices. Mobile optimization is an important consideration in the survey world today. Roughly 3 in 10 people taking SurveyMonkey surveys in the U.S. do so on a smartphone or tablet.
With such small screens and no mouse or keyboard to use, mobile devices aren’t good mediums for surveys that use text boxes or require a lot of scrolling. There’s a time and a place for open-ended answer options, but for mobile surveys, stick with simple multiple choice options.
Make sure to read SurveyMonkey’s top tips for survey mobile optimization before designing your survey.
Multiple choice questions make up the bulk of survey questions for a good reason. They’re useful in a broad range of situations, and they’re especially valuable once you understand the subtleties of how to use each type.
NPS®, Net Promoter® & Net Promoter® Score are registered trademarks of Satmetrix Systems, Inc., Bain & Company and Fred Reichheld.
Survey research associate Laura Wronski is a literal master of survey methodology (she got her master’s degree from the University of Maryland College Park). She came to SurveyMonkey by way of the Bureau of Labor Statistics, where she worked on the American Time Use Survey. Stay tuned to the SurveyMonkey blog for more survey science tidbits from Laura.