Writing Survey Questions That Avoid Bias

Remember that bad survey you wrote? The one that resulted in responses filled with blatant bias and caused you to doubt whether your respondents even understood the questions? Creating a survey may seem like a simple task, but even minor errors can result in biased results and unreliable data. If this has happened to you before, it's likely due to one or more of these common mistakes in your survey design: 1. Ambiguous Questions: Vague wording like “often” or “regularly” leads to varied interpretations among respondents. Be specific—use clear options like “daily,” “weekly,” or “monthly” to ensure consistent and accurate responses. 2. Double-Barreled Questions: Combining two questions into one, such as “Do you find our website attractive and easy to navigate?” can confuse respondents and lead to unclear answers. Break these into separate questions to get precise, actionable feedback. 3. Leading/Loaded Questions: Questions that push respondents toward a specific answer, like “Do you agree that responsible citizens should support local businesses?” can introduce bias. Keep your questions neutral to gather unbiased, genuine opinions. 4. Assumptions: Assuming respondents have certain knowledge or opinions can skew results. For example, “Are you in favor of a balanced budget?” assumes understanding of its implications. Provide necessary context to ensure respondents fully grasp the question. 5. Burdensome Questions: Asking complex or detail-heavy questions, such as “How many times have you dined out in the last six months?” can overwhelm respondents and lead to inaccurate answers. Simplify these questions or offer multiple-choice options to make them easier to answer. 6. Handling Sensitive Topics: Sensitive questions, like those about personal habits or finances, need to be phrased carefully to avoid discomfort. Use neutral language, provide options to skip or anonymize answers, or employ tactics like Randomized Response Survey (RRS) to encourage honest, accurate responses. By being aware of and avoiding these potential mistakes, you can create surveys that produce precise, dependable, and useful information. Art+Science Analytics Institute | University of Notre Dame | University of Notre Dame - Mendoza College of Business | University of Illinois Urbana-Champaign | University of Chicago | D'Amore-McKim School of Business at Northeastern University | ELVTR | Grow with Google - Data Analytics #Analytics #DataStorytelling

🎙️ Capturing the Patient’s Voice: More Complex Than It Sounds We often romanticize the idea of “hearing the patient’s voice.” But in the real world of survey design and research, that voice doesn’t reach us raw—it is translated, filtered, and sometimes muted by the very tools we use to measure it. 🏥 The journey to and from the patient We decide in advance what’s “important” for patients to express. Then we translate those priorities into predefined response options—checkboxes, Likert scales, multiple choice. By the time the patient responds, their voice has been channeled through our framework—highly filtered, curated, and, sometimes, unintentionally distorted. Even in face-to-face interviews, what the patient says is shaped by what we ask—and sometimes by what they think we want to hear. In that process, our biases sneak in too. 🎯 Four Sources of Bias to Watch 👉 Are we asking what we truly mean to ask? 👉Is the patient hearing the question as we intended it? 👉Is their answer a true reflection of how they feel? Or just what they think we want to hear? 👉Is what we recorded or coded exactly what was said? At any of these four steps, things can go wrong. Examples in Practice 🔊 Miscommunication: Due to translation or poor phrasing, we may end up asking ABC instead of XYZ. Remember: translation must capture context, not just words. 🧠Interpretation: Communication is not what is said, but what is heard. A good question must be interpreted the same way by everyone. Example: Asking “In the past year, have you seen a dentist?” can be interpreted as: 👉The calendar year (e.g., 2024), 👉The last 12 months, 👉Since the respondent's last birthday. To solve this, be specific: use terms like “in the past 12 months” 🙊When Respondents Don’t Say What They Mean Some questions—especially judgmental or sensitive ones—can produce socially desirable answers. This is especially common in face-to-face interviews. That’s why it’s crucial to pre-test questionnaires before deployment. 📝The Danger of Poor Response Options Even when the patient is ready to tell us the truth, we can still fail to capture it—if: Response options are poorly worded Categories are not mutually exclusive or not collectively exhaustive Or if important choices are simply missing This creates “orphan answers”—respondents have thoughts we haven’t given them space to express. 🧰 What Can We Do? Survey design is not just ticking boxes. It requires time, effort, and intentionality to get it right. Some tips: Use precise language Validate translations with native speakers Run pilot studies Include open-ended questions when possible This mixed-methods approach gives you the structure of fixed responses and the richness of qualitative feedback—helping you get closer to the patient’s true voice. 🎤 Final Thought The voice of the patient is never raw—it comes through a filter. But with careful design, we can minimize distortion and maximize clarity. #Validity

After more than 25 years in market research, I’ve learned that a single poorly worded survey question can mislead teams and compromise decision-making. One of my most memorable examples of this was when I had a client that had built a prototype of a device to track and monitor driving and wanted to target parents with teenage drivers. This was their question: With 8% of all fatal crashes occurring among drivers ages 15 to 20, motor vehicle deaths are the second-leading cause of death for that age group. We know your child’s safety is of utmost importance, and you are willing to do whatever you can to keep them safe.  How likely would you be to install a device in your car to track and monitor your teenage driver? I told them that question would guilt a lot of the parents into selecting a positive rating, but it would not give them an accurate, unbiased estimate of market potential. Here's the wording they finally agreed to. A manufacturer has created a device that tracks a driver’s behavior (e.g., speeding, slamming on the brakes) and their location. It allows a user to set boundaries for where a car can be driven and be notified if the boundaries are crossed. It also allows a user to talk to the driver while they are on the road. How likely would you be to install a device with those capabilities to monitor your teenage driver? The results were not very favorable, which upset the client but also prevented them from making an expensive mistake. #MarketResearch #SurveyDesign #DataDrivenDecisions