How to avoid response bias: 9 proven strategies

Customer Experience

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Just how much of a problem is response bias?

38% of collected data is discarded by researchers due to quality concerns and misinformation. This implies that over one-third of the funds allocated for survey sampling are wasted.

Similarly, receiving incorrect or misleading results in your customer surveys can have a domino effect. Strategies made on their basis might not only be ineffective but also counterproductive. 

In short, the wrong data will lead to the wrong decisions at every stage.

In this article, we provide actionable strategies to avoid response bias and ensure your surveys yield accurate and unbiased results.

What is response bias?

Response bias refers to the systematic error introduced into survey results when responses from participants don’t accurately represent their true feelings, opinions, or behaviors. 

Researchers or analysts need to recognize and address this bias proactively to ensure the validity of survey findings. You can’t have meaningful and reliable data without addressing response bias. 

What is an example of response bias?

Imagine a survey question asking individuals about their satisfaction with their online shopping experience. In the survey, you give two positive options and one negative option:

Question: “How satisfied were you with your shopping experience?”

Options:: “Very satisfied, Satisfied, Dissatisfied.”  

In this scenario, the responses will likely skew toward a biased positive.  

How can response bias be prevented?

Response bias can be prevented by employing the following strategies. Scroll down to learn more:

  • Using neutral wording
  • Avoiding leading questions
  • Using randomization
  • Pilot testing
  • Training survey administrators
  • Using AI-driven analytics

Implementing a systematic approach to continuously monitor and improve survey design and data collection processes will help prevent response bias. 

9 strategies to avoid response bias

To ensure reliable survey results, consider the following strategies to minimize response bias:

  1. Neutral wording
  2. Avoid leading questions
  3. Pilot/pre-testing
  4. Randomization
  5. Balanced scales
  6. Control for halo and horn effects
  7. Anonymity
  8. Diverse demographics
  9. Training and monitoring

Let’s explore these strategies in more detail.

1. Neutral wording

Use unbiased, neutral language in questions to avoid evoking a particular emotion for  participants. For example, instead of asking, “How amazing is  our new feature?” ask, “What are your thoughts on our new feature?” The first question evokes a feeling of excitement, which could sway the participant’s answer.

Neutral wording is the foundation of unbiased survey design. It allows respondents to provide their genuine opinions without feeling pressured to conform to a specific perspective.

2. Avoid leading questions 

Craft questions that don’t lead respondents toward a specific answer or point of view or make assumptions. Avoid questions like, “How satisfied are you with our product?” and opt for, “How would you rate our product?” The first question assumes the customer is satisfied.

Leading questions can induce confirmation bias. By using non-leading, open-ended questions, you encourage respondents to think independently and express their unfiltered views.

3. Pilot/pre-testing 

Pilot testing is an essential phase in survey development, providing an opportunity to enhance the survey by revealing and addressing any challenges or uncertainties that could impact responses. Execute a preliminary, small-scale trial of your survey to uncover and address potential biases. 

4. Randomization

Randomize the order of questions to minimize order bias. By presenting questions in a different order to each participant, you reduce the potential for systematic bias being introduced by the sequence.

Randomization ensures no specific order consistently influences responses. This technique helps uncover the true range of opinions.

5. Balanced scales

Use balanced scales and response options to prevent acquiescence bias. For instance, use a scale from 1 to 10, where 5 represents a neutral option. 

Balanced scales provide respondents with more nuanced response choices, encouraging them to express their opinions accurately rather than defaulting to a neutral or agreeable option.

6. Control for halo and horn effects

Incorporate counterbalancing questions to offset positive and negative impressions. For example, if you start with a question about a positive experience, follow it with a question probing for potential negative experiences to balance the overall feedback.

Counterbalancing questions help identify and counteract the halo and horn effects, ensuring that positive or negative experiences in one area don’t unduly influence responses in other areas.

7. Anonymity

Assure participants of anonymity to reduce social desirability bias. Let respondents know that their responses are confidential and will not be traced back to them. This encourages honest feedback. Anonymity provides a safe space for respondents to express their opinions without fear of judgment. 

8. Diverse demographics

Aim for a diverse sample to reduce nonresponse bias. When designing your survey, ensure that it reaches a representative sample of your target audience to minimize bias introduced by non-respondents. Diverse demographics in your survey sample ensure that various perspectives are represented. 

9. Training and monitoring

Train survey administrators to remain impartial and monitor data collection for potential biases. Those administering the survey should avoid conveying any biases and should be trained to maintain a neutral stance.

Ensuring that survey administrators are trained in unbiased data collection and monitoring the process for potential biases is critical. Administrators must be aware of the impact they can have on respondents and strive to remain impartial.

Incorporating advanced technologies like AI-driven analytics can further enhance your response bias mitigation efforts. AI uses contextual natural language processing (NLP) to identify and adjust for subjective language like extreme wording. Machine learning algorithms analyze survey responses based on user behavior to identify which questions need revising. For example, if a certain question is receiving an abnormally high number of neutral responses, this might mean the question needs to be reworked. 

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What are the methods to eliminate response sets?

A response set is a tendency for survey respondents to consistently answer questions in a certain way, such as always selecting the same response option, regardless of the content of the question.

To prevent response sets in a survey, vary the wording and format of questions to avoid predictability. Randomize the order of response options, making it less likely for participants to choose the same answer repeatedly. Additionally, use both positively and negatively framed questions to discourage habitual responses. Regularly review survey data to identify and address any consistent patterns that may indicate response sets.

👉AI-driven sentiment analysis software can allow you to analyze and categorize all your customer insight data points, offering an overview of sentiment beyond survey questions.

What factors affect response bias?

Response bias can be influenced by a variety of factors. The problem is often exacerbated by biased questions

Here are the most common factors that affect response bias:  

Social desirability bias

Social desirability bias occurs when respondents provide socially desirable responses rather than answering honestly. This often manifests as overreporting ‘good behavior’ and underreporting ‘bad behavior’.

This bias doesn’t just happen in a vacuum. It’s often triggered by poorly worded questions, leading questions, or sensitive topics such as alcohol consumption, personal income, and health. Affirmative actions like endorsing charity donations can also induce social desirability bias.

Take the following question: 

“Is it fashionable to use our premium beauty products every day?”


“Despite their unconventional styles, can individuals with unique beauty preferences still be seen as trendsetters?”

These examples may elicit biased responses, potentially influencing respondents to align with perceived societal expectations. The first question subtly implies social desirability towards using the company’s premium beauty products. The second question associates unconventional styles with trendsetting.

Non-response bias

Non-response bias occurs when people who don’t respond to a survey differ significantly from those who do. 

Suppose a survey is sent out to a random sample of customers, but only those who are highly dissatisfied or exceptionally pleased with the service choose to respond. 

In this scenario, non-response bias occurs because the experiences and opinions of those who didn’t respond (the moderately satisfied or indifferent customers) aren’t represented in the collected data. The survey results may overemphasize extreme viewpoints, leading to an inaccurate reflection of the overall customer satisfaction level, as well as cause you to miss “slow burn issues”—those that aren’t a huge problem at the moment, but build up, costing more time and money in the long run.

One way to counter this would be by increasing your survey’s response rate and making sure the survey applies to a broad customer base.

👉 Learn more about reducing “slow burn issues” with AI-driven ticket deflection.

Demand bias

Demand bias occurs when participants change behaviors or views based on their assumptions about the research agenda. These inferences can be made based on the survey title, researcher-participant interaction, survey process, setting, and tools used. This can bias research findings as participants may respond according to perceived demands.

For instance, customers informed that a survey aims to evaluate ‘how frustrating’ the online shopping experience can be during peak hours may be more inclined to report negative feedback.

This example illustrates how framing the survey question with a negative tone could lead respondents to provide more critical feedback about the online shopping experience.

Extreme response bias

This bias occurs when respondents choose extreme views even if their true opinion is moderate. It’s most common in satisfaction surveys, where respondents often jump to the highest or lowest rating. This can result from a desire to please or due to the wording of questions inducing a biased response.

For example, say a feedback survey asks:

“With 1 as unsatisfied and 5 as satisfied, how would you rate the quality of our service?” 

Respondents may reply with a 1 or a 5 as if they’re choosing a binary answer, even if their true opinion is more neutral.

Neutral response bias

Neutral response bias can negate all the effort put into designing a survey. It occurs when respondents consistently provide neutral responses to every question. This is often a result of participant disinterest or time constraints.

For example, general public respondents may provide neutral responses in a survey about specialized topics like health or lifestyle choices.

Acquiescence bias

Acquiescence bias occurs when respondents consistently agree with research statements irrespective of their true opinions. This bias is rooted in the perception of how researchers expect them to respond. 

For instance, respondents may provide favorable feedback to new ideas or products, assuming they align with the researchers’ preferences. 

Dissent bias

Dissent bias comes up when respondents deliberately disagree with presented statements. It can arise from factors like inattention or a desire to complete the survey in a hurry.

Voluntary response bias

Voluntary response bias manifests when the survey sample comprises of strictly volunteers. This situation can lead to an overrepresentation of specific viewpoints, especially if the volunteers hold strong opinions. 

For instance, online discussions debating controversial topics like whether freelancers using AI in their work should receive equal pay for comparable work may attract participants with distinct and often polarized perspectives.

Cognitive bias

Cognitive bias represents a subconscious error in thinking that shapes how individuals interpret information. It can impact rationality and decision accuracy. 

For instance, when users have a strong initial positive impression of a brand, they may anchor their opinions to this viewpoint. Subsequently, they might disproportionately emphasize positive feedback, neglecting or downplaying negative comments. This cognitive bias in their responses can lead to an unbalanced representation of the brand’s reputation on social media.

The role of AI in detecting and countering bias

At Idiomatic, we recognize the struggle faced by CS, CX, and product teams to effectively leverage user feedback. Our advanced algorithms go beyond generic text analytics, automatically identifying patterns and anomalies in survey responses.

Idiomatic harnesses the potential of all your existing contacts, examining every interaction within your helpdesk. This eliminates the need to depend solely on feedback from customers, as we analyze interactions from anyone who has engaged with your support team. This approach eradicates response bias. By combining support data with survey responses, we provide a comprehensive view of the customer experience, ensuring a complete understanding of the customer experience.

To further reduce response bias,  Idiomatic allows you to categorize feedback without asking the customer to do it themselves. This means you can ask more open-ended questions and allow them to get straight to giving feedback instead of selecting from categories. This means less friction for giving a response, which increases response rates. Increased response rates are inversely correlated with response bias (because you have a larger section of the population) so will reduce bias. The easier you make it to give feedback the more you’ll get and the less bias you will incur.  

Analyze all your customers’ feedback easily and reduce response bias significantly with Idiomatic. Sign up for a free, no-obligation demo to get started. 

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