3 survey bias types to avoid (and why)

Surveys are essential tools for businesses, researchers and policymakers when they want to gather insights and make data-driven decisions. However, if survey bias is present, the results can be misleading, resulting in poor conclusions and ineffective strategies. 

Imagine launching a new product based on customer feedback, only to discover that your data was skewed due to a biased survey design. The consequences can be costly, resulting in wasted resources, misguided marketing campaigns and lost revenue.

Fortunately, survey bias is preventable. We’ll review the most common types of bias in surveys and share how to avoid bias with our survey best practices.

Survey bias is a deviation of feedback owing to the surveyor’s influences on the respondent. It occurs when survey methods systematically favour specific outcomes, leading to results that inaccurately represent the target population.

Survey bias – whether deliberate or accidental – can arise at any stage, from survey design to data analysis. Leading questions, focusing on certain demographics or excluding non-respondents can all introduce bias and distort your data.

For example, if a company only surveys its loyal customers, the results may be skewed positively, giving an inaccurate picture of customer sentiment. 

Similarly, the data won’t capture respondents’ true opinions if they feel under pressure to give socially acceptable answers.

Poor survey distribution can also lead to bias. If a community service charity only distributes a survey about community concerns online, it may exclude older individuals who are less active on digital platforms, leading to results that do not accurately represent the concerns of the community as a whole.

Survey bias is commonplace in market research, customer feedback studies and public opinion polls. Identifying and reducing bias is vital for making data-driven decisions that are grounded in reality rather than based on personal perceptions.

Bias in surveys can significantly affect research by distorting findings and leading to:

We’ll show you how to address and tackle survey response bias early on so you can refine your research methods and confidently share your insights with stakeholders.

There are three common types of survey bias, each presenting its own unique challenges and implications:

Recognising and tackling these biases in survey research is key to ensuring accurate feedback from a truly representative sample.

Let’s look at each type of bias and its subcategories:

Sampling bias occurs when surveys are distributed in a way that excludes certain groups. 

To reduce this bias effectively, you should carefully consider your survey process and use a sampling method that allows inclusivity. 

Examples of sampling bias include:

Non-response bias happens when certain respondents systematically do not participate in a survey.

For example, if HR sends an employee satisfaction survey via email, disengaged or dissatisfied employees may choose not to respond, leading to overly inaccurate feedback. 

Similarly, political polling may be biased if certain demographic groups, such as young voters, are less likely to respond, thus skewing the results towards older populations.

Survivorship bias occurs when the survey data includes only those who have completed a process and ignores others. 

If a company only surveys long-term customers, it might overlook those who have stopped using the product due to dissatisfaction, thus resulting in an inaccurate assessment of the company’s customer satisfaction levels.

Response bias occurs when survey participants provide inaccurate or misleading answers due to the structure and external conditions of the survey.

Effective survey design can help you tackle response bias by encouraging respondents to answer honestly.

Extreme responding occurs when respondents consistently only choose the highest or lowest response options. Participants may exhibit this behaviour by selecting “strongly disagree” or “strongly agree” in answer to a Likert scale question.

Extreme responses can appear in satisfaction surveys, where participants may exaggerate their answers instead of selecting more moderate options. For example, an employee engagement survey might report unusually high scores because employees feel under pressure to give positive feedback.

Neutral response bias occurs when respondents consistently pick middle-of-the-road answers, avoiding extreme options even if they have strong feelings. 

Respondents might give neutral answers to a customer feedback survey when they don’t want to appear overly critical or enthusiastic.

Acquiescence bias arises when respondents agree with statements regardless of their true opinions. For instance, in an employee satisfaction survey, participants may tick “agree” for most statements, out of habit or to avoid disagreement, rather than truly expressing their views.

Question order bias occurs when the order of survey questions influences respondents’ answers. 

For example, let’s suppose that a survey asks about overall job satisfaction before asking about specific job benefits. In this case, respondents may be more likely to respond to the second question in a way that aligns with their previous response to the first question. 

Social desirability bias occurs when survey participants answer questions in a way they believe is socially acceptable rather than truthfully.

For example, in a health survey, respondents may underreport unhealthy behaviours such as smoking or fast-food consumption to appear healthier.

Interviewer bias occurs when the interviewer’s behaviour, tone or phrasing influences survey responses. 

Interviewer bias can arise as a result of the interviewer’s enthusiasm, question structure, or non-verbal cues such as facial expressions and body language.

This bias occurs when respondents subconsciously alter their answers based on the interviewer’s cues. For example, if the interviewer expresses enthusiasm about a particular product, respondents may be more likely to give positive feedback.

Reporting bias occurs when data analysis selectively emphasises or ignores specific responses. For example, a company may highlight only positive customer feedback while downplaying negative responses.

Some survey methods are more prone to bias than others. Selecting the right method will ultimately depend on your survey’s goals, your audience and the resources available to you.

Consider these method types when deciding how to distribute your survey:

Preventing survey bias requires support from every member of your research team. Create a research plan to avoid common survey question mistakes and regularly review survey responses to identify and adjust for bias. 

Biased survey questions can lead to misleading responses and distorted data. Here are some survey question examples to help you spot and correct bias:

Leading questions encourage respondents to answer in a particular manner, often favouring one perspective over another. These questions can subtly push participants towards giving a desired response, which will skew your survey results.

Double-barrelled questions ask about two different things in a single question, making it difficult for respondents to provide an accurate answer.

Loaded questions contain built-in assumptions that can pressure respondents into answering in a specific way.

Researchers who prioritise addressing and eliminating survey bias benefit from greater credibility, more effective strategies and improved data accuracy. You can take proactive steps to eliminate survey bias by following our survey design best practices. 

Create a SurveyMonkey account to get started with designing efficient, impactful surveys that yield accurate, reliable insights.