Survey Bias 101: Understanding the Impact on Data Accuracy

Surveys are a cornerstone of primary data collection across a variety of sectors. They offer an efficient way to gather insights, measure attitudes and preferences and predict product choice, especially of products pre-launch. However, the value of survey research is often deeply dependent on the accuracy of the data collected. Even the most well-crafted survey can yield misleading or inaccurate results if it falls prey to an often-underestimated problem: survey bias.

Survey bias is more than just an academic term. It's a real-world issue that can distort data, skew findings and ultimately lead to incorrect conclusions. In turn, these inaccuracies can influence policies, product launches and more — sometimes with costly repercussions. This article will explore survey bias, its various types, how it impacts data accuracy and how you can mitigate its effects.

What Is Survey Bias?

Survey bias refers to systematic errors that occur in data collection, leading to a non-random and skewed sample of responses. It can manifest in various ways—from the questions posed to the sampling methods employed—and undermines the validity and reliability of survey findings.

For marketers this is not just a theoretical concern; it's a practical problem that can significantly hinder the efficacy of marketing strategies. Imagine investing time, effort and financial resources into a comprehensive market research survey, only to end up with data that doesn't represent your target audience. 

The ramifications could be severe, ranging from an ineffective advertising campaign to a product launch that falls flat. At worst, skewed data could even lead marketers to misidentify their target demographic, a mistake that can have long-term consequences on brand image and revenue.

Survey bias essentially dilutes the truth, providing a distorted lens through which marketers view consumer behavior and preferences. The result? Marketing strategies that are built on shaky ground, leading to poor ROI and missed opportunities for business growth.

Researchers in the social sciences and economics of course are impacted as well, for similar reasons, though the focus of this article is on marketing research applications.

Types of Survey Bias with Examples

Understanding the various forms of survey bias is crucial for anyone looking to interpret data accurately. Although not exhaustive, the following list outlines some of the most common types of survey bias that marketers (as well as researchers in the social sciences and economics) should be aware of.

Survey biases come in three flavors: (1) biases that result from having a biased sample of respondents, (2) biases that come from the structure or wording of the questionnaire and (3) biases that arise from the way the survey is administered.

Selection Bias

Selection bias occurs when the participants chosen to participate in the survey are not representative of the larger population under study.

Example: A tech company conducting an online survey about smartphone usage may inadvertently exclude older adults who are less likely to be online, skewing the results.

Non-Response Bias

This bias emerges if the individuals invited to participate in the survey but who do not respond differ from those who do respond. This makes the ending sample of respondents unrepresentative of the whole population.

Learn more about population vs sample

Example: In a survey about employee satisfaction, unhappy employees may be more motivated to respond than satisfied ones, leading to an overly negative portrayal of workplace conditions.

Self-Selection Bais

Some surveys invite any respondents interested in the survey to participate, rather than inviting a carefully controlled sample to participate. Respondents who choose to participate (who “self-select”) may differ systematically from those who do not.

Example: A survey performed on a social media platform invites respondents to rate a recent travel experience. People with negative recent experiences might be more inclined to opt into the survey to express their frustration than satisfied travelers, making the survey results systematically more negative than would have been seen in a survey with a carefully controlled sample.

Question-Order Bias

Question-order bias happens when the sequence of questions in a survey influences how people respond to later questions. 

Example: Asking participants how much they enjoy exercise before asking about their general happiness can lead to inflated reports of well-being due to the positive sentiment evoked by the first question. Or again, asking respondents to rate their satisfaction with live in general after asking about their marital satisfaction may lead respondents to rate the second question as if it were about their satisfaction with everything about their lives outside of their marriage.

Leading Question Bias

This form of bias is introduced when the phrasing of a question nudges respondents toward a particular answer.

Example: Asking "Don't you think our new product is revolutionary?" will likely elicit more positive responses than a neutral question like, "What do you think of our new product?" Better still, the question could remove the questioner from the survey entirely and ask “What do you think of this new product?” because asking about “our” product makes the situation a social one wherein some respondents who don’t like the product hesitate to say so on fear of offending the questioner.

Social Desirability Bias

Social desirability bias occurs when participants answer questions in a way they believe others will view favorably.

Example: In a survey about environmental habits, respondents may over-report recycling behaviors to appear more eco-conscious.

Response Style Bias

Response Style Bias occurs with rating scale responses when different respondents confine their answers to different parts of the rating scale.

Example: In a multicultural survey, respondents from some geographic regions (Japan and South Asia in particular) are known to use the positive end of evaluative rating scales while others (particularly Germay) are known to use the more negative end. The results of a new product concept test might show a successful product in Japan but a failure in Germany, yet both results may owe more to the differing response style bias in those two countries.

Which Survey Is Most Likely Affected by Bias?

Nearly any survey can suffer from bias, but different surveys come with different risks. The format and methodology of a survey can make it more vulnerable to specific types of survey bias. Knowing which surveys are most susceptible to certain biases can help marketers take the necessary precautions.

Online Surveys

Online surveys are convenient and far-reaching but are often susceptible to selection bias. Because they are generally conducted over the internet, they may inadvertently exclude certain demographics, such as older adults or those without Internet access. Although in most developed markets this can be less of a concern.

Example: A survey about Internet usage conducted online will naturally miss insights from individuals who don't use the Internet, leading to skewed data.

Telephone Surveys

While they can reach a more diverse demographic, telephone surveys are particularly prone to non-response bias. People who don't pick up calls from unknown numbers or decline to participate may represent a segment whose views are not captured. Telephone surveys administered by a live person may also be subject to social desirability bias if they touch on socially sensitive topics.

Example: A poll conducted via telephone may miss out on younger people who are less likely to answer phone calls, thereby affecting the representativeness of the results.

In-Person Interviews

Although they allow for deeper, qualitative insights, in-person interviews are often subject to social desirability bias. Respondents are more likely to give answers that they think will be viewed favorably by the interviewer. In person interviews can also distort the sample through selection bias because busier people will be more difficult to reach and to convince to participate.
Example: During a face-to-face interview about workplace culture, an employee might overstate their satisfaction for fear of reprisal or judgment.

Self-Administered Questionnaires

These questionnaires, which participants fill out themselves, are susceptible to misunderstanding questions or leading question bias. Misinterpretations can result without an interviewer to clarify questions.

Example: A self-administered questionnaire might ask "Do you agree that our new line of organic snacks is a healthier option compared to conventional snacks?" Respondents might feel led to agree, even if they have not tried the new product or do not have enough information to make that judgment.

How Survey Bias Can Affect Outcomes

Marketers often rely on survey data to make crucial decisions — identifying target audiences, tailoring advertising messages and even guiding product development. Yet, survey bias can severely compromise marketers' efforts, leading to skewed data, misleading conclusions and, ultimately, wasted resources.

Biased Data

Biased surveys produce biased data and no matter how large your sample size, it cannot correct for bias. For instance, selection bias in an online survey could lead you to focus your marketing efforts on younger audiences, overlooking potential interest among older demographics. The consequence? A distorted understanding of your market that could result in missed opportunities and lost revenue.

Misleading Conclusions

Nothing is more dangerous in marketing than making critical decisions based on misleading information. Imagine launching a costly advertising campaign based on data marred by social desirability bias, only to discover consumers are less eco-conscious than they claimed to be. The risk here is drawing false insights that can misguide marketing strategies, leading you down the wrong path.

Wasted Resources

Time and money are of the essence in marketing. Operating on biased data can lead to wasteful spending on ineffective campaigns or targeting the wrong consumer segments. Even worse, you may invest in a full-scale product launch only to realize the demand you thought existed was merely an artifact of biased survey data.

Mitigating Survey Bias

Recognizing the negative impact of survey bias is only the first step. The next crucial phase is to mitigate its effects. In a field where the margin for mistakes can be slim, marketers can ill-afford to base their strategies on flawed data. However, you can take several steps to minimize survey bias and bolster the reliability of your data.

Importance of a Representative Sample of Sufficient Size

Size does matter when it comes to survey samples. A larger and appropriately diverse sample that represents the underlying population can help minimize selection bias, making the results more generalizable. By reaching out to various demographic groups that provide better coverage of the true population (and are represented in the correct proportions), you not only get a richer set of data but also a more accurate snapshot of consumer attitudes and behaviors.

Crafting Neutral and Clear Questions

The phrasing of your questions can make or break the validity of your survey. To avoid biased survey questions, make sure your questions are neutral and easy to understand. Avoid loaded terms and double-barreled questions that could sway respondents' answers. Surveys sponsored by political organizations of all parties are notorious for asking biased questions, and often make great examples of how NOT to word survey questions.

Randomization of Question Order

To counteract question-order bias, consider how earlier questions could affect the response to later questions. You may also consider randomizing certain groups of questions. This ensures that earlier questions don't influence the answers to subsequent ones, thus improving the quality of the data you collect. Avoid randomizing for the sake of randomizing, however, because some question sequences flow naturally and better allow respondents to think through the questions and their responses.

Anonymity to Reduce Social Desirability Bias

People are more likely to answer honestly when they believe their responses are anonymous. Assuring anonymity can help reduce social desirability bias, which is especially important for questions that touch on potentially sensitive or controversial topics. Online surveys that do not involve a live interviewer are also likely to reduce social desirability bias. For this reason, studies hoping to get an unbiased view of the brand should NOT identify the brand as the sponsor of the survey – the survey should be “blind” in this case.

Recontacts and Follow-Up Surveys to Counteract Non-Response Bias

Initial surveys might not capture the full spectrum of your target audience thanks to non-response bias. Allowing enough fielding time for recontacts of non-respondents for follow-up surveys — possibly with a larger incentive to participate — can capture data from those who were initially reluctant to respond, making the findings more representative.

A MaxDiff Approach to Combat Survey Bias

MaxDiff, or best-worst scaling, is the go-to method for gauging preferences or importance levels for a range of items such as brands or product attributes. MaxDiff offers a more nuanced differentiation among items and between individuals' preferences than traditional rating scales do. Importantly, MaxDiff gives us a way to collect attitudinal data that avoids scale use bias (the tendency noted above for different people use rating scales differently), MaxDiff requires respondents to make best and worst choices from subsets of items, in a way that eliminates this particular form of survey bias.

Frequently Asked Questions About Survey Bias

Sawtooth Software is a trusted provider of advanced analytics and insights and has a wealth of knowledge to share. Read the following questions for more information on survey bias.

How Can Bias Affect the Outcome of a Survey?

Bias can affect your pool of respondents and their responses to your questions, generating biased data and misleading conclusions.

What Is an Example of a Survey Bias?

One example of survey bias is leading question bias, in which the question influences respondents toward a specific answer. For instance, "Don't you think this product is amazing?" is a leading question.

How Do You Avoid Bias in a Survey?

You can avoid bias in a survey by carefully selecting your sample pool and thoughtfully creating neutral questions. Conducting anonymous surveys is another way to reduce the chance of bias.

Rely on Sawtooth Software for Unbiased Survey Results

Sawtooth Software has the expertise and knowledge to help you avoid the various flavors ofsurvey bias. Our products provide powerful tools that tap into your customers' preferences. Examples include functionality to randomly rotate question order and response options with questions. Contact us today to learn how we can help you generate meaningful results.