7 ways to prevent survey response bias

Creating a survey campaign aims to get reliable, actionable feedback. Unfortunately, that is not always what happens. The surveyor has a significant role to play in this. As a researcher, you must look out to avoid biased responses in your survey.

When bias seeps into survey responses, the quality and reliability of the data can be severely compromised. This ultimately affects the decisions made based on such data, whether in business, policy-making, or academic research.

In this blog, we will explore seven effective strategies to prevent survey response bias and ensure the integrity of your survey results.

 

What is Survey Response Bias

Survey response bias occurs when respondents' answers are influenced by external factors or their inclinations rather than reflecting their true thoughts or experiences. This usually occurs when a sampling or testing process is influenced by selecting or encouraging one particular outcome or response over others. This bias can distort the data collected, leading to inaccurate conclusions.

 

Types of Survey Response Bias

Understanding the different types of survey response bias is crucial for identifying and mitigating them. Here are some common types:

Agreement Bias

Acquiescence bias, also known as "yea-saying," occurs when respondents tend to agree with statements regardless of their content. This can happen due to a desire to be agreeable or because respondents find it easier to agree than to think critically about their true opinions.

For example, in a customer satisfaction survey, respondents might agree with positive statements about a product or service even if they have not used them or genuinely hold those views.

This bias can lead to an overestimation of positive responses and skew the results towards a more favorable outcome.

Social Desirability Bias

Social desirability bias occurs when respondents answer questions in a manner they believe will be viewed favorably by others. This can be due to a desire to conform to social norms or to avoid judgment.

For example, in a survey about charitable donations, respondents might overreport their contributions to appear more altruistic.

This bias can result in data that reflects socially acceptable behaviors rather than actual behaviors, leading to inaccurate conclusions.

Recency Bias

Recency bias happens when respondents are influenced by recent events or experiences when answering survey questions, rather than considering the entire timeframe in question.

For example, in a performance review survey, an employee might rate their experience based on recent projects rather than their overall experience throughout the review period.

This can lead to a skewed understanding of trends or issues, focusing too much on recent events while ignoring past performance or experiences.

Nonresponse Bias

Nonresponse bias occurs when certain types of respondents are less likely to participate in a survey, leading to a non-representative sample.

For example, if a survey about workplace satisfaction is distributed via email, employees who are less engaged or less satisfied might be less likely to respond.

This bias can result in data that does not accurately reflect the views of the entire population, skewing results toward the opinions of more responsive or engaged individuals.

Response Order Bias

Response order bias, also known as order effects, occurs when the order in which questions or response options are presented influences the answers.

For example, in a multiple-choice question, respondents might be more likely to choose the first or last options presented, regardless of their actual preference.

This bias can distort the true distribution of responses, leading to inaccurate conclusions about preferences or opinions.

Extreme Response Bias

Extreme response bias occurs when respondents tend to choose the most extreme response options, either positive or negative, regardless of their true feelings.

For example, in a satisfaction survey, some respondents might always choose "very satisfied" or "very dissatisfied" rather than more moderate options.

This bias can lead to polarized data, making it difficult to understand the nuanced views of respondents.

 

How to prevent survey response bias

Because of the several types of biased responses that can distort your data quality, it is best to consider possible tips that can help mitigate the effect of survey response bias. Here are 7 helpful tips.

1. Design Clear and Neutral Questions

The design of survey questions plays a crucial role in minimizing response bias. Clear and neutral questions are essential for obtaining accurate and reliable responses.

Clear questions are easy to understand, and neutral questions do not lead respondents toward a particular answer.

Tips for Clarity

• Use Simple Language: Avoid complex words or technical jargon that might confuse respondents.
• Be Concise: Keep questions short and to the point to avoid misinterpretation.
• Define Terms: Clearly define any terms that might be ambiguous or unfamiliar to respondents.

Tips for Neutrality

• Avoid Leading Questions: Ensure that questions do not suggest a particular response. For example, instead of asking, "How much do you love our new product?" ask, "What are your thoughts on our new product?"
• Provide Balanced Response Options: Offer a range of response options that cover the full spectrum of possible answers, including neutral options.

2. Randomize Question Order

Randomizing the order of questions in a survey can help prevent response order bias, ensuring that each question is considered independently.

Randomizing question order means presenting questions in a different sequence to each respondent. This helps prevent respondents from being influenced by the sequence of questions and ensures that each question is answered on its own merits.

While randomizing, ensure that related questions remain grouped to maintain logical flow. Many survey platforms offer features to randomize question order automatically.

 

3. Use Anonymous Surveys

Anonymity can significantly reduce social desirability bias by encouraging respondents to answer honestly without fear of judgment. Anonymous surveys do not collect identifiable information, ensuring that responses cannot be traced back to individuals.

Using anonymous surveys has been known as a way to boost response rates and maintain quality data from surveys

You can conduct an anonymous survey by communicating to respondents that their answers are anonymous and will be kept confidential. Also, use an efficient survey platform that supports anonymous surveys, and does not collect personal data.

 

4. Offer a "No Opinion" or "Not Applicable" Option

Providing respondents with the option to select "No Opinion" or "Not Applicable" can reduce the pressure to provide an answer that may not be accurate. These options allow respondents to indicate that they do not have a relevant opinion or experience related to a question. 

This reduces forced responses as respondents are not compelled to choose an option that does not reflect their true feelings. It also increases accuracy since only relevant and considered responses are included in the data.

 

5. Pilot Test the Survey

A pilot test involves running the survey with a small sample to gather feedback and identify problems. Conducting a pilot test with a small, representative group can help identify potential biases and issues before the survey is fully deployed. This allows the researcher to make necessary changes before the main survey is conducted.

Follow these steps to conduct a pilot test for surveys

• Select a small, diverse group of respondents.
• Administer the survey and gather feedback.
• Analyze the feedback to identify any issues or biases.
• Make adjustments to the survey based on the feedback.
• Look for patterns in the feedback that indicate potential biases or confusion.

6. Keep Surveys Short and Focused

A concise and relevant survey is more likely to be completed thoughtfully and helps maintain respondent engagement and accuracy. On the other hand, long surveys can lead to respondent fatigue, which increases the likelihood of biased or inaccurate answers.

Tips for Shortening Surveys:

• Remove redundant or unnecessary questions.
• Combine related questions where possible.
• Prioritize the most important questions.

7. Ensure Diverse and Representative Sampling

Representative sampling means selecting a sample that reflects the demographics and characteristics of the broader population. Using diverse and representative sampling methods helps avoid sampling bias, ensuring that survey results accurately reflect the target population.

Strategies for Representative Sampling:

• Use random sampling techniques.
• Stratify the sample to ensure the representation of key subgroups.
• Oversample underrepresented groups if necessary.
• Utilize survey platforms and statistical methods that support diverse and representative sampling.

 

Get Ready to Create Your Unbiased Survey

Don’t waste your marketing budget on gathering unreliable results. Do your best to design the most objective and neutral survey you can. Enquete offers dozens of ready-to-use templates for your most common survey needs, such as employee satisfaction surveys, customer satisfaction surveys, and many more. They are fully customizable; you can add your colors and branding (and remove ours).

Each question can have its own logic and be modified with new answer options. You can also add your own social media handles at the end.

Elevate your decision-making with valuable customer feedback insights. Try Enquete for free and access advanced features on our paid plans.