research bias type

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.

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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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.

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Understanding the different types of bias in research (2024 guide)

Last updated

6 October 2023

Reviewed by

Miroslav Damyanov

Research bias is an invisible force that overly highlights or dismisses the chosen study topic’s traits. When left unchecked, it can significantly impact the validity and reliability of your research.

In a perfect world, every research project would be free of any trace of bias—but for this to happen, you need to be aware of the most common types of research bias that plague studies.

Read this guide to learn more about the most common types of bias in research and what you can do to design and improve your studies to create high-quality research results.

• What is research bias?

Research bias is the tendency for qualitative and quantitative research studies to contain prejudice or preference for or against a particular group of people, culture, object, idea, belief, or circumstance.

Bias is rarely based on observed facts. In most cases, it results from societal stereotypes, systemic discrimination, or learned prejudice.

Every human develops their own set of biases throughout their lifetime as they interact with their environment. Often, people are unaware of their own biases until they are challenged—and this is why it’s easy for unintentional bias to seep into research projects .

Left unchecked, bias ruins the validity of research . So, to get the most accurate results, researchers need to know about the most common types of research bias and understand how their study design can address and avoid these outcomes.

• The two primary types of bias

Historically, there are two primary types of bias in research:

Conscious bias

Conscious bias is the practice of intentionally voicing and sharing a negative opinion about a particular group of people, beliefs, or concepts.

Characterized by negative emotions and opinions of the target group, conscious bias is often defined as intentional discrimination.

In most cases, this type of bias is not involved in research projects, as they are unjust, unfair, and unscientific.

Unconscious bias

An unconscious bias is a negative response to a particular group of people, beliefs, or concepts that is not identified or intentionally acted upon by the bias holder.

Because of this, unconscious bias is incredibly dangerous. These warped beliefs shape and impact how someone conducts themselves and their research. The trouble is that they can’t identify the moral and ethical issues with their behavior.

• Examples of commonly occurring research bias

Humans use countless biases daily to quickly process information and make sense of the world. But, to create accurate research studies and get the best results, you must remove these biases from your study design.

Here are some of the most common types of research biases you should look out for when planning your next study:

Information bias

During any study, tampering with data collection is widely agreed to be bad science. But what if your study design includes information biases you are unaware of?

Also known as measurement bias, information bias occurs when one or more of the key study variables are not correctly measured, recorded, or interpreted. As a result, the study’s perceived outcome may be inaccurate due to data misclassification, omission, or obfuscation (obscuring). 

Observer bias

Observer bias occurs when researchers don’t have a clear understanding of their own personal assumptions and expectations. During observational studies, it’s possible for a researcher’s personal biases to impact how they interpret the data. This can dramatically affect the study’s outcome.

The study should be double-blind to combat this type of bias. This is where the participants don’t know which group they are in, and the observers don’t know which group they are observing.

Regression to the mean (RTM)

Bias can also impact research statistics.

Regression of the mean (RTM) refers to a statistical bias that if a first clinical reading is extreme in value (i.e., it’s very high or very low compared to the average), the second reading will provide a more statistically normal result.

Here’s an example: you might be nervous when a doctor takes your blood pressure in the doctor’s surgery. The first result might be quite high. This is a phenomenon known as “white coat syndrome.” When your blood pressure is retaken to double-check the value, it is more likely to be closer to typical values.

So, which value is more accurate, and which should you record as the truth?

The answer depends on the specific design of your study. However, using control groups is usually recommended for studies with a high risk of RTM.

Performance bias

A performance bias can develop if participants understand the study’s nature or desired outcomes. This can harm the study’s accuracy, as participants may adjust their behavior outside of their normal to improve their performance. This results in inaccurate data and study results.

This is a common bias type in medical and health studies, particularly those studying the differences between two lifestyle choices.

To reduce performance bias, researchers should strive to keep members of the control and study groups unaware of the other group’s activities. This method is known as “blinding.”

Recall bias

How good is your memory? Chances are, it’s not as good as you think—and the older the memory, the more inaccurate and biased it will become.

A recall bias commonly occurs in self-reporting studies requiring participants to remember past information. While people can remember big-picture events (like the day they got married or landed their first job), routine occurrences like what they do after work every Tuesday are harder to recall.

To offset this type of bias, design a study that engages with participants on both short- and long-term periods to help keep the content more top of mind.

Researcher bias

Researcher bias (also known as interviewer bias) occurs due to the researcher’s personal beliefs or tendencies that influence the study’s results or outcomes.

These types of biases can be intentional or unintentional, and most are driven by personal feelings, historical stereotypes, and assumptions about the study’s outcome before it has even begun.

Question order bias

Survey design and question order is a huge area of contention for researchers. These elements are essential for quality study design and can prevent or invite answer bias.

When designing a research study that collects data via survey questions , the order of the questions presented can impact how the participants answer each subsequent question. Leading questions (questions that guide participants toward a particular answer) are perfect examples of this. When included early in the survey, they can sway a participant’s opinions and answers as they complete the questionnaire .

This is known as systematic distortion, meaning each question answered after the guiding questions is impacted or distorted by the wording of the questions before.

Demand characteristics

Body language and social cues play a significant role in human communication—and this also rings true for the validity of research projects . 

A demand characteristic bias can occur due to a verbal or non-verbal cue that encourages research participants to behave in a particular way.

Imagine a researcher is studying a group of new grad business students about their experience applying to new jobs one, three, and six months after graduation. They scowl every time a participant mentions they don’t use a cover letter. This reaction may encourage participants to change their answers, harming the study’s outcome and resulting in less accurate results.

Courtesy bias

Courtesy bias arises from not wanting to share negative or constructive feedback or answers—a common human tendency.

You’ve probably been in this situation before. Think of a time when you had a negative opinion or perspective on a topic, but you felt the need to soften or reduce the harshness of your feedback to prevent someone’s feelings from being hurt.

This type of bias also occurs in research. Without a comfortable and non-judgmental environment that encourages honest responses, courtesy bias can result in inaccurate data intake.

Studies based on small group interviews, focus groups , or any in-person surveys are particularly vulnerable to this type of bias because people are less likely to share negative opinions in front of others or to someone’s face.

Extreme responding

Extreme responding refers to the tendency for people to respond on one side of the scale or the other, even if these extreme answers don’t reflect their true opinion. 

This is a common bias in surveys, particularly online surveys asking about a person’s experience or personal opinions (think questionnaires that ask you to decide if you strongly disagree, disagree, agree, or strongly agree with a statement).

When this occurs, the data will be skewed. It will be overly positive or negative—not accurate. This is a problem because the data can impact future decisions or study outcomes.

Writing different styles of questions and asking for follow-up interviews with a small group of participants are a few options for reducing the impact of this type of bias.

Social desirability bias

Everyone wants to be liked and respected. As a result, societal bias can impact survey answers.

It’s common for people to answer questions in a way that they believe will earn them favor, respect, or agreement with researchers. This is a common bias type for studies on taboo or sensitive topics like alcohol consumption or physical activity levels, where participants feel vulnerable or judged when sharing their honest answers.

Finding ways to comfort participants with ensured anonymity and safe and respectful research practices are ways you can offset the impact of social desirability bias.

Selection bias

For the most accurate results, researchers need to understand their chosen population before accepting participants. Failure to do this results in selection bias, which is caused by an inaccurate or misrepresented selection of participants that don’t truly reflect the chosen population.

Self-selection bias

To collect data, researchers in many studies require participants to volunteer their time and experiences. This results in a study design that is automatically biased toward people who are more likely to get involved.

People who are more likely to voluntarily participate in a study are not reflective of the common experience of a broad, diverse population. Because of this, any information collected from this type of study will contain a self-selection bias .

To avoid this type of bias, researchers can use random assignment (using control versus treatment groups to divide the study participants after they volunteer).

Sampling or ascertainment bias

When choosing participants for a study, take care to select people who are representative of the overall population being researched. Failure to do this will result in sampling bias.

For example, if researchers aim to learn more about how university stress impacts sleep quality but only choose engineering students as participants, the study won’t reflect the wider population they want to learn more about.

To avoid sampling bias, researchers must first have a strong understanding of their chosen study population. Then, they should take steps to ensure that any person within that population has an equal chance of being selected for the study.

Attrition bias

People tend to be hard on themselves, so an attrition bias toward the impact of failure versus success can seep into research.

Many people find it easier to list things they struggle with rather than things they think they are good at. This also occurs in research, as people are more likely to value the impact of a negative experience (or failure) than that of a positive, successful outcome.

Survivorship bias

In medical clinical trials and studies, a survivorship bias may develop if only the results and data from participants who survived the trial are studied. Survivorship bias also includes participants who were unable to complete the entire trial, not just those who passed away during the duration of the study.

In long-term studies that evaluate new medications or therapies for high-mortality diseases like aggressive cancers, choosing to only consider the success rate, side effects, or experiences of those who completed the study eliminates a large portion of important information. This disregarded information may have offered insights into the quality, efficacy, and safety of the treatment being tested.

Nonresponse bias

A nonresponse bias occurs when a portion of chosen participants decide not to complete or participate in the study. This is a common issue in survey-based research (especially online surveys).

In survey-based research, the issue of response versus nonresponse rates can impact the quality of the information collected. Every nonresponse is a missed opportunity to get a better understanding of the chosen population, whether participants choose not to reply based on subject apathy, shame, guilt, or a lack of skills or resources.

To combat this bias, improve response rates using multiple different survey styles. These might include in-person interviews, mailed paper surveys, and virtual options. However, note that these efforts will never completely remove nonresponse bias from your study.

Cognitive bias

Cognitive biases result from repeated errors in thinking or memory caused by misinterpreting information, oversimplifying a situation, or making inaccurate mental shortcuts. They can be tricky to identify and account for, as everyone lives with invisible cognitive biases that govern how they understand and interact with their surrounding environment.

Anchoring bias

When given a list of information, humans have a tendency to overemphasize (or anchor onto) the first thing mentioned.

For example, if you ask people to remember a grocery list of items that starts with apples, bananas, yogurt, and bread, people are most likely to remember apples over any of the other ingredients. This is because apples were mentioned first, despite not being any more important than the other items listed.

This habit conflates the importance and significance of this one piece of information, which can impact how you respond to or feel about the other equally important concepts being mentioned.

Halo effect

The halo effect explains the tendency for people to form opinions or assumptions about other people based on one specific characteristic. Most commonly seen in studies about physical appearance and attractiveness, the halo effect can cause either a positive or negative response depending on how the defined trait is perceived.

Framing effect

Framing effect bias refers to how you perceive information based on how it’s presented to you. 

To demonstrate this, decide which of the following desserts sounds more delicious.

“Made with 95% natural ingredients!”

“Contains only 5% non-natural ingredients!”

Both of these claims say the same thing, but most people have a framing effect bias toward the first claim as it’s positive and more impactful.

This type of bias can significantly impact how people perceive or react to data and information.

The misinformation effect

The misinformation effect refers to the brain’s tendency to alter or misremember past experiences when it has since been fed inaccurate information. This type of bias can significantly impact how a person feels about, remembers, or trusts the authority of their previous experiences.

Confirmation bias

Confirmation bias occurs when someone unconsciously prefers or favors information that confirms or validates their beliefs and ideas.

In some cases, confirmation bias is so strong that people find themselves disregarding information that counters their worldview, resulting in poorer research accuracy and quality.

We all like being proven right (even if we are testing a research hypothesis ), so this is a commonly occurring cognitive bias that needs to be addressed during any scientific study.

Availability heuristic

All humans contextualize and understand the world around them based on their past experiences and memories. Because of this, people tend to have an availability bias toward explanations they have heard before. 

People are more likely to assume or gravitate toward reasoning and ideas that align with past experience. This is known as the availability heuristic . Information and connections that are more available or accessible in your memory might seem more likely than other alternatives. This can impact the validity of research efforts.

• How to avoid bias in your research

Research is a compelling, complex, and essential part of human growth and learning, but collecting the most accurate data possible also poses plenty of challenges.

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