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5 Types of Biases in Market Research and How to Avoid Them

Sophia madhavan.

Last Updated:  

8 April 2024

Table Of Contents

• Types of market research bias
• Social Desirability Bias
• Habituation Bias
• Sponsor Bias
• Confirmation Bias
• Culture Bias

How can bias impact market research?

Wrapping up.

No research can escape bias. Never has, never will.  Assuming that everyone else sees the world the same as we do is only human. That is what leads to market research bias .

However, don’t start second-guessing every study and survey you did right away! We also have ways to avoid market research bias almost completely.

Types of market research bias (and how to avoid them)

Let’s explore the types of research biases that are most common while conducting market research so that you will know how to combat them.

These biases can be grouped into two categories: respondent bias and researcher bias .

To elaborate – when it comes to market research, there are two human elements participating in it: the researcher and the respondent . Both come with individual packages of biases and presumptions which will reflect in what they do.

A. Respondent Bias

1. social desirability bias.

In this bias, the respondents give incorrect information in order to be accepted or liked. So, they answer the questions to please and show themselves in the best possible light. Thus, skewing your market research conclusions.

For example, if there are questions about drinking and driving, few would admit to doing it. Since it’s a condemned activity, the respondent might lie to avoid criticism or judgment.

How to avoid it

• Frame your questions to allow answers that may not be socially desirable.
• You can also ask indirect questions. For example, asking about how a third person (or imaginary person) would think, feel or act in a socially sensitive situation.
• Lastly, you can switch your survey setting to ‘Anonymous’, and provide a disclaimer at the start of the survey that all responses are confidential. This allows the respondents to portray their own feelings and provide honest answers.

2. Habituation Bias

Questions that are worded in a similar fashion may get the same answers. When respondents see repetitive questions, they go on auto-pilot mode and stop being responsive. Then, they start giving similar responses to different questions asked in a similar way.

For example, if you have multiple questions that require the respondents to rate the quality of the service or products, the respondents might automatically mark ‘slightly agree’ or ‘slightly disagree’ without properly reading the questions.

• Keep your questions conversational to engage your respondents.
• Frame each question differently to minimize habituation.
• Use a mix of question types to avoid repetition.

Survey tools such as SurveySparrow have lots of interesting question types  like multiple choice, image choice, open-ended, ratings, slider, etc.

What you see right below is a Product Market Research Survey template created with the platform. It is an excellent example of arranging different question types to make the survey feel like a conversation.

If you’re in a hurry, feel free to use it. You can also customize it the way you like.

But, if you want to explore, sign up and create your first survey from scratch.

Market Research Survey Template

Don’t you feel like completing it? Weren’t you hooked? That’s exactly what you should aim for!

3. Sponsor Bias

When respondents suspect or know the sponsor of the research, their opinions, experiences, and feelings about the sponsor may influence how they answer the questions concerning that particular brand.

Let’s take an example of a personal care survey where a shampoo brand is one of the sponsors. When asked about their favorite shampoo, the respondent might choose the survey sponsor out of obligation – especially if rewards are involved.

Health studies are another example. Many health studies are condemned to be skewed because they have been funded by pharmaceutical companies.

• Maintain a neutral stance. Limit the urge to reinforce positive respondent feedback.
• Avoid disclosing the name of the survey sponsors. You can remove any sign of the logos.
• If you must mention the sponsors, mention them as part of a ‘thank you’ message after the respondent has submitted the survey.

Related: How to set up personalized Thank You pages 

B. Researcher Bias

1. confirmation bias.

One of the most recognized and pervasive forms of bias in market research is confirmation bias.

In this type of bias, the researcher is convinced of a belief or hypothesis and uses respondents’ responses as evidence to confirm that belief.

Simply put, confirmation bias is seeing what you want to see and focusing on evidence that supports what you already believe.

For example, imagine that the election season is here. The articles and opinion columns you tend to seek would most probably have the candidate you support portrayed in a good light, while the opposition candidate is in a negative shade.

• Re-evaluate respondents’ impressions.
• Check for unsupported or unsubstantiated claims.
• Try to challenge and overcome your assumptions and hypotheses.

2. Culture Bias

Culture bias is an extension of confirmation bias. It stems from assumptions we have about other cultures based on the values and standards we have for our own culture.

Collected responses or generated reports are undermined or over-sensationalized because of the researcher’s culture bias.

For example, a ‘proud’ American could very well be biased when asked about issues that affect the Latino community in the US. Stereotyping undermines facts.

• Move towards cultural relativism .
• Show unconditional positive regard for all cultures.
• Be aware of your own cultural assumptions.

Bias is a pure product of human nature . It seeps into the thinking and reasoning of a human mind to reflect research that is biased too.

Nevertheless, we cannot just accept that when it comes to research. There is nothing more damaging than a skewed or biased study. Hidden research bias can change the way a researcher words questions and answer options. This could lead to data that is misleading or outright false.

No market research is 100% free from survey biases. But you can definitely reduce or avoid them with the proper tools .

You can only see the world through your own experiences and opinions. That makes it difficult to not be biased, in one way or another.

Learning more about the kind of survey biases that can creep into your market research can help you successfully counter them. The different types of market research bias above can be minimized if you ask the right market research questions at the right time while being aware of the various sources of bias.

If you’re setting up a new market research project, it’s up to you to report accurate results that have not been skewed by biased data. Want to learn how? Talk to us – we’re a chat away.

Senior Director at SurveySparrow

Mommy during the day and Marketer by the night!

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10 Examples Of Biased Questions In Surveys (To Avoid Using)

Surveys bridge the gap between customers and brands. The questions asked through the survey serve as an outlet for customers to express their emotions and opinions.

At the same time, brands can leverage survey questions to learn more about the customers and what they feel about their products and services. However, asking biased survey questions can send all your effort and time down the drain.

In this extensive guide, we take a deep dive into the world of survey questions and address the impact of biased questions on their effectiveness.

We will discuss different types of biased survey questions and learn about them through examples. Moreover, we will share tips you can implement to avoid using biased survey questions once and for all.

Let’s get started!

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What is a biased survey question?

Biased survey questions are ones that are not neutral.

As the name suggests, they drive respondents toward a specific response and hinder their ability to provide honest feedback. Biased survey questions are generally vague and unclear, and lead to confusion and chaos.

As we discuss the different types of biased survey questions and their examples, you will realize how these biases put customers in a tough spot and impact their responses.

A biased survey question can affect the effectiveness of the survey and you might even end up collecting incorrect responses unintentionally.

Such unreliable survey responses may lead to bad decision-making, especially if the brand is using surveys to identify cracks in its customer experience strategy.

How does survey bias impact business?

A company’s reputation is attached to the customer surveys. The worse the survey questions, the more the reputation declines.

As a brand, you should prioritize effective and bias-free survey question writing as modern-day customers tend to opt out of surveys if the questions are vague, confusing, and lack context.

Since answers to biased survey questions are inaccurate and often skewed, you might even end up jeopardizing any data-driven strategy running in your company.

Besides, respondents might render your brand as unethical and unprofessional if you ask them biased survey questions.

10 types & examples of biased survey questions

Let’s move forward and discuss the various types of biased survey questions. We have also included examples of biased questions to facilitate better understanding.

1. Double-barreled questions

As the name suggests, these are biased survey questions that are essentially two questions disguised as one. At the same time, the respondent can only give one answer to both the disguised questions. For example,

Double-barreled question — “Was the product sturdy and functional?”

Now this is a question that is inquiring the respondent about two different aspects of the product — material strength and functionality.

The respondent has only one opportunity to rate both metrics. The product might have got the job done, but that doesn’t necessarily mean it is sturdy, and vice versa.

2. Leading questions

Manipulation takes a whole new level with leading questions. These are biased survey questions that have only one motive — to lead the respondent to a certain answer.

Leading questions are framed as a statement seeking confirmation or denial from the respondent. For example,

Leading question — “The product helped solve your problem. Correct?”

This is quite an obvious leading question as it explicitly expresses the preferences of the interviewer.

The only way to avoid leading questions in a survey is by providing respondents with adequate freedom and refraining from adding personal opinions to the question.

3. Dichotomous questions

As the name suggests, dichotomous questions are biased survey questions that offer respondents only two answer choices.

Brands using dichotomous questions find it easier to interpret results and make analysis simpler. Here is an example of a dichotomous question,

Dichotomous question — “Do you plan to purchase our product?”

Again, the question pushes respondents into a corner. Dichotomous questions make respondents give a yes/no answer. Therefore, they will either choose one of the two answer choices or simply opt out of the survey.

The best way to deal with dichotomous question bias is by adding a “maybe” or “don’t know” option for respondents who wish to express uncertainty.

4. Social desirability bias questions

The respondents to these survey questions are humans. We are social beings and a significant percentage of our decisions are based on social approval.

Brands can leverage such social desirability bias by making respondents choose a socially approved answer to their survey questions. For example,

Social desirability bias question — “Do you plan to move into a more comfortable accommodation?”

For most respondents, the answer to the question would be “yes” because we all seek growth as individuals.

Now, there will be respondents who are happy with their present accommodation. Thus, social desirability bias in a survey question makes it difficult to solicit honest answers.

5. Assimilation bias

The way you structure a survey can also impact the answers respondents leave.

While a change in words can impact survey results, a change in structure can also deform the meaning of the questions, especially when multiple questions are stacked together. For example,

Assimilation bias — “Did the product function properly?” followed by “How would rate the product on functionality?”

Functionality can be quite a broad topic. When these two questions are stacked together, respondents might rate the product on functionality thinking that the product functions properly, as asked in the first question.

Moreover, respondents might ignore other metrics of product success since the question focuses only on functionality.

6. Absolute questions

The biased survey questions containing absoluteness are often presented using words like “all, never, none, always.” The reason behind this is the ability of these terms to push the respondent to a corner and retrieve a desired response. For example,

Absolute questions — “Have you never shopped at our store?”

These biased survey questions are framed in a way the respondent feels accused of not being part of the brand. The “pushiness” of the questions can make people leave extreme responses, without even considering their options.

However, brands can always make a conscious choice of omitting such words and sentences and rephrasing the question to avoid absoluteness.

7. Loaded questions

Another type of biased survey question worth mentioning in the post is a loaded question. Now, these are survey questions that put forward an assumption and push respondents to consider it.

One should note that the assumptions made in the question can be false for some respondents. Regardless, a loaded question functions similarly to a leading question — directing people to a desired response. For example,

Loaded question — “How much did you enjoy using our product?”

The question comes with a presumption that customers enjoyed using the product. The only thing the surveyor wants to know is how much the customers enjoyed it. This survey question falls flat for people who didn’t enjoy using the product at all.

8. Vague questions

This biased survey question type is a classic. While the other biased questions mentioned in the post only tend to limit respondents’ freedom, vague survey questions add a touch of confusion and chaos.

A vague question, as the name suggests, includes words that have no definite meaning and are open to different interpretations. For example,

Vague question — “Was the marketing campaign a success?”

Now, “success” can have different meanings for different people. Therefore, it is best to avoid vague biased survey questions. It can be achieved by focusing more on definite parameters and qualities and less on subjective interpretations.

9. Demand characteristics bias questions

Most customers have a sense of why you are running surveys and soliciting feedback. The very realization that companies are running surveys to get better can make respondents answer the surveys in a certain way.

Demand characteristics bias makes respondents answer surveys assuming what you need or what is the most favorable answer. For example,

Demand characteristics bias — “We are considering diversifying our product line-up. Should we go ahead with the decision?”

Many respondents will say “yes” because they assume diversifying is the way to go or something you need. The best way to avoid demand characteristics bias is by removing assumptions in the question.

10. Contrast effect

The contrast effect is quite similar to the assimilation effect discussed earlier. Here, respondents will have to consider two or more questions when responding to the survey.

However, respondents might assume that since you have already covered an issue in the first question, they don’t have to consider it when answering the second question, and so on. For example,

Contrast effect — “Did you purchase the product easily on our website?” followed by “How would you rate your customer experience?”

Even if the respondents found it easy to purchase the product on your website, they might not have liked the overall customer experience, and vice versa. In other words, you will only end up consolidating confusing data.

Factors contributing to biased survey questions

What are the factors that can lead you to write biased survey questions? Since there are several elements involved in survey questions, you will have to consider the following factors to avoid writing biased questions —

The choice of words and phrases in a survey question can have a major impact on how participants respond. The question can trigger biases if the words are used to imply a certain response. Use words/phrases that support the question’s intent.

We have already discussed how multiple questions stacked together can impact participants’ responses. Assimilation and contrast biases are prime examples of how the order of questions can drastically change responses.

Make sure the survey questions are in the correct order before you hit send.

3. Response

Answer choices depend on the type of survey questions.

However, the answer options you provide with the questions can lead to bias if they don’t provide adequate freedom to the respondents. We suggest you include all possible responses to help respondents provide honest customer feedback.

As a brand or surveyor, you have got to keep things neutral. The context can be simplified by providing the participants with adequate information regarding the survey’s purpose. Use neutral language and tone to not trigger assumptions and biases.

How do we avoid biased survey questions?

Since biased survey questions can significantly impact the effectiveness of responses and lead to confusion and misinformation, we highly recommend avoiding biased survey questions by following these tips.

The very first thing you’d want to ensure is that you understand the different types of biased survey questions. We have discussed some of the most important ones in the post. Read them thoroughly to make bias identification easier.

Always go for multiple question structures when drafting survey questions. This makes it easier to avoid biases as you are deliberately randomizing the question order.

Besides, you can leverage different types of questions with different rating scales. It can help participants share feedback in unique ways.

Another effective way to avoid biased survey questions is to let fresh eyes have a look at the questions. Gather feedback from your colleagues about the questions and identify any potential source of bias. Make changes accordingly.

We highly recommend you pre-test the survey to evaluate the effectiveness of the questions and survey flow. Create a small group of respondents and test the questions on them.

Lastly, you’d want to avoid any assumptions, overloading, confusing language, and jargon when writing survey questions. Make sure you write clearly and consider user experience as well.

Wrapping Up

Survey bias, as discussed in the post, can quickly flush all your hard work down the toilet. Therefore, you should stay alert and ensure that the questions are well-tested before pushing the “send” button.

We highly recommend you look at the survey questions from the perspective of respondents and see if you can answer the questions without bias.

Moreover, keep things simple and your questions neutral when drafting a customer survey. Did you find this post helpful? Stay tuned for more informative posts in the future.

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Wording Bias: What it is with Examples

When designing your questionnaire, many things can go wrong if we ignore the details. This can mean that our research returns incorrect data and invalidates our study.

In this article, we will talk about wording bias, and we will explain everything you need to know about one of the most common research biases and some tips to avoid it.

What is wording bias?

Wording bias, also called question-wording bias, happens in a survey when the wording of a question systematically influences the responses. Wording bias leads to survey data becoming less valuable as it is inaccurate.

A survey question is biased if it is phrased or formatted in a way that prepossesses people towards a specific answer. Also, if the survey questions are hard to understand, it is difficult for customers to answer honestly.

Example of Wording Bias

Wording Bias can be caused in various ways, all related to how the interviewer or the team formulates their questions and answer options, below, we share some of the most common cases and some examples so you can identify them.

Let’s say your questionnaire contains the following question:

Are you in favor of building a shopping center that could increase traffic in the area? YES | NO

In the example above, we include additional information likely to influence people’s perceptions and how they tend to answer that question. As you can see, information about the possible adverse effect of the construction in the area is included, so the response trend would surely lean NO.

If the question were:

Are you in favor of constructing a shopping center that will provide employment to more than 20 families in the area? YES | NO

We can distinguish that information that influences the respondent’s perception is also included. On this occasion, the trend is undoubtedly inclined to YES. However, this does not mean that it is correct since there is still a bias, and for the validity of any study, it is always desirable to seek neutrality in the opinion of the people surveyed, so in this example:

Are you in favor of constructing a new mall in the area? YES | NO

It would be a slightly better-posed question since unnecessary and biased information is eliminated.

This was just one example of how the way the question is formulated can impact the study results.

However, there are other ways to cause wording bias, either by altering the instructions on how to answer the survey or how you formulate the answer options.

How can I reduce wording bias?

When you create a survey , the order of both questions and answer options in your survey matters. Questions that come early in your survey might impact how respondents respond to questions later in your study.

Here are some tips to avoid making these mistakes when creating your questionnaire .

Short and clear questions and answers

Keep your questions short and precise. Ask neutrally worded questions. Your question shouldn’t have too much information, and it shouldn’t have too little. It should be just right. Respondents are less likely to answer if the question is too long. Or if they do not understand the question.

Leading questions

Avoid leading questions. Instead of asking a leading question like “Are you satisfied with our service?” you can ask about the quality of service provided.

Simple Answers

If answer options are long and unfamiliar, the respondent may face difficulties answering the questions. One technique to ask a question precisely is to use a matrix question with multiple answer choices.

Carry out a pilot survey

Before launching your questionnaire to your entire database or sending a whole fleet of interviewers throughout the city, we recommend that you do a small test where you include control and feedback questions to identify areas for improvement and if the questionnaire does not have any type of bias or error that could compromise the integrity of the study.

Are you ready to lunch your first survey?

We hope that these tips will help you when designing your survey and conducting your research. We remind you that at QuestionPro, we have all the types of questions necessary to design an ideal questionnaire for your projects.

In addition, our analysis module will allow you to see the results in real-time to identify any anomaly or manage your information to avoid including biased data or data that is not of value for the study.

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What is survey bias?

How can survey bias influence my research/results, which survey type is most likely to be affected by survey bias, what are the 3 types of survey bias, how can qualtrics prevent survey bias, try qualtrics for free, survey bias types that researchers need to know about.

23 min read You’re ready to start a new research project, but how can you ensure you get the best and most accurate results? In this article, we tackle the most common types of survey bias, and provide best-practice advice on how to address them to ensure you get honest, accurate answers from your research.

When it comes to understanding your audience at scale, few options are as good as a  market research  report or offline or online survey.

Having information on customer preferences,  behaviours , likes and dislikes, income, and  demographics  — the list goes on — helps businesses to create more tailored products and services, as well as captivating experiences.

But even if you create and distribute the surveys, how can you ensure that they’re fair, unbiased, and contain questions that are easy for respondents to answer? This is where ‘bias’ comes in — and reducing it is key to creating great surveys that gather data, encourage honest responses, and benefit your business.

Bias is  defined  as a “deviation of results or inferences from the truth, or processes leading to such a deviation” and it occurs in every survey. It’s impossible to  eradicate bias  as each person’s opinion is subjective. This includes the researcher, who thinks up the questions and plans the research, and the participants, who answer the questions and share their thoughts.

There are several ways survey bias can influence the accuracy and integrity of interviews, as well as the answers provided by participants. For example:

• Selection:  How was the survey sample selected? How many participants completed the survey? Was the sample broad enough to capture the most valuable insights?
• Response:  How are participants swayed by leading factors from the interviewer? Such as the questions asked, their format, and the respondent’s desires to be socially accepted?
• Interviewer:  Is the interviewer unconsciously sending signals to participants that could alter their answers?  Are the interviewers biased ? Are the survey questions tailored towards specific outcomes?

These are just a few ways survey response biases can creep into research projects. In this guide, we’ll share a few examples of the above and how you can reduce sampling bias and survey bias. First, how can survey bias influence your survey data,  response rate , and survey results?

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Survey bias can cause a plethora of problems for researchers, including:

Data issues:  The data produced doesn’t accurately reflect the opinions of participants, as there are less than truthful responses, extreme responses, or inaccurate answers. This data won’t help you reach your goals.

Poor strategies and investment:  As management and senior leaders base future business decisions on market research and survey insights, survey bias can affect how they invest money, time, and resources — potentially taking the wrong course of action.

Low return on investment:  Poor insights lead to poor product performance. When you’re targeting the wrong customers as a result of survey biases, e.g. leading questions, you won’t get the information you need to improve your offerings and the overall experience.

Dissatisfaction:  Stakeholders and investors will be dissatisfied with performance levels and may reduce market research budgets over the long term.

Inconclusive research:  Surveys may need to be repeated to test whether the data or the researchers are at fault, which takes time, money, and resources.

Ultimately, good actions, progress, and innovation are based on good data quality. If the management can’t rely on or trust the research results for accuracy, then it’s a lose-lose situation for all people involved.

There is no single survey type that experiences more bias than another. Bias can affect all survey types, including:

• Panel interviews
• One-on-one fact-to-face interviews
• Group interviews
• Telephone interviews
• Webinar or video polls

Survey bias is a universal issue that researchers should be aware of and plan for before every research project. The best thing to do is to think about survey design and use the right survey tools to empower respondents to answer honestly. This way, you can get accurate, valuable survey results.

1. Selection bias

Selection bias creates inaccurate or unrepresentative data. This is because it’s gained in an unfair way that’s detrimental to the accuracy and goals of the research.

For example, you could select a non-random sample, a sample that has a crucial market segment unaccounted for, or a sample that doesn’t engage can all affect data results by providing too much, not enough, or the wrong kind of feedback. You could also choose to focus on samples that validate your own viewpoints and perspectives (confirmation bias), offering no new insights for your teams to act on.

Sampling bias

Sampling bias, also known as selection bias, is when your sample is unrepresentative and will not provide the right feedback to support the goals of the survey research.

Some samples forget to include the right target customer market segment. This can lead to inaccurate data results. For example, if your brand makes toys for children and you’re wondering about their aesthetic appeal, surveying a sample made up of parents would tell you why the parents buy the toy, but not why the toy is considered appealing to children.

Sampling bias can also occur when the researcher creates a one-sided sample because they believe they know who the survey should target.

But while they might be correct, creating a one-sided sample may overstate the importance of respondents’ feedback, as well as miss the diverse viewpoints of other non-customer segments that may want to use your product or service.

Examples of sampling bias

• Certain population groups aren’t covered in polling or survey sampling, leading to skewed sample data results.
• Non-probability  sampling methods  are used incorrectly. Non-probability sampling methods don’t offer the same bias-removal benefits as probability sampling (which use a random sample).

Non-response bias

Even with a perfect sample selection (no sampling bias), respondents may not answer the survey. But why?

Well, they may not like filling in surveys, or their email could be inactive (so make sure to keep your data up to date). They might not  like your brand  or don’t understand the purpose of your survey. Or they might just hang up the phone, or throw the survey in the bin.

Whatever the reason, your results won’t be indicative of the full sample. This means that, because of unresponsive sample members, you may miss out on crucial data that would help you analyse trends or identify correlations.

For every survey, there will be those who don’t answer. The idea is to keep this to a minimum, ideally a small percentage of the full sample survey size. If the percentage of unresponsive sample members is higher than average, you know that your results have a non-response bias.  

Examples of non-response bias

• A survey that is aimed at finding out the views of criminals or closed-off groups would more than likely have low participation from these groups as they don’t want to share their illegal activities or share the information they know. Therefore, the remaining responses would be from participants that might not best represent the target market of the research.
• A survey that can be filled in and posted may have more responses than an electronic survey done over the phone if respondents live in an area with poor signal coverage.

Survivorship bias

What if your sample is in itself filled with the wrong kind of people, just because the right kind of people are no longer available to speak to (e.g. you’ve no longer got their details)? Survivorship bias is where you target the right customer market segment, but due to natural turnover, you only have the people who are left — the ‘survivors’.

These ‘survivors’ are more likely to be favourable and biased in their results. But to get the full picture, you have to hear from the people who are not around anymore, as they represent the full picture.

Example of survivorship bias

A brand is looking to understand why employee turnover is so high, so they do research with their current employees. However, the people that will give them the insights as to why they left are those that have left the organisation. As they aren’t part of the research sample, the results will have survivorship bias.

2. Response bias

Response bias is when your sample provides responses based on the survey questions, but the answers they provide aren’t what they really believe or think. Instead, participants’ survey responses are based on the  structure and language of the questions , leading them to answer in a particular way.

Some examples of response bias in action are:

• Asking about  customer satisfaction  but only providing two positive responses and one negative, e.g. Very Satisfied, Satisfied and Dissatisfied. To balance the survey questions, consider adding two positive and two negative options.
• Taking an emotional approach to questions  — e.g. “Your parents are getting old and would like to see their grandchildren. Would you consider having a child soon?”

Extreme response bias

For this response bias, some participants will choose an extreme answer value to answer a question that has a scale as an answer (e.g. Likert scale). This will increase the response bias if the question is phrased in a way that suggests that the right answer is an extreme-ended one.

This is more common where the researcher has failed to make the question neutral, or if the question has a ‘closed’ yes or no response that forces you to be extreme in your approach.  

Examples of extreme bias

• The question: ‘Is it okay to spank your children as a form of rearing education?’ would elicit a strong response bias in favour or against the practice of physical discipline.
• The question: ‘Should a family member have the right to end their own life if they have a terminal illness?’ is an emotive topic that forces a participant to think of a stressful scenario and decide on a strong position.

Neutral response bias

This type of response bias occurs when the researcher creates questions that are not specific enough, or don’t evoke a strong enough response for respondents to pick an extreme either way.

As a result, participants pick a neutral position on a Likert answer scale. This doesn’t help the overall results of the research, as you would like to have a mixture of extreme and neutral responses that tell you more about your participants’ varied views.

Example of neutral bias

On a scale of 1 to 5, how do you feel about these animals?

Dog, cat, bear, lion, goldfish

(Scale of 1-5, where 1 is Hate and 5 is Love)

For pet owners, or non-pet owners, the answers will be neutral as, without experience of owning each one, it’s unlikely they’ll have an extreme view.

Acquiescence bias

Acquiescence bias (also known as the yes bias, the friendliness bias, and the confirmation bias) tends to be one of the more commonly recognised response biases by researchers.

This bias is the tendency for survey respondents to agree with the survey questions, without their response being a true reflection of their own position or beliefs. This is because it’s easier to say yes and agree — to please a researcher or complete a survey — than to hold a disagreeable position.

This occurs when the question is phrased in a way that asks the participant to confirm a statement, or when the question is answered with opposing pairs, such as ‘Agree / disagree’, ‘True / false’, and ‘Yes / no’.

Examples of acquiescence bias

• ‘When you have your coffee, do you enjoy it with milk or without?’ This question presumes that the participant has and enjoys coffee. They may dislike coffee and enjoy another beverage instead.
• ‘Do you consider yourself a good person?’ This question would ultimately end in a ‘yes’ answer as participants are unlikely to answer in a way that makes them look unfavourable. This is an example of a leading question, one that leads you towards a particular answer.

Question order bias

Question order bias , or order-effects bias, occurs when related questions are placed in a certain order. For example, once a participant answers one question positively or negatively, the participants feel they have to answer any follow-on, related questions the same way.

This is a bias based on the participant’s desire to be consistent with their answers, whereas, in reality, there could be different answers to a set of questions on one topic.

Examples of question order bias

• Asking the primary question in a loaded way, for example: ‘Do you want kids?’ and then following up with questions about the perception of motherhood or fatherhood. The former question sets up the participant for an extreme answer (yes or no). The second question could relate to the participant’s view of their own parents, though the order of the questions suggests that this is a follow-on question to be answered similarly.
• Another example is double-barrelled questions that ask two things at the same time, implying that they’re linked. For example, What do you think of this clothing brand and the management?’

Social desirability bias or conformity bias

Survey takers may want to appear more socially desirable or attractive to the interviewer as people are careful about how they appear to others. From a survey perspective, this could be respondents answering uncharacteristically or lying to appear in a positive light.

The researcher’s choice of topic could be the source of the issue, or it could be the participant’s insecurity or comfort with the topic that affects their answers.

Examples of social desirability bias

• If participants are influenced by societal ‘norms’ for behaviour and appearance, e.g. how a person ‘should’ appear or act, this can affect their answers. For example, drinking can lead to binge drinking and health problems, yet it’s an acceptable social norm for workers and teams.
• Social desirability bias may also cause participants to go over the top and inflate their own status to seem more successful or progressive than they actually are. For example, lying about their annual income or level of education.

3. Interviewer bias

The last type of survey bias is  created by the actions of the interviewer . The way that a question is asked, or the way the interviewer makes a participant feel in the survey, can impact what results they receive back.

As the reliability of the data is on the line, the interviewer owes it to themselves to do their best to remove bias, though they may not even realize what they’re doing.

Demand characteristic bias

When a participant is doing a survey, they are aware that they’re in an interview setting and may act differently because of that. If you recall how nerve-wracking interviews are for a new job, you’ll see why you might say something that wasn’t accurate or wholly true because of the pressure on you.

As such, researchers will get biased responses from surveys that are incredibly formal or hosted in an uncomfortable setting. To help respondents and get accurate data and valuable data — researchers need to help participants forget that they are being interviewed and asked survey questions.

Examples of demand characteristic bias

• If the survey starts without a good introduction, the participant doesn’t understand the purpose of the study and spends time trying to figure it out, while still being asked questions at the same time. This can lead to pressure and stress, and you won’t get the best answers out of them during this time.
• If the setting of the interviews is unwelcoming and you don’t do your best to keep the participant comfortable, the discomfort the participant feels may come through into the way they answer their questions — rushed or anxiously.

Reporting bias

Reporting bias arises when the research team decides on the publication of the research based on the positive or negative outcome, from the analysis of the data.

Examples of reporting bias

A healthcare research team found that they can’t make a case that their medical painkiller cream decreases pain when used on test participants. The brand may choose not to publish the results sharing this information, which is unethical and doesn’t represent the facts based on the research.

How to prevent survey bias

Given how prevalent bias in surveys is, what can you do about it to protect your survey work, but to make sure you get the right answers back from survey takers? Answers based on their beliefs,  needs , and views?

Here are some suggestions that will help prevent survey response bias:

Selection bias

• Add in a ‘don’t know’ or ‘not applicable’ option in answers so that the participant doesn’t feel the need to answer a question incorrectly or not at all.
• Ensure you have an up-to-date participant list that covers the right target audiences and is a random sample. If you need help knowing what is a good sample size, try our  free sample calculator tool .
• Ask non-respondents to participate in a follow-up survey. Sometimes, people are busy and miss the email invitation. A second round may provide more ‘yeses’ that will help create a better picture of results.
• Consider the best way to reach your target audience so you can connect with them. This means that you won’t get survivorship bias where the longest-serving participants are the only ones available to interview.

Response bias

• Avoid phrasing questions emotionally or using emotional topics, unless you’re trained to provide guidance and support for participants that react badly or are emotionally triggered by the questions.
• Write questions in a neutral way that doesn’t indicate a preference for one answer or another. For example, ‘Do you like coffee?’ becomes ‘How do you feel about coffee?’
• Avoid simple ‘yes and no’ questions that don’t allow for elaboration or mixed viewpoints. Instead, use a scale or multiple choice answers.
• Ask someone in your company to review the questions for bias. A fresh pair of eyes can really help identify issues and areas for improvement.
• Provide incentives for participants to complete the survey. This will help keep them focussed and engaged with the survey until the end.
• Ask one question at a time to avoid double-barrelled questions that might confuse participants or make them respond a certain way.
• Mix topics questions up so that there are no linked groups on a topic that occur one after the other, preventing question order bias.
• Avoid emotionally charged language to prevent extreme response bias.
• If you’re always getting a lot of ‘yes’ responses, try an answer scale range that doesn’t encourage acquiescence bias. For example, “Definitely will not, Probably will not, Don’t know, Probably will and Definitely will.”
• If your participants are not happy to be interviewed for the survey, you can  try suggesting anonymous feedback  so that you’re able to  collect key insights  that you wouldn’t have gotten otherwise.

Interviewer bias

• Stay neutral and professional as you survey, so that you don’t unconsciously show a preference for one answer over another. This allows for unbiased responses that aren’t fed by unconscious body language or tone of voice.
• Provide a clear welcome and introduction without telling the participants about what’s coming up as questions. This means participants won’t have time to stress about the survey questions coming up and can take each question at a time.
• Be nice to the participants and thank them for their time. Your participants want to know their time is being taken seriously. A warm manner can help them feel at ease.

You might have sampling bias in your marketing list, or you might have inadvertently created questions that lead the participant to a specific answer. But why take the risk when you can see the hundreds of questions on offer with our  free survey templates ?

But you can go one step further. With our integrated all-in-one solutions, you can get all your surveys, customer and participant data in one place.

With the ability to improve your survey quality using AI and create research surveys by just dragging and dropping the right modules, you have everything you need. What’s more, you’ll benefit from analytics and dashboard reporting, giving you both an at-a-glance and comprehensive view of responses.

Our survey and panel management tools can help you reach the right audiences around the world, right when you need them. And with the inbuilt intelligence assisting with personalisation, you can boost response rates and show the customer that they’re front of mind.

After all, 13,000 of the world’s best brands that use our software can’t be all wrong!

But if you want to know where to start with your survey questions, we can help. Find out what kind of questions our experts have created for use in your surveys to reduce the risk of survey bias.

eBook: The Qualtrics Handbook of Question Design

Related resources

Thematic analysis 11 min read, post event survey questions 10 min read, choosing the best survey tools 16 min read, survey app 11 min read, close-ended questions 7 min read, survey vs questionnaire 12 min read, likert scales 14 min read, request demo.

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Common biased survey questions & how to avoid them

Many businesses invest time, energy, and maybe even a few virtual sweat drops into creating the perfect questions for a survey. But when the results roll in, they seem to be suspiciously one-sided. This happens due to inherent bias present in the survey questions.

Biased survey questions can infiltrate even the most well-intentioned surveys and lead you down a path of misleading data and wasted effort.

This article explains:

What is a biased question?

• Different types of survey biases to avoid
• Some biased survey question examples
• And bonus tips to help you get the honest feedback you need

According to Merriam-Webster , bias in a survey is a type of survey error where one outcome or answer is encouraged over others.

Biased survey questions are worded in a way that pushes respondents to pick a certain answer instead of giving you their honest opinion.

Some characteristics of biased survey questions include:

• A leading tone that nudges the survey taker to a particular answer option
• Emotionally charged words that sway respondent’s opinions
• Build-in assumptions about the respondents’ experiences
• Double meanings or confusing and multiple interpretations
• Limited range of responses that don’t capture the full range of survey taker’s opinions.
• Imbalance question framing that presents one side of the issue more prominently

Biased questions can lead to some major problems for your survey, such as:

• Unreliable data: If people feel pressured to answer a certain way, your data won’t reflect their true feelings.
• Wasted time and resources: You spend time and resources creating a survey. Biased questions that produce inaccurate results make the whole effort pointless.
• Frustrated respondents: Nobody likes to be manipulated. Biased questions can annoy participants and make them less likely to finish your survey.

6 types of bias in surveys and how to avoid them

Now, let’s go deeper into specific types of survey bias and understand them with biased and unbiased survey question examples.

Leading Bias

Leading question bias occurs when survey questions subtly guide respondents toward a particular answer.

These questions plant a seed in the respondent’s mind and make them more likely to pick an answer that reflects the bias in the question itself.

• Biased Question : “Don’t you agree that our new product is the best on the market?”

What makes it biased : This question assumes the respondent agrees with the statement without providing an opportunity to express differing opinions. It leads the respondent to affirm the product’s superiority, which inflates the positive feedback.

Fixed Question : “What are your thoughts on our new product compared to others available on the market?”

• Biased Question : “How much do you love our amazing customer service?”

What makes it biased : This question presupposes that the respondent loves your customer service and frames the response positively without considering differing opinions or experiences. It forces respondents to provide positive feedback regardless of their actual feelings.

Fixed Question : “How would you rate your experience with our customer service on a scale from 1 to 5”

Question-Wording Bias

This type of survey bias means your survey contains confusing, vague questions or has unnecessary technical jargon that is difficult to understand.

• Biased question : How would you rate the level of synergism within our cross-functional teams?

Why it’s biased : This question uses jargon (“synergism,” “cross-functional”) that some respondents might not understand. If they don’t know what you are asking, their answer does not reflect their true opinion.

Fixed question : How well do different departments within our company collaborate and work together?

• Biased Question : “What do you think about the recent changes?”

What makes it biased : This question is too broad and doesn’t specify which changes are being referred to. Respondents may have different interpretations of what changes are being discussed.

Fixed Question : “What are your thoughts on the recent changes to our company’s vacation policy?”

Double Barrelled Questions

Double-barreled questions combine two separate inquiries into one, forcing the respondents to answer both with a single response. Research suggests that asking double-barrelled questions nullifies the validity of the survey.

• Biased Question : “Do you think the company provides a supportive work environment and ample opportunities for career growth?”

What makes it biased : This double-barrelled question combines two distinct inquiries about the work environment and career growth opportunities. Respondents may agree with one aspect but disagree with the other, which results in confusion and potentially inaccurate responses.

Fixed Question : “Do you feel the company provides a supportive work environment? Additionally, do you believe there are ample opportunities for career growth within the company?”

• Biased Question : “How satisfied are you with the product quality and the delivery speed?”

What makes it biased : This question also addresses two separate aspects—product quality and delivery speed—without allowing respondents to differentiate their satisfaction levels for each. It assumes that satisfaction with one aspect automatically implies satisfaction with the other, which may not be true.

Fixed Question : “On a scale from very dissatisfied to very satisfied, please rate your satisfaction with the quality of the product. Separately, please rate your satisfaction with the speed of delivery.”

Question Order Bias

Question order bias refers to the way the order of your questions can influence how respondents answer later questions.

• Biased Question : “Do you agree that our product is affordable? How satisfied are you with its quality?”

What makes it biased : The order of these questions may influence respondents’ perceptions. If respondents are asked about affordability first, they might consider the price when rating quality. It might lead to a lower or higher satisfaction score.

Fixed Questio n: “How satisfied are you with the quality of our product?

Next question: Do you find our product to be affordable?”

• Biased Question : “How likely are you to recommend our product to a friend? Did you find the product easy to use?”

What makes it biased : By asking about the likelihood of recommendation before inquiring about the product’s ease of use, respondents’ perceptions of usability may be influenced by their intent to recommend and produce a biased response.

• Fixed Question : “Please rate its ease of use on a scale of 1 to 5. Then, indicate how likely you are to recommend the product to a friend on the same scale.”

Assumptive Bias

Assumptive bias occurs when your survey question makes an assumption that might not be true for all respondents. The assumptions skew your data because you don’t capture the real respondent’s beliefs, experiences, or preferences.

• Question : Since our new loyalty program launched, how much more frequently have you been shopping with us?

Why it’s biased : This question assumes the respondent is aware of and has participated in the new loyalty program. It might miss valuable feedback from those who haven’t heard about it yet.

Fixed question : Are you aware of our new loyalty program? If yes, how has it impacted your shopping frequency with us?

• Biased Question : “Given the popularity of our new product, how likely are you to recommend it to a friend?”

What makes it biased : This question presupposes that the new product is popular and assumes that popularity correlates with the likelihood of recommendation. It may influence respondents to give a positive recommendation based on perceived popularity rather than their product assessment.

• Fixed Question : “On a scale from very unlikely to very likely, how likely are you to recommend our new product to a friend based on your personal experience with it?”

Negative and Double Negative Bias

Negative bias happens when you ask biased survey questions with a negative term that forces respondents to interpret “no” as the positive answer. Double negatives take it a step further and use two downright confusing negatives.

• Biased question : Do you find the long wait times at checkout to be not bothersome?

Why it’s biased : This question uses a negative term (“not bothersome”) and makes it unclear whether a “yes” means they find the wait times acceptable.

Fixed question : How satisfied are you with the wait times at checkout? (Very Dissatisfied, Dissatisfied, Neutral, Satisfied, Very Satisfied)

• Biased question : “Don’t you agree that the new policy isn’t ineffective?”

What makes it biased : This question contains a double negative (“isn’t ineffective”), which can confuse respondents. It’s unclear whether the intention is to assess agreement with the policy’s effectiveness or ineffectiveness, leading to potentially contradictory responses.

Fixed question : “Do you believe the new policy is effective in achieving its intended goals?”

Tips to avoid biased survey questions

Here are some tips to help you create crystal-clear survey questions that get you real feedback.

• Ensure your questions are free from loaded or emotionally charged language. Avoid jargon and overly complex sentence structures.
• Provide balanced response options to avoid skewing responses toward one end of the spectrum.
• Don’t prime respondents with positive or negative questions that influence their answers later on.
• Include an “Other” or “Not applicable” option to accommodate respondents whose experiences may not align with the provided choices.
• Stick to one topic per question to prevent confusion and ensure respondents provide relevant answers.

Unbiased survey questions lead to accurate data

Unbiased surveys are essential for accurate data-driven decision-making. They save you time, resources, and frustration.

Now that you know different types of question bias, you can ensure your surveys are clear and concise, and capture honest feedback.

Formaloo is a user-friendly survey maker and form builder that lets you design clear, unbiased questions, gather valuable data, and easily analyze your findings.  

Sign up for free today.

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How to ask open-ended questions crucial tips and examples.

9 ways to make more engaging surveys

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5 Errors in surveys to avoid to collect authentic data

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Bias in Marketing Research: How to Avoid and Minimize

Author’s Note: I’ve always been interested in biases. A poster of The Cognitive Bias Codex hangs in one of our home offices; and, I’ve bookmarked and often look at that interactive…

by Karen Lynch

Head of Content at Greenbook

Author’s Note: I’ve always been interested in biases. A poster of The Cognitive Bias Codex  hangs in one of our home offices; and, I’ve bookmarked and often look at that interactive wiki to learn more. Just like I looked to two industry experts to learn more about bias in research for this article:  Jeff Henning  and  Ray Poynter . Many thanks to you both for serving GreenBook as expert sources. We value your contribution to this piece, just as we value our readers.

Have you ever asked a question while assuming you already knew the answer? That’s a simple example of a bias. As humans, we inadvertently experience bias in every aspect of our lives. Our personal biases influence the lens through which we look at the world. And while some biases are innocuous, some are harmful; and, bias in market research can have a negative effect on the findings.

What is Marketing Research Bias?

Research bias is defined as any type of feedback that skews the market research’s results. There are five types of market research bias to look out for:

• Social desirability bias – This bias boils down to research participants thinking they should answer questions in a way that paints them in a positive light. For example, if surveying on gender discrimination, no one wants to admit they have misjudged someone solely based on their gender.

Food for Thought: Challenges and Ideas for Addressing Bias

• Sponsorship bias – Most likely one of the more common types of biases, results are skewed in preference of the organization paying for the market research. Popular examples of this often appear in the food industry, such as The Coca-Cola Company funding obesity research.
• Confirmation bias – This type of bias occurs when the researcher intentionally or unintentionally seeks to “confirm” their hypothesis by crafting leading questions or cherry-picking data.
• Culture bias – When researchers analyze data and make assumptions through their own unique lens of their upbringing, heritage, and community, this is cultural bias.

What is the Most Prevalent Type of Marketing Research Bias?

“the most prevalent bias is the ‘say/do gap’ – people saying they will do things that it turns out they don’t do.”.

Experts also have differing opinions regarding the most prevalent types of bias in research. According to Roy Poynter, Chief Research Officer at Platform One, “The most prevalent bias is the ‘say/do gap’ – people saying they will do things that it turns out they don’t do.” For Jeff Henning, Chief Research Officer at Researchscape International, the most prevalent bias boils down to a nonresponse. Other opinions state confirmation bias is the most common type.

How to Reduce and Avoid Marketing Research Bias

Why even bother reducing market research bias? First, it’s problematic because it often skews results. It’s important to critically and independently collect and report on data. Second, strong biases make it difficult to come to a true scientific conclusion that can be repeated and fact-checked by third parties. Researchers should do everything they can to proactively minimize and avoid bias.

4 Ways to Minimize Marketing Research Bias

There are specific ways to avoid each specific type of market research bias. Howe, it’s important to keep your personal perspective in check in both quantitative and qualitative research .

1. Follow Random Sampling Best Practices

Avoid bias in your sampling pool by ensuring each element has an equal chance of representation. Use randomization tools to select an assortment. For digital research, work to avoid bots with layered fraud mitigation techniques.

2. Pay Attention to How Questions Are Worded

Asking the wrong questions can lead you to the wrong answers. Since survey questions are written by humans, it’s inevitable that we subconsciously put our personal perspective into our language. Use open-ended questions to avoid steering subjects in a specific direction. Henning said, “Researchers control the questionnaire, which can often add bias. Studying questionnaire writing or using an expert minimizes instrument bias.”

3. Use a Survey Method That Makes It Easy for All Types of People to Participate

For example, if you are surveying how many commuters walk to work, and you only stand out in the street with a clipboard, you inherently miss everyone who drives or bikes to work. Poynter explained, “We need to use approaches that have been developed to be robust in the face of bias, and we need to triangulate and take different readings from different points to get a more predictive result.”

4. Use an Agile Research Method

A scalable, agile research method allows for innovation and a customer-specific focus. With continuous feedback, a minimal viable product, and sprint work, research can be human-centric and easily adaptable to changes. Researchers can get more mileage out of current efforts and focus on other initiatives simultaneously.

Bias Isn’t Completely Avoidable – But It’s Mitigable

Research will never completely avoid bias, but market research experts can get better and better at spotting bias before it influences results. An agile approach can help quickly find, solve, and document biases so they’re avoided in the future. Less bias means more accurate results, which furthers your field of research and leads to true breakthrough.

Karen Lynch

72 articles

The views, opinions, data, and methodologies expressed above are those of the contributor(s) and do not necessarily reflect or represent the official policies, positions, or beliefs of Greenbook.

Comments are moderated to ensure respect towards the author and to prevent spam or self-promotion. Your comment may be edited, rejected, or approved based on these criteria. By commenting, you accept these terms and take responsibility for your contributions.

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Biased survey questions: types, examples, and ways to avoid them

The most common types of bias in event feedback surveys

Ekaterina Orlova

Read more posts by this author.

The process of collecting feedback is a dialogue between an interviewer and a respondent. And like in every dialogue, there can be misunderstandings. Due to the indirect form of the conversation, they become even more troublesome than usual.

If you want to receive valid and useful feedback from your survey, you need to express your thoughts clearly and concisely, avoiding biased questions.

What is a biased question?

A biased question is an unquiry asked in a way to solicit a certain answer from a respondent or unclear to them. Sometimes, they are born from wrong choice of words and questions order, sometimes - caused by social and psychological factors. Both surveyor and respondent might be unaware of it, still falling to its trap subconsciously.

Biased questions prevents people from sharing honest impressions and damage the feedback received. Although eliminating them completely is next to impossible as you cannot predict the reaction of each person, but knowing what types of bias are out there will help you watch out for them and mitigate their influence.

Types of biased questions

Just like there are many ways to affect a respondent's choice, there are many different types of biases and questions born from them:

• Double-barreled question

Absolute questions

Leading questions, loaded questions, dichotomous questions, vague questions, social desirability bias, demand characteristics bias, assimilation effect, contrast effect, acquiescence bias.

• Straightlining.

Double-barreled questions

A double-barreled question combines two questions in one and asks for one answer to both. It is easy to miss because adding more information often seems like a good idea.

This sentence asks about two different matters: the effectiveness of the materials and their structure. It’s not necessarily that both of them are on the same quality level. A respondent might find materials extremely helpful, but not very well organized. Thus, they will be at a loss as to what to answer. And when you will be reading their replies, you also won’t be able to pinpoint which of the two matters caused problems.

How to avoid double-barreled questions? Try to be concise and simple whenever possible. Stay away from long descriptions that use several words to define one point. Make sure that you address only one side of the topic in each field.

To check yourself, imagine how you would be analyzing results. If you receive a negative answer, will you be able to understand where the problem lies?

Absolute questions include such words as “all, always, every, none, never”. These ultimate terms carry an extreme connotation making people want to deny it.

This question sounds too pushy. If a respondent liked only some of the tasks, their first impulse will be just to say No, without looking at the other options.

For someone, it might sound almost like you are accusing them of not participating in your events earlier.

How to avoid absolute questions? It’s best not to use absolutes in your form except when it’s unavoidable. In most cases, these words can be omitted or the sentence can be rephrased.

A leading question is a statement that a surveyor wants to be confirmed or denied asking it in a way that leads to a certain answer.

It’s obvious from this sentence what answer is expected. You risk receiving negative feedback or no answer at all because no one likes being manipulated.

The example above expresses an interviewer's preferences explicitly. But not all leading questions are that obvious.

At first glance, there are two choices — old and new programs. But are there really? This sentence clearly favours the new program by emphasizing that it was enhanced. Whether your respondents subconsciously follow the lead or realize it — the outcome will be biased anyway.

How to avoid leading questions? Carefully check if you give respondents enough freedom. Do not include your personal opinion keeping the sentences neutral.

A loaded question contains a certain assumption that might be false for some respondents. It is similar to a leading question as it implies the direction of an answer too.

The interviewer is certain that all participants have enjoyed the event and all that’s left is to find out to what degree. What if someone did not enjoy it at all? Even if you include the corresponding option in the list of responses, the question will still leave a negative impression.

How to avoid loaded questions? The countermeasures are similar to those for leading questions. Take all possible outcomes into account and stay neutral allowing respondents to decide for themselves.

Dichotomous questions always have only two possible answers, with yes/no being the most frequent options.

Using two definite options is tempting because it makes the analysis of the results an easy task. But at the same time, it backs some of the respondents into a corner. They won’t be able to give a reply if they have not decided on their future attendance yet. Consequently, they will either drop out or pick an answer randomly.

How to avoid dichotomous questions? Include the middle option that expresses the uncertainty or neutral position — for example, “Maybe”. Or give a way out by adding the “I don’t know” answer. It’s not that convenient for analysis but provides a suitable choice for those who do not have a clear opinion on a matter.

In contrast with the previous types of bias that limit respondents’ freedom, a vague question gives too much of it.

A vague question includes words that can be interpreted in different ways: "good, bad, interesting, many". People define them depending on their values, beliefs, experience, and goals. If you use these words in your questionnaire, there is a high chance that each of your respondents will be answering their own question and you won’t know which one.

Are you sure that you and event participants have the same understanding of success? For you, the criteria might be delivering new knowledge and teaching new skills. And for others, it might be an engaging and lively discussion or the level of organization.

How to avoid vague questions? Focus on the qualities and parameters that matter the most for you and ask as precisely as you can. Try not to leave the room for different interpretations.

This type of bias is different from previously mentioned biased questions. Social desirability is a response bias that makes the survey participants choose a socially-approved answer.

People want to look good in front of others. Even without realizing it, they usually deny traits that can be viewed negatively by those around them. This makes it difficult to receive honest answers to sensitive questions and to make accurate statistics.

It’s considered that learning and self-improvement are essential qualities of any successful person. If a respondent doesn't have any intention to learn anything new, there is still a high chance they won’t admit it and give you a positive answer that would be socially approved.

How to avoid social desirability bias? Making a questionnaire anonymous helps people relax and share honest thoughts.

If anonymity is not an option, at least assure participants that you will keep this information private (for example, in an attached letter).

You can also try to rephrase a sentence to make it sound less aggressive.

If you list possible directions and include “I haven’t decided yet” or “I don’t know” options, it will help two groups of people. You will give hints to those who want to grow but haven’t yet decided how to proceed. And you will add a way out for those who do not intend to raise their skills for now.

Demand characteristics bias make respondents give answers based on their assumption of what you need the most. They do not reflect on their actual thoughts and emotions but try to pick the most favorable answer.

Respondents might think that they help you by choosing “Yes”, so they will mark this reply solely because of this reason. At the same time, they could be satisfied with the current location and don’t see any need to change it.

How to avoid demand characteristics bias? Do not leave the ground for assumptions and stay as neutral as possible. Ask participants to share their impressions on the current location or write their suggestions on what can be improved on this matter.

If you get a lot of negative feedback, changing a location can really be a good idea, even if you don’t ask about it directly.

Survey results can be negatively affected not only by the wording of questions but by the survey structure too.

The assimilation effect takes place when a respondent uses their reply to the first question as a basis for a reply to the next question.

Let’s look at the order of the following fields:

Knowledge of the topic can be considered as a part of a trainer's performance, which means that the detailed question here precedes the general question. In this situation, when a participant is asked to rate the performance in general, the first thing that comes to their mind is how knowledgeable the trainer was because it has been mentioned just before. And they might give their rating based on it alone, ignoring all the other aspects of the performance.

It happens because our mind always looks for a shortcut. To find an answer, we go through the data stored in our memory. Of course, we stumble upon the most recent information first. If it suits our purposes, we tend to use it and don’t try to look further.

But the most recent information is not always what we need. A participant might be impressed with the trainer’s profound knowledge, but have complaints about the presentation style or the way the trainer interacted with the audience. If they rate the performance high thinking only of knowledge, some serious problems might go unnoticed.

How to avoid the assimilation effect? Placing a general question first is the easiest solution. It helps get rid of the assimilation effect because this way you don’t give respondents any hints.

If there are several detailed questions related to one topic, it’s safe to put them before a general question. A respondent will have a broader picture and will be less susceptible to the assimilation effect.

If, for some reason, you still need to put one detailed question first, and a general question second, try providing an explanation for the second case. For example, elaborate on what you mean by performance in brackets. Not a very elegant way out, but still better than invalid results.

Contrast effect occurs when in their next answer, respondents exclude the aspect they were asked about before. That’s because they think that if you have already mentioned this one aspect you mean something else in the next field.

In their answer about the event’s venue, respondents might not take transportation into account. Thus, you will get confusing data when participants had various troubles with getting to the location, but at the same time, they gave the venue 5 stars out of 5.

How to avoid the contrast effect? The contrast effect can be avoided the same way as the assimilation effect — either put a general question first or precede a general question with several detailed questions.

In some cases, the problem is not how you ask questions or position them. Sometimes, respondents don’t feel like answering so they just pick options randomly.

You can’t really control their motivation, but you can at least help them find it. One way is to explicitly convey the importance of their feedback and design a feedback form with respondents’ convenience in mind.

Read our recommendations on how to collect event feedback effectively .

And another way is to wisely use question order and question types to keep respondents focused.

Two effects caused by low motivation are acquiescence and dissent biases. Acquiescence or agreement bias means a respondent agreeing with everything in a survey. And dissent bias is a respondent always choosing “No”.

How to avoid acquiescence bias? Maintain the balance by using different question types. It will help participants not to get bored while answering them.

Do not ask for agreement or disagreement and offer different options instead.

There is still a chance that some respondents give random replies, but others will feel interested to share their real thoughts.

Straightlining

Straightlining means marking the same option in all questions. It stems from the same reason as the acquiescence bias — respondents are not motivated enough to think about their replies.

It happens with ratings but can affect multiple choice questions as well.

How to avoid straightlining? The advice is the same here — the balance of question types. It’s better to avoid several rating scales in a row or matrix questions, asking to rate several aspects.

It will be harder for participants to click on the same answer, if a multiple choice question is followed by an open-ended question, then a Likert scale. Or when answer options in multiple-choice are different from the previous field.

It will be harder for the survey bias to get in your way if you stay alert of it. Step back to look at your questionnaire from your respondents’ perspectives before sending an email with a survey link or handing out a form.

If you are collecting feedback for your event, staying impartial might be difficult because you have put so much effort into it and you are rooting for its success.

But that’s exactly why you have to keep your head cool and your questions neutral. It will allow you to transform surveys from a mere formality into a really powerful tool that will support your events and give them additional value.

And to enhance this tool even further, you can automate the whole process of collecting feedback — sending evaluation requests, structuring and storing reviews, and converting them into testimonials.

You can easily do it with Workshop Butler's evaluation tools — a set of features designed specifically for the needs of training businesses.

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Types of Sampling Biases (And How to Avoid Them)

Sampling biases in consumer research can derail all your hard work if left unchecked. Here are some types of sampling biases you need to be aware of.

Aishwarya N K

September 8, 2023

In this Article

Consumer research isn't just about data; it's about understanding your audience—their preferences and behaviors. However, the most carefully planned and executed research can fall flat if your chosen sample population doesn't accurately represent your target market. Sampling biases can lead you astray, rendering your insights less insightful and your decisions less informed.

What is sampling bias?

Sampling bias refers to a systematic error that occurs in the process of selecting a sample from a larger population for research or survey purposes. It happens when the method of selecting the sample favors specific individuals or groups over others, leading to a sample that does not accurately represent the population it is intended to study.

Sampling bias can arise for various reasons, including flaws in the sampling method, the inclusion/exclusion criteria used, or the way the sample is recruited. The consequence of sampling bias is that the research findings or survey results may not be applicable to the entire population, and they can lead to inaccurate or misleading conclusions. To minimize sampling bias, researchers must carefully design their sampling methods, use appropriate techniques to select samples that are as representative as possible and be aware of potential sources of bias throughout the research process.

Read more: Using Data-Driven Decision Making to Decode Consumer Behavior: Here’s How to Do It

Types of sampling biases

Non-response bias.

This occurs when some people who are selected to participate in a study do not respond. This can happen for a variety of reasons, such as they are too busy, they are not interested, or they do not understand the survey. Non-response bias can lead to inaccurate results, as the study may not be representative of the population as a whole. For example, let’s assume that in a survey about a new mobile app, only 300 out of 1,000 users have responded. This could make app satisfaction seem higher than it actually is because busy users might not have replied.

How can you avoid this?

• Increase the sample size to compensate for potential non-response.

• Use follow-up reminders and incentives to encourage participation.

• Analyze and compare respondents and non-respondents to identify potential differences.

• Implement weighting techniques to adjust for non-response.

Undercoverage bias

This is one of the most common types of sampling bias and occurs when some groups of people are underrepresented in a study. This can happen if the sampling frame is not representative of the population, or if some groups of people are less likely to participate in the study. Undercoverage bias can lead to inaccurate results, as the study may not be representative of the population as a whole. For example, when studying electric car preferences, using data from one city's dealership might miss what people in rural areas or from other dealerships like.

• Use a sampling frame that is as comprehensive and representative as possible.

• Employ random or stratified sampling methods to ensure all segments of the population have a chance of being included.

• Supplement data from incomplete frames with data from other reliable sources if feasible.

Survivorship bias

This occurs when only the people who are successful are studied. This can happen in studies of businesses, where only the businesses that are still in operation are studied. Survivorship bias can lead to inaccurate results, as it ignores the experiences of the businesses that were not successful. For example, researching successful startups without considering failed ones could give a one-sided view of what makes startups work.

• Include data from both successful and unsuccessful cases, where applicable.

• Clearly define the criteria for inclusion in the study and collect data on non-survivors.

• Use historical data to analyze both outcomes, if relevant.

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Voluntary response bias

This occurs when people who choose to participate in a study are different from those who do not choose to participate. This can happen if the study is advertised in a way that only appeals to certain types of people, or if the study is conducted in a way that makes it difficult for some people to participate. Voluntary response bias can lead to inaccurate results, as the study may not be representative of the population as a whole. For example, if a customer satisfaction survey is shared mainly on social media, it may attract more active customers, making satisfaction ratings seem higher than they are.

• Use random sampling methods rather than relying solely on volunteers.

• Design surveys to be engaging and straightforward to encourage participation from a broader audience.

• Be cautious when interpreting results from surveys with voluntary response.

Recall bias

This occurs when people forget or misremember information. This can happen in studies where people are asked to recall past events or experiences. Recall bias can lead to inaccurate results, as the study may not be representative of the actual events or experiences. For example, when people are asked to remember how much detergent they used in six months, they might not recall accurately, leading to inaccurate results.

• Use more objective data sources when possible (e.g., sales records, behavioral data).

• Frame questions to be specific and focus on recent events rather than relying solely on participants' memories.

• Employ techniques like the "telescoping" method to help participants recall events more accurately.

Observer bias

This occurs when the researcher's own biases influence the results of the study. This can happen if the researcher is not objective, or if the researcher is not aware of their own biases. Observer bias can lead to inaccurate results, as the study may not be objective. For example, if a researcher really likes a soda brand and watches people buying drinks in a store, they might notice the brand more, making their observations biased.

• Train observers to be objective and aware of potential biases.

• Use standardized observation protocols and criteria.

• Implement blind or double-blind study designs where the observer is unaware of the study's objectives or the conditions being observed.

Pre-screening or advertising bias

This occurs when the way a study is advertised or pre-screened leads to certain types of people being more likely to participate. This can happen if the study is advertised in a way that only appeals to certain types of people, or if the study is pre-screened in a way that excludes certain types of people. Pre-screening or advertising bias can lead to inaccurate results, as the study may not be representative of the population as a whole.

For example, if a survey on smartphone preferences is advertised exclusively in a tech-savvy online forum, it might attract participants who are already tech enthusiasts. This could lead to biased results that don't accurately represent the broader population's smartphone preferences.

• Diversify advertising channels to reach a more representative audience.

• Clearly state the research's purpose and objectives to attract a broader range of participants.

• Consider using multiple recruitment methods, including random sampling and community-based approaches.

In conclusion

When it comes to conducting research, it is important to ensure that bias does not affect the accuracy and reliability of the results. By understanding the different types of sampling bias and how to avoid them, you can conduct research that is bias-free, and that reflects the true preferences of your target customers.

Read more: Sampling Error in Consumer Research: What Every Researcher Should Know

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Aishwarya tries to be a meticulous writer who dots her i’s and crosses her t’s. She brings the same diligence while curating the best restaurants in Bangalore. When she is not dreaming about her next scuba dive, she can be found evangelizing the Lord of the Rings to everyone in earshot.

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How to Avoid Survey Bias in Your Market Research

Survey bias is one of the biggest roadblocks in market research . Although online surveys are powerful agents in gathering primary research, they are susceptible to unreliable and sometimes inaccurate results. This is due to survey bias.

Unfortunately, survey bias is inevitable, as several aspects can contribute to inaccurate results, many of which are out of the researchers’ control. However, understanding the main types of biases that can arise is critical for avoiding them.

While no one can fully weed out bias from surveys, there are certain measures researchers can put into practice to fend them off. This article illustrates the makeup of survey biases and how to avoid them.

For more information on survey bias, read the Pollfish Ultimate Guide to Remove Sampling Bias and More . This article will cover certain biases not mentioned in the guide. 

Defining Survey Bias 

Survey bias is an encompassing term that does not simply refer to results that are unrepresentative of a studied population . Survey bias is a general term for a variety of biases that influence respondents to provide dishonest or inaccurate answers . These invalidate the findings of a survey.

There are two main types of biases that occur in survey research: survey errors and response bias.

Survey Error

This type of error is rendered when researchers use faulty means in creating a survey, i.e., its screeners, quotas, questions, etc. Referring to the survey-production process, this type of error can also occur in administering a survey and even in post-survey analysis. 

The latter is known as researcher bias. (More on this in a few sections below). 

The survey error is a type of bias that lies entirely on the researchers’ end, as they are responsible for producing the surveys and their processes. As such, it can be avoided via actions taken on the part of the in-house or external research side of the survey campaign. 

Response Bias

Unlike the survey error, which comes from the researcher side, response bias occurs from the survey participants, i.e., the respondents. Response bias occurs when respondents are influenced into providing answers on a survey . A general term, this type of bias can result in a variety of inaccurate responses. This bias can appear either deliberately or subconsciously from the respondent. 

Respondents can be influenced to answer questions in a particular way or they may exhibit reluctance to provide accurate answers themselves. In this regard, there are many types of survey respondents to look out for . 

As a market researcher or survey maker, you ought to ensure you’re implementing efforts to reduce the many varieties of these two main survey biases.

Survey Errors

Construed from the research end of the survey research process, survey errors can occur in the questionnaire, the screener, the distribution/collection stage, or from the interpretation of the researchers themselves. Here are the key survey errors prone to occur on the research side and how to avoid them:

1. Sampling Bias: 

This relates to a bias that occurs in the respondent selection process, specifically when a survey sample (group of respondents) is not chosen at random, or not completely at random. This leads to under or overrepresentation of a certain segment of your targeted population, as only certain types of respondents are taking part.

How to reduce sampling bias : Researchers need to employ several methods of distributing their surveys, so that respondents are as randomized as possible. This includes making mention of it on emails, websites, social media and even physical locations. Alternatively, researchers can use an online survey platform, one which features a large network of publishers. This will ensure that the survey is exposed to thousands, if not millions of people online. 

2. Survey Scope Error:

This error occurs when researchers leave out critical questions needed to fully complete the research around a topic. This results in incomplete findings that require follow-up surveying, which is only possible if researchers use a survey panel . Otherwise, despite deploying surveys to the same market segment, you won’t receive answers from the same respondents due to the nature of random device sampling found in many online surveys. Survey error renders survey research to lack critical aspects of a topic or inquiry.

How to reduce the survey scope error: Create a list of questions and answers before setting up your survey. Consider the answer options you give, will any of them require further probing? If so, create question paths to send respondents to the appropriate question based on their previous answer. This allows you to probe deeper into a topic or subtopic. Follow-up questions can be open-ended. If you require too many questions, making for a long survey, consider breaking up the survey based on subtopics as online users are unwilling to answer lengthy surveys, no matter how well-built the survey platform you use. 

3. Order Bias:

This refers to the bias that arises due to an ill-conceived order of questions and answer choices. This is to say that this bias occurs when questions asked early in the survey affect how respondents answer questions later on in the survey. Order bias can exist in two varieties . One is the assimilation effect , where the response to a concluding question is based on the former questions asked. 

Ex: When conducting a customer satisfaction survey , researchers ask several questions on the CX of a brand, with the final question on the overall experience. The questions leading up to the final question all deal with similar aspects of the experience, therefore influencing the response to the final question. In this case, the response to the final question would be similar to the former questions. Asked on its own, however, it would likely receive a different response.

The second type of order bias is the contrast effect , wherein the response to a concluding question is not similar to prior questions, but more severe in comparison to them than it would be if it was asked earlier on or by itself.  

Order bias can also occur due to the types of answers the researcher provides in multiple-choice questions, as respondents prefer to answer with the first few responses. 

How to reduce order bias: Randomize question and answer options. It also is key to reduce the number of scale questions per survey. When too many such question types are used, you are bound to create the assimilation or contrast effect. Ask questions that better engage respondents; include multiple and single-selection answer questions, questions with visual elements and open-ended questions. 

4. Purpose Creep Error:

This refers to the error that emerges when researchers include unnecessary items in a survey, typically for the sake of obtaining an exhaustive data collection. This action is called a purpose creep. Essentially, this kind of action creates superfluous data, the kind researchers won’t need to use when analyzing or presenting their findings. 

Furthermore, in an attempt to avoid discarding data, researchers may study unneeded aspects of the survey, such as demographics that aren’t needed or tidbits about the target market that are not entirely relevant to examine. Asking unnecessary questions can also negatively affect the respondents’ experience, leading them to answer untruthfully or leave surveys incomplete. 

How to reduce the purpose creep error: Consider the key demographics, screening questions and topics you need to base your survey on. When you prepare questions, look them over before launching the survey to see if there is anything redundant. While some aspects may seem interesting, they may not be entirely pertinent to the survey and overall study.

Survey Biases

Survey biases occur within the respondents themselves, commonly when they are influenced to answer or behave in a particular way. It can also exist from within, meaning that the respondents are inclined to misunderstand questions or easily get bored. Here are the key survey biases common on the respondents’ side of survey research and how to avoid them:

1. Acquiescence Bias:  

Also called agreement bias, this bias occurs when respondents gravitate towards positive or agreeable answers. In this bias, respondents will exhibit the propensity of frequently choosing answers with positive associations. They do this as these answers feel like the correct choices. This kind of bias is more prevalent in Asian cultures, as a study found .

This bias also rears its ugly head when respondents feel tired and thus answer questions without applying any thought to them. 

How to reduce the purpose creep error: Create questions with answers that don’t allow respondents to make positive or negative associations. As such, researchers should avoid using questions that ask if the respondent agrees with an idea. For example, instead of asking:

Do you agree with the following statement? “I found the new feature easy to use.”

What do you think about the new features in terms of ease of use?

• I think the feature is easy to use. 
• I think the feature has to be studied.

2. Prestige bias:

Prestige bias is tied to respondents’ social desirability, as it deals with responses made specifically to be seen positively. This can manifest in instances where respondents are asked about their income or their associations with notable actions. In these cases, respondents exaggerate to make their circumstances appear more socially desirable. Many times, this bias occurs indeliberately, as respondents may recall memories in a way that’s more favorable to them as a subconscious way to protect their reputation. 

How to reduce prestige bias: Screen your survey participants for their knowledge of certain things relating to the study. Also, be sure to preset the audience with demographics that fit a certain level of education. Ask questions in a way that avoids cheating positive or negative associations. Also, it will help to add a disclaimer at the beginning of your survey, that claims it is a judgment-free study and solicits respondents to answer honestly. 

3. Demand Characteristic bias: 

This bias develops when a demand characteristic is present. This phenomenon denotes cues in the survey that unintentionally influence how responders answer questions. A demand characteristic can take place if the researcher gives away the purpose of the survey or study. As such, the main purpose of a survey should not be overt in the setup of a survey. When a respondent knows the purpose of a study, they may purposely provide answers to influence the study, especially if this will benefit them. Ex: if a survey is conducted to determine a municipal law, the respondent may provide answers that would make the outcome of the survey favorable to them.

How to reduce demand characteristic bias: Researchers should keep the main purpose of a survey private. As such, they can forgo a welcome page that respondents would otherwise see before taking the questionnaire or answering the screening questions. The wording of the questions should not make the purpose of the study too obvious; therefore, researchers can use analogies and keep certain topics vague. 

4. Random Response bias: 

This bias comes into being when a respondent does not know how to answer a question, but does so anyway, creating inaccurate results that are random at worst. In this scenario, respondents guess an answer rather than providing authentic information. This bias is also exhibited when respondents don’t read the questions, deliberately randomly answering them to quickly finish the survey. This is usually seen when there is an incentive that requires completing the survey. 

How to reduce random response bias: When forming the questions, consider your target market with them. Will they understand your questions? Will they know how to respond accurately? Avoid asking questions that deal with how respondents perceive others to think or behave. To catch this bias in action, add trigger items, such as an item that is opposite to another, or reverse scoring to catch potential bias.

Staying Clear of Survey Bias with the Right Online Survey Platform

While providing a potent channel for primary research, surveys are bound to incur bias, whether it manifests in survey errors or survey bias. Often, survey errors — those which are made on the part of the researchers, generate and influence survey bias — which occurs on the part of the survey subjects, i.e., the respondents. 

Therefore, researchers should always scrutinize their survey efforts, both in the screening and questionnaire portions of the survey they’re launching. An online survey platform can help minimize errors as a potent one provides randomization of respondents and many question-and-answer option setups. 

While certain biases are unavoidable, researchers ought to carefully assess the online survey tool they intend to use for their survey research. 

Frequently asked questions

What is response bias.

Response bias is an umbrella term for a variety of survey issues that may cause survey respondents to answer questions incorrectly or inaccurately.

What is sampling bias?

Sampling bias is an effect that occurs when data is collected from a population (i.e., the sample) in a non-random way. This results in a biased sample that does not accurately represent the larger population.

What is a survey scope error?

A survey scope error is an effect that can occur when researchers omit certain important questions that are necessary in order to fully understand a topic.

What is acquiescence bias?

Acquiescence bias, also known as agreement bias, is a common survey error that occurs when individuals have a tendency to agree rather than disagree with a question.

What is random response bias?

Random response bias describes the tendency for a survey respondent to answer a question randomly, rather than accurately. This can happen if the respondent does not understand the question, does not know how to answer the question or is in a hurry to complete the survey.

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5 Examples Of Bias In Product Research & How To Get Around Them

If finding product-market fit is your Holy Grail, these five common research biases are the booby traps you'll need to dodge on the way.

When was the last time customers asked you to build a feature during an interview but none of them ended up using it? I am confident that every single PM out there has had at least one round of user interviews that have ended in a colossal flop.

The natural next question is, what went wrong during the interview ? More than likely, we encountered one of the many research biases that led to us making the wrong product decisions.

Leading a product based on biased data is dangerous. Fortunately, you can avoid it by learning what bias is, what makes it risky, and learning from these five examples of bias in research that led to undesirable outcomes.

What Is Bias In Product Research, and What Are the Risks?

The term bias refers to the set of errors that product and marketing teams make when conducting research. The result of these errors is usually incorrect analysis that leads to decisions that do not represent the true state of things for their product, market, and users.

A product manager’s decision-making carries a massive impact. A single decision can pivot the entire product line towards a new market or target persona. There are also daily execution-related decisions that impact the effectiveness of the engineering, marketing, AI, and other teams’ efforts.

Therefore, making decisions on biased data is especially dangerous, considering that you can lead people in the wrong direction and fail miserably as a PM.

Luckily, mitigating research bias is not as hard as it might seem. The hard part is to admit that your data is biased. Mitigation itself is relatively easy.

Types Of Research Bias In Quantitative Data Analysis

Let’s begin with the common types of bias that you encounter when working with data. They are usually related to errors in the way you gather, analyze, and act upon quantitative data.

Classical scientific research identifies a wide variety of research biases , including:

• Publication bias
• Measurement bias
• Analysis bias
• Design bias
• Observer bias
• Recall bias
• Social desirability bias
• Demographic and cultural bias
• Response bias, and others.

But, these are a bit too theoretic for us to talk about. So, I’ll focus on the three types of bias that you will encounter in real life as a product manager.

#1: Sampling And Selection Bias

As the name suggests, sampling bias is about getting incorrect data because of the sample you have selected.

If your sample contains data skewed towards a certain characteristic, then the analysis you make will not be representative of the general population. You will end up incorrectly extrapolating the behavior of that skewed sample and applying it to the entire data set.

Netflix is one of the big tech companies that has been guilty of sampling bias. People in the United States loved its recommendation algorithm as it was able to suggest shows relevant to their interests. The rest of the world, however, hated it. The suggestions were simply terrible.

What was the reason behind it? Apparently, the AI model trained to make these suggestions consumed only U.S. viewer data and suggested things that were relevant to Americans.

How To Mitigate Sampling Bias

In Netflix's case, they simply started feeding international data to their models and retrained them based on a less-skewed study population.

But, if you want a more systematic approach to mitigating this type of bias, I can suggest a bit of an unorthodox approach that has worked for me in the past: running something statisticians call a cluster analysis .

A cluster analysis can find common traits in your sample population and cluster them based on these traits.

In the image above, you can see that the population is clearly divided into three different clusters based on the traits chosen.

What you do is select a wide variety of traits and create these charts for each of them. Your data will be skewed and prone to sampling bias if you see that, for certain traits, you only have a single cluster. In the example of Nexflix, the viewer country trait had a single cluster: United States.

Careful, though! Sometimes, it’s natural for your entire population to have a single cluster for a trait if it is describing their persona. For instance, if we ran a cluster analysis for the readers of theproductmanager.com, the majority of them would be product managers. This is natural as PMs are the persona the website is aimed at. So, you should always remove these types of traits from your analysis.

#2: Procedural Bias

This type of bias refers to the errors you have made in your study design and research process. It can be due to your usage of incorrect tools, statistical formulas, or even your data collection methods.

From my experience, procedural bias is especially prevalent in user surveys. If you have any leading questions in it, then your respondents will most likely fall victim to your “push” towards a certain type of answer.

Here’s what a typical leading survey question will look like:

How much do you love our product?

With the way you phrase it, you are suggesting users give a more positive answer, even if they hate what you do.

The more neutral variant of the same question will look like this:

How would you rate our product?

In this case, we clearly indicate that you are ok with negative answers.

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How To Mitigate Procedural Bias

The best way to make sure that your research is rid of procedural bias is to pick the right tools and methodologies for the specific type of analysis you are doing.

Randomize Answers

If you're conducting surveys, apart from avoiding leading questions, you will also need to randomize the question order because there’s a chance that people will get bored midway through the questionnaire and start giving meaningless answers. Without randomization, the last couple of questions will get near zero useful answers.

Pay Attention to Statistical Significance

Moreover, consider that surveys are partially quantitative in their nature. So, don’t rely too much on the statistical analysis of that data. For instance, if you improved a certain feature and your NPS score increased by 2%, that is too low a difference to be considered a statistically significant result. A 2-fold increase, on the other hand, would be reliable.

Choose the Right Tools

Now, let’s talk about selecting the wrong tools, too. If you are measuring the conversion rate of your checkout page, then using a heatmap tool would create procedural bias, as the data you get from it is unreliable.

In these cases, it is best to use heatmap software to debug the behavioral problem, and A/B test the behavior change. For CR measurement, on the other hand, you should rely on product analytics tools such as Amplitude or GA4.

#3: Survivorship Bias

Biased research methods have been with us for a long time. There’s an interesting story about American bomber planes of World War 2. Each time the bombers returned to base, the engineers would examine the anti-air gun holes on their hull and plot it like this:

The natural course of action was to reinforce the spots with most bullet hits with thicker armor plates. But this reinforcement had nearly zero effect on the survivability of American bombers.

After carefully examining the case, engineers realized that they were dealing with only those planes that had managed to survive and come back to the base. So, the parts they were supposed to reinforce were actually the ones where the planes had no bullet holes.

How To Mitigate Survivorship Bias

Let’s shift from WW2 planes to digital products. As a PM, when managing your data collection process, you will most likely deal with these two potential instances where bias sneaks in:

Filtering Data For Analysis

When analyzing user behavior, you can end up filtering out users who have performed certain actions that are not relevant to your analysis.

I faced this case fairly recently. We were trying to improve the activation rate for our influencer marketing product. The data analytics team built a funnel report where the conversion rates from one activation step to another were fairly high (somewhere around 50-70%).

This data baffled us because we were seeing an overall low rate of people engaging with the product compared to the number of signups. After carefully analyzing the data, we understood that the data team had applied a filter and only left the data for our power users.

Of course, power users were most likely the ones who had passed the activation flow with ease. So, the conversion rates on the funnel were not really representative of the real situation. When we looked at the funnels, taking all of our signups into account, the rates dropped significantly.

Lesson learned :

• Always consider the analysis's context and goal when filtering your data.

Analyzing Pre-screened Survey Data

Another interesting case in research design that I had to deal with recently was related to a survey we ran to understand the needs and pains of influencer marketers. Again, the study results were a bit baffling to us because the needs of respondents did not match the insights we had from our user interviews.

The problem here was our pre-screening process for users who took the survey. Apart from asking if they were influencers (to avoid getting responses from irrelevant people), we also asked about the size of their audience/followers.

For some reason, we had disqualified influencers with less than 50,000 followers and only considered the answers for the relatively larger ones. Our product was supposed to target small influencers as well. So, the data we got from the survey was not accurate for us.

Lessons learned:

• Cross-check your research findings with qualitative data. If there is a big difference between the findings of these two, you might have done something wrong.
• Be careful with your pre-screening proces s; you might end up disqualifying relevant users, too .

Types Of Bias In Qualitative Research 

With the systematic errors related to quantitative research covered, let’s move on to another important category of research—qualitative studies.

For product managers, the single most important type of qualitative research is interviewing users . So, I’ll focus on the two major biases you usually encounter when talking to your customers and users.

Note: These are also the two types of biases that you can manage and mitigate using a proper product ideation tool .

#4: Confirmation Bias

One of the greatest traits of product managers is strongly believing in something when others don’t. That’s how you create innovative products and revolutionize your industry. However, tending to stick to their opinions is also a major weakness for PMs.

The reason is that PMs often suffer from confirmation bias. This describes a situation when you are actively trying to hear the parts of the conversation that align with your beliefs and discarding the rest.

This is quite dangerous for people who then make strategic decisions on what the product will look like and how to take it to market. When confirmation bias is at play, your user interviews become completely meaningless, and you end up making blind decisions (and you can forget about user-centricity).

How To Mitigate Confirmation Bias

I have to admit, I am guilty of “practicing” confirmation bias too. But there is a solution that has helped me mitigate it fairly well.

You can call it the “code review” of user interviews. It is a process when product people cross-review some of the interviews of their peers and suggest adding or removing learnings.

If you’re the only product manager in the company (common for small startups), then do this exercise with your founder. Don’t forget that founders are essentially product people too.

#5: Participant Bias

While confirmation bias is a dangerous beast, it is among the easiest ones to fix. The next one I want to talk about, however, is much harder to mitigate.

Participant bias occurs when your respondents say things based on what they think you want to hear instead of what they really think about that particular topic.

For instance, when you are conducting an interview, you can ask the research participants if they would buy your product assuming that you added the feature they have just requested. Can you guess what the answer would be? From my experience, the vast majority will say Yes.

Does that mean you should run to the engineering team and ask them to build that feature ASAP? No! The question you asked is flawed and naturally pushes people to say something that will please you.

I agree, your feature will be able to cover a major pain for some of these interviewees and they will end up paying for it. But the majority will simply use that feature for free or not use it at all.

Apart from asking flawed research questions, study participants will also give you biased answers if you are paying them for the research study. They usually tend to “earn the paycheck” by giving favorable answers and making you happy without realizing that they are doing more harm than good.

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As I already mentioned, participant bias is quite hard to mitigate. This is because you don’t really know if the participant is being sincere with you or not.

But, two tactics can help you decrease the chances of getting bad answers.

Asking questions about their past experience

If you ask questions about a theoretical event in the future, interviewees will most likely say things that they will never actually do in real life.

If you ask someone on January 2nd if they would be willing to go to the gym for 12 months in a row, they will say yes. But we all know that most people end up ditching their membership only after a couple of visits.

To avoid this, PMs prefer asking questions about things that have happened in the past. This way, the PM can learn about user behavior based on what users have done and not what they wish they did.

Setting the right expectations

Sometimes, the nature of your question or study does not allow you to ask about your users’ past. For example, you want to know how they would feel if you increased the price of your product.

In this case, you can make it clear that it is okay for you to hear negative answers, and their honest feedback is the only way they can “earn their keep.”

Admitting Your Biases Is 80% Of The Battle

That’s right. The worst part of being biased is that most of us genuinely believe that we are right. So, the single most important step that you can take to mitigate your own biases is to recognize them.

The moment you do this, you will naturally start battling them and eventually reach a bias-free state. (Or at least a "bias- managed " state!)

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How To Identify & Avoid Bias in Research | Top 5 Types

by Premise Team | Dec 11, 2020

Types of Bias

Acquiescence bias (agreement bias).

You may know it as “friendliness bias” or “yea-saying.” Acquiescence bias occurs when a respondent agrees with every aspect of a survey or interview regardless if it matches their personal opinion. 

There are a variety of different factors that can cause this behavior in research participants. Some individuals have acquiescent personalities, and various factors like education can impact a respondent’s tendency toward acquiescence bias. 

Aquiescening can also seem like an easy way out when respondents get survey fatigue. To reduce survey fatigue, it is key that questions are concise and to the point. Moreover, it’s important to exclude any survey questions that are not directly linked to the research question to make it as short as possible for the respondent. Another way to limit this scenario is reducing yes/no style questions and instead substituting them for options that allow the respondent to express their genuine opinion.

Social Desirability Bias

This bias happens when the respondent answers questions not based on their true opinions but on how they believe others will view their responses to be accepted and liked. People are more likely to respond inaccurately on personal or sensitive topics, which is where ensuring a respondent that providing socially undesirable answers is acceptable. Additionally, ensuring respondents know that their information will remain confidential may help to get respondents to answer honestly. 

Another way to avoid social desirability bias is through indirect questioning, which you can do by asking what the third-party may think and how they will behave. It allows an individual to project their thinking onto others while giving honest answers. Additionally, validating responses with other datasets can help provide confidence in the information you gather. 

Being transparent about the usage of the data and allowing respondents to be anonymous can also increase a respondent’s likelihood of providing their true opinion. 

Confirmation Bias

One of the common and pervasive types of bias in research is confirmation bias , which is the tendency of people to believe or favor information that supports their beliefs or hypothesis. This can happen when people weigh the evidence that confirms their opinions more than disproves them and also when selectively recalling information.  

Most individuals exhibit some tendency toward confirmation bias as it is how we often filter and sort information. Due to this bias, it can often lead to focusing only on one hypothesis at a time. Researchers need to reevaluate respondents’ information and challenge preexisting hypotheses and assumptions constantly to solve this problem.

Sampling Bias

For most research, the goal is to collect data from the audience members you are attempting to learn about. Sampling bias occurs when you only collect information for a specific group (though sometimes researchers require collecting data from a subset of the population) while leaving out the data from individuals that are important for the sample. 

Examining your research design and data collection methods can help you ensure that you collect data from your target population. 

Question Order Bias 

The order in which respondents answer questions can cause bias in their responses. Different words or questions can prime respondents’ thoughts and feelings and impact how they react to subsequent questions. Though it can be difficult to avoid all instances of order bias, asking general questions before specific questions and reviewing the order of questions can help limit the impact of this bias. 

You should start a questionnaire with easy questions that are not threatening. Then, toward the end of the survey, add more threatening or sensitive issues. 

Bottom Line

It’s near impossible to eliminate all bias from research, but concentrating on designing high-quality research studies is one way to limit bias in the research process. 

Check out our blog post, 7 Tips for Designing A Great Survey , to get more actionable tips to help you design an effective survey. 

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• 6 Types of Market Research Bias and How to Remove Them

Bhavika Sharma Survey Designer at SurveySparrow

Tendencies, trends, inclinations, and preconceived notions – all are forms of human bias that are consciously or unconsciously affecting the outcome of your survey. Whether you are a researcher or a consumer, survey bias can easily sneak into any research and become a problem even before you realize it.

It’s very difficult to identify and avoid bias in your research unless you’re using a market research platform which is designed to eliminate human error.

So before you send out your survey, consider the following 6 types of bias that appear in market research so you can identify them and minimize their impact on the quality of your data:

1. Conformity bias

Confirmation is one of the most pervasive forms of bias in market research and it occurs when a researcher forms a strong belief and uses the respondent data to validate that belief. This takes place in real-time when the responses are being weighed as relevant or reliable and further extends into the analysis.

The only way to mitigate the effect of confirmation bias is to consistently re-evaluate the responses while challenging pre-conceived notions and hypotheses.

2. Cultural bias

Assumptions about influences and motivations are often colored by a cultural lens. People often judge another culture purely on the basis of their individual values and the standards of their own culture, and the researcher is no exception.

To minimize this cultural bias, researchers must adopt cultural relativism and be cognizant of their own assumptions to straddle potential cultural divides. Researchers can also consider using in-country analysts to capture local nuances.

3. Social desirability bias

This survey bias occurs when respondents tend to answer a survey in a manner that they believe is socially accepted and liked. This leads to inaccurate responses in case of sensitive subjects or personal topics where people like to present themselves in the positive light.

To minimize this bias in market research, the questions need to be phrased in a manner that explicitly states that the answers need not be socially desirable. The researcher can also frame indirect questions and ask about how a third-party would react or feel in a socially sensitive situation. This format will allow the respondent to express their own feelings onto a third-party and still provide unbiased, honest answers.

4. Acquiescence bias

Commonly known as the friendliness bias in market research, it typically occurs when a respondent exhibits a tendency to agree with the moderator on every aspect that is proposed. This happens because they perceive the moderator to be an expert.

To avoid the acquiescence bias from blurring your survey results, the researcher must rephrase

5. Sponsor bias

When survey respondents know the sponsor then their views and opinions may become biased. The respondent’s perception of the organization’s mission, vision or values can easily influence related answers. To avoid this situation, researchers can take a neutral stance.

Limiting the researcher’s reinforcement to positive answers and emphasizing on an independent status will also help in mitigating the influence of affiliation and elicit unbiased responses.

6. Irrational escalation bias

Irrational escalation motivates people to dismiss the results of a survey if they overrule or undermine already established decisions. This leads to biased interpretations and misdirected assumptions.

Irrational escalation often prevents respondents from expressing their honest views. The only way to safeguard qualitative research against irrational escalation bias is by ensuring that the survey questionnaire has clearly written questions that don’t mislead respondents and don’t create any ambiguity.

100% elimination of survey bias is impossible but its impact can be significantly minimized with market research software. Market research software lets you customize your questions, make the survey more engaging, group the questions by topic, randomize questions, and keep the answers anonymous to extract unbiased responses.

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Bhavika Sharma

Bhavika Sharma is a survey designer at SurveySparrow , where she designs surveys that work by making them more conversational and engaging, fetching 40% more responses. When she isn’t designing killer surveys, she loves to travel in search of adventure and to meet new people. Her travel experiences have taught her the importance of conversation and that is what she brings to the table while creating surveys or content for her readers.

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Further Reading

Why Didn’t They Buy? A Deep Dive into Buyer Preferences

6 Types of Data Sampling (and the Best Practices for Each)

The 2020 Online Marketplaces Report

The State of Marketing

4 Kinds of Market Research Bias and How to Avoid Them

Jun 21, 2016

Like middle-aged relatives on Facebook, biases are pervasive and difficult to ignore. People can’t help but see the world through their own experiences and opinions, so it’s pretty difficult to not be a little biased in one way or another. And since consumers and researchers are actually people (shocking, I know), you can bet market research is probably a little biased as well.

The good news is there are plenty of ways to anticipate and reduce the different kinds of biases that affect quantitative and qualitative research methods, and impact respondents. In this exciting two-part series, we’ll outline eight biases that are particular to market research and how you can avoid, or at least mitigate, their effects.

1. Confirmation Bias

What it is:  Confirmation bias comes from our natural tendency to interpret data in a way that is consistent with what we already believe. But this keeps analysts from getting the whole story of the data, because they inadvertently read it—and draw conclusions—through their own subjective context.

How to avoid it:  If your data is telling you exactly what you want to hear, you might need to listen more closely. Interpreting through a moderator offers a more articulate understanding and leaves less room for your own interpretation. For example, biased reading of vague words like “good” can skew data, so further probing a respondent to elaborate will help reveal a less ambiguous meaning. For more tips on avoiding confirmation bias, check out  this article .

2. Culture Bias

What it is:  An extension of confirmation bias, culture bias derives from assumptions about motivations and influences that are based in how we view other cultures. It’s a form of ethnocentrism, where someone judges another culture solely by the values and standards of their own culture.

How to avoid it:  The best way to avoid culture bias is to conduct and analyze research through a partner who can straddle any potential cultural divides. Around here, we minimize this bias by using in-country moderators and analysts to fairly capture local, cultural nuances.

3. Survivorship Bias

What it is:  Survivorship bias occurs when analysts concentrate on the concepts or stimuli that won the respondents’ favor, or “survived,” and dismiss those that lost. The problem is that focusing on only the winners means you lose the lessons of the failures, or are even distracted from any faults in your research design just because you found a winner.

How to avoid it:  While dwelling only on winners doesn’t sound so bad, it would be kind of like if after losing the Super Bowl, the defeated team suddenly ceased to exist. It’s just as important to understand why a concept won as it is to understand why another lost, so it’s helpful to think of your results on a spectrum of success.

4. Social Desirability Bias

What it is:  This bias occurs when respondents answering in the presence of others give more socially acceptable or responsible answers than they ordinarily would.

How to avoid it:  A common symptom of traditional focus groups, this bias can be remedied through online market research, where participants may feel less inhibited to honestly discuss even their worst habits and qualities. For instance, we employ Instant Research Groups in which respondents interact with each other digitally while answering open-ended questions and follow-ups posted by a trained moderator, offering anonymity and reducing social pressure.

As you can see, there are a ton of factors that can skew your market research conclusions. And, we’re here to help you avoid their damaging effects. Tune in this Thursday for the exciting conclusion to our research bias saga. In the meantime,  check out this handy eGuide  to maximizing your research results by combining quantitative and qualitative research.

You can now read about four more kinds of research bias in part two, found  here .

Sarah Welty

Research Operations Manager

When I’m not living and breathing market research (rare occurrences, to be sure), I enjoy playing with Waffles (my dog), performing stand-up comedy, and dominating pub trivia.

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Biased Survey - How to Avoid Biased Survey Questions

• February 10, 2022
• Best practice

The importance of avoiding biased survey questions

1. leading questions bias: what are leading questions and how to avoid them, 2. assumptive questions, 3. double-barreled questions: what is a double-barreled question in research, 4. ambiguous questions, 5. dichotomous-biased questions: why are absolute questions not the best option for some surveys, 6. survey questions that include jargon language, how to avoid biased survey questions and, ultimately, a biased survey.

Gaining important insights through survey research is an efficient decision-making strategy. It’s a sensible way to make good calls when a difficult decision is required, one requiring relevant feedback from stakeholders such as customers or employees.

However, there are many ways to go astray when surveying respondents, including using biased survey questions that can completely ruin research and lead to incorrect conclusions.

Creating a survey is a task that should be taken seriously. Data accuracy is always essential to good research. Luckily, if you want to become a seasoned survey writer who successfully avoids different types of survey bias, you’re in the right place.

Are you ready to collect reliable feedback from respondents?

Understanding biased survey questions: What is a biased survey?

A biased survey includes bad survey questions or phrasing that confuse respondents and leads to inaccurate data since respondents are directed to answer in a certain way. This can be done purposefully or inadvertently by survey authors.

There are many potential disadvantages to ignoring biased questions in your survey, the main being they waste time and resources because the results end up being useless and inaccurate.

Creating a well-designed, unbiased questionnaire requires making sure questions are written clearly and straightforwardly. They also need to align with your respondents’ knowledge and motivations.

Surveys are a popular method of gathering feedback. However, they can suffer from response biases. Biased survey questions can introduce misleading survey results and distort the picture of what your examinees really think.

When it comes to questionnaire research, everything is about objective data. Avoiding response bias is the key to getting accurate viewpoints and opinions, whether for science or business purposes.

An important part of a customer’s experience is how the business communicates and questionnaires are no exception. How questions are answered will depend heavily on respondents’ previous customer service experiences.​

Biased survey questions—such as loaded questions or leading questions—can seriously jeopardize your customer experience survey results. One small word change can completely alter the response to a question, leading to inaccurate data. The same goes for any other type of survey research.

There is no secret formula for a perfect survey design; however, there are a couple of tips and tricks you can use to recognize bad ones and avoid them in the future.

Dealing with the biased survey: How to recognize biased survey questions?

Unfortunately, there is no exact method to catch all the potential flaws when formulating survey questions (at least not without the help of a seasoned researcher).

However, there are some indicators you should look for when wording questions. Biased survey questions usually have two types of issues: respondents do not interpret the wording consistently or the wording influences respondents’ answers. Always read questions carefully—more than once—before sending them out as part of a survey.

Knowing the most common types of biased survey questions is a good start. Learn how to recognize them so you can avoid them in the first place.

Leading questions include phrasing that contains the statement the researcher wants to pull from respondents, which pushes the person taking the survey to answer in a certain way.

The key element to be on the lookout for is leading questions often contain an assumption and tend to be overly definitive about acquiring desired information.

• Do you support the new tax proposal? (This question is biased because it implies that the respondent should support the proposal.)
• More employees working remotely reported being happy compared to office-based workers. What do you have to say about it? (This question is biased because it implies that the respondent should prefer working remotely.)

To avoid leading questions, it is important to phrase your questions neutrally and balance the options for answering. A better way to word these questions might be:

• What are your thoughts on the new tax proposal?
• Would you prefer working remotely or in an office?

Assumptive questions contain an assumption about a respondent’s habits, preferences, values, etc. When answering these types of questions, respondents usually end up being pushed to agree or disagree with the statement included in the question.

Also known as loaded survey questions, assumptive questions not only prevent you from getting honest feedback but also lead to a higher dropout rate (the number of respondents that choose not to finish).

• What is your favorite cocktail bar? (This question is biased because it implies that the respondent drinks.)
• How much did you enjoy attending our conference? (This question is biased because it implies that the respondent enjoyed attending.)

To avoid assumptive questions, do not make baseless assumptions about your respondents. A better way to word these questions might be:

• Do you have a favorite cocktail bar, and which one is it if you do?
• Did you enjoy attending our conference?

Double-barreled questions, also known as double-direct questions, are two questions (issues or topics) asked at once. The unenviable task for recipients is to answer the question with only one answer.

By splitting up one double-barreled question, you make your questionnaire less complex and this will help respondents not get confused. The essential tip for creating an excellent survey—one that will produce a great experience for respondents—is to not overcomplicate your research design.

• Do you think our products are innovative and practical? (This question is biased because the attributes “innovative” and “practical” are not synonymous.)

To avoid double-barreled questions, ensure that only one piece of information is sought per question A better way to get this information is to ask two separate questions:

• Do you think our products are innovative?
• Do you think our products are practical?

Ambiguous questions can be interpreted in more than one way. They can confuse respondents and easily lead to inaccurate research results.

Also known as unclear or vague questions, ambiguous questions don’t have a specific query. This means respondents can answer the question correctly even when providing information about entirely different topics.

• How are you doing today? (This question is biased because respondents could understand the question as asking them how they are feeling or asking about their health.)
• Have you bought anything in the past six months? (This question is biased because it is unclear and doesn’t reveal what the purchase was, where it was made, etc.)

To avoid ambiguous questions, you must keep the overall research goal constantly in your mind. Write brief questions that gather relevant information for your specific research topic.

Dichotomous questions contain only two possible response variables. Often these are “Yes/No” or “True/False” questions. While they can be helpful in some research scenarios, they often tend to result in inaccurate data.

• Do you know how to play the piano? (This question is biased because it doesn’t allow the respondents to describe their piano-playing skills. Instead, they end up picking an answer that may not resonate with them completely.)

To avoid the problem of dichotomous questions, try using multiple-choice questions or a Likert scale instead. A better way to word these questions might be:

• Not very well

Jargon is a word or expression used by a group that is difficult for people outside of that group to understand (such as legal or sports terminology). When used in a questionnaire, respondents may not understand what is being asked.

If the jargon is specific to a particular field, respondents may also become frustrated, leading to a higher dropout rate. Finally, if respondents misinterpret the questions, this can lead to inaccurate responses.

• Are you satisfied with our website’s UX? (This question is biased because the question is probably inadequately worded for a wider population or your target audience .)

Here’s a quick tip. To ensure you’re not using jargon, find a few friends or colleagues with different demographic profiles and see if they understand the questions.

When designing survey questions, word choice may profoundly change the research results. To gather reliable data, questions need to be clear and concise.

If you don’t have experience in this field, then the best way to produce well-written surveys is to “steal” our best practices and avoid biased survey questions. You can browse through our survey examples or SurveyPlanet’s helpful blog posts . Our idea is to make survey research “easy peasy,” a task that anyone can carry out in a professional manner. Start your survey-making journey by signing up today !

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Market Research Biases and How to Remove Them

9 common market research biases.

The Association of Qualitative Research based in the UK describes bias as a “systematic error.” The definition suggests that this systematic error is responsible for deviating results from the factual findings of the research.

Various biases can impact qualitative and quantitative studies and potentially occur at any research phase, from study design and sampling to data collection and analysis.

 One important note to consider when discussing market research biases is that they are not dichotomous variables. This condition means that researchers can’t interpret the tendencies simply by asking whether it’s present or not. A 2010 study on the identification and avoidance of research bias suggests that surveyors and researchers must focus on the degree to which the bias affects the research and how it was and how to avoid it.

Understanding bias is critical information for any researcher engaged in market research as some degree of bias affects most studies and can potentially distort their findings and conclusions .

While this seems like an end-all situation and may prompt one to rethink the study design, market research experts know that biases can find their way in.

Before we delve right in, we must get familiar with the human elements that drive said market research biases, namely the respondents and researcher. Today, we’ll explore the primary sources of these biases and what researchers can do to minimize them to prevent your study from inferring skewed results.

Researcher Market Research Biases

We’ll begin with researcher biases as they begin at the study design source – the researcher or surveyor. While many forms of researcher biases exist, we focus on those relevant to the marketing field.

Confirmation Bias

Peter Cathcart Wason, an English cognitive psychologist, is responsible for coining the term “confirmation bias” to describe a form of cognitive bias he observed.

Also referred to as confirmatory and myside bias , it describes a systematic error that stems from the researcher’s tendency to form hypotheses that agree with their preconceived notions. The researcher then uses their study’s findings to confirm that belief – hence the name.

In other words, the researcher sees what they want to see.

Confirmation bias is a fundamental and more challenging market research bias to watch for when conducting research. A researcher afflicted by confirmation bias tends to dismiss responses that sway away from their hypothesis.

From there, the bias extends to other phases of research, including analysis as they use their findings to dispel other hypotheses, severely distorting study conclusions.

Since this form of bias stems from a very baseline natural human tendency to filter relevant information that suits their beliefs, it can be hard to eliminate. This is why preventing its emergence is most effective.

Researchers need to reevaluate their intentions for the study and change how they perceive respondent answers. They must challenge and overcome any preexisting notions about the research objective and hypotheses. Research advisors often play the third-party observer’s role that can identify confirmation bias in research before it’s complete.

Culture Bias

Culture bias often comes after confirmation bias in social research as it is considered an extension of the latter. Instead of preconceived notions and beliefs, the researcher defaults to make assumptions about other cultures based on his own cultural values.

For instance, an American researcher may undermine his Indian respondent’s answers based on the stereotyped understanding of Indian culture that his American cultural beliefs influence.

Culture bias falls on the spectrum of ethnocentrism , which is the phenomenon of judging another culture solely based on one’s own culture. To remove culture bias from your study, shifting towards cultural relativity and reassessing your personal assumptions about other cultures.

Cultural relativism refers to assessing and understanding an individual’s cultural values based on said individual’s cultural context and not your own.

Question-Order Bias

As a researcher, when you form questions for your responders to answer, you put an idea in their head. The words you choose and how you present an idea prime the respondents’ feelings and thoughts towards the subject.

How you structure (or order) your questions can change how one idea changes the respondent’s attitude towards subsequent survey questions. This creates a question-order bias and unknowingly prompts the respondent to choose options or answers that are not completely accurate and answer relatively than objectively.

Let’s consider you are conducting brand tracking on smartphone brands. You may ask your respondents to rate two different smartphone brands on a satisfaction Likert scale . Whatever rating they give the first product will be based on what they think about it, but they are more likely to rate the second product on how it compares to the first rather than how good or bad it is.

This type of market research bias slips into surveys very easily, and it’s oftentimes unavoidable. One way to minimize this bias is by asking general questions before specific and positive questions before negative.

Irrational Escalation Bias

While observed less frequently than other forms of biases, irrational escalation can be quite damaging for marketers who have made a financial investment in an idea.

This bias is similar to confirmation bias in that the researcher has already developed a hypothesis they believe in and dismiss findings and results that override or undermine them. How it differs is that financial investment becomes sunken debt.

Instead of changing methodologies based on research findings, they escalate the importance of their own beliefs and convince themselves that the research is wrong.

For example, let’s consider that a concept testing study forecasts a new product’s performance in the market. The brand or company might have already rolled out the products and made them available to customers.

Now let’s consider that research findings project product failure. Instead of accepting their mistake and cutting their losses by ceasing production, they reject research findings and continue pushing the products, convinced that it might work.

This bias is quite strong and often hard to eliminate. The solution for eliminating this bias is to keep an open mind and relying on research before making such critical decisions.

Respondent Market Research Biases

Biases also incur on the respondent’s side while they fill out survey forms and answer researcher questions.

Social Desirability Bias

Social desirability comes naturally to most human beings. It has roots in our inherent desire to have people like us. This need creeps into respondents’ subconscious while they answer questions, distorting survey findings.

Respondents end up answering questions in a way they think will lead to them being liked. Regardless of your market research format, questions about their personality or sensitive subjects can cause social desirability bias to occur.

This bias is often apparent in “would you rather” questions.

To prevent social desirability biases , phrase the question with positive affirmations so that the respondents know that it’s okay to answer honestly. Or you can ask it indirectly.

For example, instead of asking what they think about a certain situation, ask what a third-party would think. This way, the respondents project their own beliefs without feeling the need to come off as socially desirable.

Acquiescence Bias

Also known as the friendliness bias , this occurs when respondents want tend to say yes or agree with whatever the researcher asks or presents. They believe that whatever idea they are presented with is good.

There are several reasons for this bias to occur:

• One of the more obvious ones comes from social desirability. Respondents think that agreeing with or responding positively will get the researcher to like them.
• The second reason could be that the respondent has an acquiescent personality, and they tend to agree with others in most cases. Third, the respondent might agree because they think the researcher or surveyor is the expert and has the right answers.
• And lastly, acquiescence comes with laziness. Whether it’s a long interview/ questionnaire or lack of interest, respondents may choose to acquiesce to get over it.

Eliminating this bias requires the researcher to ask questions that ask respondents’ for their opinion instead of presenting them with dichotomous options.

Sponsor Bias

This form of bias can occur easily in market research as it is often sponsored or supported by a notable brand or organization. If respondents are aware of this sponsor, their feelings or beliefs about the sponsor may influence their answers.

If keeping that information from respondents for objectivity is not possible, researchers should focus on maintaining a neutral stance or reiterating their independent status.

Habituation

Habituation bias occurs when the respondents’ brain goes into an autopilot mode, and they begin answering similarly-worded or intended questions the same way. This may be due to the sheer length of the survey or how the researcher phrases his or her questions.

Instead of paying attention to each question, the respondents glance to find commonalities between statements and give the same answer out of habit.

Rephrasing the questions to make them more engaging and creating more concise surveys can reduce habituation.

Halo Effect

We’ve mentioned this particular type of market research bias separately because it can occur on either side. The halo effect can influence both researchers and respondents. It’s easy for either to form an opinion about a product or brand based on one positive attribute.

Moderators and researchers may form positive opinions about certain respondents based on one positive answer. Similarly, respondents may respond to a question about a brand or product positively based on one positive quality, disregarding objectivity and non-consideration of other attributes.

For researchers, preventing the halo effect on their side requires maintaining neutral opinions about the respondents. To prevent it from occurring among respondents, researchers should ask questions concerning one brand before the second.

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You are here, wording your research questions.

Comparing wording of questions used in qualitative and quantitative research

For research questions in qualitative research, the use of open words such as “how” or “what” allow for greater exploration of issues, as opposed to words such as “why” which imply exploration of cause and effect, which is more characteristic of quantitative research. Suggested verbs to use in qualitative research questions are: discover, understand, describe, explore. Suggested verbs to use in quantitative research questions are those which convey the idea of cause and effect i.e. they indicate the link between variables: compare, relate, cause and influence. However it should be noted that this is only a guide and there are many examples of research questions that do not fit these ‘rules’.

Making your questions researchable

Research questions should be researchable , that is, they should allow you to do research in relation to them (Cresswell 2011). That means that they should not be formulated in terms that are so abstract that they cannot be translated into practice (Bryman 2008). Also the wording of research questions should be as simple as possible so that it can be easily understood. There should be no terms that are ambiguous, therefore it is important to think through the terminology used and ensure that the best possible choice of wording has been selected.

This example research question illustrates the need to change an ambiguous term:

Do children with behaviour problems benefit from the use of concrete materials in mathematics?

The term ‘benefit’ needs to be more specific as it is not possible to take the research forward without first considering what type of benefit is to be investigated, for example:

Do children with behaviour problems show increased levels of engagement when concrete materials are used in mathematics?

Questions that are too ‘wordy’

Also it is important that research questions should be written as concisely as possible. The next example shows how by rewording a research question to make it more concise, the meaning can be made clearer:

Do children learn more effectively in science in practical sessions compared to lessons using worksheets and textbooks?

This question is too wordy and as a consequence of this the main focus of the research is not clear. Below the reworked research question carries the same meaning, but is more concise and easier to understand:

Possible reworked question:

Does scientific enquiry improve children’s learning in science?

Avoiding bias and assumptions

The way a question is worded can reflect biased views or assumptions held by the researcher. It is often very difficult for you to identify your own bias or assumptions that you have made and it is necessary to consider your wording carefully. Also it may be helpful to run questions by a colleague to identify these. It is important to realise that an operational definition of a concept is context –specific and that if you were to research the same issue in a different context e.g. psychology rather than education, the definition may be need to change (Punch, 1998, cited in Robson, 2002).

Below is an example of a question which illustrates how researcher bias can impact on the research design

Do teachers use worksheets in science more than practical activities because they lack confidence in performing investigations in a classroom?

The wording of this question indicates that the researcher strongly believes that lack of teacher confidence is the root cause of a high incidence of use of worksheets rather than carrying out practical activities in science. If this is not addressed at an early stage it will impact on all aspects of the research design including how questions are phrased in the research tools, which will impact on the validity of the research. Also it could limit the potential findings because the researcher does not ask questions that could identify other issues contributing to lack of practical activities.

Key points to consider when developing research questions:

The process of developing your research questions is the foundation of all your subsequent research.

The wording of the central research question needs to be particularly carefully thought through as it will directly influence your choice of methods.

Throughout the early stages of planning your research, reading will form an integral part of the process as it will inform your thinking and help direct the development of your research questions and research design.  

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Research Bias 101: What You Need To Know

By: Derek Jansen (MBA) | Expert Reviewed By: Dr Eunice Rautenbach | September 2022

If you’re new to academic research, research bias (also sometimes called researcher bias) is one of the many things you need to understand to avoid compromising your study. If you’re not careful, research bias can ruin the credibility of your study. 

In this post, we’ll unpack the thorny topic of research bias. We’ll explain what it is , look at some common types of research bias and share some tips to help you minimise the potential sources of bias in your research.

Overview: Research Bias 101

• What is research bias (or researcher bias)?
• Bias #1 – Selection bias
• Bias #2 – Analysis bias
• Bias #3 – Procedural (admin) bias

So, what is research bias?

Well, simply put, research bias is when the researcher – that’s you – intentionally or unintentionally skews the process of a systematic inquiry , which then of course skews the outcomes of the study . In other words, research bias is what happens when you affect the results of your research by influencing how you arrive at them.

For example, if you planned to research the effects of remote working arrangements across all levels of an organisation, but your sample consisted mostly of management-level respondents , you’d run into a form of research bias. In this case, excluding input from lower-level staff (in other words, not getting input from all levels of staff) means that the results of the study would be ‘biased’ in favour of a certain perspective – that of management.

Of course, if your research aims and research questions were only interested in the perspectives of managers, this sampling approach wouldn’t be a problem – but that’s not the case here, as there’s a misalignment between the research aims and the sample .

Now, it’s important to remember that research bias isn’t always deliberate or intended. Quite often, it’s just the result of a poorly designed study, or practical challenges in terms of getting a well-rounded, suitable sample. While perfect objectivity is the ideal, some level of bias is generally unavoidable when you’re undertaking a study. That said, as a savvy researcher, it’s your job to reduce potential sources of research bias as much as possible.

To minimize potential bias, you first need to know what to look for . So, next up, we’ll unpack three common types of research bias we see at Grad Coach when reviewing students’ projects . These include selection bias , analysis bias , and procedural bias . Keep in mind that there are many different forms of bias that can creep into your research, so don’t take this as a comprehensive list – it’s just a useful starting point.

Bias #1 – Selection Bias

First up, we have selection bias . The example we looked at earlier (about only surveying management as opposed to all levels of employees) is a prime example of this type of research bias. In other words, selection bias occurs when your study’s design automatically excludes a relevant group from the research process and, therefore, negatively impacts the quality of the results.

With selection bias, the results of your study will be biased towards the group that it includes or favours, meaning that you’re likely to arrive at prejudiced results . For example, research into government policies that only includes participants who voted for a specific party is going to produce skewed results, as the views of those who voted for other parties will be excluded.

Selection bias commonly occurs in quantitative research , as the sampling strategy adopted can have a major impact on the statistical results . That said, selection bias does of course also come up in qualitative research as there’s still plenty room for skewed samples. So, it’s important to pay close attention to the makeup of your sample and make sure that you adopt a sampling strategy that aligns with your research aims. Of course, you’ll seldom achieve a perfect sample, and that okay. But, you need to be aware of how your sample may be skewed and factor this into your thinking when you analyse the resultant data.

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Bias #2 – Analysis Bias

Next up, we have analysis bias . Analysis bias occurs when the analysis itself emphasises or discounts certain data points , so as to favour a particular result (often the researcher’s own expected result or hypothesis). In other words, analysis bias happens when you prioritise the presentation of data that supports a certain idea or hypothesis , rather than presenting all the data indiscriminately .

For example, if your study was looking into consumer perceptions of a specific product, you might present more analysis of data that reflects positive sentiment toward the product, and give less real estate to the analysis that reflects negative sentiment. In other words, you’d cherry-pick the data that suits your desired outcomes and as a result, you’d create a bias in terms of the information conveyed by the study.

Although this kind of bias is common in quantitative research, it can just as easily occur in qualitative studies, given the amount of interpretive power the researcher has. This may not be intentional or even noticed by the researcher, given the inherent subjectivity in qualitative research. As humans, we naturally search for and interpret information in a way that confirms or supports our prior beliefs or values (in psychology, this is called “confirmation bias”). So, don’t make the mistake of thinking that analysis bias is always intentional and you don’t need to worry about it because you’re an honest researcher – it can creep up on anyone .

To reduce the risk of analysis bias, a good starting point is to determine your data analysis strategy in as much detail as possible, before you collect your data . In other words, decide, in advance, how you’ll prepare the data, which analysis method you’ll use, and be aware of how different analysis methods can favour different types of data. Also, take the time to reflect on your own pre-conceived notions and expectations regarding the analysis outcomes (in other words, what do you expect to find in the data), so that you’re fully aware of the potential influence you may have on the analysis – and therefore, hopefully, can minimize it.

Bias #3 – Procedural Bias

Last but definitely not least, we have procedural bias , which is also sometimes referred to as administration bias . Procedural bias is easy to overlook, so it’s important to understand what it is and how to avoid it. This type of bias occurs when the administration of the study, especially the data collection aspect, has an impact on either who responds or how they respond.

A practical example of procedural bias would be when participants in a study are required to provide information under some form of constraint. For example, participants might be given insufficient time to complete a survey, resulting in incomplete or hastily-filled out forms that don’t necessarily reflect how they really feel. This can happen really easily, if, for example, you innocently ask your participants to fill out a survey during their lunch break.

Another form of procedural bias can happen when you improperly incentivise participation in a study. For example, offering a reward for completing a survey or interview might incline participants to provide false or inaccurate information just to get through the process as fast as possible and collect their reward. It could also potentially attract a particular type of respondent (a freebie seeker), resulting in a skewed sample that doesn’t really reflect your demographic of interest.

The format of your data collection method can also potentially contribute to procedural bias. If, for example, you decide to host your survey or interviews online, this could unintentionally exclude people who are not particularly tech-savvy, don’t have a suitable device or just don’t have a reliable internet connection. On the flip side, some people might find in-person interviews a bit intimidating (compared to online ones, at least), or they might find the physical environment in which they’re interviewed to be uncomfortable or awkward (maybe the boss is peering into the meeting room, for example). Either way, these factors all result in less useful data.

Although procedural bias is more common in qualitative research, it can come up in any form of fieldwork where you’re actively collecting data from study participants. So, it’s important to consider how your data is being collected and how this might impact respondents. Simply put, you need to take the respondent’s viewpoint and think about the challenges they might face, no matter how small or trivial these might seem. So, it’s always a good idea to have an informal discussion with a handful of potential respondents before you start collecting data and ask for their input regarding your proposed plan upfront.

Let’s Recap

Ok, so let’s do a quick recap. Research bias refers to any instance where the researcher, or the research design , negatively influences the quality of a study’s results, whether intentionally or not.

The three common types of research bias we looked at are:

• Selection bias – where a skewed sample leads to skewed results
• Analysis bias – where the analysis method and/or approach leads to biased results – and,
• Procedural bias – where the administration of the study, especially the data collection aspect, has an impact on who responds and how they respond.

As I mentioned, there are many other forms of research bias, but we can only cover a handful here. So, be sure to familiarise yourself with as many potential sources of bias as possible to minimise the risk of research bias in your study.

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This is really educational and I really like the simplicity of the language in here, but i would like to know if there is also some guidance in regard to the problem statement and what it constitutes.

Do you have a blog or video that differentiates research assumptions, research propositions and research hypothesis?

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• What is Response Bias and How Can Y ...

What is Response Bias and How Can You Avoid It?

Market researchers must be aware of any potential threats to the validity of their study. Response bias is a common factor – it can skew the results of your study, and send your marketing team down the wrong path. An ineffective marketing campaign means time and money wasted, not to mention having to start back at square one.

Whether you’re considering an ambitious new round of market research, or looking to fine-tune and expound upon extant information, being aware of response bias is a must. Learn to understand what response bias is, how to spot it, and stop it in its tracks.

What is Research Response Bias?

In market research, response bias is a blanket term for the different factors that cause respondents to answer surveys incorrectly. This is a particularly glaring issue for online surveys that are interpreted and filled out directly by the participant. Any survey methodology is vulnerable to response bias, making it particularly hard to combat.

What Type of Bias is Response Bias?

While sub-categories do exist, there are generally three major types of bias to be aware of when conducting your market research: information, selection, and confounding.

• Information Bias: This occurs when the variables of the study skew the information so drastically that the results are no longer reflective of the target population.
• Selection Bias: This occurs when the selection of subjects or participants isn’t randomized or diverse enough to get effective measurements or results.
• Confounding Bias: This is a variable related to both the presentation of the survey and the outcome. How the survey is organized and distributed changes the results drastically.

Response bias typically falls under the category of “confounding” because how the survey is worded can be received in various ways, triggering biases. Humans are unpredictable, and we operate on impulse. This means several factors can trigger bias and cause distorted data.

Response Bias Triggering Factors

Before launching your survey, it’s important to consider what may cause response bias. Understanding response bias triggers helps to preemptively troubleshoot common mistakes when constructing your survey.

• Polarizing Language or Subject Matter: Politics, religion, family planning, global events – all are hot-button issues that provoke intense emotional responses. If your survey deals with any of these or other divisive topics, it’s prone to plenty of response bias.
• Advance Knowledge: If your subject knows the survey’s purpose, they may begin pre-determining answers. This leads to demand or social desirability bias, which will be discussed later.
• Mistrust: Subjects that aren’t confident in the security of the information they’re sharing may be inclined to answer dishonestly, or not at all.
• Incentives: Offered incentives should be good enough to encourage participants, but a balance must be struck. Incentives that are too attractive may cause respondents to click through your survey quickly to get to the incentive.
• Survey Length: Participants dislike long, drawn-out, and complex surveys. A good survey should only take about ten minutes to complete. Any longer than that might trigger a speed-run or random answers, or nonresponse bias.
• Complicated/Confusing Wording: The wording you choose for your questions plays a major role in bias potential. Questions that are hard to understand will often be answered randomly, with a neutral response, or cause complete survey abandonment.

Types of Response Bias and How to Avoid Them

There are several major types of response bias that can hurt your chances of securing meaningful responses.

Extreme Response

Extreme response bias is most common for surveys that utilize a numerical scale, with one number representing “worst” and another being the “best.” An “extreme response” is the repeated selection of the highest or lowest point on the scale. Since this doesn’t offer a realistic, nuanced look at the participant, extreme responses are typically considered invalid.

Factors at play in “extreme response” cases are varied. There are cultural aspects to consider, as well as education level. Neither of these can be influenced by the surveyor, so how can you hope to allay the extreme response?

How to Avoid Extreme Response Bias: Use what you can influence! Carefully worded questions that are clear and neutral in tone help to provoke a thoughtful response. Don’t lead your participants or use polarizing language that elicits an overly emotional response.

Neutral Response

An equally frustrating response to numerical scale surveys is the “all neutral” response. It is, in many ways, less helpful than the extreme response, though both are unusable. This is often a result of a participant not understanding the question fully. It also might be a question that doesn’t engage the participant at all.

How to Avoid Neutral Response Bias: This, once again, can be handled with careful wording. Complex concepts should be broken down into easily understood ideas that appeal to respondents.

Dissent/Acquiescence Bias

Dissent and acquiescence bias are two sides of the same coin. This occurs often in surveys with two possible options. Dissent bias is when a participant answers “negatively” to every question, while acquiescence participants answer all “positively.” This isn’t always obvious but can be spotted when the answers contradict each other. If someone chooses “yes” to conflicting statements, it’s acquiescence bias.

How to Avoid Dissent and Acquiescence Bias: Randomizing and diversifying the types of questions and responses keep the participant engaged mentally and will help avoid a speed-run of repeated answers.

Prestige/Social Desirability Bias

While these are different types of bias, they both deal with the perception.

Firstly, social desirability bias is the result of social conditioning. In questions about personal experiences or beliefs, it’s common for respondents to give what they believe is the “socially acceptable” option in an effort to not look bad.

Meanwhile, prestige bias results from the very human impulse to want to appear in a more flattering light. Participants may inflate their education level, income, social influence, and other similar factors.

How to Avoid Prestige or Social Desirability Bias: This can be tricky! Use indirect questions, anonymity, and third-party survey companies to avoid the impulse for social desirability bias answers. Complete circumvention of prestige bias is not possible, as it’s so ingrained in the human condition. However, it can be allayed with neutrally worded questions that strip away the emotional incentive to be dishonest.

Demand Bias

Demand bias occurs when respondents answer questions with the responses they believe the survey wants. This has everything to do with leading language and expectation. It doesn’t even make sense to take time running a survey with questions that give you the answers you want!

How to Avoid Demand Bias: Avoid leading the participant. Include a text response box so that the participant knows you actually want to hear their opinion. Write-in responses may surprise you with unique insights and can provide the building blocks for innovation.

Bonus: Nonresponse Bias

Response bias suggests that there is a response, albeit a distorted one. Nonresponse bias occurs when a considerable difference exists between the participants who submitted responses and those who didn’t.

Nonresponse bias results from a potential respondent being unable or unwilling to supply information. This can be accidental – like an email sent to the “junk” folder without even being seen – or it can be completely intentional on the respondent’s part. If the survey is longer or more complicated than they had anticipated, you may get a lot of bail-outs!

Avoiding Nonresponse Bias: Offering reasonable incentives and reminders can help to encourage participants to finish and return a survey. Breaking the survey into smaller sections – or shortening it altogether – can also decrease nonresponse as a factor.

CheckMarket Helps Foresee and Avoid Response Bias

CheckMarket can help create effective, carefully worded surveys that bring in real results. Our survey templates are short and easy to understand. They’re designed to elicit thoughtful responses, not knee-jerk reactions.

Some bias is inevitable, but you can account for it, avoid the most damaging kinds, and secure reliable results with CheckMarket’s help. Even better – we operate commitment-free , meaning you pay for what you need, and that’s all.

Checkmarket is trusted around the world – come sign up for free, no credit card required, and launch your marketing study with an advantage.

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The art of market research interviewing: How to avoid bias and leading questions

Why conduct market research interviews.

Conducting interviews is a great way to gather insights about your products, market and competition. Interviews are used mainly as a form of primary research . They involve speaking directly with current or potential customers to learn more about their pain points and desired gains. Although interviews are a great source of information and provide valuable insights, it is important to avoid interviewer bias and to refrain from asking leading questions.

This article provides insights on avoiding interviewer bias and leading questions, as well as a checklist and tips & tricks.

What is interviewer bias?

Interviewer bias stems from trying to get specific answers or information from the interviewee that will directly support your hypothesis. It interferes with being objective, both negatively and positively, and clouds the interviewee’s judgment when they provide answers. This bias can be shown through body language, attitude, inconsistency in questioning or particular wording (i.e., leading questions) that leads the interviewee to answer a specific way.

Dismissing an interviewee’s response from the collected data because it doesn’t align with your hypothesis or it contradicts other interviewees’ responses is another common type of bias. Outliers often tell a bigger story than the collected interviewees’ responses.

What is a leading question?

A leading question may influence interviewees to provide a desired answer. This can occur through the use of specific wording or body language.

For example, “You do enjoy working here, right?” is a leading question. It uses specific language that implies you want the interviewee to answer they do enjoy working there, instead of allowing them to answer honestly in their own words. This type of questioning often pressures the interviewee and limits the integrity of the interview, as it might not produce honest responses.

Importance of framing questions

When framing questions, consider the ultimate goal of the project and the research questions to ensure each question provides value . Use the same set of interview questions for every interviewee to avoid bias and ensure a level of consistency between interviewees.

Questions should be short, direct and to the point. Break big ideas or concepts into smaller, multiple questions to avoid confusing the interviewee and ensure you receive a clear answer. Use direct language to ask one question at a time. If you want to know how someone feels about two different things, ask two separate questions.

Checklist to ensure questions are framed properly:

• Is this question clear and concise?
• Is this question open to interpretation from the interviewee?
• Does this question inform an assumption?
• Does this question lean toward confirmation of my personal bias?
• Does this question have a pretext phrase to sway answers? (“I’m sure you already know the answer to this but….”)

Market research interview tips and tricks

• Allow plenty of time for questions to be answered: According to the NNG , world leaders in research-based user experience, “Apply intentional silences to create space for interviewees to think and respond in a thoughtful, unhurried manner… don’t finish interviewees’ sentences in an attempt to read their minds to fill the silence.”
• Be flexible and responsive: Use interviewees’ answers to ask follow-up questions and dig deeper.
• Use a rating scale to gather quantitative data: Instead of asking whether interviewees agree or disagree, ask them to rate on a scale of 1-5 and why they rated it that way to compare responses.
• Conduct a mock interview before the actual session: Conduct a mock interview session with colleagues before the interviews begin. It takes practice to get comfortable with being agile and easily reframing a question if the interviewee misinterprets it. Saying the questions out loud will give you an idea of the interview flow and whether any questions need to be rearranged. Time yourself for the duration of the interview and see if any questions seem repetitive, redundant or won’t provide sufficient insights. This will give you a sense of the interview length and help to perfect your discussion guide.

It is crucial to stay open-minded to all possibilities. Let the information lead you to the proper conclusion, rather than leading the interview to the conclusion you believe to be correct .

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