Response biases are responsible for the ineffectiveness of surveys conducted to improve the service quality of a business. These biases could be inherent or a result of several other issues associated with the survey process. There is always this possibility even issues and biases can be avoided to ensure that your customers have the best experience.

This article focuses on the definition of response bias, examples, types, and ways to help remove any possible bias in a survey.

What is Response Bias?

Response bias is concerned with the ability of respondents in a survey to answer questions accurately. It is often concerned with the level of bias that influences each answer when asked certain questions during a survey. The response bias highlights the indicators of such untruthfulness, especially when respondents are asked to report on personal behaviors concerning social acceptance.

Thus, response bias is commonly used to refer to conditions that influence survey responses. It shows that there are varying reasons for incorrect survey responses from respondents concerning social acceptance.

Why is Response Bias a Problem?

When surveys are conducted, it helps improve your customer service, and so when there is a bias in the response, it hinders such. This then affects your organization as you cannot measure the experience offered to the customers appropriately.

Response bias is a problem as it leads to overestimating the level at which your customers or the respondents are satisfied with your services.

Types of Response Bias

Surveys are a professional way to improve customer satisfaction with the help of analytic data. However, a response bias questions the level of professionals engaged while conducting the survey. Response bias connects to dishonest responses to survey questions, and several types of this bias exist.

Here are 7 types of response bias:

Social Response Bias

This is also known as the social desirability bias, and those respondents are influenced by this often over-report. The bias makes the respondents exaggerate their good behaviors while concealing the bad ones.

Some social response biases could be misreported cases of sexual behavior, abilities, religion, financial earning, or unlawful behavior. The respondents would want to give answers that they think would make them more acceptable.

Non-response Bias

The participation bias, also known as the non-response, happens when a survey sample doesn’t properly represent the target population. In such instances, the opinions the respondents share are not aligned with those of a much larger population.

The survey result would produce a much-biased data set which negatively influences your research outcomes.

Extreme Responses

With the name, it is clear that such bias goes one way, either positive or negative. However, either of the extremes can render the data collected ineffective for the research outcomes.

Such bias often occurs when the survey provides a scale for the responses to individual rate components. It could be in numbers ranging from a ‘1 to 5’ star rating or a selection of statements as a means for analysis.

In extreme responses, the participants chose just the endpoints, and the middle options were only considered on a few occasions. Through response bias studies, culture has influenced such behaviors even though it is not all that is to be blamed.

Sometimes, the wording could be a reason for such bias as there are sensitive questions. These sorts of questions may offer to blame something or someone for a difficult situation which may cause extreme responses.

Acquiescence Bias

Acquiescence is a kind of bias where the respondents agree with all the questions asked during the survey. Even with a well-designed survey, the participants agree with all the questions with just two statements that contradict. Such answers would no longer be truthful or accurate.

People are not expected to agree with everything on a survey as there are unique viewpoints. So, when there is a total agreement, it could be to please the researcher and a signifier that there is an acquiescence bias. Note that it is bad for your survey outcome.

Cognitive Bias

Cognitive bias starts as a subconscious error in thinking that makes people misinterpret general information. This then affects their rationality and how accurately their decisions are made as they alter facts to fit a personal way of looking at information. Thus, when answering a survey question, such respondents try to fit into their predetermined thoughts.

Such bias could manifest in different forms, from how emphasis is placed on certain recent events to an irrational escalation. With this, the survey would produce under or over-reported samples in the data collection, influencing the decisions made using the research responses.

As a type of bias it is frequently expected and you should look out for it when doing your survey.

Voluntary Response Bias

With the voluntary response bias, the respondents to your survey are often made up of those that have volunteered to engage in the survey. This is directly detrimental to your survey and the process of data collection. Those who have volunteered may be highly opinionated, so it could overreport one research angle.

Particularly, this response bias may include those that feel the same way about a particular topic or issue and thus the reason for volunteering. At the end of such, you would have just similar responses with just a few variations.

Dissent Bias

Just as the name suggests, the dissent bias is the opposite of the acquiescence bias, as the people in the survey constantly agree with every statement. The dissent bias includes respondents that don’t give their true opinions when faced with a question and just choose the negative.

Sometimes the decent bias is intentional and shows a respondent’s lack of attention or desire to complete the survey much faster. This attitude and lack of opinion could harm the data analysis and, eventually, the research outcome.

Neutral Responses

While extreme responses focus on a constant agreement, the neutral response bias means sticking with the middle option for every question. Such responses may occur when the participant is not so interested in the survey or wants to save time. Thus, they answer the questions as quickly as possible.

For this reason, the survey questions must be brief and direct to reduce the time spent listening to provide answers. Another thing to do is to ensure that the people selected are enthusiastic about getting involved in the survey.

What are Examples of Response Bias?

There are two prominent  examples of response bias that can be identified:

●  Information Bias: Information bias includes information or terms that would ordinarily trigger a bias in the respondents. Such examples cover the inherent sources of bias.

●  Selection Bias: The selection bias has to do with the process of choosing the participants to be part of the survey. As highlighted in the previous section, such bias could occur by choosing the wrong respondents just because they are willing or available.

How to Avoid Response Bias

Response bias can be avoided when certain tips are engaged. For a call center organizing the survey, these tips would make sure that each survey result is optimized:

1.   Consider your Demographic

Certain demographics are more accepting of certain kinds of biases, which is critical in the survey process. Therefore, pay attention to the ‘who’ you are involved with and what you intend to ask the respondents.

There are a few questions that should be asked, such as:

●  What do the respondents share in common?

●  Are there enough reasons for them to want to answer my survey?

●  What about them is interesting?

With the questions answered, it would be easier to determine the demographics of those needed to participate in the survey.

2.   Diversify the Survey Questions

Use a diverse range of questions to ensure that the respondents remained focused on the survey. You can mix the ‘yes or no’ binary responses with those requiring other answers.

This ensures that the respondents don’t simply give the same answers every time by forcing them to think about their responses. Note that this applies to more than just the style of the questions asked and emphasizes engaging the respondents while going through the survey.

Avoid asking questions about the same issues over and again as they often lead to having ill-thought answers. Mix the topics so that there would be breaks to reduce the number of reflex answers.

3.   Reduce the Wording Bias

How a question is worded is as important as the question itself as it helps the respondents offer appropriate answers. Carefully phrase the questions to ensure that the responses are critical and unbiased.

Some questions create inherent bias as they can trigger certain emotive responses, thus enabling an acquiescence bias. For this reason, certain practices would ensure minimal or zero bias.

One of the ways to handle this is to establish a balance in the questions and include as many negative and positive responses as possible. You can also make some negative responses seem like positive responses in a way that certain emotions would not be triggered.

4.   Give Room for ‘No’

Respondents often would love to give the best possible answers; if the survey doesn’t have all the answers for some reason, it becomes inaccurate. It would ordinarily cause them to choose an inaccurate, and it does not promise the best answers for the survey. Therefore, it is best to include all the possible answers to ensure the survey is thorough.

Including an ‘undecided’ or ‘don’t know’ option lets them answer all questions honestly without having wrong answers. Although it may seem minimal, this addition can prove useful when checking out the validity of results or even for subsequent survey analysis.

5.   Maintain Survey Neutrality and Integrity

Several forms of bias stem from the researchers and generally reflect their motives behind engaging in the process. For this reason, neutrality and integrity are ways to avoid such response bias.

Digital approaches can eliminate such risks that may come naturally from the researcher. Thus, the questions can be asked using certain call center tools applicable to organizations. This would help maintain a certain level of professionalism or unbiased demeanor.

Regarding integrity, the participants are to feel confident when answering the questions. The motive for the survey should be communicated to the participants, so there are no misconceptions.

6.   Avoid Using Sensitive Terms

All questions should be precise, clear, and voiced in a way that can be easily understandable. This means that words or terms that are sentimental should be avoided to avoid creating an inherent bias.

Another way to achieve this is to limit the number of negative words in the survey. Here, positive words can be used even to achieve negative responses to the questions asked. This would directly affect how the participants see the survey and their responses.

Learn to be transparent by avoiding word tricks to allow them to construct their answers.

7.   Offer Anonymity

Participants may be less reluctant to offer wrong or socially acceptable answers when their personal information is protected. Response bias in psychology identifies that the pressure to seem more acceptable creates a basis for incorrect answers.

To avoid such occurrences, convince the responders that they will remain anonymous when the results are published. You can also provide a legally binding document proving you are being transparent. All of this would automatically influence the response gotten from the participants.


The response bias definition has made it clear that the sources of the bias could be the research questions or the participants chosen. When there is a bias, it could result in an exaggeration of the level of customer satisfaction. There is always this possibility even when the surveys are conducted over the phone or other means.

Do you want a seamless survey with minimal bias? ULTATEL has a cloud phone system that would serve as a solution for administering the survey to your callers. Some tools would also be efficient in the analysis of the data collected.