Using leading questions in your survey is not good form, and it can lead to questionable data. Leading questions show bias, and they dilute your results.
In this article, we look at what clarifies a leading question and how to avoid them. First, let’s define the leading question.
What is a Leading Question?
A question of this type in your survey is one that influences your respondents to provide a particular answer. This is the last thing you want to do when creating your survey.
The whole point of survey research is to gain feedback from your customers that is objective and true. When you craft leading questions, you move respondents away from their valid answers. (tweet this)
For example, if you were to ask the question, “How likely are you to purchase brand A (your brand) over brand Z?” This question assumes your customer is happy with your brand and would be more likely to buy from you again.
Leading questions assume your question is true even though you have no valid data proving it’s so.
Let’s look at another example of a leading question, “How good was the service you got from our team?” Again, this assumes your customer got good service. This assumes you’ll get a positive answer.
You’ll find, though, that respondents may completely avoid providing you with constructive feedback because you worded the question poorly.
Types of Leading Questions
While we’ve looked at some examples, let’s put some names to specific types of leading questions so you can avoid them.
Leading questions change your survey results because they have an inherent intent and answer. Here are the types:
- Assumption-Based Leading Questions: These are questions asked based on an assumption. You may ask a question like, “How much did you enjoy…?” This assumes your customers enjoyed something. Instead, you can ask a question that says, “Please share your experience….”
- Interlinked Statement Leading Questions: These are questions you ask that have two closely connected statements. For example, “Most customers hate waiting for more than 10 minutes on hold. What do you think?” Here you’ve linked something they hate and put a time limit on it. Perhaps you have a really patient customer who’d wait 20 minutes. Instead ask a question that says, “What’s the maximum amount of time you’d accept on hold?”
- Leading Questions Based on a Direct Implication: Here you ask a question that leads respondents to consider the result that would happen in the case of a particular incident. For example, “If you purchased this product, should we make another just like it in a different color?” You’ve implied they’re purchasing when in fact, they haven’t.
- Coercive Leading Questions: These questions come right out and force your respondents to answer in a very forceful manner. For example, you might craft a question that says, “Your experience with our customer support team was satisfactory, right?” Or, it might be, “You’ve enjoyed your purchase, now rate it.” Instead, let them tell you about their experience.
Common Characteristics of Leading Questions
You know that leading questions prompt and encourage respondents to answer in a specific manner contrary to what they might have answered.
Now let’s look at the characteristics so you can avoid them.
- Leading questions are framed intentionally to cultivate bias. They are often created with a plan by the survey creator to get specific results or confirmations. Conversely, survey creators may unknowingly craft their questions this way.
- Leading questions make assumptions.
- You’ll find that leading questions require personal input, but they often make respondents uncomfortable especially when they don’t agree.
- Sometimes survey creators use leading questions to understand the exact consequences of a situation.
- Usually leading questions are forceful and demanding of feedback, even when it’s not correct.
Avoiding a Biased Survey
The last thing you want to do is send out thousands of surveys that are biased by their very nature.
Your survey design hinges on open, honest questions without bias and without leading respondents.
Biased surveys often have a higher drop out rate. Why? You confuse respondents, and sometimes frustrate them as you’ve given the answer before they can give it.
By creating good survey questions, you allow respondents to answer truthfully and without fear. You allow them to avoid bias, so they aren’t pulled in one direction or another.
Avoiding Leading Questions
By putting some extra thought into your survey questions, you can avoid the leading question.
Here are some suggestions:
- Be simple, clear, and concise when writing your questions.
- Don’t lead someone to a specific answer.
- Always offer an “other” option.
- Keep your survey short.
- Analyze each question and test it before sending.
- Make sure others, both in and outside your company, look over your survey before you send it.
- Don’t use jargon or colloquial wording. This isn’t an “insider” survey. Make sure it’s understandable to everyone.
- Don’t get too technical.
- Make sure your questions ask only one question.
- Keep your sentences as short as possible.
- Avoid using leading words. For example, don’t ask how respondents like your “excellent” products. Ask them to rate it, but don’t use leading keywords.
- Ask specific questions that allow your respondents to come up with their own answer.
- Take out words that reflect your own opinions or preferences.
- Write questions that allow for two answers – both a negative and a positive one.
- Don’t include unnecessary modifiers. For example, don’t label people, places, or things.
Bias in the form of leading questions can sneak into the most well-intentioned surveys. Leading questions push your respondents to answer in a specific way because of how they are framed.
Many survey creators intentionally craft questions they want confirmed. This assumes they know the answer and makes it nearly impossible for respondents to answer.
Don’t dilute your survey with leading questions. Use the tips here to create a survey without bias and one that provides you with the most valid, actionable data to grow your business.
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