Have you ever collected data that looked accurate on paper but failed to provide a reliable picture once you analyzed it?
Surveys today are an essential tool for organizations to clearly understand their customers and their needs. But designing a survey without proper planning is like trying to navigate with a misleading map: the results may look correct, but they steer you away from the truth.
In this article, we will explore the most common errors in survey design and share practical tips to avoid them, ensuring that every question you set is a step toward collecting accurate and reliable information.
For more detail, we will divide survey errors into three main categories:
Errors related to question wording
Errors in scale and response design
General design and user experience errors
1. Errors Related to Question Wording
1.1 Avoid leading and loaded questions: Phrase your questions neutrally
The goal of a survey is to encourage respondents to answer freely and honestly. Therefore, questions must not be phrased in a way that nudges them toward a particular side.
• Leading Questions:
These suggest a certain answer or push respondents toward a specific response by using biased terms.
Bad Question: How would you rate our exceptional customer service?
Good Question: How would you rate your experience with our customer service?
Bad Question: Was the waiting time very short?
Good Question: How would you describe the waiting time you experienced?
Bad Question: Was the product too expensive?
Good Question: How do you view the product’s price compared to its value?
Leading questions impose a prior assumption, such as assuming the service is exceptional, the wait time is short, or the product is expensive. Even if the response options are neutral, such wording can still distort the accuracy of the evaluation.
• Loaded Questions:
These forces respondents into answers that don’t reflect their actual situation, because they contain a built-in assumption that may not apply to everyone.
Examples:
• What is your favorite place to drink coffee?
(Assumes the respondent drinks coffee.)
• Which of our products do you use daily?
(Assumes the respondent regularly buys your products.)
• What is your favorite dish at our restaurant?
(Assumes the respondent has already dined there.)
✦ The best solution is to create sequential (funnel) questions phrased neutrally and use skip logic instead of assuming.
• First, ask a qualifying question (e.g., “Do you drink coffee?”).
• Then direct respondents accordingly (Yes → coffee-related questions, No → skip this section).
Adding preliminary or branching questions makes the survey more neutral, avoids incorrect assumptions, and improves the accuracy and quality of the collected data.
1.2 Avoid double-barreled questions: Ask about one thing at a time
One of the most common mistakes is asking about two things at once, which makes it impossible to know what the respondent is addressing.
Example:
“What do you think about the price and quality of this product?”
This asks about two different aspects: price and quality. The respondent may answer about only one or both, leaving you unsure which factor they meant.
✦ Better approach: Split each aspect into a separate question, e.g.:
• “How do you rate the price of this product?”
• “How do you rate the quality of this product?”
Ensure every question focuses on a single aspect, even if that increases the total number of questions. This makes it easier for respondents and yields clearer, more reliable results.
1.3 Avoid absolute terms in questions
These are often yes/no questions containing words like “always,” “every,” and “never.” Such absolutes force respondents into narrow corners, making answers less accurate.
Example:
“Do you always brush your teeth before bed?” (Yes/No)
Most people will choose “No,” even if they usually brush, because “always” implies an absolute standard that is hard to meet.
✦ Correct approach: Convert the question into a frequency scale for more accurate data:
How many times per week do you brush your teeth before bed?
• 7 days
• 5–6 days
• 3–4 days
• 1–2 days
• Rarely or never
2. Errors in Scale and Response Design
2.1 Overlapping or non-sequential categories
Response options must be mutually exclusive so that only one answer applies to each respondent. Otherwise, confusion arises.
Example (problematic):
How many children do you have?
• None
• 1–2
• 2–3
• 3+
Someone with 2 children wouldn’t know which to choose.
✦ Correct approach: Ensure non-overlapping, sequential categories.
How many children do you have?
• None
• 1–2
• 3–4
• 5+
2.2 Leaving gaps between categories
Sometimes the scale doesn’t cover all possible answers, leaving some respondents without a valid choice.
Example (problematic):
What is your annual income?
• No income
• 20,000–30,000 SAR
• 30,001–50,000 SAR
• 50,000+ SAR
Here, people earning less than 20,000 SAR annually have no valid option.
✦ Correct approach: Add missing ranges, such as “Less than 20,000 SAR,” to ensure inclusiveness.
2.3 Not providing sufficient options
If respondents don’t see their situation reflected, they may either skip the question or provide inaccurate answers. Not including options like “Other,” “All of the above,” or “None of the above” forces respondents into inaccuracy.
✦ Always ensure comprehensive coverage and provide “Other” with an open text field when necessary.
Example:
What type of housing do you currently live in?
• Apartment
• Villa
• Detached house
• Shared housing
• Other: ……………
3. General Design Errors
• Survey length: Very long surveys cause fatigue, leading respondents to drop out or answer carelessly, reducing data quality.
• Lack of logical flow: Randomly ordered questions confuse respondents, especially when topics jump without structure. Clear sections improve clarity and responses.
• Complex or unfamiliar language: Jargon or abbreviations may confuse respondents. Use clear, simple wording suited to an average reading level.
• No progress indicators or time estimates: Without knowing how much is left, respondents may feel discouraged and quit.
• Forcing responses: Making all questions mandatory can push respondents to answer inaccurately. Sensitive questions should be optional, with answers like “I don’t know” or “Prefer not to answer.”
• Unclear visual design: Small fonts, poor color contrast, or lack of spacing between questions make surveys harder to read, reducing respondent focus.
• No pilot testing: Not testing on a small sample risks undetected technical issues or confusing questions, which may harm data accuracy once fully launched.
• Poor device compatibility: If surveys don’t function smoothly on mobile or tablets, accessibility and response rates suffer.
In Conclusion..
Accurate survey design is not just a formality; it’s the foundation for collecting reliable data that enables informed decisions and tangible results. By avoiding common mistakes, you ensure that respondents leave the survey feeling satisfied that they gave honest and clear answers, guaranteeing your decisions are built on solid, real-world data.
Start today with BSure: create your survey or use our ready-made templates and get trustworthy data that powers your smart decisions.
