One of the most common mistakes in survey design is ignoring bias. Bias directly affects the accuracy of results and the reliability of data, leading to conclusions that may not reflect the participants’ actual reality. Previously, we discussed the definition of bias and covered its first type, Sampling Bias, which can result in data that is less representative of reality and make understanding and analyzing results more difficult.
In this article, we will focus on Response Bias, one of the most impactful types of survey bias, and explore its various forms, with practical examples illustrating how each type appears and affects data quality. We will also outline effective strategies to avoid it and ensure accurate, reliable data collection.
What is Survey Bias?
Bias is any deviation that causes survey results not to accurately reflect reality. It can be intentional or unintentional and may appear at any stage of the survey, from question design to data analysis. To fully understand the impact of bias and how to handle it, you can refer to our previous article: Definition of Bias and Its Impact.
Three Common Types of Survey Bias?
There are three common types of survey bias, each with its challenges and effects:
Occurs when the selected sample does not accurately represent all segments of the target population.
2. Response Bias:
Occurs when respondents answer questions inaccurately or untruthfully, or under the influence of various pressures or assumptions.
3. Interviewer Bias:
Happens when survey designers or interviewers, knowingly or unknowingly, influence the survey process, leading to skewed results.
Understanding and addressing these biases in your survey is critical to obtaining accurate responses from a representative sample.
What is the Response Bias?
Response bias occurs when participants provide inaccurate or misleading answers that do not reflect what they actually think or believe. It usually arises from three main reasons:
• They do not want to answer honestly.
• They do not know the correct answer.
• The survey structure or question phrasing influences them.
This bias reduces result accuracy and affects survey credibility. Therefore, it is important to understand its types and how to manage it to minimize its likelihood and achieve more reliable results.
8 Types of Response Bias:
Based on the factors causing it, response bias can appear in the following forms:
1. Extreme Response Bias
In this type, participants tend to give exaggerated or extreme answers instead of moderate opinions, especially when using scales like Likert. It increases if the question implies a “correct” answer at the scale ends or if the question is closed-ended with limited options like “Yes/No,” forcing extreme choices.
Example:
If a university survey asks, “Do you think the professor’s lectures are excellent?” and only gives two options: Yes or No, the student may be forced to choose an extreme response, even if their true opinion is moderate (e.g., good in some aspects, average in others).
2. Neutral Response Bias
This bias occurs when unclear or weak questions lead participants to frequently choose middle answers. This may indicate lack of interest and reduces research value, as the goal is to collect a diverse range of opinions.
Example:
• In a dental clinic survey, if the question says, “How was your experience?” without specifying whether it refers to treatment quality, doctor behavior, or waiting time, many patients may choose a neutral option.
• In an employee survey asking, “How do you rate the company’s work environment?” employees may consistently select “Average,” not because it reflects their true opinion, but because it is the easiest choice.
3. Acquiescence Bias (Agreeing Bias)
Also known as “Yes Bias,” politeness bias, or affirmation bias, it occurs when participants agree with statements regardless of their true opinions or beliefs. Saying “Yes” is often easier to please the researcher or complete the survey than taking a dissenting stance.
Examples:
• If a question starts with, “To what extent do you agree with the following statement: ‘Management always considers your opinions’?” with only two options (Agree/Disagree), participants may lean toward “Agree” even if unsure.
• A question like, “When you drink your coffee, do you enjoy it with or without milk?” assumes the participant drinks coffee and enjoys it, which may not be true.
Read more about the top 3 survey design mistakes and how to avoid them.
4. Dissent Bias
The opposite of acquiescence bias, here the respondent rejects all statements or questions, even if they match their true opinions. It may result from lack of interest or a desire to finish the survey quickly.
Examples:
• In an employee satisfaction survey, the participant may choose “Strongly Disagree” for all questions, even if some points are true, simply due to disinterest.
• In a customer service survey, the client may answer “No” to all service-related questions, even if some services were satisfactory, just to reject participation.
5. Question Order Bias
Also known as “order effect,” it occurs when the order of questions influences participants’ responses. They may answer similarly to the first or last questions to appear consistent, even if their answers would differ if the order changed.
Examples:
• In a parenting survey, asking first, “Do you want children?” followed by “Do you feel your parents expect you to have children soon?” may affect the second answer due to a desire to remain consistent.
• Asking first about overall job satisfaction and then about specific job benefits may bias the second answer based on the first.
6. Social Desirability or Conformity Bias
Occurs when participants answer sensitive questions in a way they believe is socially acceptable or desirable, rather than expressing their true opinions or behaviors. They often exaggerate good behaviors and downplay bad ones due to discomfort or insecurity.
Examples:
• In a health survey, “Do you exercise regularly?” participants may answer “Yes” to appear healthy even if they don’t.
• “Have you ever gotten angry or hit someone?” may get a “No” to appear polite, even if it occurred.
7. Courtesy Bias
The tendency to give positive feedback and minimize criticism, often to avoid conflict or appear polite. Common when there is a power difference, e.g., between a client and service provider or participant and researcher.
Examples:
• In a health survey, “Was everything in the service good?” participants may answer “Yes” to appear polite.
• In a workplace survey, “Are you satisfied with management?” participants may say “Yes” to avoid issues, even if dissatisfied.
8. Demand Characteristics Bias
Occurs when participants alter their behavior or responses because they believe they know the study’s purpose, such as its title, tools used, or researcher interaction, responding based on what they think the researcher wants.
Examples:
• In a survey about healthy eating, participants may underreport unhealthy foods if they know the goal is to assess healthy habits.
• In a study on stress response, participants may overreport stress levels to align with the perceived study objective.
Best Practices to Reduce Response Bias:
1. Segment your survey audience: classify participants by their knowledge and familiarity with the product or service. Allow knowledgeable respondents to skip introductory questions while less informed participants get introductory guidance.
2. Use varied question types: combine Likert scales, binary, multiple-choice, and open-ended questions to avoid extreme or agreeing biases.
3. Carefully craft neutral questions: avoid showing preference for a particular answer. Example: instead of “Do you like coffee?” use “What is your opinion on coffee?”
4. Keep questions short and clear to reduce cognitive load.
5. Avoid leading questions: Example: instead of “Don’t you agree that smoking is harmful?” use “Do you think smoking affects health?”
6. Avoid double-barreled questions: split questions containing multiple aspects. Example: instead of “Is the product useful and affordable?” ask separate questions about usefulness and price.
7. Avoid absolute questions: allow gradation in responses.
8. Avoid emotionally loaded language: use neutral, simple wording.
9. Provide flexible answer options, e.g., “I don’t know,” to avoid forcing inaccurate responses.
10. Structure the survey effectively: place personal questions at the end, randomize answer order, and group related questions.
11. Avoid repetitive questions on the same topic; mix topics to prevent previous answers influencing subsequent ones.
12. Ensure confidentiality to encourage honest responses.
13. Have questions reviewed by another team member to detect potential bias.
14. Offer incentives to encourage focus and completion.
15. Allow anonymous feedback when necessary to collect insights participants may otherwise withhold.
16. Use deception techniques cautiously: e.g., mislead participants about the study topic to avoid demand characteristics bias.
Conclusion:
No survey is completely free of some level of bias. However, awareness of its different types and actively minimizing it through neutral question design, smart survey structuring, and ensuring response confidentiality makes results more accurate and reliable. Addressing response bias not only improves data quality but also increases participants’ trust, making surveys a stronger tool for informed decision-making based on real opinions.
Use BSure today and make decisions based on clear, accurate, and unbiased data.
