Research bias results from any deviation from the truth, causing distorted results and wrong conclusions. Bias can occur at any phase of your research, including during data collection, data analysis, interpretation, or publication. Research bias can occur in both qualitative and quantitative research.
Understanding research bias is important for several reasons.
Bias exists in all research, across research designs, and is difficult to eliminate.
Bias can occur at any stage of the research process.
Bias impacts the validity and reliability of your findings, leading to misinterpretation of data.
It is almost impossible to conduct a study without some degree of research bias. It’s crucial for you to be aware of the potential types of bias, so you can minimize them.
Actor–observer bias occurs when you attribute the behavior of others to internal factors, like skill or personality, but attribute your own behavior to external or situational factors.
In other words, when you are the actor in a situation, you are more likely to link events to external factors, such as your surroundings or environment. However, when you are observing the behavior of others, you are more likely to associate behavior with their personality, nature, or temperament.
Confirmation bias is the tendency to seek out information in a way that supports our existing beliefs while also rejecting any information that contradicts those beliefs. Confirmation bias is often unintentional but still results in skewed results and poor decision-making.
Information bias, also called measurement bias, arises when key study variables are inaccurately measured or classified. Information bias occurs during the data collection step and is common in research studies that involve self-reporting and retrospective data collection. It can also result from poor interviewing techniques or differing levels of recall from participants.
The main types of information bias are:
Regression to the mean (RTM)
Recall bias is a type of information bias. It occurs when respondents are asked to recall events in the past and is common in studies that involve self-reporting.
As a rule of thumb, infrequent events (e.g., buying a house or a car) will be memorable for longer periods of time than routine events (e.g., daily use of public transportation). You can reduce recall bias by running a pilot survey and carefully testing recall periods. If possible, test both shorter and longer periods, checking for differences in recall.
Observer bias is the tendency of research participants to see what they expect or want to see, rather than what is actually occurring. Observer bias can affect the results in observational and experimental studies, where subjective judgment (such as assessing a medical image) or measurement (such as rounding blood pressure readings up or down) is part of the data collection process.
Observer bias leads to over- or underestimation of true values, which in turn compromise the validity of your findings. You can reduce observer bias by using double- and single-blinded research methods.
Performance bias is unequal care between study groups. Performance bias occurs mainly in medical research experiments, if participants have knowledge of the planned intervention, therapy, or drug trial before it begins.
Studies about nutrition, exercise outcomes, or surgical interventions are very susceptible to this type of bias. It can be minimized by using blinding, which prevents participants and/or researchers from knowing who is in the control or treatment groups. If blinding is not possible, then using objective outcomes (such as hospital admission data) is the best approach.
When the subjects of an experimental study change or improve their behavior because they are aware they are being studied, this is called the Hawthorne (or observer) effect. Similarly, the John Henry effect occurs when members of a control group are aware they are being compared to the experimental group. This causes them to alter their behavior in an effort to compensate for their perceived disadvantage.
Regression to the mean (RTM)
Regression to the mean (RTM) is a statistical phenomenon that refers to the fact that a variable that shows an extreme value on its first measurement will tend to be closer to the center of its distribution on a second measurement.
Medical research is particularly sensitive to RTM. Here, interventions aimed at a group or a characteristic that is very different from the average (e.g., people with high blood pressure) will appear to be successful because of the regression to the mean. This can lead researchers to misinterpret results, describing a specific intervention as causal when the change in the extreme groups would have happened anyway.
Interviewer bias stems from the person conducting the research study. It can result from the way they ask questions or react to responses, but also from any aspect of their identity, such as their sex, ethnicity, social class, or perceived attractiveness.
Interviewer bias distorts responses, especially when the characteristics relate in some way to the research topic. Interviewer bias can also affect the interviewer’s ability to establish rapport with the interviewees, causing them to feel less comfortable giving their honest opinions about sensitive or personal topics.
Publication bias occurs when the decision to publish research findings is based on their nature or the direction of their results. Studies reporting results that are perceived as positive, statistically significant, or favoring the study hypotheses are more likely to be published due to publication bias.
Publication bias is related to data dredging (also called p-hacking), where statistical tests on a set of data are run until something statistically significant happens. As academic journals tend to prefer publishing statistically significant results, this can pressure researchers to only submit statistically significant results. P-hacking can also involve excluding participants or stopping data collection once a p value of 0.05 is reached. However, this leads to false positive results and an overrepresentation of positive results in published academic literature.
Researcher bias occurs when the researcher’s beliefs or expectations influence the research design or data collection process. Researcher bias can be deliberate (such as claiming that an intervention worked even if it didn’t) or unconscious (such as letting personal feelings, stereotypes, or assumptions influence research questions).
The unconscious form of researcher bias is associated with the Pygmalion (or Rosenthal) effect, where the researcher’s high expectations (e.g., that patients assigned to a treatment group will succeed) lead to better performance and better outcomes.
Researcher bias is also sometimes called experimenter bias, but it applies to all types of investigative projects, rather than only to experimental designs.
Response bias is a general term used to describe a number of different situations where respondents tend to provide inaccurate or false answers to self-report questions, such as those asked on surveys or in structured interviews.
This happens because when people are asked a question (e.g., during an interview), they integrate multiple sources of information to generate their responses. Because of that, any aspect of a research study may potentially bias a respondent. Examples include the phrasing of questions in surveys, how participants perceive the researcher, or the desire of the participant to please the researcher and to provide socially desirable responses.
Response bias also occurs in experimental medical research. When outcomes are based on patients’ reports, a placebo effect can occur. Here, patients report an improvement despite having received a placebo, not an active medical treatment.
Acquiescence bias is the tendency of respondents to agree with a statement when faced with binary response options like “agree/disagree,” “yes/no,” or “true/false.” Acquiescence is sometimes referred to as “yea-saying.”
This type of bias occurs either due to the participant’s personality (i.e., some people are more likely to agree with statements than disagree, regardless of their content) or because participants perceive the researcher as an expert and are more inclined to agree with the statements presented to them.
Demand characteristics are cues that could reveal the research agenda to participants, risking a change in their behaviors or views. Ensuring that participants are not aware of the research goals is the best way to avoid this type of bias.
Social desirability bias
Social desirability bias is the tendency of participants to give responses that they believe will be viewed favorably by the researcher or other participants. It often affects studies that focus on sensitive topics, such as alcohol consumption or sexual behavior.
Note that while social desirability and demand characteristics may sound similar, there is a key difference between them. Social desirability is about conforming to social norms, while demand characteristics revolve around the purpose of the research.
Courtesy bias stems from a reluctance to give negative feedback, so as to be polite to the person asking the question. Small-group interviewing where participants relate in some way to each other (e.g., a student, a teacher, and a dean) is especially prone to this type of bias.
Question order bias
Question order bias occurs when the order in which interview questions are asked influences the way the respondent interprets and evaluates them. This occurs especially when previous questions provide context for subsequent questions.
When answering subsequent questions, respondents may orient their answers to previous questions (called a halo effect), which can lead to systematic distortion of the responses.
Extreme responding is the tendency of a respondent to answer in the extreme, choosing the lowest or highest response available, even if that is not their true opinion. Extreme responding is common in surveys using Likert scales, and it distorts people’s true attitudes and opinions.
Disposition towards the survey can be a source of extreme responding, as well as cultural components. For example, people coming from collectivist cultures tend to exhibit extreme responses in terms of agreement, while respondents indifferent to the questions asked may exhibit extreme responses in terms of disagreement.
Selection bias is a general term describing situations where bias is introduced into the research from factors affecting the study population.
Sampling bias occurs when your sample (the individuals, groups, or data you obtain for your research) is selected in a way that is not representative of the population you are analyzing. Sampling bias threatens the external validity of your findings and influences the generalizability of your results.
The easiest way to prevent sampling bias is to use a probability sampling method. This way, each member of the population you are studying has an equal chance of being included in your sample.
Sampling bias is often referred to as ascertainment bias in the medical field.
Attrition bias occurs when participants who drop out of a study systematically differ from those who remain in the study. Attrition bias is especially problematic in randomized controlled trials for medical research because participants who do not like the experience or have unwanted side effects can drop out and affect your results.
You can minimize attrition bias by offering incentives for participants to complete the study (e.g., a gift card if they successfully attend every session). It’s also a good practice to recruit more participants than you need, or minimize the number of follow-up sessions or questions.
Volunteer or self-selection bias
Volunteer bias (also called self-selection bias) occurs when individuals who volunteer for a study have particular characteristics that matter for the purposes of the study.
Volunteer bias leads to biased data, as the respondents who choose to participate will not represent your entire target population. You can avoid this type of bias by using random assignment—i.e., placing participants in a control group or a treatment group after they have volunteered to participate in the study.
Closely related to volunteer bias is nonresponse bias, which occurs when a research subject declines to participate in a particular study or drops out before the study’s completion.
Survivorship bias occurs when you do not evaluate your data set in its entirety: for example, by only analyzing the patients who survived a clinical trial.
This strongly increases the likelihood that you draw (incorrect) conclusions based upon those who have passed some sort of selection process—focusing on “survivors” and forgetting those who went through a similar process and did not survive.
Note that “survival” does not always mean that participants died! Rather, it signifies that participants did not successfully complete the intervention.
Nonresponse bias occurs when those who do not respond to a survey or research project are different from those who do in ways that are critical to the goals of the research. This is very common in survey research, when participants are unable or unwilling to participate due to factors like lack of the necessary skills, lack of time, or guilt or shame related to the topic.
You can mitigate nonresponse bias by offering the survey in different formats (e.g., an online survey, but also a paper version sent via post), ensuring confidentiality, and sending them reminders to complete the survey.
Undercoverage bias occurs when you only sample from a subset of the population you are interested in. Online surveys can be particularly susceptible to undercoverage bias. Despite being more cost-effective than other methods, they can introduce undercoverage bias as a result of excluding people who do not use the internet.
How to avoid bias in research
While very difficult to eliminate entirely, research bias can be mitigated through proper study design and implementation. Here are some tips to keep in mind as you get started.
Clearly explain in your methodology section how your research design will help you meet the research objectives and why this is the most appropriate research design.
In quantitative studies, make sure that you use probability sampling to select the participants. If you’re running an experiment, make sure you use random assignment to assign your control and treatment groups.
Account for participants who withdraw or are lost to follow-up during the study. If they are withdrawing for a particular reason, it could bias your results. This applies especially to longer-term or longitudinal studies.
Phrase your survey or interview questions in a neutral, non-judgmental tone. Be very careful that your questions do not steer your participants in any particular direction.
Consider using a reflexive journal. Here, you can log the details of each interview, paying special attention to any influence you may have had on participants. You can include these in your final analysis.
Research bias affects the validity and reliability of your research findings, leading to false conclusions and a misinterpretation of the truth. This can have serious implications in areas like medical research where, for example, a new form of treatment may be evaluated.
Observer bias occurs when the researcher’s assumptions, views, or preconceptions influence what they see and record in a study, while actor–observer bias refers to situations where respondents attribute internal factors (e.g., bad character) to justify other’s behavior and external factors (difficult circumstances) to justify the same behavior in themselves.
Response bias is a general term used to describe a number of different conditions or factors that cue respondents to provide inaccurate or false answers during surveys or interviews. These factors range from the interviewer’s perceived social position or appearance to the the phrasing of questions in surveys.
Nonresponse bias occurs when the people who complete a survey are different from those who did not, in ways that are relevant to the research topic. Nonresponse can happen because people are either not willing or not able to participate.
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