Common Types of Survey Bias – How to Avoid Them

When it comes to market research, obtaining reliable data can be paramount for businesses as it affects their return on investment. To ensure that you get the most reliable insights from your surveys, you’ll want to make sure to mitigate survey bias.

Survey bias is when respondents provide answers in a way that aren’t indicative of how they truly feel or respond. This can occur due to many factors such as recall bias (having difficulty remembering an event) or response bias (giving answer based on someone else’s expectations).

Another form of survey bias which is important to be aware of is social desirability bias where participants answer in a manner they think would paint them in a more favorable light. Therefore, when designing surveys, one should aim to provide supplemental information along with questions and create questions which are easy-to-understand and non-leading so as to reduce any potential survey biases.

Survey Bias

Utilizing data to make decisions has become more important than ever for businesses seeking to succeed in a competitive marketplace. This is evidenced by the fact that, according to SPGlobal, an astonishing 44% of companies are now basing most of their decisions on data.

By learning to interpret this data correctly and apply it effectively to their decision-making process, organizations can gain invaluable insights for better understanding how customers interact with them and how to optimize key operations within the company. With data playing such an integral role in today’s corporate world, wise firms will start taking advantage of its potential or be left behind.

Why Survey Bias Matters

Good marketing practices rely heavily on understanding the needs of customers and prospects. But what happens when surveys are conducted with biased questions that distort people’s answers? The results are no longer valid and decisions can be made on potentially faulty data.

Survey bias can influence how a company positions its brand, products or services to the public – inviting frustration from customers if their expectations aren’t met or if inefficiencies exist due to inaccurate research. Inaccurate surveys risk damaging customer relationships, leading to reduced sales, lost opportunities and decreased customer loyalty. Companies need to ensure that the surveys they ask of customers are as objective as possible so that gathered data is reliable and trustworthy.

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Making Misguided Business Decisions

Making business decisions based on faulty data can be a costly mistake for any organization. All too often, companies invest resources in market research without realizing the data they’re collecting is lopsided and far from reliable.

The only way to make sure that the data your team is using to inform decisions is credible and unbiased is to conduct vigorous market research. Investing in good quality research tasks like surveys, focus groups, experiments, and field tests will set your organization up with the right foundation for fact-based decision-making. If you take the time to properly assess your studies, statistics, and customer feedback, you will reap the rewards of your analysis down the line!

Missing Out on Important Information

When planning surveys, it’s important to remember that your respondents may not be able to give you an honest reply. They might be unable to choose from the options you’ve provided, or opt for a more conversational text-based answer.

What’s more, if they feel your bias has been expressed through the questions, it’s likely that certain key insights will go unheard. Ultimately, designing surveys with objectivity in mind is essential if you want to capture all of the available data and make informed decisions based on quality research.

Risk of Discriminating Against Certain Groups

As businesses continue to grow, it’s important for brands to be aware of the potential risk that comes with discriminating against certain groups. Let’s look at an example: surveys that are created without sufficient thought in the context.

If a brand were sending out a survey to determine if their employees had family members affected by immigration laws, it could lead to believed discrimination if 95% of those surveyed responded “no” while the 5% who said “yes” receive lesser consideration. This is why being mindful of context and making recognition is so important!

Types of Survey Bias

• Selection Bias: occurs when the sample of respondents is not representative of the target population due to the method of selection or nonresponse.
• Non-response Bias: happens when selected individuals refuse to participate or are unavailable to be surveyed, which can skew the results.
• Response Bias: results from respondents providing answers that do not reflect their true opinions or experiences, often due to social desirability, acquiescence, or leading questions.
• Sampling Bias: occurs when the sample is not randomly selected, which can result in over- or under-representation of certain groups in the population.
• Recall Bias: happens when respondents have difficulty recalling past events accurately, leading to inaccurate responses.
• Reporting Bias: occurs when respondents provide inaccurate information intentionally or unintentionally, such as exaggerating or minimizing their experiences.
• Hawthorne Effect: happens when respondents change their behavior or responses simply because they know they are being studied.
• Confirmation Bias: occurs when researchers interpret or report survey results in a way that confirms their preconceived notions or hypotheses, instead of presenting the data objectively.

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Below, we discuss eight common survey bias types. In a nutshell, they circle around four main factors:

• The survey creator’s own beliefs.
• Lack of responses to certain questions.
• Respondents telling you what they think you want to hear.
• Small sample sizes or non-representative respondent groups.

1. Sampling Bias

Sampling bias is a common type of bias in survey research that occurs when the sample used in the study is not representative of the population being studied. This can lead to inaccurate or misleading results, as the sample may not accurately reflect the views, opinions, or characteristics of the population as a whole.

Some common causes of sampling bias include:

• Non-random sampling methods: If the sample is selected using a non-random method, such as purposive or convenience sampling, it may not be representative of the population.
• Exclusion of certain groups: If certain groups are excluded from the sample, such as those with low income or education levels, the results may not accurately reflect the views of the entire population.
• Self-selection bias: If participants are allowed to self-select into the study, such as by responding to an online survey, the sample may be biased towards those who are more interested or motivated to participate.

To avoid sampling bias, researchers should use random sampling methods whenever possible, such as simple random sampling or stratified random sampling. This ensures that every member of the population has an equal chance of being included in the sample. Additionally, researchers should make every effort to include all relevant groups in the sample, even if it means increasing the sample size or using more resources to recruit participants.

Pro tips for avoiding sampling bias include:

1. Clearly define the population being studied: This helps to ensure that the sample is representative of the entire population, rather than just a subset of it.
2. Use random sampling methods: This helps to ensure that every member of the population has an equal chance of being included in the sample, and reduces the risk of bias.
3. Consider using stratified sampling: This can be especially useful when studying populations that are diverse or have distinct subgroups, as it helps to ensure that each subgroup is represented in the sample.
4. Use a large enough sample size: A larger sample size can help to reduce sampling bias, as it increases the chances of including a diverse range of participants.
5. Analyze and report on non-response rates: If a significant number of individuals refuse to participate in the study, non-response rates should be reported as this could potentially impact the results.
6. Choose an appropriate sampling method: Depending on the research goals, different sampling techniques may be more suitable than others. Choosing the most suitable methodology can help to ensure that the sample is representative of the population being studied.

2. Non-Response Bias

Non-response bias occurs when individuals who choose not to participate in a survey differ from those who do participate in important ways, leading to an inaccurate representation of the population. This bias can compromise the validity and generalizability of the study results. Here are some pro tips for minimizing non-response bias:

1. Increase response rates: One of the most effective ways to minimize non-response bias is to increase response rates. This can be achieved through various methods, such as offering incentives, sending reminders, and ensuring the survey is easy to complete.
2. Understand non-respondents: Collect information on non-respondents, such as age, gender, and socioeconomic status. This can help to identify potential sources of non-response bias and adjust for them in the analysis.
3. Use multiple contact methods: Using multiple contact methods, such as mail, phone, and email, can increase the likelihood of reaching potential respondents and reduce non-response bias.
4. Minimize survey length: Longer surveys are more likely to result in non-response bias. Minimize the length of the survey to increase response rates and reduce the likelihood of non-response bias.
5. Analyze non-response bias: Analyze the potential impact of non-response bias by comparing the characteristics of respondents and non-respondents. This can help to identify potential sources of bias and adjust for them in the analysis.
6. Use weighting: Weighting can be used to adjust for non-response bias. This involves adjusting the weights of the respondents to ensure they are representative of the population.
7. Consider alternative data sources: If non-response bias is a significant concern, consider using alternative data sources, such as administrative data or existing survey data.

By following these pro tips, you can help to minimize non-response bias in your research and ensure that your results are accurate and reliable.

Example of Non-Response Bias

A non-response survey bias example that you’re probably familiar with happens during elections.

Some people don’t make it to a polling station because they have other obligations, like work or childcare, or they don’t feel their vote makes a difference.

These individuals might have completely different worldviews from those who do vote. However, since they don’t vote, the party whose electorate does may win, even though many people disagree with them.

Pro tip: Survey delivery matters. Before you send your survey, test it. Run it on various devices to verify what the experience will look like for your respondents. Once you’re sure the links work, watch the surveys you’re sending and their response rates.

3. Survivorship Bias

Survivorship bias is a type of bias that occurs when the focus is only on the successful outcomes or surviving members of a group, while ignoring those who did not succeed or did not survive. This can lead to overestimating the success rate of a group or a particular intervention, and can result in flawed conclusions. Here’s an example of survivorship bias:

Example: A study is conducted on the success rates of entrepreneurs who have started their own companies. The study focuses only on successful entrepreneurs who have made millions of dollars and have become successful in their respective fields. The study concludes that entrepreneurship is a highly successful path, but ignores the vast number of entrepreneurs who have failed and gone bankrupt.

Pros:

1. Encourages success: Survivorship bias can provide motivation and inspiration to individuals to strive for success, as they are presented with successful role models.
2. Simplifies analysis: By focusing only on successful outcomes, data analysis can be simplified and more straightforward.
3. Can be useful in some contexts: In some contexts, such as studying successful companies or individuals, survivorship bias may be useful.

However, it is important to be aware of survivorship bias and its potential impact on research conclusions. To avoid survivorship bias, it’s important to include a representative sample of the entire population, including both successful and unsuccessful outcomes. By including all members of the population, researchers can better understand the factors that contribute to both success and failure, leading to more accurate conclusions.

4. Question Order Bias

Question order bias is a type of bias that occurs when the order of questions in a survey affects how respondents answer subsequent questions. This bias can arise when questions are asked in a particular order that influences the way respondents think about the topic or influences their response. Here’s an example of question order bias:

Example: A survey is conducted to gather opinions on two political candidates. The first question asks respondents to rate one of the candidates on a scale of 1 to 10. The second question asks respondents to rate the other candidate on the same scale. Because the first candidate is rated before the second, the order of the questions may influence respondents to rate the second candidate lower than they would have if they were asked to rate the second candidate first.

Pros:

1. Provides structure: Having a specific order of questions can provide structure and clarity to the survey.
2. Reduces response burden: By ordering questions in a logical and concise manner, respondents may be more likely to complete the survey.
3. Can highlight patterns: Question order can help to identify patterns and relationships between questions.

However, it’s important to be aware of question order bias and its potential impact on the survey results. To avoid question order bias, researchers should:

1. Randomize question order: Randomizing the order of questions can help to reduce question order bias and ensure that responses are not influenced by the order of the questions.
2. Use branching questions: Branching questions can be used to ask follow-up questions based on previous responses. This can help to ensure that responses are not influenced by the order of the questions.
3. Pretest the survey: Pretesting the survey with a small group of participants can help to identify potential sources of bias, including question order bias.

By being aware of question order bias and taking steps to minimize its impact, researchers can ensure that their survey results are accurate and reliable.

5. Conformity Bias

Conformity bias can be tricky for questionnaire creators. This form of bias occurs when respondents opt for what they deem to be the most popular opinion rather than their true feelings. Reasons behind this choice could include possible repercussions, a feeling that their opinion doesn’t matter, or even an obligation to complete the survey with a positive outlook if they are financially incentivized.

Such motivations can lead to inaccurate results and data from your surveys, so it’s important to take precautionary measures into account.

Example of Conformity Bias

Conformity bias occurs when judgments are based on the social pressure to accede to a majority’s opinion. An example of this is when customers of your B2B company decide to rate their overall satisfaction with your services higher than what their true feelings might be.

Respondents may select a “4” or “5” rating even if they have encountered difficulties in the past during the cooperation process – late invoices, miscommunications with account managers, etc. In order to keep your business relationships strong and beneficial for all parties involved, it is important that customers are always honest and candid in assessing their level of satisfaction with you.

Pro tip:When it comes to getting honest and diverse feedback, one of the best ways to tackle conformity bias is anonymizing your survey responses. Nobody likes having their opinions put into a box, so make it clear that your survey responses will remain anonymous. Your respondents will feel comfortable giving you honest answers without fear of judgement or retribution. Plus, this allows for greater diversity in the survey feedback, allowing for deeper insights that can inform decisions more accurately. Anonymizing survey responses ensures everyone gets to have a say in shaping the future!

6. Dissent Bias

Dissent bias can be a major issue for survey takers and needs to be addressed. Without the right kind of attentiveness, it can be easy to fall victim to hitting the “disagree” option too quickly or too often. Several variables are likely at work here – including a desire on behalf of respondents to complete surveys as quickly as possible or needing to take breaks due to survey fatigue.

Ultimately, however, recognizing true responses versus those resulting from dissent bias can become very tricky. To truly get the most accurate data out of your surveys, it’s important to pay attention to any potential signs of dissent bias throughout the process.

Example of Dissent Bias

As an ecommerce business, you want to know more about your customers, which is why offering rewards in exchange for surveys makes sense. Giving customers a 10% coupon code in return for their participation can be an effective way to motivate them to provide valuable feedback. However, one downside of this plan could be the phenomenon of “dissent bias”.

When people only care about redeeming their discount code and don’t take the time to actually go through the questionnaire, they could simply click to mark the lowest score and submit their answers without much further thought – resulting in effectively useless results for the company that are not truly reflective of customers’ opinions. Make sure you do all you can to increase survey participation and reward thoughtful responses!

Pro tip: If you’re looking to get the most out of your survey, a smaller survey sample that is more tailored to your target audience can make all the difference. Your CRM and surveys of the past can be invaluable resources in determining which customers would make the best candidates to be part of this group. Long-term customers or those who have taken part in customer support are highly likely to invest more time and thought each of their responses, making them ideal candidates. Selecting the right participants with an eye towards detail can lead to valuable insights and actionable data.

7. Neutral Response Bias

Neutral responses can seem like a harmless consequence of your survey, but they could be indicative of something more serious. That’s why it’s essential to conduct research with the right respondents and ask questions that are specific, clear, and relevant. For example, asking a random group if they like the taste of Brie de Meaux would likely result in neutral responses as most people don’t know anything about French cheese. By conducting surveys in a strategic manner, we can make sure that responses stay meaningful and help our team optimize their approach!

Example of Neutral Response Bias

On a scale from 1–5, how do you feel about the following exercises?

Cycling / Running / Swimming / Stretching

(On a scale from 1 to 5, where 1 is Hate and 5 is Love)

Those who exercise regularly will probably have a favorite sport, and their rankings will vary. But what about respondents who aren’t physically active? Their answers will most probably be neutral.

They’re unlikely to have an extreme attitude, as they don’t do sports.

Pro tip: Choose your sample wisely. Make sure that the topic of the survey is relevant to them. As in the example above, if you’re running a survey about sports, don’t send it to people who don’t engage in any physical activity.

8. Voluntary Response Bias

Voluntary response bias can be a real problem when conducting a questionnaire, especially when individuals are eager to share their opinion on the matter. This type of bias might lead to an over-reporting on a particular area of focus, as well as extreme viewpoints being expressed without taking into account those who may take more moderate positions. To ensure that your research produces balanced results, it is important to keep voluntary response bias in mind and be aware of how it can affect your outcomes.

Example of Voluntary Response Bias

Let’s say that you’re an editor at a popular online news site and want to hear your readers’ opinions on abortion rights.

If the survey has multiple questions or requires open-ended replies, it’s going to require determination to complete the survey. This will likely eliminate those who don’t have strong views on the subject.

Pro tip: Consider the channels you can distribute your survey in to gather more objective responses. Think of social media channels or forums where members seem to display a wider variety of viewpoints.

Eliminating Survey Bias

Survey bias can significantly compromise the validity and generalizability of study results. It is crucial to identify and minimize the different types of survey bias to ensure accurate and reliable findings. Here are some pro tips for eliminating survey bias:

1. Pretest the survey: Pretesting the survey with a small group of participants can help to identify potential sources of bias, including question order bias, response bias, and sampling bias.
2. Use clear and concise language: Use clear and concise language to ensure that participants understand the questions being asked and can provide accurate responses.
3. Randomize question order: Randomize the order of questions to minimize the influence of question order bias.
4. Use multiple contact methods: Using multiple contact methods, such as mail, phone, and email, can increase the likelihood of reaching potential respondents and reduce non-response bias.
5. Offer incentives: Offering incentives, such as gift cards or discounts, can increase response rates and reduce non-response bias.
6. Use balanced scales: Use balanced scales to avoid response bias. A balanced scale ensures that the midpoint is neutral, and respondents are not forced to choose between positive or negative responses.
7. Minimize social desirability bias: Use anonymous surveys or assure participants of confidentiality to minimize social desirability bias.
8. Use representative samples: Use representative samples to ensure that the results can be generalized to the population of interest and avoid sampling bias.
9. Analyze the data: Analyze the data for potential sources of bias and adjust for them in the analysis.
10. Consider alternative data sources: Consider using alternative data sources, such as administrative data or existing survey data, if survey bias is a significant concern.

By following these pro tips, researchers can help to eliminate survey bias and ensure that their findings are accurate and reliable. It is essential to be aware of the different types of survey bias and take steps to minimize their impact on the study results.