types of bias qualitative research

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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The Ultimate Guide to Qualitative Research - Part 1: The Basics

• Introduction and overview
• What is qualitative research?
• What is qualitative data?
• Examples of qualitative data
• Qualitative vs. quantitative research
• Mixed methods
• Qualitative research preparation
• Theoretical perspective
• Theoretical framework
• Literature reviews
• Research question
• Conceptual framework
• Conceptual vs. theoretical framework
• Data collection
• Qualitative research methods
• Focus groups
• Observational research
• Case studies
• Ethnographical research
• Ethical considerations
• Confidentiality and privacy

What is research bias?

Understanding unconscious bias, how to avoid bias in research, bias and subjectivity in research.

• Power dynamics
• Reflexivity

Bias in research

In a purely objective world, research bias would not exist because knowledge would be a fixed and unmovable resource; either one knows about a particular concept or phenomenon, or they don't. However, qualitative research and the social sciences both acknowledge that subjectivity and bias exist in every aspect of the social world, which naturally includes the research process too. This bias is manifest in the many different ways that knowledge is understood, constructed, and negotiated, both in and out of research.

Understanding research bias has profound implications for data collection methods and data analysis , requiring researchers to take particular care of how to account for the insights generated from their data .

Research bias, often unavoidable, is a systematic error that can creep into any stage of the research process , skewing our understanding and interpretation of findings. From data collection to analysis, interpretation , and even publication , bias can distort the truth we seek to capture and communicate in our research.

It’s also important to distinguish between bias and subjectivity, especially when engaging in qualitative research . Most qualitative methodologies are based on epistemological and ontological assumptions that there is no such thing as a fixed or objective world that exists “out there” that can be empirically measured and understood through research. Rather, many qualitative researchers embrace the socially constructed nature of our reality and thus recognize that all data is produced within a particular context by participants with their own perspectives and interpretations. Moreover, the researcher’s own subjective experiences inevitably shape how they make sense of the data. These subjectivities are considered to be strengths, not limitations, of qualitative research approaches, because they open new avenues for knowledge generation. This is also why reflexivity is so important in qualitative research. When we refer to bias in this guide, on the other hand, we are referring to systematic errors that can negatively affect the research process but that can be mitigated through researchers’ careful efforts.

To fully grasp what research bias is, it's essential to understand the dual nature of bias. Bias is not inherently evil. It's simply a tendency, inclination, or prejudice for or against something. In our daily lives, we're subject to countless biases, many of which are unconscious. They help us navigate our world, make quick decisions, and understand complex situations. But when conducting research, these same biases can cause significant issues.

Research bias can affect the validity and credibility of research findings, leading to erroneous conclusions. It can emerge from the researcher's subconscious preferences or the methodological design of the study itself. For instance, if a researcher unconsciously favors a particular outcome of the study, this preference could affect how they interpret the results, leading to a type of bias known as confirmation bias.

Research bias can also arise due to the characteristics of study participants. If the researcher selectively recruits participants who are more likely to produce desired outcomes, this can result in selection bias.

Another form of bias can stem from data collection methods . If a survey question is phrased in a way that encourages a particular response, this can introduce response bias. Moreover, inappropriate survey questions can have a detrimental effect on future research if such studies are seen by the general population as biased toward particular outcomes depending on the preferences of the researcher.

Bias can also occur during data analysis . In qualitative research for instance, the researcher's preconceived notions and expectations can influence how they interpret and code qualitative data, a type of bias known as interpretation bias. It's also important to note that quantitative research is not free of bias either, as sampling bias and measurement bias can threaten the validity of any research findings.

Given these examples, it's clear that research bias is a complex issue that can take many forms and emerge at any stage in the research process. This section will delve deeper into specific types of research bias, provide examples, discuss why it's an issue, and provide strategies for identifying and mitigating bias in research.

What is an example of bias in research?

Bias can appear in numerous ways. One example is confirmation bias, where the researcher has a preconceived explanation for what is going on in their data, and any disconfirming evidence is (unconsciously) ignored. For instance, a researcher conducting a study on daily exercise habits might be inclined to conclude that meditation practices lead to greater engagement in exercise because that researcher has personally experienced these benefits. However, conducting rigorous research entails assessing all the data systematically and verifying one’s conclusions by checking for both supporting and refuting evidence.

What is a common bias in research?

Confirmation bias is one of the most common forms of bias in research. It happens when researchers unconsciously focus on data that supports their ideas while ignoring or undervaluing data that contradicts their ideas. This bias can lead researchers to mistakenly confirm their theories, despite having insufficient or conflicting evidence.

What are the different types of bias?

There are several types of research bias, each presenting unique challenges. Some common types include:

Confirmation bias: As already mentioned, this happens when a researcher focuses on evidence supporting their theory while overlooking contradictory evidence.

Selection bias: This occurs when the researcher's method of choosing participants skews the sample in a particular direction.

Response bias: This happens when participants in a study respond inaccurately or falsely, often due to misleading or poorly worded questions.

Observer bias (or researcher bias): This occurs when the researcher unintentionally influences the results because of their expectations or preferences.

Publication bias: This type of bias arises when studies with positive results are more likely to get published, while studies with negative or null results are often ignored.

Analysis bias: This type of bias occurs when the data is manipulated or analyzed in a way that leads to a particular result, whether intentionally or unintentionally.

What is an example of researcher bias?

Researcher bias, also known as observer bias, can occur when a researcher's expectations or personal beliefs influence the results of a study. For instance, if a researcher believes that a particular therapy is effective, they might unconsciously interpret ambiguous results in a way that supports the efficacy of the therapy, even if the evidence is not strong enough.

Even quantitative research methodologies are not immune from bias from researchers. Market research surveys or clinical trial research, for example, may encounter bias when the researcher chooses a particular population or methodology to achieve a specific research outcome. Questions in customer feedback surveys whose data is employed in quantitative analysis can be structured in such a way as to bias survey respondents toward certain desired answers.

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Identifying and avoiding bias in research

As we will remind you throughout this chapter, bias is not a phenomenon that can be removed altogether, nor should we think of it as something that should be eliminated. In a subjective world involving humans as researchers and research participants, bias is unavoidable and almost necessary for understanding social behavior. The section on reflexivity later in this guide will highlight how different perspectives among researchers and human subjects are addressed in qualitative research. That said, bias in excess can place the credibility of a study's findings into serious question. Scholars who read your research need to know what new knowledge you are generating, how it was generated, and why the knowledge you present should be considered persuasive. With that in mind, let's look at how bias can be identified and, where it interferes with research, minimized.

How do you identify bias in research?

Identifying bias involves a critical examination of your entire research study involving the formulation of the research question and hypothesis , the selection of study participants, the methods for data collection, and the analysis and interpretation of data. Researchers need to assess whether each stage has been influenced by bias that may have skewed the results. Tools such as bias checklists or guidelines, peer review , and reflexivity (reflecting on one's own biases) can be instrumental in identifying bias.

How do you identify research bias?

Identifying research bias often involves careful scrutiny of the research methodology and the researcher's interpretations. Was the sample of participants relevant to the research question ? Were the interview or survey questions leading? Were there any conflicts of interest that could have influenced the results? It also requires an understanding of the different types of bias and how they might manifest in a research context. Does the bias occur in the data collection process or when the researcher is analyzing data?

Research transparency requires a careful accounting of how the study was designed, conducted, and analyzed. In qualitative research involving human subjects, the researcher is responsible for documenting the characteristics of the research population and research context. With respect to research methods, the procedures and instruments used to collect and analyze data are described in as much detail as possible.

While describing study methodologies and research participants in painstaking detail may sound cumbersome, a clear and detailed description of the research design is necessary for good research. Without this level of detail, it is difficult for your research audience to identify whether bias exists, where bias occurs, and to what extent it may threaten the credibility of your findings.

How to recognize bias in a study?

Recognizing bias in a study requires a critical approach. The researcher should question every step of the research process: Was the sample of participants selected with care? Did the data collection methods encourage open and sincere responses? Did personal beliefs or expectations influence the interpretation of the results? External peer reviews can also be helpful in recognizing bias, as others might spot potential issues that the original researcher missed.

The subsequent sections of this chapter will delve into the impacts of research bias and strategies to avoid it. Through these discussions, researchers will be better equipped to handle bias in their work and contribute to building more credible knowledge.

Unconscious biases, also known as implicit biases, are attitudes or stereotypes that influence our understanding, actions, and decisions in an unconscious manner. These biases can inadvertently infiltrate the research process, skewing the results and conclusions. This section aims to delve deeper into understanding unconscious bias, its impact on research, and strategies to mitigate it.

What is unconscious bias?

Unconscious bias refers to prejudices or social stereotypes about certain groups that individuals form outside their conscious awareness. Everyone holds unconscious beliefs about various social and identity groups, and these biases stem from a tendency to organize social worlds into categories.

How does unconscious bias infiltrate research?

Unconscious bias can infiltrate research in several ways. It can affect how researchers formulate their research questions or hypotheses , how they interact with participants, their data collection methods, and how they interpret their data . For instance, a researcher might unknowingly favor participants who share similar characteristics with them, which could lead to biased results.

Implications of unconscious bias

The implications of unconscious research bias are far-reaching. It can compromise the validity of research findings , influence the choice of research topics, and affect peer review processes . Unconscious bias can also lead to a lack of diversity in research, which can severely limit the value and impact of the findings.

Strategies to mitigate unconscious research bias

While it's challenging to completely eliminate unconscious bias, several strategies can help mitigate its impact. These include being aware of potential unconscious biases, practicing reflexivity , seeking diverse perspectives for your study, and engaging in regular bias-checking activities, such as bias training and peer debriefing .

By understanding and acknowledging unconscious bias, researchers can take steps to limit its impact on their work, leading to more robust findings.

Why is researcher bias an issue?

Research bias is a pervasive issue that researchers must diligently consider and address. It can significantly impact the credibility of findings. Here, we break down the ramifications of bias into two key areas.

How bias affects validity

Research validity refers to the accuracy of the study findings, or the coherence between the researcher’s findings and the participants’ actual experiences. When bias sneaks into a study, it can distort findings and move them further away from the realities that were shared by the research participants. For example, if a researcher's personal beliefs influence their interpretation of data , the resulting conclusions may not reflect what the data show or what participants experienced.

The transferability problem

Transferability is the extent to which your study's findings can be applied beyond the specific context or sample studied. Applying knowledge from one context to a different context is how we can progress and make informed decisions. In quantitative research , the generalizability of a study is a key component that shapes the potential impact of the findings. In qualitative research , all data and knowledge that is produced is understood to be embedded within a particular context, so the notion of generalizability takes on a slightly different meaning. Rather than assuming that the study participants are statistically representative of the entire population, qualitative researchers can reflect on which aspects of their research context bear the most weight on their findings and how these findings may be transferable to other contexts that share key similarities.

How does bias affect research?

Research bias, if not identified and mitigated, can significantly impact research outcomes. The ripple effects of research bias extend beyond individual studies, impacting the body of knowledge in a field and influencing policy and practice. Here, we delve into three specific ways bias can affect research.

Distortion of research results

Bias can lead to a distortion of your study's findings. For instance, confirmation bias can cause a researcher to focus on data that supports their interpretation while disregarding data that contradicts it. This can skew the results and create a misleading picture of the phenomenon under study.

Undermining scientific progress

When research is influenced by bias, it not only misrepresents participants’ realities but can also impede scientific progress. Biased studies can lead researchers down the wrong path, resulting in wasted resources and efforts. Moreover, it could contribute to a body of literature that is skewed or inaccurate, misleading future research and theories.

Influencing policy and practice based on flawed findings

Research often informs policy and practice. If the research is biased, it can lead to the creation of policies or practices that are ineffective or even harmful. For example, a study with selection bias might conclude that a certain intervention is effective, leading to its broad implementation. However, suppose the transferability of the study's findings was not carefully considered. In that case, it may be risky to assume that the intervention will work as well in different populations, which could lead to ineffective or inequitable outcomes.

While it's almost impossible to eliminate bias in research entirely, it's crucial to mitigate its impact as much as possible. By employing thoughtful strategies at every stage of research, we can strive towards rigor and transparency , enhancing the quality of our findings. This section will delve into specific strategies for avoiding bias.

How do you know if your research is biased?

Determining whether your research is biased involves a careful review of your research design, data collection , analysis , and interpretation . It might require you to reflect critically on your own biases and expectations and how these might have influenced your research. External peer reviews can also be instrumental in spotting potential bias.

Strategies to mitigate bias

Minimizing bias involves careful planning and execution at all stages of a research study. These strategies could include formulating clear, unbiased research questions , ensuring that your sample meaningfully represents the research problem you are studying, crafting unbiased data collection instruments, and employing systematic data analysis techniques. Transparency and reflexivity throughout the process can also help minimize bias.

Mitigating bias in data collection

To mitigate bias in data collection, ensure your questions are clear, neutral, and not leading. Triangulation, or using multiple methods or data sources, can also help to reduce bias and increase the credibility of your findings.

Mitigating bias in data analysis

During data analysis , maintaining a high level of rigor is crucial. This might involve using systematic coding schemes in qualitative research or appropriate statistical tests in quantitative research . Regularly questioning your interpretations and considering alternative explanations can help reduce bias. Peer debriefing , where you discuss your analysis and interpretations with colleagues, can also be a valuable strategy.

By using these strategies, researchers can significantly reduce the impact of bias on their research, enhancing the quality and credibility of their findings and contributing to a more robust and meaningful body of knowledge.

Impact of cultural bias in research

Cultural bias is the tendency to interpret and judge phenomena by standards inherent to one's own culture. Given the increasingly multicultural and global nature of research, understanding and addressing cultural bias is paramount. This section will explore the concept of cultural bias, its impacts on research, and strategies to mitigate it.

What is cultural bias in research?

Cultural bias refers to the potential for a researcher's cultural background, experiences, and values to influence the research process and findings. This can occur consciously or unconsciously and can lead to misinterpretation of data, unfair representation of cultures, and biased conclusions.

How does cultural bias infiltrate research?

Cultural bias can infiltrate research at various stages. It can affect the framing of research questions , the design of the study, the methods of data collection , and the interpretation of results . For instance, a researcher might unintentionally design a study that does not consider the cultural context of the participants, leading to a biased understanding of the phenomenon being studied.

Implications of cultural bias

The implications of cultural bias are profound. Cultural bias can skew your findings, limit the transferability of results, and contribute to cultural misunderstandings and stereotypes. This can ultimately lead to inaccurate or ethnocentric conclusions, further perpetuating cultural bias and inequities.

As a result, many social science fields like sociology and anthropology have been critiqued for cultural biases in research. Some of the earliest research inquiries in anthropology, for example, have had the potential to reduce entire cultures to simplistic stereotypes when compared to mainstream norms. A contemporary researcher respecting ethical and cultural boundaries, on the other hand, should seek to properly place their understanding of social and cultural practices in sufficient context without inappropriately characterizing them.

Strategies to mitigate cultural bias

Mitigating cultural bias requires a concerted effort throughout the research study. These efforts could include educating oneself about other cultures, being aware of one's own cultural biases, incorporating culturally diverse perspectives into the research process, and being sensitive and respectful of cultural differences. It might also involve including team members with diverse cultural backgrounds or seeking external cultural consultants to challenge assumptions and provide alternative perspectives.

By acknowledging and addressing cultural bias, researchers can contribute to more culturally competent, equitable, and valid research. This not only enriches the scientific body of knowledge but also promotes cultural understanding and respect.

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Keep in mind that bias is a force to be mitigated, not a phenomenon that can be eliminated altogether, and the subjectivities of each person are what make our world so complex and interesting. As things are continuously changing and adapting, research knowledge is also continuously being updated as we further develop our understanding of the world around us.

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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.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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9 types of research bias and how to avoid them

To reduce the risk of bias in qual, researchers must focus on the human elements of the research process in order to identify and avoid the nine core types of bias.

Editor’s note: Rebecca Sarniak is a moderating services specialist iModerate , a Denver research firm.

Seasoned research experts know that bias can find its way into any research program – it’s naïve to think that any research could be 100 percent free from it. But when does bias become a problem? And how do we identify and control the sources of bias to deliver the highest-quality research possible?

The goal of reducing bias isn’t to make everyone the same but to make sure that questions are thoughtfully posed and delivered in a way that allows respondents to reveal their true feelings without distortions. The risk of bias exists in all components of qualitative research and can come from the questions, the respondents and the moderator. To reduce bias – and deliver better research – let’s explore its primary sources.  

When we focus on the human elements of the research process and look at the nine core types of bias – driven from the respondent, the researcher or both – we are able to minimize the potential impact that bias has on qualitative research.

Respondent bias

1. Acquiescence bias: Also known as “yea-saying” or the friendliness bias, acquiescence bias occurs when a respondent demonstrates a tendency to agree with and be positive about whatever the moderator presents. In other words, they think every idea is a good one and can see themselves liking, buying and acting upon every situation that is proposed. Some people have acquiescent personalities, while others acquiesce because they perceive the interviewer to be an expert. Acquiescence is the easy way out, as it takes less effort than carefully weighing each option. This path escalates if fatigue sets in – some people will agree just to complete the interview. To avoid it, researchers must replace questions that imply there is a right answer with those that focus on the respondent’s true point of view.

2. Social desirability bias 1 : This bias involves respondents answering questions in a way that they think will lead to being accepted and liked. Regardless of the research format, some people will report inaccurately on sensitive or personal topics to present themselves in the best possible light. Researchers can minimize this bias by focusing on unconditional positive regard. This includes phrasing questions to show it’s okay to answer in a way that is not socially desirable. Indirect questioning – asking about what a third-party thinks, feels and how they will behave – can also be used for socially sensitive questions. This allows respondents to project their own feelings onto others and still provide honest, representative answers.

3. Habituation 2 : In cases of habituation bias, respondents provide the same answers to questions that are worded in similar ways. This is a biological response: being responsive and paying attention takes a lot of energy. To conserve energy, our brains habituate or go on autopilot. Respondents often show signs of fatigue, such as mentioning that the questions seem repetitive, or start giving similar responses across multiple questions. Moderators must keep the engagement conversational and continue to vary question wording to minimize habituation.

4. Sponsor bias 3 : When respondents know – or suspect – the sponsor of the research, their feelings and opinions about that sponsor may bias their answers. Respondents’ views on the sponsoring organization’s mission or core beliefs, for example, can influence how they answer all questions related to that brand. This is an especially important type of bias for moderators to navigate by maintaining a neutral stance, limiting moderator reinforcement to positive respondent feedback that can be construed as moderator affiliation to brand and reiterating, when possible, the moderator’s independent status.   

Researcher bias

5. Confirmation bias 4 : One of the longest-recognized and most pervasive forms of bias in research, confirmation bias occurs when a researcher forms a hypothesis or belief and uses respondents’ information to confirm that belief. This takes place in-the-moment as researchers’ judge and weight responses that confirm their hypotheses as relevant and reliable, while dismissing evidence that doesn’t support a hypothesis. Confirmation bias then extends into analysis, with researchers tending to remember points that support their hypothesis and points that disprove other hypotheses. Confirmation bias is deeply seated in the natural tendencies people use to understand and filter information, which often lead to focusing on one hypothesis at a time. To minimize confirmation bias, researchers must continually reevaluate impressions of respondents and challenge preexisting assumptions and hypotheses.

6. Culture bias 5 : Assumptions about motivations and influences that are based on our cultural lens (on the spectrum of ethnocentricity or cultural relativity) create the culture bias. Ethnocentrism is judging another culture solely by the values and standards of one's own culture. Cultural relativism is the principle that an individual's beliefs and activities should be understood by others in terms of that individual's own culture. To minimize culture bias, researchers must move toward cultural relativism by showing unconditional positive regard and being cognizant of their own cultural assumptions. Complete cultural relativism is never 100 percent achievable.

7. Question-order bias: One question can influence answers to subsequent questions, creating question-order bias . Respondents are primed by the words and ideas presented in questions that impact their thoughts, feelings and attitudes on subsequent questions. For example, if a respondent rates one product a 10 and is then asked to rate a competitive product, they will make a rating that is relative to the 10 they just provided. While question-order bias is sometimes unavoidable, asking general questions before specific, unaided before aided and positive before negative will minimize bias.

8. Leading questions and wording bias 6 : Elaborating on a respondent’s answer puts words in their mouth and, while leading questions and wording aren’t types of bias themselves, they lead to bias or are a result of bias. Researchers do this because they are trying to confirm a hypothesis, build rapport or overestimate their understanding of the respondent. To minimize this bias, ask questions that use the respondents’ language and inquire about the implications of a respondent’s thoughts and reactions. Avoid summarizing what the respondents said in your own words and do not take what they said further. Try not to assume relationships between a feeling and a behavior.

9. The halo effect 7 : Moderators and respondents have a tendency to see something or someone in a certain light because of a single, positive attribute. There are several cognitive reasons for the halo effect, so researchers must work to address it on many fronts. For example, a moderator can make assumptions about a respondent because of one positive answer they’ve provided. Moderators should reflect on their assumptions about each respondent: Why are you asking each question? What is the assumption behind it? Additionally, respondents may rate or respond to a stimulus positively overall due to one factor. Researchers should address all questions about one brand before asking for feedback on a second brand, as when respondents are required to switch back and forth rating two brands, they are likely to project their opinion on one attribute to their opinion of the brand as a whole.

Bias in qualitative research can be minimized if you know what to look for and how to manage it. By asking quality questions at the right time and remaining aware and focused on sources of bias, researchers can enable the truest respondent perspectives and ensure that the resulting research lives up to the highest qualitative standards.  

1 Dodou, D., & de Winter, J. C. F. (2014). Social desirability is the same in offline, online and paper surveys: A meta-analysis. Computers in Human Behavior, 36, 487–495. doi: 10.1016/j.chb.2014.04.005.  https://www.iser.essex.ac.uk/research/publications/working-papers/iser/2013-04.pdf  

2 Habituation of event related potentials: a tool for assessment of cognition in headache patients Neelam Vaney, Abhinav Dixit, Tandra Ghosh, Ravi Gupta, M.S. Bhatia Departments of Physiology and Psychiatry, University College of Medical Sciences & G.T.B. Hospital, Dilshad Garden, Delhi-110095,  http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2605404/ .

3 Essentials or Marketing Research, An Applied Orientation By Naresh Malhotra, John Hall, Mike Shaw, Peter Oppenheim. Pp 227.  http://www.readexresearch.com/understanding-survey-data/ .

4 http://psy2.ucsd.edu/~mckenzie/nickersonConfirmationBias.pdf;  http://www.anderson.ucla.edu/faculty/keith.chen/negot.%20papers/RabinSchrag_ConfirmBias99.pdf  UCLA

5 Pirkey, W. (2015, May 6). Personal Interview. 

6 Essentials or Marketing Research, An Applied Orientation By Naresh Malhotra, John Hall, Mike Shaw, Peter Oppenheim. Pp 227.

7 Halo effects in consumer theories, Master Thesis, Erasmus University Rotterdam, thesis.eur.nl/pub/11759/Luttin,%20L.V.%20(352879ll).pdf  

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How to Avoid Bias in Qualitative Research

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Research bias occurs when researchers try to influence the results of their work, in order to get the outcome they want. Often, researchers may not be aware they are doing this. Whether they are aware or not, such behavior clearly severely affects the impartiality of a study and greatly reduces the value of the results.

The Issues in Qualitative Research

Recently, I discussed the problem of bias with a researcher friend.

“I heard that research bias is a bigger problem for qualitative research than quantitative research.”

“Why is that?”

“Qualitative research relies more on the experience and judgment of the researcher. Also, the type of data collected is subjective and unique to the person or situation. So it is much harder to avoid bias than in quantitative research.”

“Are there ways to avoid bias ?”

“A good start is to recognize that bias exists in all research. We can then try to predict what type of bias we might have in our study, and try to avoid it as much as possible.”

Types of Bias in Research

“Are there different types of bias to watch out for?”

• There’s design bias , where the researcher does not consider bias in the design of the study. Factors like sample size , the range of participants, for example – all of these can cause bias.
• Next there’s also selection or sampling bias . For example, you might omit people of certain ages or ethnicities from your study. This is called omission bias. The other type, inclusive bias, is when you select a sample just because it is convenient. For example, if the people you select for your study are all college students, they are likely to share many characteristics.”

“Are there more?”

“Yes, there are lots of different types of bias.

• There’s procedural bias , where the way you carry out a study affects the results. For example, if you give people only a short time to answer questions, their responses will be rushed.
• There’s also measurement bias that can happen if the equipment you are using is faulty, or you are not using it correctly.”

“That’s a lot to think about.”

“I can think of three more.

• There’s interviewer bias , which is very hard to avoid. This is when an interviewer subconsciously influences the responses of the interviewee. Their body language might indicate their opinion, for example.
• Furthermore, there’s response bias , where someone tries to give the answers they think are “correct.”
• Finally, there’s reporting bias . This is often outside the researcher’s control. It means that research with positive, or exciting, results is far more likely to be reported, so can seem more critical.”

How to Avoid Bias in Research

“With so many types of bias, how can it be avoided?”

“There are a number of things the researcher can do to avoid bias.

• Read the guidelines : Check the guidelines of your institution or sponsor and make sure you follow them.
• Think about our objectives : Plan your study early. Be clear about what you want to achieve, and how. This will help to avoid bias when you start collecting data.”

“And next?”

• Maintain records : Keep detailed records. This reduces the chance of making mistakes.
• Be honest when reporting : Make sure you include all your results in your report. Even the results that don’t seem important. Finally, be honest about the limitations of your study in your report.”

Avoiding Participant Bias

“That explains what researchers can do. But what about participant bias?”

“Try asking indirect questions. People might change their answers to direct questions to make a good impression. But if you ask them what a friend or colleague might think, you might get a more honest response.”

“Are open-ended questions useful?”

“Yes. They allow information to flow more freely, by not forcing a limited set of answers. But even these should be used with caution . You should try to be impartial about all parts of the study, and avoid implying that there is a right answer. It might help to ask people to rate their responses on a scale of 1-5, for example, rather than agree/disagree.”

Reducing Researcher Bias

“All researchers should try to avoid confirmation bias. This is when you interpret your data in a way that supports your hypothesis. Secondly, you should make sure to analyze all your data, even if it doesn’t seem useful. Finally, always get an independent person to check your work, ideally several times during your study.”

Identifying and avoiding research bias in qualitative research is clearly tricky, with many different factors to consider. However, it is also vital. Biased research has little value; it is a waste of researchers’ valuable time and resources.

Learn even more about bias here . How did you overcome bias in your research? Share your experiences and thoughts in the comment section below.

Can you please tell me who is the author of this article: How to Avoid Bias in Qualitative Research Last updated May 3, 2019

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• Research Bias: Definition, Types + Examples

Sometimes, in the cause of carrying out a systematic investigation, the researcher may influence the process intentionally or unknowingly. When this happens, it is termed as research bias, and like every other type of bias , it can alter your findings. 

Research bias is one of the dominant reasons for the poor validity of research outcomes. There are no hard and fast rules when it comes to research bias and this simply means that it can happen at any time; if you do not pay adequate attention. 

The spontaneity of research bias means you must take care to understand what it is, be able to identify its feature, and ultimately avoid or reduce its occurrence to the barest minimum. In this article, we will show you how to handle bias in research and how to create unbiased research surveys with Formplus. 

What is Research Bias? 

Research bias happens when the researcher skews the entire process towards a specific research outcome by introducing a systematic error into the sample data. In other words, it is a process where the researcher influences the systematic investigation to arrive at certain outcomes. 

When any form of bias is introduced in research, it takes the investigation off-course and deviates it from its true outcomes. Research bias can also happen when the personal choices and preferences of the researcher have undue influence on the study. 

For instance, let’s say a religious conservative researcher is conducting a study on the effects of alcohol. If the researcher’s conservative beliefs prompt him or her to create a biased survey or have sampling bias , then this is a case of research bias. 

Types of Research Bias 

• Design Bias

Design bias has to do with the structure and methods of your research. It happens when the research design, survey questions, and research method is largely influenced by the preferences of the researcher rather than what works best for the research context. 

In many instances, poor research design or a pack of synergy between the different contributing variables in your systematic investigation can infuse bias into your research process. Research bias also happens when the personal experiences of the researcher influence the choice of the research question and methodology. 

Example of Design Bias  

A researcher who is involved in the manufacturing process of a new drug may design a survey with questions that only emphasize the strengths and value of the drug in question. 

• Selection or Participant Bias

Selection bias happens when the research criteria and study inclusion method automatically exclude some part of your population from the research process. When you choose research participants that exhibit similar characteristics, you’re more likely to arrive at study outcomes that are uni-dimensional. 

Selection bias manifests itself in different ways in the context of research. Inclusion bias is particularly popular in quantitative research and it happens when you select participants to represent your research population while ignoring groups that have alternative experiences. 

Examples of Selection Bias  

• Administering your survey online; thereby limiting it to internet savvy individuals and excluding members of your population without internet access. 
• Collecting data about parenting from a mother’s group. The findings in this type of research will be biased towards mothers while excluding the experiences of the fathers. 
• Publication Bias

Peer-reviewed journals and other published academic papers, in many cases, have some degree of bias. This bias is often imposed on them by the publication criteria for research papers in a particular field. Researchers work their papers to meet these criteria and may ignore information or methods that are not in line with them. 

For example, research papers in quantitative research are more likely to be published if they contain statistical information. On the other hand, Non-publication in qualitative studies is more likely to occur because of a lack of depth when describing study methodologies and findings are not presented. 

• Analysis Bias

This is a type of research bias that creeps in during data processing. Many times, when sorting and analyzing data, the researcher may focus on data samples that confirm his or her thoughts, expectations, or personal experiences; that is, data that favors the research hypothesis. 

This means that the researcher, albeit deliberately or unintentionally, ignores data samples that are inconsistent and suggest research outcomes that differ from the hypothesis. Analysis bias can be far-reaching because it alters the research outcomes significantly and provides a false presentation of what is obtainable in the research environment. 

Example of Analysis Bias  

While researching cannabis, a researcher pays attention to data samples that reinforce the negative effects of cannabis while ignoring data that suggests positives.

• Data Collection Bias

Data collection bias is also known as measurement bias and it happens when the researcher’s personal preferences or beliefs affect how data samples are gathered in the systematic investigation. Data collection bias happens in both q ualitative and quantitative research methods. 

In quantitative research, data collection methods can occur when you use a data-gathering tool or method that is not suitable for your research population. For example, asking individuals who do not have access to the internet, to complete a survey via email or your website. 

In qualitative research, data collection bias happens when you ask bad survey questions during a semi-structured or unstructured interview . Bad survey questions are questions that nudge the interviewee towards implied assumptions. Leading and loaded questions are common examples of bad survey questions. 

• Procedural Bias

Procedural is a type of research bias that happens when the participants in a study are not given enough time to complete surveys. The result is that respondents end up providing half-thoughts and incomplete information that does not provide a true representation of their thoughts. 

There are different ways to subject respondents to procedural respondents. For instance, asking respondents to complete a survey quickly to access an incentive, may force them to fill in false information to simply get things over with. 

Example of Procedural Bias

• Asking employees to complete an employee feedback survey during break time. This timeframe puts respondents under undue pressure and can affect the validity of their responses.  

Bias in Quantitative Research

In quantitative research, the researcher often tries to deny the existence of any bias, by eliminating any type of bias in the systematic investigation. Sampling bias is one of the most types of quantitative research biases and it is concerned with the samples you omit and/or include in your study. 

Types of Quantitative Research Bias

Design bias occurs in quantitative research when the research methods or processes alter the outcomes or findings of a systematic investigation. It can occur when the experiment is being conducted or during the analysis of the data to arrive at a valid conclusion. 

Many times, design biases result from the failure of the researchers to take into account the likely impact of the bias in the research they conduct. This makes the researcher ignore the needs of the research context and instead, prioritize his or her preferences. 

• Sampling Bias

Sampling bias in quantitative research occurs when some members of the research population are systematically excluded from the data sample during research. It also means that some groups in the research population are more likely to be selected in a sample than the others. 

Sampling bias in quantitative research mainly occurs in systematic and random sampling. For example, a study about breast cancer that has just male participants can be said to have sampling bias since it excludes the female group in the research population. 

Bias in Qualitative Research

In qualitative research, the researcher accepts and acknowledges the bias without trying to deny its existence. This makes it easier for the researcher to clearly define the inherent biases and outline its possible implications while trying to minimize its effects. 

Qualitative research defines bias in terms of how valid and reliable the research results are. Bias in qualitative research distorts the research findings and also provides skewed data that defeats the validity and reliability of the systematic investigation. 

Types of Bias in Qualitative Research

• Bias from Moderator

The interviewer or moderator in qualitative data collection can impose several biases on the process. The moderator can introduce bias in the research based on his or her disposition, expression, tone, appearance, idiolect, or relation with the research participants. 

• Biased Questions

The framing and presentation of the questions during the research process can also lead to bias. Biased questions like leading questions , double- barrelled questions, negative questions, and loaded questions , can influence the way respondents provide answers and the authenticity of the responses they present. 

The researcher must identify and eliminate biased questions in qualitative research or rephrase them if they cannot be taken out altogether. Remember that questions form the main basis through which information is collected in research and so, biased questions can lead to invalid research findings. 

• Biased Reporting

Biased reporting is yet another challenge in qualitative research. It happens when the research results are altered due to personal beliefs, customs, attitudes, culture, and errors among many other factors. It also means that the researcher must have analyzed the research data based on his/her beliefs rather than the views perceived by the respondents. 

Bias in Psychology

Cognitive biases can affect research and outcomes in psychology. For example, during a stop-and-search exercise, law enforcement agents may profile certain appearances and physical dispositions as law-abiding. Due to this cognitive bias, individuals who do not exhibit these outlined behaviors can be wrongly profiled as criminals. 

Another example of cognitive bias in psychology can be observed in the classroom. During a class assessment, an invigilator who is looking for physical signs of malpractice might mistakenly classify other behaviors as evidence of malpractice; even though this may not be the case. 

Bias in Market Research

There are 5 common biases in market research – social desirability bias, habituation bias, sponsor bias, confirmation bias, and cultural bias. Let’s find out more about them.

• Social desirability bias happens when respondents fill in incorrect information in market research surveys because they want to be accepted or liked. It happens when respondents are seeking social approval and so, fail to communicate how they truly feel about the statement or question being considered. 

A good example will be market research to find out preferred sexual enhancement methods for adults. Some persons may not want to admit that they use sexual enhancement drugs to avoid criticism or disapproval.

• Habituation bias happens when respondents give similar answers to questions that are structured in the same way. Lack of variety in survey questions can make respondents lose interest, become non-responsive, and simply regurgitate answers.  

For example, multiple-choice questions with the same set of answer options can cause habituation bias in your survey. What you get is that respondents just choose answer options without reflecting on how well their choices represent their thoughts, feelings, and ideas. 

• Sponsor bias takes place when respondents have an idea of the brand or organization that is conducting the research. In this case, their perceptions, opinions, experiences, and feelings about the sponsor may influence how they answer the questions about that particular brand. 

For example, let’s say Formplus is carrying out a study to find out what the market’s preferred form builder is. Respondents may mention the sponsor for the survey (Formplus) as their preferred form builder out of obligation; especially when the survey has some incentives.

• Confirmation bias happens when the overall research process is aimed at confirming the researcher’s perception or hypothesis about the research subjects. In other words, the research process is merely a formality to reinforce the researcher’s existing beliefs. 

Electoral polls often fall into the confirmation bias trap. For example, civil society organizations that are in support of one candidate can create a survey that paints the opposing candidate in a bad light to reinforce beliefs about their preferred candidate. 

• Cultural bias arises from the assumptions we have about other cultures based on the values and standards we have for our own culture . For example, when asked to complete a survey about our culture, we may tilt towards positive answers. In the same vein, we are more likely to provide negative responses in a survey for a culture we do not like. 

How to Identify Bias in a Research

• Pay attention to research design and methods. 
• Observe the data collection process. Does it lean overwhelmingly towards a particular group in the survey population? 
• Look out for bad survey questions like loaded questions and negative questions. 
• Observe the data sample you have to confirm if it is a fair representation of your research population.

How to Avoid Research Bias 

• Gather data from multiple sources: Be sure to collect data samples from the different groups in your research population. 
• Verify your data: Before going ahead with the data analysis, try to check in with other data sources, and confirm if you are on the right track. 
• If possible, ask research participants to help you review your findings: Ask the people who provided the data whether your interpretations seem to be representative of their beliefs. 
• Check for alternative explanations: Try to identify and account for alternative reasons why you may have collected data samples the way you did. 
• Ask other members of your team to review your results: Ask others to review your conclusions. This will help you see things that you missed or identify gaps in your argument that need to be addressed.

How to Create Unbiased Research Surveys with Formplus 

Formplus has different features that would help you create unbiased research surveys. Follow these easy steps to start creating your Formplus research survey today: 

• Go to your Formplus dashboard and click on the “create new form” button. You can access the Formplus dashboard by signing into your Formplus account here. 

• After you click on the “create new form” button, you’d be taken to the form builder. This is where you can add different fields into your form and edit them accordingly. Formplus has over 30 form fields that you can simply drag and drop into your survey including rating fields and scales. 

• After adding form fields and editing them, save your form to access the builder’s customization features. You can tweak the appearance of your form here by changing the form theme and adding preferred background images to it. 

• Copy the form link and share it with respondents. 

Conclusion 

The first step to dealing with research bias is having a clear idea of what it is and also, being able to identify it in any form. In this article, we’ve shared important information about research bias that would help you identify it easily and work on minimizing its effects to the barest minimum. 

Formplus has many features and options that can help you deal with research bias as you create forms and questionnaires for quantitative and qualitative data collection. To take advantage of these, you can sign up for a Formplus account here. 

Connect to Formplus, Get Started Now - It's Free!

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Minimizing Bias in Qualitative Research: Strategies for Ensuring Validity and Reliability

Qualitative research, with its emphasis on understanding human experiences and perspectives, plays a vital role in various fields. However, like any research methodology, it is susceptible to bias, which can threaten the validity and reliability of findings. Therefore, researchers must actively strive to minimize bias throughout the research process. This blog post delves into the importance of minimizing bias in qualitative research and explores various strategies to achieve this goal.

Recognizing and Understanding Bias 

The first step towards minimizing bias is acknowledging its existence. Bias can creep into research in various ways, often subconsciously, influencing how researchers design studies, collect data, analyze results, and interpret findings. Recognizing potential sources of bias is crucial for developing strategies to mitigate their impact.

Types of Bias in Qualitative Research

Researchers should be aware of different types of bias that can affect qualitative research. These include:

• Design bias: Occurs when the research design itself favors certain outcomes or perspectives. For instance, if a study is designed in such a way that it only includes participants from a specific demographic or uses a particular method that leans towards a certain result, it can lead to design bias. This can skew the results and make them less representative of the broader population or phenomenon being studied.
• Selection bias: Arises when the sample of participants is not representative of the population being studied. For example, if a study on dietary habits only includes participants from a health club, the results may not represent the dietary habits of the general population, as people who attend a health club may have different dietary habits compared to those who do not.
• Omission bias: Occurs when important data is excluded from the analysis. For example, if a researcher conducting a study on the effects of a new drug only includes positive results and excludes negative ones, this would be an example of omission bias. The results of the study would then be skewed, as they do not take into account all relevant data.
• Inclusive bias: Occurs when irrelevant data is included in the analysis. For instance, if a researcher is studying the impact of diet on heart disease and includes data about participants’ favorite colors, this would be an example of inclusive bias. The favorite color is likely irrelevant to the development of heart disease and its inclusion could confuse the analysis.
• Procedural bias: Occurs due to inconsistencies in data collection or analysis procedures. For example, if a researcher uses different methods to collect data from different participants, or if the criteria used to analyze data changes during the course of the study, this could introduce procedural bias. The results may then not accurately reflect the phenomenon being studied, but rather the inconsistencies in the procedures used.
• Measurement bias: Occurs when the research instruments or methods used to collect data are flawed. For example, if a researcher uses a faulty scale to measure weight in a study on obesity, the data collected would be inaccurate, leading to measurement bias. Similarly, if a survey question is poorly worded or ambiguous, it could lead to inconsistent responses, introducing measurement bias.
• Interviewer bias: Occurs when the interviewer’s own beliefs or expectations influence how they interact with participants and interpret their responses. For example, if an interviewer has strong opinions about a topic, they may unconsciously lead participants to respond in a certain way, or they may interpret responses in a way that aligns with their own beliefs. Similarly, characteristics such as the interviewer’s age, gender, or race may influence how participants respond.
• Response bias: Occurs when participants intentionally or unintentionally misrepresent their experiences or opinions. For example, participants might give socially desirable responses, or they might try to guess what the researcher is looking for and tailor their responses accordingly. They might also misunderstand the question, forget relevant information, or exaggerate their responses.
• Reporting bias: Occurs when researchers selectively report findings that support their hypotheses or preconceived notions. For example, if a researcher only reports positive outcomes of a clinical trial and neglects to mention negative or neutral outcomes, this would be an example of reporting bias. The results of the study would then not accurately reflect the true effects of the treatment being studied.

Strategies for Minimizing Bias

Several strategies can be employed to minimize bias in qualitative research. These include:

• Multiple Coders: Using multiple researchers to code the data can help identify and address individual biases. Consistency in interpretation across coders strengthens the validity of findings.
• Participant Review: Allowing participants to review and provide feedback on the research findings can help ensure that their perspectives are accurately represented.
• Reflexivity, Peer Debriefing, and Triangulation: Engaging in reflexivity, where researchers critically examine their own biases and assumptions, can help mitigate their influence on the research process. Additionally, peer debriefing with colleagues and using triangulation, where data is collected from multiple sources and methods, can further strengthen the objectivity of the research.
• Changing Experimental Design: When unavoidable omission bias is identified, researchers can consider modifying the research design to address it. This may involve expanding the scope of the study or including additional data sources.
• Respecting Participants: Ensuring that participants are treated with respect and are not coerced into participating is essential for protecting them from exploitation and minimizing response bias.

Minimizing bias in qualitative research is an ongoing process that requires researchers to be vigilant and proactive. By employing the strategies discussed above, researchers can enhance the validity and reliability of their findings, ensuring that they accurately reflect the lived experiences and perspectives of the participants they study. Remember, while complete elimination of bias may be challenging, these strategies can help ensure that research findings remain as unbiased and faithful representations of participants’ perspectives as possible.

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What types of bias exist in qualitative research?

A brief introduction to bias in qualitative research, introduction to bias in qualitative research, researcher bias, reflexivity bias, selection bias, participant bias, self-selection bias, response bias, non-response bias, conclusion on types of bias in qualitative research.

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Social desirability bias in qualitative health research

José patrício bispo, júnior.

I Universidade Federal da Bahia, Instituto Multidisciplinar em Saúde, Vitória da Conquista BA , Brasil, Universidade Federal da Bahia. Instituto Multidisciplinar em Saúde. Vitória da Conquista, BA, Brasil

The objective of this essay is to discuss the social desirability bias in qualitative health research. The social desirability bias consists of a systematic research error, in which the participant presents answers that are more socially acceptable than their true opinions or behaviors. Qualitative studies are very susceptible to this type of bias, which can lead to distorted conclusions about the studied phenomenon. Initially, I present the theoretical-conceptual aspects of the social desirability bias. I discuss how its occurrence can be intentional or unintentional, with a distinction between the concepts of self-deception and impression management. Then, I discuss the determining factors of this bias from four dimensions: study design; study context; interviewee's characteristic; interviewer's posture. Finally, I present a systematization of six strategies to be used by qualitative researchers for identifying and controlling social desirability bias.

INTRODUCTION

Biases can exist in health research, in both quantitative and in qualitative research 1 . Although it is not a new topic, the discussion about biases in qualitative research is still timid and demands greater attention and depth from researchers. According to Althubaiti 2 , the problem of bias is still often ignored in practice. For the author, in most cases bias is introduced unintentionally by researchers in a study, which makes it difficult to recognize. Thus, it is a matter of great relevance for the debate on enhancing the consistency of qualitative studies.

Research bias can be defined as the influence of a factor that causes distortions in the results of the study 3 . These are systematic errors that can occur at all stages of research development 4 : in the planning and design of the study; in the collection of data; and in the analysis and interpretation of information. In any of the steps, the existence of this type of error can compromise the rigor and consistency of the findings of the research.

In qualitative research, the discussion about the existence and treatment of biases is controversial and not consensual. Characteristics inherent to the method itself, such as obtaining data through verbal interaction or observation and the interpretive nature of the analysis, are often pointed out as uncontrollable sources of biases that threaten the credibility of the research 5 . According to Roulston and Shelton 6 , this is due to the presumption of positivist and quantitative values over the qualitative method.

In this debate, it is important to set the boundaries of the epistemic aspects of qualitative health research. According to Guba and Lincoln 7 , qualitative research seeks to encompass the historical, cultural and subjective dimension of human phenomena. Therefore, objectivity and neutrality, so reified in the natural sciences, are not a foundation for qualitative studies. Qualitative research has as its epistemological reference the interpretive constructivist paradigm, in which reflexivity and subjectivity are valued as means for interpreting complex social phenomena 7 , 8 .

Trad 9 underscores that epistemic vigilance consists in balancing the valorization of subjectivity with the imperative of producing scientific knowledge. Thus, it is necessary to bear in mind the contradictions of informants and to consider how they might be attempting to manipulate information. Several strategies are used to try to mitigate the intervening factors that could inadequately influence the observed realities or the reports of the study participants 10 , 11 .

The data collection phase is very susceptible to response biases. Response bias is understood as the existence of research errors resulting from intentionally or unintentionally misrepresented statements 12 . In this situation, respondents alter, censor or misrepresent their true opinions, thoughts and beliefs. As a result, the answers to the questions are not representative of how participants actually behave, think or feel 12 , 13 .

There are several types of response bias. Some examples are 2 , 12 , 14 the memory bias; the acquiescence bias, also known as the “yea-saying” bias; the apathy bias or the habituation bias; and the social desirability bias. The social desirability bias is understood as the tendency of a study participant to present himself or his social context in a way that is socially acceptable, but not fully corresponding to reality 15 .

The existence of this type of bias is motivated by the predisposition of people to deny socially undesirable traits and claim other socially desirable ones. Therefore, it relates to the desire to say things that will make a good impression on the people with whom they are interacting in a given situation 16 .

Given the existence of socially reproved behaviors or violations of laws and norms, obtaining honest and reliable reports is a major challenge in qualitative research. As highlighted, qualitative health studies are very susceptible to the social desirability bias. However, this is a topic that has received little attention in the debate on the methodological aspects of health research.

The objective of this essay is to discuss the social desirability bias in qualitative health research and to present potential control strategies for this type of bias. The text is structured into three sections. In the first part, I present the conceptual discussion and theoretical-methodological reflections on socially desirable responses. In the second section, I address the determining factors of this type of bias, considering the characteristics of a qualitative study. In the third section, I systematize some strategies for identifying and controlling the social desirability bias.

Social Desirability Bias: Conceptual and Methodological Aspects

The social desirability bias describes the behavior by study participants of describing themselves in positive terms to create socially appropriate images of themselves or certain situations, instead of responding truthfully and accurately 14 . Motivated by a variety of factors, participants tend to overestimate socially acceptable behaviors, attitudes, and traits and underestimate true opinions and behaviors if they are socially undesirable 13 , 17 .

Responses that disclose deviations from social norms are viewed reproachfully and, therefore, are difficult to obtain in scientific investigations. In this sense, the results obtained by the studies may fail to reveal many of the aspects of the study object. Consequently, the conclusions presented by the authors may be distorted or not adequately express people's behavior, the functioning of health services or the development of public policies.

Reports of behaviors aligned with socially established patterns are seen as capable of avoiding reactions of contempt and are often associated with potential gains for one's good image. Thus, respondents can distort their responses towards the social norm to maintain a socially favorable self-image 17 .

The social desirability bias is related to controversial issues or behaviors that elude legal, cultural and ethical standards established in each society. Krumpal 17 uses the denomination of sensitive topics to express subjects considered taboo, reveal illegal behavior or express antisocial attitudes. Thus, obtaining reliable information while researching sensitive topics is a challenge for the social sciences in health care. The author presents three dimensions of sensitive topics: (1) intrusion, since certain issues can be perceived as private or personal; (2) fear of disclosure, regarding the respondents’ concerns about potential risks and consequences of disclosing answers outside the research environment; and (3) social desirability, regarding the distortion of answers relative to the social norm to present a socially favorable self-image.

An important issue in this debate is that the social desirability bias can be intentional or unintentional. According to Paulhus 18 , socially appropriate responses can result from two situations: self-deception and impression management. In self-deception, distortions of responses are motivated by inflated personality attributes and high self-esteem 15 , which favors the tendency of respondents to always see themselves in a positive way 12 . Thus, respondents actually believe that a statement about themselves is true, even if the answer is inaccurate 15 . Self-deception responses are motivated by the constant need for social approval, regardless of what is being addressed 18 .

In turn, impression management concerns the intentional act of misrepresenting the truth as a way of making a good impression 15 . In this situation, respondents deliberately and consciously manage a response to present themselves in a positive way, omitting and misrepresenting facts that may generate unfavorable situations 14 .

Distinguishing these two perspectives is of great relevance in social health research since it allows the separation of determinants that can be controlled and interfered with by the researcher. Self-deception situations are less easily controlled and, in most cases, can only be detected 16 . In turn, impression management is triggered by a specific situation or item, which the study participant attempts to hide or misrepresent 17 . As they are influenced by the characteristics of an item, they are more easily identifiable and also enable the researcher to develop strategies to prevent or circumvent this type of bias.

Determinants of the Social Desirability Bias

Obtaining information in qualitative research involves the actors (interviewer, respondents and potential viewers), the relationship established between them, the social segments or institutions to which they are linked, the environment in which the study was conducted and the sociocultural norms established. Thus, some characteristics of the study, the circumstances in which it is performed and the positions of the actors involved can facilitate the occurrence of biases. The following is a systematization of the determinants of the social desirability bias.

Study Design

Before conducting any scientific study, the researcher must carefully defined the proposed objectives, the research methods, the techniques to be used in obtaining data and the selection of participants. An important step to avoid the social desirability bias is to analyze the pertinence and coherence between the objectives and the methodological elements to be followed.

The choice of technique for obtaining data can facilitate or hinder biased responses. Two collection techniques in qualitative research are prone to the social desirability bias: interview and focus group.

Interviews are characterized as a conversation with a purpose and are as the most used technique in qualitative fieldwork 19 . Several factors can contribute to respondents not formulating answers truthfully, including the desire to omit socially reproved opinions and behaviors, the willingness to demonstrate mastery of the content, or even the desire to please the interviewer. The dynamic nature of the interview and the possibility of the interviewer to identify traces of deviations in the respondent's speech allow the targeting and use of resources to minimize biased approaches.

A focus group is a qualitative research technique for collecting information through group interactions 20 . It is based on generating information through the interaction between the participants, rather than asking them question individually 20 , 21 . Thus, one of the main challenges of focus groups is precisely to promote interaction and debate among the participants and make them not interact only with the moderator 21 . Given the peculiarities of focus groups, they can constitute a technique capable of mitigating or potentiating the bias of social desirability. In the interaction between the participants, a process of self-control of the group can develop, with the ability to inhibit opinions and positions that do not correspond to the reality under discussion. On the other hand, a kind of social micro-pact may develop to collectively hide certain behaviors or practices that may be considered inappropriate.

Regarding the study participants, three groups of people are most commonly requested in qualitative health research: users and caregivers; health professionals; and managers. The bias of the participants’ responses is strongly related to how sensitive the topics covered in the studies are and the criteria for selecting participants. For example, users may feel uncomfortable disclosing risky sexual behaviors or embarrassed to report domestic violence situations. The situation may be even more difficult to handle with managers of aspects related to the way resources are managed or, if any, about illegal behaviors, omissions and fraud.

In fact, obtaining honest answers corresponding to the reality being studied is not an easy task. It is not rare for participants, when asked about a particular practice or the functioning of a particular service, to describe an ideal situation or present the parameters of a given policy instead of reporting the daily reality that they experience.

The mechanisms of participant selection can also influence the occurrence of the social desirability bias. There are two basic ways of selecting participants 22 , 23 : to previously set the number and characteristics of respondents and to select them according to the needs and questions that appear in the course of the study. In both ways, properly selecting participants imbricated and willing to share true opinions and behaviors is a challenge to ensure rigor and to reduce the occurrence of bias. Inadequate selection can affect subsequent stages of fieldwork and data analysis, as well as hindering actions to control the social desirability bias.

Another important aspect related to the design of the study is the proper elaboration of the instruments. The writing of the script can induce the content of the answer. According to Kaminska and Foulsham 13 , the formulation of the question may imply that there is a socially undesirable behavior or attitude, leading people to respond in a biased way. That is, certain words or phrases in the instrument suggest certain types of answers 12 . Also, the order of the questions can generate biases, since the answer given to a question can influence the answers to subsequent questions.

Study Context

Contextual factors of the field step have great potential to generate biases. Two main contextual factors influence the occurrence of the social desirability bias: the bystander effect and data confidentiality.

The bystander effect is the presence of one or more people, in addition to the researcher and the participant, at the time of data collection. Given the high probability of negative repercussions, in the presence of a third party the respondent will report fewer socially undesirable responses 17 . In qualitative health research, it is not uncommon for researchers to find themselves in contexts with a third person at the time of data collection. For example, interviews conducted in the users’ homes are almost always followed by other residents, which can be a difficult situation control and may negatively affect the quality of the information provided. The bystander effect can also occur indirectly, as a result of lack of privacy in the research environment. In certain environments, such as health facilities or administrative spaces, where speech can be heard by people in other spaces, there is a greater propensity for distortion of responses.

Regarding the confidentiality of data, it is necessary to assure respondents and make sure that they understand and trust that their anonymity will be preserved and their personal information will be kept absolutely confidential. Situations of distrust about the seriousness and the purpose of the research generate fear and insecurity in the participants about how the information provided can be used. As a result, it is common for people to try to protect themselves by giving untrue answers.

Characteristic of the Respondents

Some of the elements involving the characteristics of the respondents were previously addressed, when discussing self-deception and impression management. Another characteristic related to the respondents is the so-called demand effect. This situation happens when the respondent gives an answer that they believe will please the interviewer and when they try to give what they believe to be the expected answers 24 . This behavior is related to the acquiescence bias or the yea-saying bias, in which the respondent has a tendency to be positive and agree with everything the interviewer presents. This attitude is considered easier because it requires less effort than carefully thinking and elaborating each answer.

The Interviewer's Position

Reporting socially reproved opinions and events to a person who does not inspire confidence is unlikely to happen. In this sense, the interviewer's characteristics, attitude and way of conducting the interview are strong determinants of the social desirability bias. Even the interviewer's personal characteristics, such as social class, ethnicity, gender, and personality traits can induce biased responses 1 . In addition, the researcher bears definitions, a specific language and a culture that dictates habits, ways of proceeding, preferences and norms to be followed 25 , which can influence the participants’ responses.

When it comes to people in communication, there is always a relational aspect, which is produced in the act of affecting and being affected by another person in the narrative mediation 26 . For that, it is essential to develop a relationship of trust between interviewer and interviewee. Aspects such as empathy, respect, good humor and warmth help the interviewee feel secure respond on sensitive topics.

Another important determinant concerns the interviewer's reactions to the answers given. The way the interviewer reacts to responses can encourage or inhibit certain positions. Graeff 12 points out that a smile, a frowning countenance or even the raising of an eyebrow can indicate which answers the interviewer expects or disapproves. Consequently, respondents may censor or distort other positions.

The interviewer's skill is also critical to identifying biased responses and encouraging respondents to respond truthfully. Some behaviors of the interviewee may indicate the existence of bias, such as excessive discomfort, acquiescent responses and responses that contradict already identified evidence.

Control Strategies and Interpretive Reflections of the Social Desirability Bias in Qualitative Health Research

Given the determining factors that influence the existence of social desirability bias, eight reflections aimed at identifying, reducing and interpreting this type of bias in qualitative health research were systematized.

Firstly, the planning phase of the research project should be carefully developed. Special attention and rigor should be given to the definition of objectives, the choice of research techniques, the selection of participants and the elaboration of the instruments. Situations of inadequate study design may imply systematic errors when obtaining information. Such situations can be irreversible and compromise the quality of the results achieved. Whenever possible, the researcher should choose more than one source of information in order to triangulate the data and identify socially desirable responses. In these cases, it is recommended that the interview or focus group take place after having access to information from other sources, such as participant observation and document analysis.

Secondly, special attention should be paid to the preparation of the interview script or focus group. Qualitative health research encompasses values, practices, beliefs, habits and attitudes of professional users and managers 8 , as objects often sensitive and difficult to approach. Thus, the questions should be formulated in such a way as to clarify that there is no problem in sharing positions or revealing socially disapproved actions. The order of the questions must also be noted 13 . It is recommended to start the interview with comprehensive questions rather than immediately asking questions about the topic of the research. This helps to break the initial tension and allows respondents to relax and gain confidence. When addressing specific content, it is suggested to start with more general questions about the content and then introduce sensitive subjects. In addition, words and expressions that are emotionally charged or imply value judgment about a particular behavior should be avoided.

Thirdly, ensuring privacy and a conducive atmosphere for the research context are key to reducing social desirability bias. The research environment must be protected from external influences, interruptions or the presence of third parties. In health studies, participants are often asked about highly sensitive topics, such as sexual practices, family relationships or situations of violence, which reinforces the need for a private environment for data collection. The researcher must have good judgment when chosing the spaces, ensuring that the participants are not heard by people in other environments. It is also necessary to properly prepare the space, with chairs, a table, water and other amenities that make the environment comfortable and help to avoid interruptions. Data should always be collected only in the presence of the research team and participants, with exceptions allowed to suit the respondent's needs, especially users.

Fourth, the confidentiality of data and information must always be safeguarded and users must be assured of their anonymity. Given the sensitivity of the topics of qualitative health research, personal identification of certain disclosures can generate moral, social, family, financial and legal losses. Therefore, it is necessary to assure respondents and make sure that they understand and trust that their anonymity will be preserved and their personal information will be kept absolutely confidential. Research involving human beings requires approval by a research ethics committee and compliance with the ethical aspects of the legislation in force. However, in many situations, the signing of the free and informed consent form and ethical and confidentiality clarifications occur as a mere procedural and bureaucratic step.

Fifth, unexpected participation in interviews and focus groups should be avoided. Respondents who are more familiar with their interviewers opt less for socially desirable responses 14 . That is, the continuity of the researcher in the field and the development of familiarity with the participants benefit the development of honest responses. When continuity in the field is not possible, it is recommended to contact the participants beforehand, when clarifications should be provided about the study, and to schedule participation for a later time.

Sixth, attention should be paid to the atitude and qualification of the researcher. Researchers should always seek to build a good relationship with participants and to promote a respectful and relaxed atmosphere. Scott et al. 27 recommend using verbal and nonverbal language to make respondents feel comfortable and less hestitant to express unpleasant positions. When identifying socially desirable positions, it is important to avoid confrontation and use strategies to make the respondent understand the scientific nature of the research. As health research often deals with very specific topics that raise questions in the participants, it is essential to master the specific contents of the nature of the object. During the interview, doubts, questions and misunderstandings may arise that require specific knowledge from the researcher to provide proper clarification. Thus, proper cognitive and relational training of the researchers responsible for fieldwork is required.

Seventh, having the sensitivity to identify situations of desirability bias and reflect critically on the participants’ positions. Although instrumental and procedural resources should always be observed, it is not always possible to control the existence of bias, and in some situations, it is desirable not to do so. In certain contexts of qualitative health research, participants may deliberately distort situations experienced, such as political beliefs, when confronted by situations of oppression or in defense of certain cultural and community aspects, or even in actions aimed at transforming health services. In this sense, the existence of the social desirability bias takes on another perspective. The researcher is not expected to try to avoid it or control it. In these cases, the bias reveals important aspects to consider and analyze in depth, and the researcher must broaden the reflections and theorizations about the phenomenon under study and the revelations manifested through “biased opinions”.

Eighth, ethical-political and social attitude of the researcher in social sciences in health care. Social research, by nature, must be connected with everyday problems and committed to building a more just society. In this perspective, the recommendations to control the bias of social desirability are not only technical-procedural requirements, aimed at increasing the rigor in qualitative research, but also political stances with the goal of better understanding the world and finding ways to transform local and global realities.

FINAL CONSIDERATIONS

This essay addressed the occurrence, determinants and strategies of approaches to the social desirability bias in qualitative health research. The existence of biases can compromise the consistency of the results of a scientific study and lead to conclusions that do not correspond exactly to the characteristics of the phenomenon being studied. In some situations, the existence of this type of bias may reveal situations of oppression and deliberate political stance experienced by the participants.

I highlight the importance of the attention and attitude of the researcher in social sciences and humanities in health in being alert to the possibility of the existence of this type of bias. Norms, customs, values and the social context exert a strong influence on the elaboration of responses by participants, and this cannot be ignored by researchers. The researcher must adopt strategies to minimize the occurrence of bias or, based on them, to interpret aspects of the participants’ experiences and meanings in depth.

Funding Statement

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Capes - funding code 001).

Funding: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Capes - funding code 001).

• Rev Saude Publica. 2022; 56: 101.

Viés de desejabilidade social na pesquisa qualitativa em saúde

Ensaio com o objetivo de discutir o viés de desejabilidade social na pesquisa qualitativa em saúde. O viés de desejabilidade social consiste em um erro sistemático de pesquisa, no qual o participante apresenta respostas que são mais socialmente aceitáveis do que suas opiniões ou comportamentos verdadeiros. Estudos qualitativos são muito suscetíveis a esse tipo de viés, que pode levar a conclusões distorcidas sobre o fenômeno em estudo. Inicialmente, apresento os aspectos teórico-conceituais do viés de desejabilidade social. Discuto como sua ocorrência pode ser intencional ou não intencional, com diferenciação entre os conceitos de autoengano e gerenciamento de impressão. Em seguida, discuto os fatores determinantes desse viés a partir de quatro dimensões: desenho do estudo; contexto do estudo; característica do entrevistado; postura do entrevistador. Por fim, apresento uma sistematização de seis estratégias a serem utilizadas por pesquisadores qualitativos para a identificação e controle do viés de desejabilidade social.

INTRODUÇÃO

Vieses podem existir em pesquisas em saúde, tanto na pesquisa quantitativa quanto na pesquisa qualitativa 1 . Embora não seja um tema novo, a discussão sobre vieses na pesquisa qualitativa é ainda tímida e demanda a necessidade de maior atenção e aprofundamento por parte dos pesquisadores. Segundo Althubaiti 2 , o problema do viés é ainda frequentemente ignorado na prática. Para o autor, na maior parte dos casos, o viés é introduzido de maneira não intencional por pesquisadores em um estudo, situação que o torna difícil de ser reconhecido. Desse modo, constitui-se numa questão de grande relevância para o debate sobre o fortalecimento da consistência dos estudos qualitativos.

Viés de pesquisa pode ser definido como a influência de algum fator que provém distorções nos resultados do estudo 3 . Trata-se de erros sistemáticos que podem ocorrer em todas as etapas de desenvolvimento da pesquisa 4 : no planejamento e desenho do estudo; na coleta de dados; e na análise e interpretação das informações. Em qualquer uma das fases, a existência desse tipo de erro pode comprometer o rigor e a consistência dos achados da investigação.

Na pesquisa qualitativa, a discussão sobre a existência e o tratamento de vieses é controversa e não consensual. Características inerentes ao próprio método, como a obtenção de dados por interação verbal ou observação e a natureza interpretativa da análise, são frequentemente apontados como fontes incontroláveis de vieses que ameaçam a credibilidade da pesquisa 5 . Segundo Roulston e Shelton 6 , isso é devido à presunção dos valores positivistas e quantitativos sobre o método qualitativo.

Nesse debate, é importante delimitar os aspectos epistêmicos da pesquisa qualitativa em saúde. Segundo Guba e Lincoln 7 , a pesquisa qualitativa busca abranger a dimensão histórica, cultural e subjetiva dos fenômenos humanos. Portanto, a objetividade e neutralidade, tão reificadas nas ciências naturais, não se constituem em fundamento para as investigações qualitativas. A pesquisa qualitativa possui como referencial epistemológico o paradigma construtivista interpretativo em que a reflexividade e a subjetividade são valorizados como meios para interpretação de fenômenos sociais complexos 7 , 8 .

Trad 9 ressalta que a vigilância epistêmica consiste em equilibrar a valorização da subjetividade com o imperativo de produzir conhecimento científico. Assim, é necessário se ater às contradições dos informantes e considerar as manipulações que possam tentar fazer. Diversas estratégias são utilizadas para tentar amenizar os fatores intervenientes que possam influenciar inadequadamente as realidades observadas ou os relatos dos participantes do estudo 10 , 11 .

A fase de coleta de dados é muito suscetível aos vieses de resposta. Por viés de resposta, entende-se a existência de erro de pesquisa resultante de afirmações deturpadas de maneira intencional ou não intencional 12 . Nessa situação, os entrevistados alteram, censuram ou deturpam suas verdadeiras opiniões, pensamentos e crenças. Como resultado, as respostas das questões não são representativas de como os participantes realmente se comportam, pensam ou sentem 12 , 13 .

Existem vários tipos de viés de resposta. Como exemplos, podem ser destacados 2 , 12 , 14 : o viés de memória; o viés de aquiescência, também conhecido como viés de dizer sim; viés de apatia ou viés de habituação; e o viés de desejabilidade social. Por viés de desejabilidade social entende-se a tendência de um participante do estudo em apresentar a si mesmo ou o seu contexto social de uma forma que seja socialmente aceitável, mas não totalmente correspondente à realidade 15 .

A existência desse tipo de viés é motivada pela predisposição das pessoas em negar traços socialmente indesejáveis e reivindicar outros socialmente desejáveis. Refere-se, assim, ao desejo de dizer coisas que causam uma boa impressão para pessoas com quem interagem em uma determinada situação 16 .

Diante de comportamentos socialmente reprováveis ou de violações de leis e normas, a obtenção de relatos sinceros e confiáveis constitui-se num grande desafio da pesquisa qualitativa. Conforme ressaltado, os estudos qualitativos em saúde são muito suscetíveis ao viés de desejabilidade social. No entanto, esse é um tema que tem recebido pouca atenção no debate sobre os aspectos metodológicos da pesquisa em saúde.

Este ensaio tem por objetivo discutir o viés de desejabilidade social na pesquisa qualitativa em saúde e apresentar potenciais estratégias de controle para esse tipo de viés. O texto está estruturado em três seções. Na primeira parte, apresento a discussão conceitual e as reflexões teórico-metodológicas sobre as respostas socialmente desejáveis. Na segunda seção, abordo os fatores determinantes desse tipo de viés, considerando as características de uma investigação qualitativa. Na terceira seção, apresento uma sistematização de algumas estratégias a serem utilizadas para identificação e controle do viés de desejabilidade social.

Viés de Desejabilidade Social: Aspectos Conceituais e Metodológicos

O viés de desejabilidade social descreve um comportamento dos participantes de um estudo em fazer autodescrições positivas, a fim de criar imagens socialmente adequadas de si ou de determinadas situações em vez de responder de forma verdadeira e precisa 14 . Motivados por diversos fatores, os participantes tendem a superestimar comportamentos, atitudes e traços socialmente aceitáveis e subestimar opiniões e comportamentos verdadeiros, caso sejam socialmente indesejáveis 13 , 17 .

Respostas com revelações de desvios de normas sociais são vistas de maneira reprovável e, portanto, difícil de serem obtidas nas investigações científicas. Nesse sentido, os resultados obtidos pelos estudos podem não ser capazes de revelar muitos dos aspectos do objeto em investigação. Consequentemente, as conclusões apresentadas pelos autores podem estar distorcidas ou não expressar adequadamente o comportamento das pessoas, o funcionamento dos serviços de saúde ou o desenvolvimento das políticas públicas.

Relatos de comportamentos alinhados aos padrões socialmente estabelecidos são vistos como capazes de evitar reações de desprezo e muitas vezes associados a potenciais ganhos de uma boa imagem. Assim, os entrevistados podem distorcer suas respostas em direção à norma social a fim de manter uma autoapresentação socialmente favorável 17 .

O viés de desejabilidade social se relaciona com assuntos polêmicos ou comportamentos que fogem aos padrões legais, culturais e éticos estabelecidos em cada sociedade. Krumpal 17 utiliza a denominação de temas sensíveis para expressar assuntos considerados tabu, revelam comportamentos ilegais ou expressam atitudes antissociais. Desse modo, obter informações confiáveis na investigação de temas sensíveis é um desafio para as ciências sociais em saúde. O autor apresenta três dimensões dos temas sensíveis: (1) intrusão, visto que certas questões podem ser percebidas como de natureza privada ou de fórum íntimo; (2) medo de divulgação, referente às preocupações dos entrevistados sobre potenciais riscos e consequências da divulgação das respostas para além do ambiente da pesquisa; e (3) desejabilidade social, referente à distorção das respostas em relação à norma social, a fim de manter uma autoapresentação socialmente favorável.

Uma questão importante desse debate é que o viés de desejabilidade social pode ser intencional ou não-intencional. De acordo com Paulhus 18 , respostas socialmente adequadas podem ser resultantes de duas situações: autoengano e gerenciamento de impressão. No autoengano, as distorções das respostas são motivadas por atributos de personalidade inflada e elevada autoestima 15 , o que favorece a tendência do entrevistado em se ver sempre de maneira positiva 12 . Assim, a pessoa realmente acredita que a afirmação sobre si é verdadeira, mesmo que a resposta seja imprecisa 15 . As respostas por autoengano são motivadas pela necessidade constante de aprovação social independentemente do que esteja sendo abordado 18 .

Por sua vez, o gerenciamento de impressão diz respeito à ação intencional de deturpar a verdade como forma de causar uma boa impressão 15 . Nessa situação, os entrevistados administram deliberada e conscientemente uma resposta a fim de se apresentar de maneira positiva, omitindo e deturpando fatos que possam gerar situações desfavoráveis 14 .

Distinguir essas duas perspectivas se mostra de grande relevância na pesquisa social em saúde, pois permite a separação dos determinantes passíveis de controle e interferência por parte do pesquisador. As situações de autoengano são menos facilmente controladas e podem, na maioria dos casos, apenas ser detectadas 16 . Por sua vez, o gerenciamento de impressão desenvolve-se a partir de uma situação ou de um item específico, o qual o participante do estudo busca esconder ou deturpar 17 . Como são influenciados pelas características de um item, são mais facilmente identificáveis e também possibilitam ao pesquisador desenvolver estratégias para prevenir ou contornar esse tipo de viés.

Determinantes do Viés de Desejabilidade Social

A obtenção de informações na pesquisa qualitativa envolve os atores (entrevistador, entrevistados e potenciais espectadores), a relação estabelecida entre eles, os segmentos sociais ou as instituições às quais estão vinculados, o ambiente de realização do estudo e as normas socioculturais estabelecidas. Desse modo, algumas características do estudo, as circunstâncias de realização do mesmo e os posicionamentos dos atores envolvidos podem facilitar a ocorrência de vieses. Apresento a seguir uma sistematização dos fatores determinantes do viés de desejabilidade social.

Desenho do Estudo

Antes da realização de qualquer estudo científico é necessária a cuidadosa atenção do pesquisador na definição dos objetivos propostos, dos métodos de pesquisa, das técnicas a serem utilizadas na obtenção de dados e da seleção dos participantes. Um importante passo para evitar o viés de desejabilidade social é analisar a pertinência e coerência entre os objetivos e os elementos metodológicos a serem seguidos.

A escolha da técnica de obtenção de dados pode facilitar ou dificultar respostas enviesadas. Duas técnicas de coleta na pesquisa qualitativa estão sujeitas ao viés de desejabilidade social: entrevista e grupo focal.

A entrevista é caracterizada como uma conversa com finalidade e constitui-se como a técnica mais utilizada no trabalho de campo qualitativo 19 . Vários fatores podem contribuir para os entrevistados não formularem as respostas com sinceridade, incluindo o desejo de omitir opiniões e comportamentos socialmente reprováveis, a vontade de demonstrar o domínio do conteúdo ou até mesmo querer agradar ao entrevistador. A natureza dinâmica da entrevista e a possibilidade do entrevistador identificar traços de desvios na fala do participante, permite o direcionamento e o uso de recursos para minimizar as abordagens enviesadas.

Por grupo focal, entende-se uma técnica de pesquisa qualitativa que coleta informações por meio das interações grupais 20 . O mesmo se baseia em gerar informações a partir da interação entre os participantes, ao invés de fazer a mesma pergunta de maneira individual 20 , 21 . Assim, um dos principais desafios do grupo focal é justamente promover a interação e o debate entre os participantes e fazer com que não interajam apenas com o moderador 21 . Frente às peculiaridades dos grupos focais, os mesmos podem se constituir em técnica capaz de amenizar ou potencializar o viés de desejabilidade social. Na interação entre os participantes, pode se desenvolver um processo de autocontrole do grupo, com capacidade para inibir opiniões e posicionamentos que não correspondam com a realidade em discussão. Por outro lado, é também possível que se desenvolva uma espécie de micropacto social para coletivamente esconder determinados comportamentos ou práticas que possam ser consideradas inadequadas.

Sobre os participantes do estudo, três grupos de pessoas são mais comumente requisitados na pesquisa qualitativa em saúde: usuários e cuidadores; profissionais de saúde; e gestores. O enviesamento das respostas dos participantes guarda forte relação com a sensibilidade dos temas abordados nos estudos e os critérios de seleção dos participantes. Por exemplo, usuários podem se sentir incomodados em revelar comportamentos sexuais de risco ou envergonhados de relatar situações de violência doméstica. A situação talvez seja ainda mais difícil para tratar com gestores de aspectos relacionados à forma de administração dos recursos ou, caso existam, sobre comportamentos ilegais, omissões e fraudes.

Com efeito, obter respostas sinceras e correspondentes com a realidade que se investiga não é uma tarefa fácil. Não raro, os participantes quando questionados sobre uma determinada prática ou o funcionamento de um determinado serviço adotam a postura de descrever uma situação ideal ou apresentar os parâmetros de uma dada política em vez de relatar a realidade cotidiana vivenciada por eles.

Os mecanismos de seleção dos participantes também podem influenciar na ocorrência do viés de desejabilidade social. Existem dois tipos básicos de definição dos participantes 22 , 23 : estabelecer a priori a quantidade e as características dos respondentes e selecionar conforme as necessidades e questões que aparecerem no decorrer da pesquisa. Em ambas as situações, selecionar adequadamente participantes imbricados e dispostos a revelar opiniões e comportamentos verdadeiros mostra-se um desafio para a garantia do rigor e para a diminuição da ocorrência de viés. A seleção inadequada pode repercutir nas etapas subsequentes do trabalho de campo e da análise de dados e obstaculizar ações para o controle do viés de desejabilidade social.

Outro importante aspecto relacionado ao desenho do estudo é a adequada elaboração dos instrumentos. A forma de redação do roteiro pode induzir o teor da resposta. De acordo com Kaminska e Foulsham 13 , a formulação da questão pode implicar que existe um comportamento ou atitude socialmente indesejável, levando as pessoas a responder de maneira tendenciosa. Ou seja, determinadas palavras ou frases contidas no instrumento sugerem determinados tipos de respostas 12 . Também a ordem das questões pode gerar vieses, uma vez que a resposta dada a uma questão pode influenciar nas respostas das questões subsequentes.

Contexto do Estudo

Fatores contextuais da etapa de campo têm grande potencial para gerar vieses. Dois principais fatores contextuais influenciam a ocorrência do viés de desejabilidade social: o efeito espectador e a confidencialidade dos dados.

Por efeito espectador, entende-se a presença de uma ou mais pessoas, além do pesquisador e do participante, no momento da coleta de dados. Em face à elevada probabilidade de repercussões negativas, na presença de uma terceira parte, o entrevistado reportará menos respostas socialmente indesejáveis 17 . Nas pesquisas qualitativas em saúde, não é incomum pesquisadores se depararem com contextos que incluem uma terceira pessoa no momento da coleta. Por exemplo, entrevistas nos domicílios dos usuários quase sempre são acompanhadas de outros moradores, o que pode se constituir numa situação de difícil controle e afetar negativamente a qualidade das informações prestadas. O efeito espectador também pode ocorrer de maneira indireta, relacionado a falta de privacidade do ambiente da pesquisa. Em determinados ambientes, a exemplo de unidades de saúde ou espaços administrativos, em que as falas podem ser ouvidas por pessoas em outros espaços, existe uma maior propensão para a distorção das respostas.

Sobre a confidencialidade dos dados, é necessário assegurar e fazer com que o entrevistado compreenda e confie que terá o anonimato preservado e as suas informações pessoais mantidas em absoluto sigilo. Situações de desconfiança sobre a seriedade e finalidade da pesquisa geram receio e insegurança nos participantes sobre como as informações prestadas poderão ser utilizadas. Desse modo, é comum as pessoas buscarem se resguardar por meio de respostas não verdadeiras.

Característica do Entrevistado

Alguns dos elementos que envolvem as características do entrevistado foram anteriormente abordados, quando discutidos o autoengano e o gerenciamento de impressão. Outra característica relativa ao entrevistado é o chamado efeito de demanda. Essa situação se caracteriza por uma resposta do entrevistado visando agradar ao entrevistador e buscar as respostas que acredita serem as esperadas 24 . Tal postura se relaciona com o viés de aquiescência ou viés de dizer sim, em que o entrevistado apresenta uma tendência de ser positivo e concordar com tudo o que o entrevistador apresentar. Trata-se de um posicionamento considerado mais fácil, pois exige menos esforço do que pensar e elaborar cuidadosamente cada resposta.

Postura do Entrevistador

Relatar opiniões e acontecimentos socialmente reprováveis para uma pessoa que não inspira confiança é algo pouco provável de acontecer. Nesse sentido, as características, posturas e forma de condução do entrevistador são fortes determinantes do viés de desejabilidade social. As próprias características individuais do entrevistador, como posição social, etnia, gênero e traços de personalidade podem induzir a respostas enviesadas 1 . Além do mais, o pesquisador é um ser portador de definições, de uma linguagem própria e de uma cultura que lhe dita hábitos, maneiras de proceder, preferências e normas a seguir 25 , o que pode influenciar nas respostas dos participantes.

Quando se trata de sujeitos em comunicação, há sempre um aspecto relacional, que é produzido na ação de afetar e ser afetado por outra pessoa na mediação narrativa 26 . Para tanto, é fundamental o desenvolvimento de uma relação de confiança entre entrevistador e entrevistado. Aspectos como empatia, respeito, bom humor e cordialidade favorecem a segurança do entrevistado para responder sobre temas sensíveis.

Outro importante determinante diz respeito às reações do entrevistador ante as respostas proferidas. A forma como o entrevistador reage às respostas pode encorajar ou inibir determinados posicionamentos. Graeff 12 ressalta que um sorriso, um semblante carrancudo ou até mesmo o levantar de uma sobrancelha podem indicar quais as respostas esperadas ou reprovadas pelo entrevistador. Consequentemente, os entrevistados podem censurar ou distorcer outros posicionamentos.

A habilidade do entrevistador também é fundamental para identificar respostas enviesadas e encorajar os entrevistados a responderem com sinceridade. Alguns comportamentos do entrevistado podem indicar a existência de viés a exemplo de desconforto excessivo, respostas aquiescentes e respostas que contrariam evidências já identificadas.

Estratégias de Controle e Reflexões Interpretativas do Viés de Desejabilidade Social na Pesquisa Qualitativa em Saúde

Diante dos fatores determinantes e que influenciam na existência do viés de desejabilidade social, foram sistematizadas oito reflexões destinadas à identificação, redução e interpretação desse tipo de viés na pesquisa qualitativa em saúde.

Primeira, a fase de planejamento do projeto de pesquisa deve ser cuidadosamente desenvolvida. Deve-se destinar especial atenção e rigor para a definição dos objetivos, a escolha das técnicas de investigação, a seleção dos participantes e a elaboração dos instrumentos. Situações de desenho inadequado do estudo podem implicar em erro sistemático de obtenção das informações. Tais situações podem ser irreversíveis e comprometer a qualidade dos resultados alcançados. Sempre que possível, o pesquisador deve optar por mais de uma fonte de informação a fim de triangular os dados e identificar respostas socialmente desejáveis. Nesses casos, é recomendado que a entrevista ou grupo focal ocorra após se ter acesso às informações das outras fontes, a exemplo da observação participante e da análise de documentos.

Segunda, deve-se dedicar especial atenção à elaboração do roteiro da entrevista ou grupo focal. A pesquisa qualitativa em saúde engloba valores, práticas, crenças, hábitos e atitudes de usuários profissionais e gestores 8 , como objetos muitas vezes sensíveis e de difícil abordagem. Assim, as perguntas devem ser formuladas de modo a esclarecer que não há problema em apresentar posicionamentos ou revelar ações socialmente reprováveis. Também deve ser observado a ordem das questões 13 . No início da entrevista, é recomendado realizar perguntas abrangentes e não questionar de imediato o tema da pesquisa. Isso ajuda a quebrar a tensão inicial e possibilita o relaxamento e ganho de confiança dos entrevistados. Ao abordar os conteúdos específicos, sugere-se iniciar com perguntas mais gerais sobre o conteúdo e em seguida introduzir os assuntos sensíveis. Além do mais, devem ser evitadas palavras e expressões carregadas de emoção ou que impliquem juízo de valor sobre determinado comportamento.

Terceira, a garantia da privacidade e de um clima adequado ao contexto da pesquisa são fundamentais para reduzir o viés de desejabilidade social. O ambiente da pesquisa deve estar resguardado de influências externas, interrupções ou presença de terceiros. Com frequência, nas investigações em saúde os participantes são questionados sobre temas de grande sensibilidade, como a respeito de práticas sexuais, relações familiares ou situações de violência, o que reforça a necessidade de ambiente privativo para coleta de dados. É necessário que o pesquisador seja criterioso na escolha dos espaços, assegurando que os participantes não sejam ouvidos por pessoas em outros ambientes. É necessário também preparar adequadamente o espaço, com cadeiras, mesa, água e outras comodidades que tornem o ambiente confortável e ajude a evitar interrupções e quebras de raciocínios. Os dados devem ser sempre coletados apenas na presença da equipe de pesquisa e dos participantes, com exceções apenas para situações de necessidade do entrevistado, especialmente usuários.

Quarta, a confidencialidade dos dados e informações devem ser sempre resguardadas e os usuários devem estar convencidos da garantia do anonimato. Frente à sensibilidade dos assuntos da pesquisa qualitativa em saúde, a identificação pessoal de determinadas revelações pode gerar prejuízos morais, sociais, familiares, financeiros e legais. Assim, é necessário assegurar e fazer com que o entrevistado compreenda e confie que terá o anonimato preservado e as suas informações pessoais mantidas em absoluto sigilo. Pesquisas envolvendo seres humanos necessitam da aprovação em comitê de ética em pesquisa e da observância dos aspectos éticos da legislação em vigor. No entanto, em muitas situações, a assinatura do Termo de Consentimento Livre e Esclarecido e os esclarecimentos éticos e sobre a confidencialidade ocorre apenas como etapa procedimental e burocrática.

Quinta, deve ser evitada a participação repentina nas entrevistas e grupos focais. Entrevistados que estão mais familiarizados com os entrevistadores optam menos por respostas socialmente desejáveis 14 . Ou seja, a continuidade do pesquisador em campo e o desenvolvimento da familiaridade com os participantes favorecem o desenvolvimento de respostas sinceras. Quando a continuidade no campo não é possível, é recomendado o contato prévio com os participantes, quando se deve prestar esclarecimentos sobre o estudo e agendar a participação para um momento posterior.

Sexta, deve-se estar atento à postura e qualificação do pesquisador. Os pesquisadores devem sempre buscar desenvolver uma boa relação com os participantes e promover um clima respeitoso e descontraído. Scott et al. 27 recomendam fazer o uso de linguagens verbais e não verbais para deixar os entrevistados à vontade e menos desconfortáveis para expressar posicionamentos desagradáveis. Ao identificar posicionamentos socialmente desejáveis, é importante evitar o confronto e buscar estratégias para fazer o entrevistado compreender o caráter científico da pesquisa. Como a pesquisa em saúde muitas vezes trata de temas muitos específicos que suscitam dúvidas dos participantes é imprescindível o domínio sobre os conteúdos específicos da natureza do objeto. Durante a entrevista podem surgir dúvidas, questionamentos e incompreensões que demandam do pesquisador conhecimentos específicos para prestar os devidos esclarecimentos. Assim, é necessária a adequada formação cognitiva e relacional dos pesquisadores responsáveis pelo trabalho de campo.

Sétima, ter a sensibilidade de identificar situações de viés de desejabilidade e refletir de maneira crítica sobre os posicionamentos dos participantes. Embora recursos instrumentais e procedimentais devam sempre ser observados, nem sempre é possível controlar a existência do viés e em algumas situações é desejável que não se faça. Em determinados contextos da pesquisa qualitativa em saúde, os participantes podem deliberadamente distorcer situações vivenciadas, como posicionamento político, frente a situações de opressão ou em defesa de determinados aspectos culturais e comunitários ou ainda em ação destinada à transformação dos serviços de saúde. Nesse sentido, a existência do viés de desejabilidade social assume outra perspectiva. Não cabe ao pesquisador buscar evitá-lo ou controlá-lo. Nesses casos, o viés é revelador de aspectos importantes a se considerar e aprofundar, cabendo ao pesquisador ampliar as reflexões e teorizações sobre o fenômeno em estudo e as revelações manifestadas por meio de “opiniões enviesadas”.

Oitava, postura ético-política e social do pesquisador em ciências sociais em saúde. A pesquisa social, por natureza, deve ser conectada com os problemas cotidianos e comprometida com a construção de uma sociedade mais justa. Nessa perspectiva, as recomendações para controlar o viés de desejabilidade social não são apenas requisitos técnico-procedimentais, destinados a ampliar o rigor na pesquisa qualitativa, são, antes, posicionamentos políticos com a finalidade de melhor compreensão do mundo e de buscar caminhos para a transformação das realidades locais e global.

CONSIDERAÇÕES FINAIS

O ensaio abordou a ocorrência, os determinantes e as estratégias de abordagens frente ao viés de desejabilidade social na pesquisa qualitativa em saúde. A existência de vieses pode comprometer a consistência dos resultados de uma investigação científica e levar a conclusões que não correspondem com exatidão às características do fenômeno em estudo. Em algumas situações a existência desse tipo de viés pode ser reveladora de situações de opressão e de posicionamento político deliberado dos participantes.

Destaco a importância da atenção e postura do pesquisador em ciências sociais e humanas em saúde de estar em alerta para a possibilidade de existência desse tipo de viés. Normas, costumes, valores e o contexto social exercem forte influência na elaboração das respostas pelos participantes e isso não pode ser ignorado pelos pesquisadores. Cabe ao pesquisador adotar estratégias para minimizar a ocorrência do viés ou, a partir deles, interpretar e aprofundar aspectos das vivências e significados dos participantes.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Capes - código de financiamento 001).

Financiamento: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Capes - código de financiamento 001).

What are the various types of research bias in qualitative research? Give a solution to overcome these bias

In- b rief:.

• In research, bias take place when regular or common errors introduced in selecting sampling or testing by supporting particular results or out come.
• Selection of samples occur when the presence of observations in the sample depends on the value of the variable of interest.
• Qualitative research  is an descriptive scientific method of study to collect non-numeric data.

Introduction:

If it is the situation then samples are no longer randomly drawn from the population being studied, and any inferences or conclusions about that population are based on the samples selected will be biased. It involves characteristics, meanings and description of  particular object or study.  Most of the case researcher should handled objective type then it is difficult to separate from the complete data, means that maintaining the objectivity and avoid bias. Therefore qualitative research and Data analysis  facing criticisms due to lack of transparency. There are many potential causes of bias in research. As a result vague results and wrong statements and conclusions are identified which leads to major damage especially in clinical and social researches. Basically, there are three types of bias such as information bias, selection bias and confounding bias.

Information bias:

Information bias may happed in the Data collection , observational, recall, recording and data handing which includes missing data also. It may also occur due to wrong classification.  Observational and missing data are more impact particularly those relying on self-reports and retrospective data collection. To over come these problem by taking care of using multiple source of data collection, use standard measurements to collect information like questionnaire automatic instruments for recording measurements.  Maintain similarities between the groups to collect information. Use study design tools for gathering information. An important element to minimize information bias is to ensure that blinding of intervention status (or exposure status in observational studies) is maintained while outcomes are measured and recorded.

Selection bias:

It occurs when comparison is made between competed study with the targeted population. it compares an association between coverage population and outcome of the population.  Some case it also involves risk factor such as health outcome differs in dropouts compared with study participants. In some situation its magnitude and direction of effect is very hard to determine.  To assess the degree of selection biases the researcher should consider random techniques when selecting the sub groups. Because any thing happened after randomization is due to chance cause. Baseline comparison between  intervention or exposure groups. Define exactly what procedure was followed to prevent prediction of future allocation based on the knowledge of previous allocation. It is more clear that selected subgroups are equivalent to the large population characteristics. Handled the missing data in a systematic way may leads to reduce bias.

Confounding bias:

Confounding bias occurs when experimental variables affects the control variables being studied therefore the results may not reflect the actual relationship exists between independent and dependent variables. That means exposure and outcome are influencing the an additional variable called confounder. Simply saying that when the person wants to prove a predetermined assumption.  These kind of biases mostly arises in epidemiology studies. This can be avoided by implementing randomization, study design, data analysis, restriction and matching etc.

Most of the cases the researcher is having the Questionnaire hypothesis  that he should prefer particular outcome or expectations then he should trying to carryout his work to get the expected results which leads to the entire research process is bias. When the experiment or qualitative research is considering population point of view then he should be impartial so that the results are very significant. If it is quantitative research numerical values may not change until the researcher purposively adjust the results.

Ways to reduce the risk of bias:

In order to reduce the risk of bias the researcher should focus on human errors appeared in the process of research. Beside of the above three biases there are few other biases exists in the qualitative research such as channeling bias, interviewer bias, culture bias, chronology bias, performance bias, citation bias etc., once if you recognize and identify the various biases then it is easier to make measures to avoid the biases.

However, a complete unbiased is not possible, but can be reduced  to some extent. In research if the study is completely unbiased then it will be the ultimate qualitative research. But it can not be possible in all cases. Bias may occur at any stage of research. Most importantly the researcher should consider and outline all kinds possible biases will probably may occur in the experiment or study. in qualitative studies the researcher should maintain the records of every step of his research work.  He should be more concentrated on study plan, Sampling design in qualitative research methodology , sample size, qualitative data collection, questionnaire and surveys to avoid bias. A complete elimination or minimizing bias provide benefits to business, community and society. Publishing false statements can leads more harm than good to the people and organizations. Some times lack of resources and time may drives researchers to neglect these unfair practices.

Finally, the researcher should pay attention to objective, transparency, selecting participants, qualitative questioning, analysis ,reporting and writing manuscripts to minimize biases in the complete research process.   Qualitative research analysis  more depends on researcher experience and judgment. Also he is trying to collect data for subjective point of view it may be unique to persons  or situation. Hence it is very difficult for the researcher to handle or avoid bias comparatively quantitative research. As there in quantitative research numerical values may not same in every situation. Therefore its always better to identify the bias exists in the research and try to predict what kind of bias is that having in our study and try to avoid the bias as much as possible. There are few general solutions to avoid bias is that take third person view, through understanding is required on the subject as well as study when comparison takes place, better to use people first language in questionnaire preparation, so that they can understand in proper way, be specific when writing about people etc.,

References:

• Collier, D., & Mahoney, J. (1996). Insights and pitfalls: Selection bias in qualitative research.  World Politics ,  49 (1), 56-91.
• Novick, G. (2008). Is there a bias against telephone interviews in qualitative research?.  Research in nursing & health ,  31 (4), 391-398.
• Buetow, S. (2019). Apophenia, unconscious bias and reflexivity in nursing qualitative research.  International journal of nursing studies ,  89 , 8-13.

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Home » Qualitative Research – Methods, Analysis Types and Guide

Qualitative Research – Methods, Analysis Types and Guide

Table of Contents

Qualitative Research

Qualitative research is a type of research methodology that focuses on exploring and understanding people’s beliefs, attitudes, behaviors, and experiences through the collection and analysis of non-numerical data. It seeks to answer research questions through the examination of subjective data, such as interviews, focus groups, observations, and textual analysis.

Qualitative research aims to uncover the meaning and significance of social phenomena, and it typically involves a more flexible and iterative approach to data collection and analysis compared to quantitative research. Qualitative research is often used in fields such as sociology, anthropology, psychology, and education.

Qualitative Research Methods

Qualitative Research Methods are as follows:

One-to-One Interview

This method involves conducting an interview with a single participant to gain a detailed understanding of their experiences, attitudes, and beliefs. One-to-one interviews can be conducted in-person, over the phone, or through video conferencing. The interviewer typically uses open-ended questions to encourage the participant to share their thoughts and feelings. One-to-one interviews are useful for gaining detailed insights into individual experiences.

Focus Groups

This method involves bringing together a group of people to discuss a specific topic in a structured setting. The focus group is led by a moderator who guides the discussion and encourages participants to share their thoughts and opinions. Focus groups are useful for generating ideas and insights, exploring social norms and attitudes, and understanding group dynamics.

Ethnographic Studies

This method involves immersing oneself in a culture or community to gain a deep understanding of its norms, beliefs, and practices. Ethnographic studies typically involve long-term fieldwork and observation, as well as interviews and document analysis. Ethnographic studies are useful for understanding the cultural context of social phenomena and for gaining a holistic understanding of complex social processes.

Text Analysis

This method involves analyzing written or spoken language to identify patterns and themes. Text analysis can be quantitative or qualitative. Qualitative text analysis involves close reading and interpretation of texts to identify recurring themes, concepts, and patterns. Text analysis is useful for understanding media messages, public discourse, and cultural trends.

This method involves an in-depth examination of a single person, group, or event to gain an understanding of complex phenomena. Case studies typically involve a combination of data collection methods, such as interviews, observations, and document analysis, to provide a comprehensive understanding of the case. Case studies are useful for exploring unique or rare cases, and for generating hypotheses for further research.

Process of Observation

This method involves systematically observing and recording behaviors and interactions in natural settings. The observer may take notes, use audio or video recordings, or use other methods to document what they see. Process of observation is useful for understanding social interactions, cultural practices, and the context in which behaviors occur.

Record Keeping

This method involves keeping detailed records of observations, interviews, and other data collected during the research process. Record keeping is essential for ensuring the accuracy and reliability of the data, and for providing a basis for analysis and interpretation.

This method involves collecting data from a large sample of participants through a structured questionnaire. Surveys can be conducted in person, over the phone, through mail, or online. Surveys are useful for collecting data on attitudes, beliefs, and behaviors, and for identifying patterns and trends in a population.

Qualitative data analysis is a process of turning unstructured data into meaningful insights. It involves extracting and organizing information from sources like interviews, focus groups, and surveys. The goal is to understand people’s attitudes, behaviors, and motivations

Qualitative Research Analysis Methods

Qualitative Research analysis methods involve a systematic approach to interpreting and making sense of the data collected in qualitative research. Here are some common qualitative data analysis methods:

Thematic Analysis

This method involves identifying patterns or themes in the data that are relevant to the research question. The researcher reviews the data, identifies keywords or phrases, and groups them into categories or themes. Thematic analysis is useful for identifying patterns across multiple data sources and for generating new insights into the research topic.

Content Analysis

This method involves analyzing the content of written or spoken language to identify key themes or concepts. Content analysis can be quantitative or qualitative. Qualitative content analysis involves close reading and interpretation of texts to identify recurring themes, concepts, and patterns. Content analysis is useful for identifying patterns in media messages, public discourse, and cultural trends.

Discourse Analysis

This method involves analyzing language to understand how it constructs meaning and shapes social interactions. Discourse analysis can involve a variety of methods, such as conversation analysis, critical discourse analysis, and narrative analysis. Discourse analysis is useful for understanding how language shapes social interactions, cultural norms, and power relationships.

Grounded Theory Analysis

This method involves developing a theory or explanation based on the data collected. Grounded theory analysis starts with the data and uses an iterative process of coding and analysis to identify patterns and themes in the data. The theory or explanation that emerges is grounded in the data, rather than preconceived hypotheses. Grounded theory analysis is useful for understanding complex social phenomena and for generating new theoretical insights.

Narrative Analysis

This method involves analyzing the stories or narratives that participants share to gain insights into their experiences, attitudes, and beliefs. Narrative analysis can involve a variety of methods, such as structural analysis, thematic analysis, and discourse analysis. Narrative analysis is useful for understanding how individuals construct their identities, make sense of their experiences, and communicate their values and beliefs.

Phenomenological Analysis

This method involves analyzing how individuals make sense of their experiences and the meanings they attach to them. Phenomenological analysis typically involves in-depth interviews with participants to explore their experiences in detail. Phenomenological analysis is useful for understanding subjective experiences and for developing a rich understanding of human consciousness.

Comparative Analysis

This method involves comparing and contrasting data across different cases or groups to identify similarities and differences. Comparative analysis can be used to identify patterns or themes that are common across multiple cases, as well as to identify unique or distinctive features of individual cases. Comparative analysis is useful for understanding how social phenomena vary across different contexts and groups.

Applications of Qualitative Research

Qualitative research has many applications across different fields and industries. Here are some examples of how qualitative research is used:

• Market Research: Qualitative research is often used in market research to understand consumer attitudes, behaviors, and preferences. Researchers conduct focus groups and one-on-one interviews with consumers to gather insights into their experiences and perceptions of products and services.
• Health Care: Qualitative research is used in health care to explore patient experiences and perspectives on health and illness. Researchers conduct in-depth interviews with patients and their families to gather information on their experiences with different health care providers and treatments.
• Education: Qualitative research is used in education to understand student experiences and to develop effective teaching strategies. Researchers conduct classroom observations and interviews with students and teachers to gather insights into classroom dynamics and instructional practices.
• Social Work : Qualitative research is used in social work to explore social problems and to develop interventions to address them. Researchers conduct in-depth interviews with individuals and families to understand their experiences with poverty, discrimination, and other social problems.
• Anthropology : Qualitative research is used in anthropology to understand different cultures and societies. Researchers conduct ethnographic studies and observe and interview members of different cultural groups to gain insights into their beliefs, practices, and social structures.
• Psychology : Qualitative research is used in psychology to understand human behavior and mental processes. Researchers conduct in-depth interviews with individuals to explore their thoughts, feelings, and experiences.
• Public Policy : Qualitative research is used in public policy to explore public attitudes and to inform policy decisions. Researchers conduct focus groups and one-on-one interviews with members of the public to gather insights into their perspectives on different policy issues.

How to Conduct Qualitative Research

Here are some general steps for conducting qualitative research:

• Identify your research question: Qualitative research starts with a research question or set of questions that you want to explore. This question should be focused and specific, but also broad enough to allow for exploration and discovery.
• Select your research design: There are different types of qualitative research designs, including ethnography, case study, grounded theory, and phenomenology. You should select a design that aligns with your research question and that will allow you to gather the data you need to answer your research question.
• Recruit participants: Once you have your research question and design, you need to recruit participants. The number of participants you need will depend on your research design and the scope of your research. You can recruit participants through advertisements, social media, or through personal networks.
• Collect data: There are different methods for collecting qualitative data, including interviews, focus groups, observation, and document analysis. You should select the method or methods that align with your research design and that will allow you to gather the data you need to answer your research question.
• Analyze data: Once you have collected your data, you need to analyze it. This involves reviewing your data, identifying patterns and themes, and developing codes to organize your data. You can use different software programs to help you analyze your data, or you can do it manually.
• Interpret data: Once you have analyzed your data, you need to interpret it. This involves making sense of the patterns and themes you have identified, and developing insights and conclusions that answer your research question. You should be guided by your research question and use your data to support your conclusions.
• Communicate results: Once you have interpreted your data, you need to communicate your results. This can be done through academic papers, presentations, or reports. You should be clear and concise in your communication, and use examples and quotes from your data to support your findings.

Examples of Qualitative Research

Here are some real-time examples of qualitative research:

• Customer Feedback: A company may conduct qualitative research to understand the feedback and experiences of its customers. This may involve conducting focus groups or one-on-one interviews with customers to gather insights into their attitudes, behaviors, and preferences.
• Healthcare : A healthcare provider may conduct qualitative research to explore patient experiences and perspectives on health and illness. This may involve conducting in-depth interviews with patients and their families to gather information on their experiences with different health care providers and treatments.
• Education : An educational institution may conduct qualitative research to understand student experiences and to develop effective teaching strategies. This may involve conducting classroom observations and interviews with students and teachers to gather insights into classroom dynamics and instructional practices.
• Social Work: A social worker may conduct qualitative research to explore social problems and to develop interventions to address them. This may involve conducting in-depth interviews with individuals and families to understand their experiences with poverty, discrimination, and other social problems.
• Anthropology : An anthropologist may conduct qualitative research to understand different cultures and societies. This may involve conducting ethnographic studies and observing and interviewing members of different cultural groups to gain insights into their beliefs, practices, and social structures.
• Psychology : A psychologist may conduct qualitative research to understand human behavior and mental processes. This may involve conducting in-depth interviews with individuals to explore their thoughts, feelings, and experiences.
• Public Policy: A government agency or non-profit organization may conduct qualitative research to explore public attitudes and to inform policy decisions. This may involve conducting focus groups and one-on-one interviews with members of the public to gather insights into their perspectives on different policy issues.

Purpose of Qualitative Research

The purpose of qualitative research is to explore and understand the subjective experiences, behaviors, and perspectives of individuals or groups in a particular context. Unlike quantitative research, which focuses on numerical data and statistical analysis, qualitative research aims to provide in-depth, descriptive information that can help researchers develop insights and theories about complex social phenomena.

Qualitative research can serve multiple purposes, including:

• Exploring new or emerging phenomena : Qualitative research can be useful for exploring new or emerging phenomena, such as new technologies or social trends. This type of research can help researchers develop a deeper understanding of these phenomena and identify potential areas for further study.
• Understanding complex social phenomena : Qualitative research can be useful for exploring complex social phenomena, such as cultural beliefs, social norms, or political processes. This type of research can help researchers develop a more nuanced understanding of these phenomena and identify factors that may influence them.
• Generating new theories or hypotheses: Qualitative research can be useful for generating new theories or hypotheses about social phenomena. By gathering rich, detailed data about individuals’ experiences and perspectives, researchers can develop insights that may challenge existing theories or lead to new lines of inquiry.
• Providing context for quantitative data: Qualitative research can be useful for providing context for quantitative data. By gathering qualitative data alongside quantitative data, researchers can develop a more complete understanding of complex social phenomena and identify potential explanations for quantitative findings.

When to use Qualitative Research

Here are some situations where qualitative research may be appropriate:

• Exploring a new area: If little is known about a particular topic, qualitative research can help to identify key issues, generate hypotheses, and develop new theories.
• Understanding complex phenomena: Qualitative research can be used to investigate complex social, cultural, or organizational phenomena that are difficult to measure quantitatively.
• Investigating subjective experiences: Qualitative research is particularly useful for investigating the subjective experiences of individuals or groups, such as their attitudes, beliefs, values, or emotions.
• Conducting formative research: Qualitative research can be used in the early stages of a research project to develop research questions, identify potential research participants, and refine research methods.
• Evaluating interventions or programs: Qualitative research can be used to evaluate the effectiveness of interventions or programs by collecting data on participants’ experiences, attitudes, and behaviors.

Characteristics of Qualitative Research

Qualitative research is characterized by several key features, including:

• Focus on subjective experience: Qualitative research is concerned with understanding the subjective experiences, beliefs, and perspectives of individuals or groups in a particular context. Researchers aim to explore the meanings that people attach to their experiences and to understand the social and cultural factors that shape these meanings.
• Use of open-ended questions: Qualitative research relies on open-ended questions that allow participants to provide detailed, in-depth responses. Researchers seek to elicit rich, descriptive data that can provide insights into participants’ experiences and perspectives.
• Sampling-based on purpose and diversity: Qualitative research often involves purposive sampling, in which participants are selected based on specific criteria related to the research question. Researchers may also seek to include participants with diverse experiences and perspectives to capture a range of viewpoints.
• Data collection through multiple methods: Qualitative research typically involves the use of multiple data collection methods, such as in-depth interviews, focus groups, and observation. This allows researchers to gather rich, detailed data from multiple sources, which can provide a more complete picture of participants’ experiences and perspectives.
• Inductive data analysis: Qualitative research relies on inductive data analysis, in which researchers develop theories and insights based on the data rather than testing pre-existing hypotheses. Researchers use coding and thematic analysis to identify patterns and themes in the data and to develop theories and explanations based on these patterns.
• Emphasis on researcher reflexivity: Qualitative research recognizes the importance of the researcher’s role in shaping the research process and outcomes. Researchers are encouraged to reflect on their own biases and assumptions and to be transparent about their role in the research process.

Advantages of Qualitative Research

Qualitative research offers several advantages over other research methods, including:

• Depth and detail: Qualitative research allows researchers to gather rich, detailed data that provides a deeper understanding of complex social phenomena. Through in-depth interviews, focus groups, and observation, researchers can gather detailed information about participants’ experiences and perspectives that may be missed by other research methods.
• Flexibility : Qualitative research is a flexible approach that allows researchers to adapt their methods to the research question and context. Researchers can adjust their research methods in real-time to gather more information or explore unexpected findings.
• Contextual understanding: Qualitative research is well-suited to exploring the social and cultural context in which individuals or groups are situated. Researchers can gather information about cultural norms, social structures, and historical events that may influence participants’ experiences and perspectives.
• Participant perspective : Qualitative research prioritizes the perspective of participants, allowing researchers to explore subjective experiences and understand the meanings that participants attach to their experiences.
• Theory development: Qualitative research can contribute to the development of new theories and insights about complex social phenomena. By gathering rich, detailed data and using inductive data analysis, researchers can develop new theories and explanations that may challenge existing understandings.
• Validity : Qualitative research can offer high validity by using multiple data collection methods, purposive and diverse sampling, and researcher reflexivity. This can help ensure that findings are credible and trustworthy.

Limitations of Qualitative Research

Qualitative research also has some limitations, including:

• Subjectivity : Qualitative research relies on the subjective interpretation of researchers, which can introduce bias into the research process. The researcher’s perspective, beliefs, and experiences can influence the way data is collected, analyzed, and interpreted.
• Limited generalizability: Qualitative research typically involves small, purposive samples that may not be representative of larger populations. This limits the generalizability of findings to other contexts or populations.
• Time-consuming: Qualitative research can be a time-consuming process, requiring significant resources for data collection, analysis, and interpretation.
• Resource-intensive: Qualitative research may require more resources than other research methods, including specialized training for researchers, specialized software for data analysis, and transcription services.
• Limited reliability: Qualitative research may be less reliable than quantitative research, as it relies on the subjective interpretation of researchers. This can make it difficult to replicate findings or compare results across different studies.
• Ethics and confidentiality: Qualitative research involves collecting sensitive information from participants, which raises ethical concerns about confidentiality and informed consent. Researchers must take care to protect the privacy and confidentiality of participants and obtain informed consent.

Also see Research Methods

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Unmasking bias in artificial intelligence: a systematic review of bias detection and mitigation strategies in electronic health record-based models.

Chen F, Wang L, Hong J, et al. Unmasking bias in artificial intelligence: a systematic review of bias detection and mitigation strategies in electronic health record-based models. J Am Med Inform Assoc. 2024;Epub Mar 23. doi:10.1093/jamia/ocae060.

When biased data are used for research, the results may reflect the same biases if appropriate precautions are not taken. In this systematic review, researchers describe possible types of bias (e.g., implicit, selection) that can result from research with artificial intelligence (AI) using electronic health record (EHR) data. Along with recommendations to reduce introducing bias into the data model, the authors stress the importance of standardized reporting of model development and real-world testing.

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SYSTEMATIC REVIEW article

This article is part of the research topic.

Ethnic Inequalities in Diabetes Care and Outcomes

Acceptability of Community Health Worker and Peer Supported Interventions for Minoritized Populations with Type 2 Diabetes: A Qualitative Systematic Review Provisionally Accepted

• 1 University of Birmingham, United Kingdom

The final, formatted version of the article will be published soon.

Ethnic minority groups in high income countries in North America, Europe, and elsewhere are disproportionately affected by T2DM with a higher risk of mortality and morbidity. The use of community health workers and peer supporters offer a way of ensuring the benefits of self-management support observed in the general population are shared by those in minoritized communities.The major databases were searched for existing qualitative evidence of participants' experiences and perspectives of self-management support for type 2 diabetes delivered by community health workers and peer supporters (CHWPs) in ethnically minoritized populations. The data were analysed using Sekhon's Theoretical Framework of Acceptability.The results are described within five domains of the framework of acceptability collapsed from seven for reasons of clarity and concision: Affective attitude described participants' satisfaction with CHWPs delivering the intervention including the open, trusting relationships that developed in contrast to those with clinical providers. In considering Burden and Opportunity Costs, participants reflected on the impact of health, transport, and the responsibilities of work and childcare on their attendance, alongside a lack of resources necessary to maintain healthy diets and active lifestyles. In relation to Cultural Sensitivity participants appreciated the greater understanding of the specific cultural needs and challenges exhibited by CHWPs. The evidence related to Intervention Coherence indicated that participants responded positively to the practical and applied content, the range of teaching materials, and interactive practical sessions. Finally, in examining the impact of Effectiveness and Self-efficacy participants described how they changed a range of healthrelated behaviours, had more confidence in dealing with their condition and interacting with senior clinicians and benefitted from the social support of fellow participants and CHWPs.Many of the same barriers around attendance and engagement adherence towith usual self-management support interventions delivered to general populations were observed, including lack of time and resource. However, the insight of CHWPs, their culturallysensitive and specific strategies for self-management and their development of trusting relationships presented considerable advantages.

Keywords: type 2 diabetes, self-management, Community Health, health inequalities, Ethnic minorities Library, Places of Worship, Sports centres, Community Halls, a Mixed methods studies

Received: 03 Oct 2023; Accepted: 26 Feb 2024.

Copyright: © 2024 Grant and Litchfield. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mx. Ian Litchfield, University of Birmingham, Birmingham, United Kingdom

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Published on 18.4.2024 in Vol 26 (2024)

Evaluating Algorithmic Bias in 30-Day Hospital Readmission Models: Retrospective Analysis

Authors of this article:

Original Paper

• H Echo Wang 1 , DrPH   ; 
• Jonathan P Weiner 1, 2 , DrPH   ; 
• Suchi Saria 3 , PhD   ; 
• Hadi Kharrazi 1, 2 , MD, PhD  

1 Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States

2 Johns Hopkins Center for Population Health Information Technology, Baltimore, MD, United States

3 Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States

Corresponding Author:

Hadi Kharrazi, MD, PhD

Bloomberg School of Public Health

Johns Hopkins University

624 N Broadway, Hampton House

Baltimore, MD

United States

Phone: 1 443 287 8264

Email: [email protected]

Background: The adoption of predictive algorithms in health care comes with the potential for algorithmic bias, which could exacerbate existing disparities. Fairness metrics have been proposed to measure algorithmic bias, but their application to real-world tasks is limited.

Objective: This study aims to evaluate the algorithmic bias associated with the application of common 30-day hospital readmission models and assess the usefulness and interpretability of selected fairness metrics.

Methods: We used 10.6 million adult inpatient discharges from Maryland and Florida from 2016 to 2019 in this retrospective study. Models predicting 30-day hospital readmissions were evaluated: LACE Index, modified HOSPITAL score, and modified Centers for Medicare & Medicaid Services (CMS) readmission measure, which were applied as-is (using existing coefficients) and retrained (recalibrated with 50% of the data). Predictive performances and bias measures were evaluated for all, between Black and White populations, and between low- and other-income groups. Bias measures included the parity of false negative rate (FNR), false positive rate (FPR), 0-1 loss, and generalized entropy index. Racial bias represented by FNR and FPR differences was stratified to explore shifts in algorithmic bias in different populations.

Results: The retrained CMS model demonstrated the best predictive performance (area under the curve: 0.74 in Maryland and 0.68-0.70 in Florida), and the modified HOSPITAL score demonstrated the best calibration (Brier score: 0.16-0.19 in Maryland and 0.19-0.21 in Florida). Calibration was better in White (compared to Black) populations and other-income (compared to low-income) groups, and the area under the curve was higher or similar in the Black (compared to White) populations. The retrained CMS and modified HOSPITAL score had the lowest racial and income bias in Maryland. In Florida, both of these models overall had the lowest income bias and the modified HOSPITAL score showed the lowest racial bias. In both states, the White and higher-income populations showed a higher FNR, while the Black and low-income populations resulted in a higher FPR and a higher 0-1 loss. When stratified by hospital and population composition, these models demonstrated heterogeneous algorithmic bias in different contexts and populations.

Conclusions: Caution must be taken when interpreting fairness measures’ face value. A higher FNR or FPR could potentially reflect missed opportunities or wasted resources, but these measures could also reflect health care use patterns and gaps in care. Simply relying on the statistical notions of bias could obscure or underplay the causes of health disparity. The imperfect health data, analytic frameworks, and the underlying health systems must be carefully considered. Fairness measures can serve as a useful routine assessment to detect disparate model performances but are insufficient to inform mechanisms or policy changes. However, such an assessment is an important first step toward data-driven improvement to address existing health disparities.

Introduction

Background of algorithmic bias.

Predictive algorithms and machine learning tools are increasingly integrated into clinical decision-making and population health management. However, with the increasing reliance on predictive algorithms comes a growing concern of exacerbating health disparities [ 1 - 3 ]. Evidence has shown that widely used algorithms that use past health care expenditures to predict high-risk patients have systematically underestimated the health care needs of Black patients [ 4 ]. In addition, studies have shown that predictive performances of models predicting intensive care unit mortality, 30-day psychiatric readmission, and asthma exacerbation were worse in populations with lower socioeconomic status [ 5 , 6 ].

With algorithmic bias as a potentially pervasive issue, a few checklists have been published to qualitatively identify and understand the potential biases derived from predictive models [ 7 , 8 ]. However, no agreed-upon quantitative method exists to routinely assess whether deployed models will lead to biased results and exacerbate health disparities faced by marginalized groups [ 2 , 9 ]. In this study, we define algorithmic bias as the differential results or performance of predictive models that may lead to differential allocation or outcomes between subgroups [ 10 - 12 ]. In addition, we define disparity as the difference in the quality of health care (the degree to which health services increase the likelihood of desired health outcomes) received by a marginalized population that is not due to access-related factors, clinical needs, preferences, and appropriateness of intervention [ 10 , 13 ]. Fairness metrics, which are a set of mathematical expressions that formalize certain equality between groups (eg, equal false negative rates [FNRs]), were proposed to measure and detect biases in machine learning models [ 12 , 14 ]. Although the machine learning community has shown that fairness metrics are a promising way to identify algorithmic bias, these metrics are criticized for being insufficient to reflect the heterogeneous and dynamic nature of health care [ 15 , 16 ]. Fairness metrics can also be misleading or conflicting due to their narrow focus on equal rates between groups [ 12 , 15 ]. Furthermore, these metrics could be interpreted without context-specific judgment or domain knowledge, thus failing to connect predictions to interventions and the downstream health care disparity [ 15 , 17 ]. Most importantly, these measures are often not fully tested in real-world predictive tasks and lack evidence on how well these measures’ interpretation could guide intervention planning.

Background of Disparity in 30-Day Hospital Readmission

Predicting hospital readmissions is widely studied in health care management and delivery [ 18 - 21 ]. Hospital readmissions, especially unplanned or avoidable readmissions, are not only associated with a high risk of in-hospital mortality but also costly and burdensome to the health care system [ 19 , 22 ]. Since 2012, the Hospital Readmission Reduction Program by the Centers for Medicare & Medicaid Services (CMS) has imposed financial penalties for hospitals with excessive readmission rates [ 22 ]. CMS has consequently incentivized hospitals to segment patients by risk so that hospitals can target the delivery of these resource-intensive interventions to the patients at greatest risk, such as transitional care intervention and better discharge planning [ 19 , 23 , 24 ]. Many hospital readmission predictive models have been published, with >350 models predicting 30-day readmission identified in prior systematic reviews and our prior work [ 7 , 18 , 19 , 21 , 25 ]. The disparity in hospital readmission rates is well studied. For example, past studies have shown that Black patients have higher readmission rates after adjusting for demographic and clinical characteristics [ 26 - 29 ]. In addition to racial disparity, patients receiving care at racial and ethnic minority-serving hospitals [ 29 , 30 ] or living in disadvantaged neighborhoods have higher rates of readmission [ 31 - 33 ]. Research has also shown that disparity in health care use, including hospital readmission, is related to not only individuals’ racial and ethnic identity but also their communities [ 34 ]. Other research has also suggested that social environments, either the place of residence or the hospital where one receives care, may explain a meaningful portion of health disparity [ 35 , 36 ].

Despite model abundance and known disparity in hospital readmissions, research has been limited in evaluating how algorithmic bias or the disparate performances of these predictive models may impact patient outcomes and downstream health disparities once deployed. Lack of evidence is more prominent in how the model-guided intervention allocation may reduce or aggravate existing health disparities between different populations. To address this gap in evidence, in this study, we aimed to (1) implement a selection of fairness metrics to evaluate whether the application of common 30-day readmission predictive models may lead to bias between racial and income groups and (2) interpret the selected fairness metrics and assess their usefulness in the context of facilitating equitable allocation of interventions. In this paper, we represent the perspective of a health system or payer who uses an established, validated algorithm to identify patients at high risk of unplanned readmission so that targeted intervention can be planned for these patients. Thus, our main concern for algorithmic bias is the unequal allocation of intervention resources and the unequal health outcome as a result. Specifically, we are concerned about risk scores systematically underestimating or overestimating needs for a certain group, assuming the model we deploy is validated and has acceptable overall predictive performance.

Study Population and Data

This retrospective study included 1.9 million adult inpatient discharges in Maryland and 8.7 million inpatient discharges in Florida from 2016 to 2019. The State Inpatient Databases (SIDs) are maintained by the United States Agency for Healthcare Research and Quality, as part of the Healthcare Cost and Utilization Project (HCUP), were used for this analysis. The SIDs include longitudinal hospital care data in the United States, inclusive of all insurance payers (eg, Medicare, Medicaid, private insurance, and the uninsured) and all patient ages [ 37 ]. The SIDs capture >97% of all eligible hospital discharges in each state [ 38 ]. Maryland and Florida were selected due to their different population sizes, compositions (eg, racial and ethnic distribution and urban to rural ratio), and health care environment (Maryland’s all-payer model vs Medicaid expansion not adopted in Florida) [ 39 , 40 ]. In addition, Maryland and Florida are among a small subset of states in which the SIDs contain a “VisitLink” variable that tracks unique patients within the state and across years from 2016 to 2019, allowing for the longitudinal analysis of readmissions across hospitals and different calendar years [ 41 ]. The SIDs were also linked to the American Hospital Association’s Annual Survey Database to obtain hospital-level information. The study population excluded admissions where patients were aged <18 years, died in hospitals, were discharged against medical advice, or had insufficient information to calculate readmission (eg, missing the VisitLink variable or length of stay).

Study Outcome

The calculation of 30-day readmission followed the definition used by the HCUP [ 42 ]. Any inpatient admission was counted as an index admission. The all-cause 30-day readmission rate was defined as the number of admissions with at least 1 subsequent hospital admission within 30 days, divided by the total number of admissions during the study period. Unplanned, all-cause 30-day hospital readmissions were identified using the methodology developed by CMS [ 43 , 44 ]. The study cohort selection process and determination of unplanned readmission are outlined in Figure 1 .

Predictive Models

The LACE index [ 45 ], the HOSPITAL score [ 46 ], and the CMS hospital-wide all-cause readmission measure [ 43 ] were included in the analysis as they were validated externally and commonly used in practice based on our prior review [ 7 ]. The LACE index and the HOSPITAL score were designed for hospital staff to identify patients at high risk of readmission for targeted intervention efforts and have been converted to a scoring system and extensively validated. Thus, the 2 models were applied to obtain the predicted risk scores without retraining, to mimic how the models were used in practice. In total, 2 of the HOSPITAL score predictors—low hemoglobin and low sodium levels at discharge—were not available in the SIDs, and thus were excluded. The total risk scores were adjusted as a result. Details of model variables and how the 2 models were implemented are reported in Multimedia Appendices 1 and 2 . The CMS measure was evaluated using 2 approaches: applied as-is with existing coefficients and retrained to generate new coefficients using 50% of the sample. To ensure comparability between the CMS measure and other models, the predicted patient-level risk was used without the hospital-level effect from the original measure, and the CMS measure was limited to the “medicine cohort” [ 43 ]. On the basis of the CMS measure’s specification report, the patient population was divided into 5 mutually exclusive cohorts: surgery or gynecology, cardiorespiratory, cardiovascular, neurology, and medicine. The cohorts were determined using the Agency for Healthcare Research and Quality Clinical Classifications Software categories [ 43 ]. The medicine cohort was randomly split 50-50 into a retraining and testing data set. The CMS measure includes age and >100 variables, representing a wide range of condition categories. The measure was trained on the retraining data set with 5 cross-validations and then run on the testing data set using the new coefficients to obtain the performance and bias metrics for the CMS retrained model. Separately, the CMS measure with the published coefficients was run on the full medicine cohort data set to obtain performance and bias metrics for the CMS as-is model. The existing model thresholds were used to classify a positive, or high-risk, class: 10 points for LACE, and high-risk (5 in the adjusted scoring) for modified HOSPITAL. The optimal threshold identified using the Youden Index [ 47 ] on the receiver operating characteristic curve was used for the 2 CMS measures.

We measured predictive performances and biases between Black and White subpopulations and between low-income and other-income subpopulations. Race is a normalized variable in the HCUP that indicates race and ethnicity. The low-income group was defined as the fourth quartile of the median state household income, whereas the remaining 3 quartiles were grouped as other income. The median state income quartiles were provided in HCUP SIDs and were calculated based on the median income of the patient’s zip code. Predictive performances of each model were derived for all population and each subpopulation using area under the curve (AUC), Brier statistic, and Hosmer-Lemeshow goodness of fit. Bias was represented by the group difference of the mathematical measures: false positive rate (FPR) difference (eg, FPR between Black and White patients), FNR difference, 0-1 loss difference, and generalized entropy index (GEI). FNR was calculated as the ratio between false negatives (those predicted as low risk while having an unplanned 30-day readmission) and the total number of positives. Similarly, the FPR was calculated as the ratio of false positives out of the total number of negative cases. Normalized total error rates is 0-1 loss, and it is calculated as the percentage of incorrect predictions. Bias measured by FPR, FNR, and 0-1 loss differences focus on unequal error rates. The GEI is a measure of income inequality and proposed to measure algorithm fairness between groups with a range between 0 and infinity, in which lower scores represent more equity [ 48 ].

Ethical Considerations

This study was not human subjects research, as determined by the Johns Hopkins School of Public Health Institutional Review Board. No compensation was provided.

Statistical Analysis

Primary analyses were conducted using R (version 4.0.2; R Foundation for Statistical Computing). The aggregate condition categories required to calculate unplanned readmission and CMS measures were calculated in SAS software (version 9.4; SAS Institute) using the programs provided by the agencies [ 49 , 50 ]. GEI measures were calculated using the AI Fairness 360 package published by IBM Corp [ 51 ]. The unit of analysis was admission. FNR and FPR results were first stratified by individual hospital and visualized in a scatter plot. The racial bias results were then stratified by hospital population composition (eg, percentage of Black patients), which was shown to associate with the overall outcome of a hospital [ 35 ]. Hospitals were binned by the percentage of Black patients served in a hospital (eg, >10% and >20%), and the racial bias measures with their 95% CIs were calculated for each bin. For FNR difference, FPR difference, and 0-1 loss difference, the distribution across 2 groups was calculated, and the significance of the measure difference was assessed using the Student t test (2-tailed) under the null hypothesis that the group difference was equal to 0. For all statistical tests, an α of .05 was used.

Demographic and Clinical Characteristics

As presented in Table 1 , among the 1,857,658 Maryland inpatient discharges from 2016 to 2019, a total of 55.41% (n=1,029,292) were White patients and 33.71% (n=626,280) were Black patients, whereas in Florida, 64.49% (5,632,318/8,733,002) of the inpatient discharges were White patients and 16.59% (1,448,620/8,733,002) were Black patients.

White patients in both states were older, more likely to be on private insurance, and less likely to reside in large metropolitan areas or be treated in major teaching or large hospitals in urban areas. Compared to White patients, Black patients in Maryland had a longer length of inpatient stay, more inpatient procedures, fewer inpatient diagnoses, higher inpatient charges, and more comorbidities and were more likely to be discharged to home or self-care. However, Black patients in Florida had fewer inpatient diagnoses, fewer procedures, and fewer total charges. These patients also had longer lengths of inpatient stays, more comorbidities, and were more likely to be discharged to home or self-care. In both Maryland and Florida, those in the lowest income quartile were younger, had a longer length of inpatient stay, had higher inpatient charges, had more comorbidities, and had fewer procedures than other-income groups. The low-income group was less likely to reside in metropolitan areas but was more likely to be treated in major teaching hospitals. Except for those noted in footnote c of Table 1 , all characteristics showed statistically significant differences between racial and income groups (all P values <.001).

a MD: Maryland.

b FL: Florida.

c P values were computed between racial groups and between income groups, respectively. All P values are <.001 except for the ones in this footnote: P value for female between income groups=.80 and for discharge type between income groups=.99.

d CCI: Charlson Comorbidity Index.

Predictive Performance

The observed 30-day unplanned readmission rates in Maryland were higher in the Black and low-income patient groups (ie, 11.13% for White patients, 12.77% for Black patients, 10.59% for other-income patients, and 12.73% for low-income patients; Table 2 ).

a Predicted: the predicted readmission rates for LACE and HOSPITAL were calculated as the percentage of patients at high risk of unplanned readmission based on the model output for the group; and the predicted readmission rates for the two CMS models were the predicted probability of being at high risk of unplanned readmission for the group.

b LACE: The LACE Index for readmission risk.

c HOSPITAL: The modified HOSPITAL score for readmission risk.

d CMS: Centers for Medicare & Medicaid Services (readmission measure).

e MD: Maryland.

f FL: Florida.

A fair and well-calibrated predictive model would be assumed to overpredict or underpredict readmission rates to a similar degree across racial or income groups. Compared to the observed readmission rates, the LACE index overestimated readmission rates in all subpopulations and was more pronounced in Black and low-income populations. The readmission rates estimated by the modified HOSPITAL score were closest to the observed rates. The CMS as-is model underestimated across subpopulations, and the estimated rates of readmission were similar between subpopulations, while the retrained CMS model overestimated in all subpopulations to a similar degree. In Florida, the observed 30-day unplanned readmission rates were higher than those in Maryland in all populations. Similar to Maryland, Florida’s observed readmission rates were also higher in the Black and low-income groups (ie, 13.94% for White populations, 17.14% for Black populations, 13.6% for other-income populations, and 16.03% for low-income populations) and had similar overestimation and underestimation patterns ( Table 2 ).

As presented in Table 3 , in Maryland, the retrained CMS model had better predictive performance (AUC 0.74 in all subpopulations) than the other 3 models, which only achieved moderate predictive performance (AUC between 0.65 and 0.68). The modified HOSPITAL score had the best calibration (Brier score=0.16−0.19 in all subpopulations), whereas the CMS as-is model performed poorly on the Brier score. Calibration was better in the White (compared to the Black) population and other-income (compared to low-income) populations in both states, and the AUC was higher or similar in the Black (compared to the White) population. In Florida, the CMS retrained model also performed better than the other models in all subpopulations (AUC 0.68-0.72), and the modified HOSPITAL score had the best calibration (Brier score 0.19-0.21). All models demonstrated excellent goodness of fit across subpopulations ( Table 3 ).

a LACE: The LACE Index for readmission risk.

b HOSPITAL: The modified HOSPITAL score for readmission risk.

c CMS: Centers for Medicare & Medicaid Services (readmission measure).

d MD: Maryland.

e FL: Florida.

f AUC: area under the curve.

Bias Measures

Misclassification rates (ie, FPR difference and FNR difference) indicate relative between-group bias, whereas 0-1 loss differences indicate the overall error rates between groups. The between-group GEI indicates how unequally an outcome is distributed between groups [ 48 ]. In Maryland, the retrained CMS model and the modified HOSPITAL score had the lowest racial and income bias ( Table 4 ).

Specifically, the modified HOSPITAL score demonstrated the lowest racial bias based on 0-1 loss, FPR difference, and GEI, and the lowest income bias based on FPR and GEI. Retrained CMS demonstrated the lowest racial bias based on 0-1 loss and FNR difference, and the lowest income bias on all 4 measures. In Florida, racial biases based on FPR and FNR differences was generally greater than that in Maryland, especially for FNR differences. In Florida, the modified HOSPITAL score showed the lowest racial bias based on 0-1 loss, FPR difference, and GEI; the LACE index showed the lowest racial bias in FNR difference. Each model scored the best in at least one measure of income bias, but the overall HOSPITAL score and retrained CMS showed the lowest income bias in Florida. In both states, the White and other-income patient groups had a higher FNR, indicating that they were more likely to be predicted as low risk while having a 30-day unplanned readmission. The Black and low-income patient groups had a higher FPR, indicating that they were more likely to be predicted to be high-risk and not have a 30-day unplanned readmission. The overall error rates were higher in the Black and low-income patient groups compared to the White and other-income patient groups, respectively. Except for GEI and the values noted with a footnote in Table 4 , all other measures showed statistically significant differences (all P values <.001) between racial and income groups, respectively.

a The columns Difference (B-W) and Difference (L-O) indicate algorithmic bias measured as the difference in the bias measure (eg, FNR and FPR) between Black and White patients and between low-income and other-income groups.

c FNR: false negative rate.

d All P values of the bias measures are <.001 except for the ones in this footnote: the P value for FNR difference of LACE in MD is .41, and the FNR difference of CMS retrained in MD is .45, and FNR difference of CMS retrained in FL is .005. Statistical tests were not conducted for the GEI as this measure produces one value for the population.

e FPR: false positive rate.

f GEI: generalized entropy index.

g N/A: not applicable.

h HOSPITAL: The modified HOSPITAL score for readmission risk.

i CMS: Centers for Medicare & Medicaid Services (readmission measure).

Stratification Analyses

The results were first stratified by hospital and then by patient population composition (percentage of Black patients). As shown in Figure 2 , the models’ FNR differences and FPR differences between the Black and White patients varied by hospital within the state, indicating hospital shifts when applying the same model. The modified HOSPITAL score was more likely to cluster near the “equality lines” (ie, when the FNR or FPR difference is 0) than other models in both states. Colors representing LACE and CMS as-is were mostly distributed in the first quadrant in Maryland, indicating that the majority of hospitals had a positive FPR difference (ie, Black patients with higher FPR) and a negative FNR difference (ie, White patients with higher FNR) when applying these 2 models ( Figure 2 ). Despite most hospitals falling in the first quadrant, the variance between hospitals appeared to be greater in Florida ( Figure 3 ). In addition, more hospitals in Florida fell in the far corners of the first and fourth quadrants than those in Maryland, indicating more hospitals with severe bias (eg, large racial differences in FPR or FNR). Refer to Multimedia Appendix 3 for the measures of income bias and hospital distribution for Maryland and Florida.

Hospitals with a higher percentage of Black patients have been shown to be associated with low resources and poorer outcomes for their patients [ 35 ]; thus, the results were stratified by the proportion of Black patients served in a hospital. In Figures 4 and 5 , each data point represents the racial bias (FNR difference or FPR difference) in a stratum of hospitals with a certain percentage of Black patients (eg, hospitals with at least 20% of Black patients). The error bars show the 95% CI of the bias measure in the strata. In both figures, the racial biases of all models, represented as FNR and FPR differences, decreased and approached zero as the hospital population became more diverse. In Maryland, the diminishing racial bias was particularly notable in hospitals where >50% of patients were Black ( Figure 4 ). The diminishing racial bias was also observed in Florida’s hospitals ( Figure 5 ). The direction of bias flipped for the LACE index and the modified HOSPITAL score in Florida hospitals with >50% of Black patients. In hospitals with a lower percentage of Black patients, Black patients had a lower FNR compared to White patients, while in hospitals with a higher percentage of Black patients, White patients had a higher FNR ( Figure 4 ). In Florida, the widening gap shown in the 2 CMS models for hospitals serving >60% of Black patients was likely attributed to the small number of hospitals and small sample size in the strata ( Figure 5 ). Refer to Multimedia Appendix 4 for the details on the bias measures stratified by payers for both Maryland and Florida.

Overall Findings

The abundance of research on fairness and bias has provided potential means to quantify bias, but there has been a gap to operationalize these metrics, interpret them in specific contexts, and understand their impact on downstream health disparity [ 7 ]. Our analysis demonstrated a practical use case for measuring algorithmic bias when applying or deploying previously validated 30-day hospital readmission predictive models in a new setting. Our approach to testing the fairness measures could serve as a framework for routine assessment of algorithmic bias for health care predictive models, and our results also revealed the complexity and limitations of using mathematical bias measures. According to these bias measures, the retrained CMS model and the modified HOSPITAL score showed the best predictive performance and the lowest bias in Maryland and Florida. However, the CMS as-is model showed subpar performance in both states, indicating that retraining on the local data not only improved predictive performance but also reduced group bias. In addition, large variations were detected between hospitals, and system- or hospital-level factors needed to be considered when interpreting algorithmic bias.

Measure Interpretation

Caution must be taken when using algorithmic bias to guide equitable intervention allocation, as the bias measures may not include key context. When designing a risk-based intervention based on model output, we would be naturally more concerned about FNR, as a higher FNR means a groups that is more likely to be predicted low in risk of readmission will indeed be readmitted, indicating missed opportunities for intervention [ 52 ]. Looking at bias measures alone, our results suggest that the risk to White and higher-income patients has a systematically higher proportion of false negatives estimated by common readmission models, suggesting more missed opportunities to intervene and prevent unplanned readmissions. This observation is contrary to our assumption, and other parts of the results show that White and higher-income patients were less sick with lower readmission rates. An explanation would suggest that the higher FNR observed in the White and higher-income patient groups might be attributed to health care use patterns. For example, research has shown that White individuals and higher socioeconomic patient groups were more likely to overuse health care resources, while Black patients and disadvantaged groups tended to underuse them [ 53 - 55 ]. The overutilizers could have more unplanned visits to the hospital when the risk was not high, while the underusing group may be more likely to defer or skip care and only use costly hospital resources when they must. Similarly, a higher FPR in Black and low-income patient groups would indicate more wasted resources on “false positives.” However, such a conclusion did not align with the rest of the study findings. These subpopulations, on average, had more chronic comorbidities and longer inpatient stays, indicating that Black and low-income patient groups were more likely to have conditions that warrant an unplanned readmission but did not show up in the observed data, potentially alluding to a health care access gap in these groups. In this case, drawing a conclusion simply based on the face value of higher FPR would lead to a reduction in the resources allocated to the sicker, more vulnerable populations. It is also important to note that, despite the racial difference in health behaviors and outcomes, race merely represents a social classification rather than the driver of the observed differences [ 56 ]. Although the performance of the evaluated readmission models differed by race, we do not recommend including race as a variable in a predictive model unless race is a biological or clinical risk factor for the predictive outcome.

The interpretation of measurable bias requires considering models’ predictive performance, the nature of health data, analytic frameworks, and the underlying health care delivery system. In our analysis, all models had modest performance, and the high FNRs may deter their application in a real setting, especially in the score-based models of LACE and HOSPITAL (ie, FNR ranges from 0.63 to 0.75). When calculating these measures, we assumed the observed outcome (ie, 30-day unplanned readmission) as the ground truth; however, it was important to recognize the key limitations of this truth and the measured bias. First, despite the HCUP state inpatient data being one of the most comprehensive and high-quality data for studying readmission, no guarantee existed that all readmissions and their causes were captured. It is possible that a patient had conditions that warranted an unplanned revisit to the hospital but either did not occur due to the patient’s unwillingness to seek treatment in time [ 57 , 58 ] or did not get documented (eg, out-of-state admissions were not captured in HCUP’s state-wide inpatient data by design). Such underdocumentation was more likely to impact disadvantaged populations and those with fragmented care, thus introducing embedded bias into the underlying data. Second, a higher percentage of Black patients sought care in academic teaching institutions (eg, 120,649/626,280, 19.26% of Black patients in Maryland and 231,379/1,448,620, 15.97% of Black patients in Florida, compared to 181,493/1,029,292, 17.63% of White patients in Maryland and 576,819/5,632,318, 10.24% of White patients in Florida), which were generally considered to deliver high-quality care [ 35 , 59 , 60 ]. These hospitals may have a more effective readmission prevention program while serving sicker patients, contributing to a higher FPR among Black and low-income patients. Third, as shown in Figure 2 , we observed that hospitals that served a high proportion of Black patients had a lower algorithmic bias. For example, in Maryland, the majority Black hospitals (>70% of patients served are Black) were in resource-poor neighborhoods, and both White and Black patients had similar higher-than-average readmission rates in these hospitals (data not shown). The fairer model performance in these hospitals was not necessarily a reflection of a higher quality of care, as all patients served in those hospitals had higher unplanned readmission rates. Finally, whether a readmission was unplanned or planned was determined using a well-established algorithm developed by CMS [ 43 , 44 ], which categorized readmissions based on the nature of the diagnoses and procedures (eg, acute vs routine). Research demonstrated that different diagnosis intensities existed between regions and hospitals, and a higher intensity of services was associated with a higher prevalence of common chronic diseases [ 61 ]. If diagnosis was not just a patient attribute but indeed reflected the systematic characteristics of the health care environment [ 62 ], the quality of unplanned readmission classification and other predictors in our models would be subject to encoded bias in the health care system. In fact, in our population, the average number of diagnoses was higher in White patients than in Black patients and higher in Maryland than in Florida, indicating the presence of such systematic variation ( Table 1 ). Of course, this is not a unique issue with our data set; electronic health records and other health data sets also reflect histories of unequal access to health care and carry racial, ethnic, socioeconomic, and other societal biases due to how the data are collected [ 2 , 3 , 63 ].

Utility of Bias Measures

Once the limitations of real-world health data are acknowledged, the expectation of equity and interpretation of the measurable bias should adjust accordingly. First, it will be too restrictive to expect mathematical equality for measurable bias; rather, it is best viewed as a relative value to aid in the selection of a less biased model. Most real-world problems are based on imperfect data, and pushing the model to perform equally on these measures will inevitably create unintended results (eg, sacrificing accuracy and potentially increasing bias for other subpopulations) [ 15 ]. Second, a validated and accurate model may reveal the gap between the “supposed-to-be” state and the reality in the underlying data, showing areas of unmet needs [ 16 , 64 ], as we observed in our Black and low-income populations. Finally, the bias measures alone provide limited evidence about which group is being biased against and in which way. A conclusion based solely on the face value of a few bias measures can be misleading and may exacerbate the disparity already faced by marginalized groups. These quantitative bias measures are useful to evaluate a model’s disparate group performance on a given data set, but they are insufficient to inform the intervention allocation or mechanisms of potential bias, which are key to the mitigation strategies [ 15 ]. In addition, our study did not evaluate other definitions of bias, such as calibration or predictive parity, which do not focus on error rates and may require unique interpretation considerations.

This analysis addressed a fundamental gap in operationalizing fairness techniques. The selection of a bias definition and appropriate bias measures is as important as detecting bias itself, yet it has remained a blind spot in practice [ 2 ]. In addition to the fact that these mathematical notions cannot be satisfied simultaneously, using the appropriate measures is also highly contextual and data dependent [ 65 , 66 ]. For example, having a model with equal positive predictions across groups (known as demographic or statistical parity) would not be a meaningful measure for inherently unbalanced outcomes such as 30-day readmissions; however, based on the fairness concept, satisfying any of the bias measures would mean a fair model. In this study, the 4 evaluated bias measures showed consistent results, despite each measuring a different definition of bias. All selected measures were able to demonstrate the magnitude of bias, but FNR and FPR differences were the most informative, as they indicated the direction of bias and were more interpretable in the context of mitigation actions. In our attempt to translate the algorithmic bias findings to intervention planning, we found that the bias measures could serve as a quick and routine assessment to compare algorithms, subpopulations, or localities (eg, hospitals) to help target further investigation of drivers of potential disparity. However, simply relying on these statistical notions to make decisions could obscure or underplay the causes of health care disparities, and a more comprehensive approach is necessary. In real-world applications, the practical goal of predictive modeling must incorporate predictive accuracy and algorithmic bias, among other operational considerations. As there is usually a trade-off between these 2 model performance goals, the best model is likely the one that balances the 2 goals rather than the one achieving the highest possible accuracy or fairness alone.

Limitations

Our analysis has several limitations and caveats. First, none of the models evaluated in this analysis had high accuracy, which may affect the measurement of misclassifications. For simplicity and the focus on interpreting the bias measures, we did not evaluate machine learning models that usually improve local accuracy [ 20 ]. While the LACE index and the HOSPITAL score were used by hospitals to manage readmissions, the CMS measure was mostly used in payer operations or population health management in addition to CMS purposes (eg, budget allocation and hospital penalties); thus, it was not used as a typical predictive model. Although we believe the models evaluated in this study represented practical scenarios, we were unable to assess if a particular type of models, variables, weights, or modeling structures were more likely to be algorithmically biased. Second, we did not evaluate the scenario in which models can be optimized to minimize and constraint bias during training or retraining. Model optimization has been a popular approach to developing fair models but, it was considered out of scope as this analysis focused on model application and bias identification. Third, we only included bias measures that are algorithm-agnostic and can be routinely calculated; thus, they were not comprehensive or exclusive. Fourth, the conclusion was based on Maryland and Florida data, which would not represent all states nor the national average. For example, Maryland is a small state with an all-payer model payment system [ 39 ] and a high percentage of patients seeking care in neighboring states, whereas Florida is a large state with a large Hispanic population and has not adopted Medicaid expansion [ 40 ]. In addition, the data set we used was administrative in nature and did not have the detailed medical information (eg, medications, laboratory results, and clinical notes) to fully evaluate the potential drivers of our results, such as selection bias [ 67 ], data quality factors [ 68 ], and more accurate ascertainment of the outcome (ie, unplanned readmissions).

Conclusions

In conclusion, our analysis found that fairness metrics were useful to serve as a routine assessment to detect disparate model performance in subpopulations and to compare predictive models. However, these metrics have limited interpretability and are insufficient to inform mechanisms of bias or guide intervention planning. Further testing and demonstration will be required before using mathematical fairness measures to guide key decision-making or policy changes. Despite these limitations, demonstrating the differential model performances (eg, misclassification rates) is often the first step in recognizing potential algorithmic bias, which will be necessary as health care organizations move toward data-driven improvement in response to existing health care disparities. The potential subtle—and not so subtle—imperfections of underlying health data, analytic frameworks, and the underlying health care delivery system must be carefully considered when evaluating the potential bias that exists within predictive models. Finally, future research is required to improve the methodology of measuring algorithmic bias and to test more fairness definitions and measures (eg, calibration parity) through an operational lens. Future studies should also explore how modeling factors influence algorithmic bias (eg, how variable inclusions, weights, or scoring schemes affect the model’s differential performance). We hope that algorithmic bias assessment can be incorporated into routine model evaluation and ultimately inform meaningful actions to reduce health care disparity.

Acknowledgments

The authors acknowledge the contributions of Dr Darrell Gaskin and Dr Daniel Naiman of Johns Hopkins University for their input into the study conceptualization and results interpretation.

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Data Availability

The data sets analyzed during this study are available for a fee from the Agency for Healthcare Research and Quality Healthcare Cost and Utilization Project [ 37 ].

Authors' Contributions

HEW and HK conceived the study concept, and all authors contributed to the study design. HEW analyzed the data and wrote the manuscript. HK and HEW interpreted the results, and all authors provided input for the interpretations. All authors reviewed and contributed to the final manuscript.

Conflicts of Interest

HEW is an employee of Merck & Co, and the employer had no role in the development or funding of this work. SS has received funding from NIH, NSF, CDC, FDA, DARPA, AHA, and Gordon Betty Moore Foundation. She is an equity holder in Bayesian Health, a clincial AI platform company; Duality Tech, a privacy preserving technology comlany and sits on the scientific advisory board of large life sciences (Eg Sanofi) and digital health startups (eg Century Health). She has received honoraria for talks from a number of biotechnology, research and health-tech companies. This arrangement has been reviewed and approved by the Johns Hopkins University in accordance with its conflict-of-interest policies.

The LACE index.

The modified HOSPITAL score.

Income bias and hospital distribution in Maryland and Florida.

Racial and income bias measures by payer in Maryland and Florida.

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Abbreviations

Edited by A Mavragani; submitted 11.03.23; peer-reviewed by D Nerenz, J Herington; comments to author 07.12.23; revised version received 28.12.23; accepted 27.02.24; published 18.04.24.

©H Echo Wang, Jonathan P Weiner, Suchi Saria, Hadi Kharrazi. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 18.04.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

Artificial intelligence in qualitative research methods

A one-day community-building gathering.

• Starts: 09:30, 26 April 2024
• Ends: 16:00, 26 April 2024
• Location: BI - campus Oslo, room: A2-Red 13
• Contact: Renate Kratochvil ([email protected])

The purpose of this event is to bring together qualitative researchers from different parts of Norway, other Nordic countries, and beyond. The main objective is to establish relationships, learn from each other, and build a community. 

During the day, you will hear from some prestigious scholars, learn what others are working on, receive (and give) feedback on work in progress, and socialize with like-minded colleagues. 

This year, our main focus will be on the utilization of AI and machine learning in qualitative research. The recent advancements in these technologies present an array of opportunities and challenges for qualitative researchers and their traditional skills, as they can automate  the processes of data collection, analysis, and presentation of findings. Through this event, we aim to delve deeper into the current hype surrounding AI and its possible implications for qualitative research. 

Established scholars, early career researchers, and PhD students from all parts of Norway, other Nordic countries, and beyond are welcome to attend.

Keynote speakers

Catherine Welch, Katharina Cepa, Heidi Karlsen and David Morgan.

Work in progress

If you want to receive feedback on your work in progress (any management topic),  please share a document of up to 2,000 words by April 16th. We welcome both early-stage ideas and proposals, as well as more developed drafts.

Participation is free, coffee & tea will be served.

The event is organized jointly by BI Qualitative Research Forum (Renate Kratochvil, Davide Nicolini & Victor Renza) and NHH (Inger Stensaker, Vidya Oruganti).

Provisional Programme

Room: A2 - Red 13

• Key notes by Catherine Welch (Trinity), Katharina Cepa (VU), Heidi Karlsen (BI) & David Morgan (Portland, zoom)
• Research speed dating
• Feedback on participants papers (Round  tables, any topic)
• AI Showcasing discussions

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