{"id":50233,"count":56,"description":"<strong>Research bias<\/strong> results from any deviation from the truth, causing distorted results and wrong conclusions. Bias can occur at any phase of your research, including during <a href=index-2779.html>data collection<\/a>, <a href=index-3133.html>data analysis<\/a>, interpretation, or publication. Research bias can occur in both <a href=index-3558.html>qualitative<\/a> and <a href=index-2992.html>quantitative research<\/a>.\r\n\r\nUnderstanding research bias is important for several reasons.\r\n<ol>\r\n \t<li aria-level=\"1\">Bias exists in all research, across <a href=index-3133.html>research designs<\/a>, and is difficult to eliminate.<\/li>\r\n \t<li aria-level=\"1\">Bias can occur at any stage of the <a href=index-2946.html>research process<\/a>.<\/li>\r\n \t<li aria-level=\"1\">Bias impacts the <a href=index-4126.html>validity<\/a> and <a href=index-4127.html>reliability<\/a> of your findings, leading to misinterpretation of data.<\/li>\r\n<\/ol>\r\nIt 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.\r\n<figure class=\"kb-textbox border-left purple\"><figcaption>Example: Bias in research<\/figcaption>Suppose that you are researching whether a particular weight loss program is successful for people with diabetes. If you focus purely on whether participants complete the program, you may bias your research.\r\n\r\nFor example, the success rate of the program will likely be affected if participants start to drop out (<a href=index-4128.html#what-is-attrition\">attrition<\/a>). Participants who become disillusioned due to not losing weight may drop out, while those who succeed in losing weight are more likely to continue. This in turn may bias the findings towards more favorable results.<strong>\u00a0<\/strong>\r\n\r\nAccounting for the differences between people who remain in a study and those who withdraw is important so as to avoid bias.<\/figure>\r\n<!--more-->\r\n<h2 id=\"information\">Information bias<\/h2>\r\n<strong><a href=index-4129.html>Information bias<\/a><\/strong>, also called measurement bias, arises when key study <a href=index-4130.html>variables <\/a>are inaccurately measured or classified. Information bias occurs during the <a href=index-2779.html>data collection<\/a> 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.\r\n\r\nThe main types of information bias are:\r\n<ul>\r\n \t<li aria-level=\"1\"><a href=index.html#recall\">Recall bias<\/a><\/li>\r\n \t<li aria-level=\"1\"><a href=index.html#observer\">Observer bias<\/a><\/li>\r\n \t<li aria-level=\"1\"><a href=index.html#performance\">Performance bias<\/a><\/li>\r\n \t<li aria-level=\"1\"><a href=index.html#regression\">Regression to the mean (RTM)<\/a><\/li>\r\n<\/ul>\r\n<figure class=\"kb-textbox border-left purple\"><figcaption>Example: Information bias in research<\/figcaption>You are researching the <a href=_.html>correlation<\/a> between smartphone use and musculoskeletal symptoms among high school students.\r\n\r\nOver a period of four weeks, you ask students to keep a journal, noting how much time they spent on their smartphones along with any symptoms like muscle twitches, aches, or fatigue.\r\n\r\nAt the end of the study, you compare the self-reports with the usage data registered on their smartphones. You notice that for usage of less than three hours a day, self-reports tended to overestimate the duration of smartphone use. Conversely, for usage of more than three hours a day, self-reports tended to underestimate the duration of smartphone use. This goes to show that information bias can operate in more than one direction within a study group.<\/figure>\r\n<h3 id=\"recall\">Recall bias<\/h3>\r\n<strong>Recall bias<\/strong> 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.\r\n\r\nAs 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.\r\n<figure class=\"kb-textbox border-left purple\"><figcaption>Example: Recall bias in research<\/figcaption>You are conducting a case-control study examining the association between the diet of young children and the diagnosis of childhood cancer. You examine two groups:\r\n<ul>\r\n \t<li aria-level=\"1\">A group of children who have been diagnosed, called the case group<\/li>\r\n \t<li aria-level=\"1\">A group of children who have not been diagnosed, called the <a href=index-4131.html>control group<\/a><\/li>\r\n<\/ul>\r\nSince the parents are being asked to recall what their children generally ate over a period of several years, there is high potential for recall bias in the case group.\r\n\r\nThe best way to reduce recall bias is by ensuring your control group will have similar levels of recall bias to your case group. Parents of children who have childhood cancer, which is a serious health problem, are likely to be quite concerned about what may have contributed to the cancer.\r\n\r\nThus, if asked by researchers, these parents are likely to think very hard about what their child ate or did not eat in their first years of life. Parents of children with other serious health problems (aside from cancer) are also likely to be quite concerned about any diet-related question that researchers ask about.\r\n\r\nTherefore, these parents can be expected to recall their children\u2019s diet in a way that is more comparable with parents of children who have cancer. In contrast, parents of children who have no health problems or parents of children with only minor health problems are less likely to be concerned with carefully recalling their children\u2019s eating habits.<\/figure>\r\n<h3 id=\"observer\">Observer bias<\/h3>\r\n<a href=index-4132.html><strong>Observer bias<\/strong><\/a> 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<a href=index-4133.html> observationa<\/a>l and <a href=index-2782.html>experimental<\/a> 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 d<a href=index-2779.html>ata collection<\/a> process.\r\n\r\nObserver bias leads to over- or underestimation of true values, which in turn compromise the <a href=index-4134.html>validity<\/a> of your findings. You can reduce observer bias by using<a href=index-4135.html#double\"> double-blinded<\/a>\u00a0and <a href=index-4135.html#single\">single-blinded <\/a>research methods.\r\n<figure class=\"kb-textbox border-left purple\"><figcaption>Example: Observer bias in research<\/figcaption>You and a colleague are investigating communication behavior in a hospital. You are observing eight doctors and two nurses, seeking to discover whether they prefer interruptive communication mechanisms\u2014face-to-face discussion or telephone calls\u2014over less interruptive methods, such as emails. You and your colleague follow the 10 members of staff for a month. Each time you observe someone making a call, walking to a colleague to ask something, etc., you make a note.\r\n\r\nBased on discussions you had with other researchers before starting your <a href=index-4133.html>observations<\/a>, you are inclined to think that medical staff tend to simply call each other when they need specific patient details or have questions about treatments.\r\n\r\nAt the end of the observation period, you compare notes with your colleague. Your conclusion was that medical staff tend to favor phone calls when seeking information, while your colleague noted down that medical staff mostly rely on face-to-face discussions. Seeing that your expectations may have influenced your observations, you and your colleague decide to conduct <a href=index-3387.html>semi-structured interviews<\/a> with medical staff to clarify the observed events.<\/figure>\r\n<figure class=\"kb-textbox border-left yellow\"><figcaption>Note:<\/figcaption>Observer bias and <a href=index.html#actor-observer\">actor\u2013observer bias<\/a> are not the same thing.\r\n\r\nObserver bias arises from the opinions and expectations of the observer, influencing data collection and recording, while actor\u2013observer bias has to do with how we interpret the same behavior differently depending on whether we are engaging in it or others are.<\/figure>\r\n<h3 id=\"performance\">Performance bias<\/h3>\r\n<a href=index-4136.html><strong>Performance bias <\/strong><\/a>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.\r\n\r\nStudies about nutrition, exercise outcomes, or surgical interventions are very susceptible to this type of bias. It can be minimized by using<a href=index-4137.html> blinding<\/a>, 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.\r\n\r\nWhen the subjects of an experimental study change or improve their behavior because they are aware they are being studied, this is called the <a href=index-2963.html><strong>Hawthorne effect<\/strong><\/a> (or observer effect). Similarly, the <strong>John Henry effect<\/strong> 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.\r\n<figure class=\"kb-textbox border-left purple\"><figcaption>Example: Performance bias in research<\/figcaption>You are investigating whether a high-protein diet can help people lose weight. The experimental group is provided with a high-protein meal plan, while the control group follows their regular diet. The control group does not know that the study is about the link between protein and weight loss, but they can easily guess that it\u2019s about nutrition. If participants know this beforehand, they can potentially change their behavior, e.g., by increasing their protein intake or seeking to eat more healthily than they normally do.<\/figure>\r\n<h3 id=\"regression\">Regression to the mean (RTM)<\/h3>\r\n<a href=index-4138.html><strong>Regression to the mean (RTM)<\/strong><\/a> is a statistical phenomenon that refers to the fact that a <a href=index-4130.html>variable<\/a> that shows an extreme value on its first measurement will tend to be closer to the center of its distribution on a second measurement.\r\n\r\nMedical 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.\r\n<figure class=\"kb-textbox border-left purple\"><figcaption>Example: Regression to the mean (RTM)<\/figcaption>You are researching a new intervention for people with depression.\r\n\r\nIn general, among people with depression, certain physical and mental characteristics have been observed to deviate from the population <a href=index-3020.html>mean<\/a>.\r\n\r\nThis could lead you to think that the intervention was <a href=index-1757.html>effective<\/a> when those treated showed improvement on measured post-treatment indicators, such as reduced severity of depressive episodes.\r\n\r\nHowever, given that such characteristics deviate more from the <a href=index-4139.html#population\">population mean<\/a> in people with depression than in people without depression, this improvement could be attributed to RTM.\r\n\r\nIn order to differentiate between RTM and true improvement, consider introducing a control group, such as an untreated group of similar individuals or a group of similar individuals in an alternative treatment.<\/figure>\r\n<h2 id=\"interviewer\">Interviewer bias<\/h2>\r\n<strong>Interviewer bias<\/strong> 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.\r\n\r\nInterviewer bias distorts responses, especially when the characteristics relate in some way to the research topic. Interviewer bias can also <a href=index-3409.html>affect<\/a> 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.\r\n<figure class=\"kb-textbox border-left purple\"><figcaption>Example: Interviewer bias in research<\/figcaption>Suppose you are interviewing people about how they spend their free time at home.\r\n\r\n<strong>Participant:<\/strong> \u201cI like to solve puzzles, or sometimes do some gardening.\u201d\r\n\r\n<strong>You:<\/strong> \u201cI love gardening, too!\u201d\r\n\r\nIn this case, seeing your enthusiastic reaction could lead the participant to talk more about gardening.\r\n\r\nEstablishing trust between you and your interviewees is crucial in order to ensure that they feel comfortable opening up and revealing their true thoughts and feelings. At the same time, being overly empathetic can influence the responses of your interviewees, as seen above.\r\n\r\nA better approach here would be to use neutral responses that still show that you\u2019re paying attention and are engaged in the conversation. Some examples could include \u201cThank you for sharing\u201d or \u201cCan you tell me more about that?\u201d<\/figure>\r\n<h2 id=\"publication\">Publication bias<\/h2>\r\n<strong><a href=index-4140.html>Publication bias<\/a><\/strong> 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,<a href=index-3039.html> statistically significant<\/a>, or favoring the study <a href=index-4141.html>hypotheses <\/a>are more likely to be published due to publication bias.\r\n\r\nPublication bias is related to<strong> data dredging <\/strong>(also called<strong><em> p<\/em>-hacking<\/strong>), 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. <em>P<\/em>-hacking can also involve excluding participants or stopping data collection once a <a href=index-3032.html><em>p<\/em> value<\/a> of 0.05 is reached. However, this leads to false positive results and an overrepresentation of positive results in published academic literature.\r\n<figure class=\"kb-textbox border-left purple\"><figcaption>Example: Publication bias in research<\/figcaption>A researcher testing a drug for Alzheimer\u2019s discovers that there is no statistically significant difference between the patients in the control and the treatment groups. Fearing that this will impact their chances for securing funding and career promotion, they decide not to publish their findings.<\/figure>\r\n<h2 id=\"researcher\">Researcher bias<\/h2>\r\n<strong>Researcher bias <\/strong>occurs when the researcher\u2019s 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\u2019t) or unconscious (such as letting personal feelings, stereotypes, or assumptions influence <a href=index-1762.html>research questions<\/a>).\r\n\r\nThe unconscious form of researcher bias is associated with the<a href=index-4142.html><strong> Pygmalion effect<\/strong><\/a> (or <a href=index-4143.html>Rosenthal effect<\/a>), where the researcher\u2019s high expectations (e.g., that patients assigned to a treatment group will succeed) lead to better performance and better outcomes.\r\n<figure class=\"kb-textbox border-left yellow\"><figcaption>Note<\/figcaption>Although researcher bias and observer bias may seem similar, they are not the same thing. Observer bias affects how behaviors are recorded or measurements are taken, often in the data collection and interpretation stages. Researcher bias is a broader term and can influence any part of the <a href=index-3133.html>research design<\/a>.<\/figure>\r\nResearcher bias is also sometimes called experimenter bias, but it applies to all types of investigative projects, rather than only to <a href=index-2782.html>experimental designs<\/a>.\r\n<figure class=\"kb-textbox border-left purple\"><figcaption>Example: Researcher bias\u00a0<\/figcaption>Suppose you want to study the effects of alcohol on young adults. If you are already convinced that alcohol causes young people to behave in a reckless way, this may influence how you phrase your survey questions. Instead of being neutral and non-judgmental, they run the risk of reflecting your preconceived notions around alcohol consumption. As a result, your survey will be biased.\r\n<ul class=\"kb-checklist\">\r\n \t<li aria-level=\"1\"><strong>Good question:<\/strong> What are your views on alcohol consumption among your peers?<\/li>\r\n<\/ul>\r\n<ul class=\"kb-errorlist\">\r\n \t<li aria-level=\"1\"><strong>Bad question:<\/strong> Do you think it\u2019s okay for young people to drink so much?<\/li>\r\n<\/ul>\r\n<\/figure>\r\n<h2 id=\"response\">Response bias<\/h2>\r\n<a href=index-4144.html><strong>Response bias<\/strong><\/a> 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 <a href=_-3.html>surveys<\/a> or in <a href=index-4145.html>structured interviews<\/a>.\r\n\r\nThis happens because when people are asked a question (e.g., during an<a href=_-4.html> interview<\/a>), they <a href=index-3697.html>integrate multiple sources<\/a> 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 <a href=index-4146.html>socially desirable<\/a> responses.\r\n\r\nResponse bias also occurs in experimental medical research. When outcomes are based on patients' reports, a<a href=index-3047.html><strong> placebo effect<\/strong><\/a> can occur. Here, patients report an improvement despite having received a placebo, not an active medical treatment.\r\n<figure class=\"kb-textbox border-left purple\"><figcaption>Example: Response bias<\/figcaption>You are researching factors associated with cheating among college students.\r\n\r\nWhile interviewing a student, you ask them:\r\n\r\n\u201cDo you think it\u2019s okay to cheat on an exam?\u201d\r\n\r\nSince cheating is generally regarded as a bad thing, the word itself is negatively charged. Here, the student may feel the need to hide their true feelings, conforming to what is considered most socially acceptable\u2014that cheating is not okay.<\/figure>\r\nCommon types of response bias are:\r\n<ul>\r\n \t<li aria-level=\"1\"><a href=index.html#acquiescence\">Acquiescence bias<\/a><\/li>\r\n \t<li aria-level=\"1\"><a href=index.html#demand-characteristics\">Demand characteristics<\/a><\/li>\r\n \t<li aria-level=\"1\"><a href=index.html#social-desirability\">Social desirability bias<\/a><\/li>\r\n \t<li aria-level=\"1\"><a href=index.html#courtesy\">Courtesy bias<\/a><\/li>\r\n \t<li aria-level=\"1\"><a href=index.html#question-order\">Question-order bias<\/a><\/li>\r\n \t<li aria-level=\"1\"><a href=index.html#extreme\">Extreme responding<\/a><\/li>\r\n<\/ul>\r\n<h3 id=\"acquiescence\">Acquiescence bias<\/h3>\r\n<strong>Acquiescence bias<\/strong> is the tendency of respondents to agree with a statement when faced with binary response options like \u201cagree\/disagree,\u201d \"yes\/no,\u201d or \u201ctrue\/false.\u201d Acquiescence is sometimes referred to as \"yea-saying.\"\r\n\r\nThis type of bias occurs either due to the participant\u2019s 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.\r\n<figure class=\"kb-textbox border-left purple\"><figcaption>Example: Acquiescence bias in research<\/figcaption>Suppose you are researching introversion and extroversion among students. You include the following question in your <a href=_-3.html>survey<\/a>:\r\n\r\n<strong>Q:<\/strong> Are you a social person?\r\n<ul class=\"kb-checklist\">\r\n \t<li aria-level=\"1\">Yes<\/li>\r\n<\/ul>\r\n<ul class=\"kb-errorlist\">\r\n \t<li aria-level=\"1\">No<\/li>\r\n<\/ul>\r\nPeople who are inclined to agree with statements presented to them are at risk of selecting the first option, even if it isn\u2019t fully supported by their lived experiences.\r\n\r\nIn order to control for acquiescence, consider tweaking your phrasing to encourage respondents to make a choice truly based on their preferences. Here\u2019s an example:\r\n\r\n<strong>Q:<\/strong> What would you prefer?\r\n<ol>\r\n \t<li aria-level=\"1\">A quiet night in<\/li>\r\n \t<li aria-level=\"1\">A night out with friends<\/li>\r\n<\/ol>\r\n<\/figure>\r\n<h3 id=\"demand-characteristics\">Demand characteristics<\/h3>\r\n<a href=index-4147.html><strong>Demand characteristics<\/strong><\/a> 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 <a href=index-3268.html>research objectives<\/a> is the best way to avoid this type of bias.\r\n<figure class=\"kb-textbox border-left purple\"><figcaption>Example: Demand characteristics<\/figcaption>A researcher is investigating whether a spinal operation can reduce back pain complaints. Patients are interviewed by the surgeon who conducted the operation six weeks, three months, and one year post-op, and their levels of pain are assessed.\r\n\r\nOn each occasion, patients reported their pain as being less than prior to the operation. While at face value this seems to suggest that the operation does indeed lead to less pain, there is a demand characteristic at play. During the interviews, the researcher would unconsciously frown whenever patients reported more post-op pain. This increased the risk of patients figuring out that the researcher was hoping that the operation would have an advantageous effect.\r\n\r\nSensing this, the patients downplayed any complaints in an effort to please the researcher. The researcher\u2019s frowns served as cues (demand characteristics) that helped participants figure out that the research agenda was lessened pain.<\/figure>\r\n<h3 id=\"social-desirability\">Social desirability bias<\/h3>\r\n<strong><a href=index-4146.html>Social desirability bias<\/a> <\/strong>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.\r\n<figure class=\"kb-textbox border-left purple\"><figcaption>Example: Social desirability bias<\/figcaption>You are designing an employee well-being program for a technology start-up. You want to gauge employees\u2019 interest in various activities and components that could be included in this program.\r\n\r\nYou are conducting face-to-face <a href=index-3387.html>semi-structured interviews<\/a> with a number of employees from different departments. When asked whether they would be interested in a smoking cessation program, there was widespread enthusiasm for the idea.\r\n\r\nHowever, when you leave the building at the end of the day, you run into a few members of the interview group smoking outside. You overhear them saying how they don\u2019t like the idea of the smoking cessation program, but they felt they couldn\u2019t really say it because smoking is considered a bad habit in this day and age.<\/figure>\r\nNote 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.\r\n<h3 id=\"courtesy\">Courtesy bias<\/h3>\r\n<strong>Courtesy bias<\/strong> 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.\r\n<figure class=\"kb-textbox border-left purple\"><figcaption>Example: Courtesy bias<\/figcaption>You are researching cases of disrespectful behavior towards women who gave birth at hospitals. If you ask women about their experiences while in or near the facility in which they received care, it is possible that some women may avoid giving negative feedback.\r\n\r\nCourtesy bias, including fear of repercussions, may lead some women to avoid sharing any negative experiences. Conducting interviews to capture women\u2019s experiences of disrespect in a more neutral setting is the best approach here.<\/figure>\r\n<h3 id=\"question-order\">Question order bias<\/h3>\r\n<strong>Question order bias<\/strong> 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.\r\n\r\nWhen answering subsequent questions, respondents may orient their answers to previous questions (called a <strong><a href=index-3278.html>halo effect<\/a><\/strong>), which can lead to <a href=index-4148.html>systematic <\/a>distortion of the responses.\r\n<figure class=\"kb-textbox border-left purple\"><figcaption>Example: Question order bias<\/figcaption>If a respondent is asked how satisfied he is with his marriage, this increases the probability that he will also take his marriage into account when answering the question about his satisfaction with life in general.\r\n\r\nIn this case, you can minimize question order bias by asking general questions (satisfaction with life) prior to specific ones (marriage).<\/figure>\r\n<h3 id=\"extreme\">Extreme responding<\/h3>\r\n<strong>Extreme responding<\/strong> 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 <a href=_-3.html>surveys<\/a> using<a href=index-2952.html> Likert scales<\/a>, and it distorts people's true attitudes and opinions.\r\n\r\nDisposition 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.\r\n<figure class=\"kb-textbox border-left purple\"><figcaption>Example: Extreme responding<\/figcaption>You want to find out what students think of on-campus counseling services via a survey using Likert scales. There are 40 questions, with potential responses ranging from \u201cstrongly agree\u201d to \u201cstrongly disagree.\u201d\r\n\r\nIn your pilot study, you notice that a number of respondents only select the extreme options for each question. To mitigate this, you decide to shorten the <a href=index-3568.html>questionnaire<\/a> and diversify the questions. Instead of solely using Likert scales, you also add some multiple-choice and open questions.<\/figure>\r\n<h2 id=\"selection\">Selection bias<\/h2>\r\n<a href=index-4149.html><strong>Selection bias<\/strong><\/a> is a general term describing situations where bias is introduced into the research from factors affecting the study population.\r\n\r\nCommon types of selection bias are:\r\n<ul>\r\n \t<li aria-level=\"1\"><a href=index.html#sampling\">Sampling or ascertainment bias<\/a><\/li>\r\n \t<li aria-level=\"1\"><a href=index.html#attrition\">Attrition bias<\/a><\/li>\r\n \t<li aria-level=\"1\"><a href=index.html#volunteer\">Self-selection (or volunteer) bias<\/a><\/li>\r\n \t<li aria-level=\"1\"><a href=index.html#survivorship\">Survivorship bias<\/a><\/li>\r\n \t<li aria-level=\"1\"><a href=index.html#nonresponse\">Nonresponse bias<\/a><\/li>\r\n \t<li aria-level=\"1\"><a href=index.html#undercoverage\">Undercoverage bias<\/a><\/li>\r\n<\/ul>\r\n<figure class=\"kb-textbox border-left purple\"><figcaption>Example: Selection bias<\/figcaption>You are investigating elderly people\u2019s self-perceived physical health in your city. You go outside a public pool and interview elderly people as they exit.\r\n\r\nCollecting your data only from senior citizens at the pool will lead to selection bias in your data. In this case, you are excluding elderly people who are not willing or able to maintain an active lifestyle.<\/figure>\r\n<h3 id=\"sampling\">Sampling or ascertainment bias<\/h3>\r\n<a href=index-3009.html><strong>Sampling bias<\/strong><\/a> occurs when your <a href=index-2997.html>sample<\/a> (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<a href=index-3011.html> external validity<\/a> of your findings and influences the <a href=index-2770.html><strong>generalizability<\/strong><\/a> of your results.\r\n\r\nThe easiest way to prevent sampling bias is to use a <a href=index-4150.html>probability sampling method<\/a>. This way, each member of the population you are studying has an equal chance of being included in your sample.\r\n\r\nSampling bias is often referred to as <a href=index-4151.html><strong>ascertainment bias<\/strong><\/a> in the medical field.\r\n<figure class=\"kb-textbox border-left purple\"><figcaption>Example: Sampling (or ascertainment) bias<\/figcaption>You are researching the likelihood of heart disease in your area. You decide to collect your data by interviewing people entering and leaving a local shopping mall.\r\n\r\nThis collection method does not include people who are bedridden or very ill from heart disease. Because many of them are more likely confined at their homes or in a hospital, and not walking around a mall, your sample is biased.<\/figure>\r\n<h3 id=\"attrition\">Attrition bias<\/h3>\r\n<a href=index-4152.html><strong>Attrition bias<\/strong><\/a> occurs when participants who drop out of a study <a href=index-4148.html>systematically<\/a> 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.\r\n\r\nYou 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\u2019s also a good practice to recruit more participants than you need, or minimize the number of follow-up sessions or questions.\r\n<figure class=\"kb-textbox border-left purple\"><figcaption>Example: Attrition bias in research<\/figcaption>Using a <a href=index-3134.html>longitudinal design<\/a>, you investigate whether a stress management training program can help students with anxiety regulate their stress levels during exams.\r\n\r\nYou provide a treatment group with weekly one-hour sessions over a two-month period, while a <a href=index-4131.html>control group<\/a> attends sessions on an unrelated topic. You complete five waves of data collection to compare outcomes: a pretest survey, three surveys during the program, and a posttest survey.\r\n\r\nDuring your study, you notice that a number of participants drop out, failing to attend the training sessions or complete the follow-up surveys. You check the baseline survey data to compare those who leave against those who remain, finding that participants who left reported significantly higher levels of anxiety than those who stayed. This means your study has attrition bias.<\/figure>\r\n<h3 id=\"volunteer\">Self-selection or volunteer bias<\/h3>\r\n<a href=index-3010.html><strong>Self-selection bias<\/strong><\/a> (also called <strong>volunteer bias<\/strong>) occurs when individuals who volunteer for a study have particular characteristics that matter for the purposes of the study.\r\n\r\nVolunteer 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 <a href=index-4153.html>random assignment<\/a>\u2014i.e., placing participants in a <a href=index-4131.html>control group<\/a> or a treatment group after they have volunteered to participate in the study.\r\n\r\nClosely related to volunteer bias is <strong><a href=index-3555.html>nonresponse bias<\/a><\/strong>, which occurs when a research subject declines to participate in a particular study or drops out before the study's completion.\r\n<figure class=\"kb-textbox border-left purple\"><figcaption>Example: Self-selection (or volunteer) bias in research<\/figcaption>You want to study whether fish consumption can reduce the risk of cognitive decline in elderly people. In order to recruit volunteers, you place posters in the area around the hospital where the experiment will take place.\r\n\r\nConsidering that the hospital is located in an affluent part of the city, volunteers are more likely to have a higher socioeconomic standing, higher education, and better nutrition than the general population.\r\n\r\nThis means that volunteer bias may affect your findings as the participants will differ significantly from non-participants in ways that relate to the study objectives (i.e., the relationship between nutrition and cognitive decline).<\/figure>\r\n<h3 id=\"survivorship\">Survivorship bias<\/h3>\r\n<a href=index-3275.html><strong>Survivorship bias<\/strong><\/a> occurs when you do not evaluate your data set in its entirety: for example, by only analyzing the patients who survived a clinical trial.\r\n\r\nThis strongly increases the likelihood that you draw (incorrect) conclusions based upon those who have passed some sort of selection process\u2014focusing on \u201csurvivors\u201d and forgetting those who went through a similar process and did not survive.\r\n\r\nNote that \u201csurvival\u201d does not always mean that participants died! Rather, it signifies that participants did not successfully complete the intervention.\r\n<figure class=\"kb-textbox border-left purple\"><figcaption>Example: Survivorship bias in research<\/figcaption>You are researching which factors contribute to a successful career as an entrepreneur. Looking into the r\u00e9sum\u00e9s of well-known entrepreneurs, you notice that most of them were college dropouts. This could make you think that having a good idea and leaving college to pursue it is all that it takes to set off your career.\r\n\r\nHowever, most college dropouts do not become billionaires. In fact, there are many more aspiring entrepreneurs who dropped out of college to start companies and failed than succeeded.\r\n\r\nWhen you focus on the people who left school and succeeded, ignoring the far larger group of dropouts who did not, you are succumbing to survivorship bias. This means that a visible \u201csuccessful\u201d subgroup is mistaken as an entire group due to the \u201cfailure\u201d subgroup's not being visible.<\/figure>\r\n<h3 id=\"nonresponse\">Nonresponse bias<\/h3>\r\n<strong>Nonresponse bias<\/strong> 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 <a href=_-3.html>survey <\/a>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.\r\n\r\nYou 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 <a href=index-4154.html>confidentiality<\/a>, and sending them reminders to complete the survey.\r\n<figure class=\"kb-textbox border-left purple\"><figcaption>Example: Nonresponse bias in research<\/figcaption>You are investigating the average age of people in your city who have a landline in their homes. You attempt to conduct a phone survey of 1,000 individuals, dialed randomly from the population of landline-owning residents. After 1,000 attempts, you are in possession of 746 valid responses, while 254 individuals never answered the phone. Is this sample representative?\r\n\r\nYou notice that your surveys were conducted during business hours, when the working-age residents were less likely to be home.\r\n\r\nIf working-age respondents are underrepresented in your sample, then the average among the 746 valid age responses will <a href=index-4155.html>skew<\/a> older than the true population average. In this case, the difference between the biased average and the true, but unobserved, average age among all landline owners is due to nonresponse bias.<\/figure>\r\n<h3 id=\"undercoverage\">Undercoverage bias<\/h3>\r\n<strong><a href=index-4156.html>Undercoverage bias <\/a><\/strong>occurs when you only<a href=index-2948.html> sample<\/a> from a subset of the population you are interested in. Online <a href=_-3.html>surveys<\/a> 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.\r\n<figure class=\"kb-textbox border-left purple\"><figcaption>Example: Undercoverage bias in research<\/figcaption>You are running a web survey on self-reported health, focusing on smoking habits and binge drinking. However, the method of your survey means that you are systematically excluding non-internet users. Undercoverage would not be a problem here if internet users did not differ from non-internet users.\r\n\r\nHowever, you know from previous studies that the proportion of non-internet use has a positive relationship with age and a negative relationship with education level. This means that you run a risk of excluding older and less educated respondents from your sample. Since the differences between internet users and non-internet users can play a significant role in influencing your study variables, you will not be able to draw valid conclusions from your web survey.<\/figure>\r\n<h2 id=\"cognitive\">Cognitive bias<\/h2>\r\n<strong>Cognitive bias<\/strong> refers to a set of predictable (i.e., nonrandom) errors in thinking that arise from our limited ability to process information objectively. Rather, our judgment is influenced by our values, memories, and other personal traits. These create \u201c mental shortcuts\u201d that help us process information intuitively and decide faster. However, cognitive bias can also cause us to misunderstand or misinterpret situations, information, or other people.\r\n<figure class=\"kb-textbox border-left purple\"><figcaption>Example: Cognitive bias<\/figcaption>When asked if their favorite team will win a game, most people will be positive. However, fewer people will still claim that they had predicted a win when asked again after their team has lost. Most people will likely say that they knew it all along the team was going to lose.\r\n\r\nBecause of cognitive bias, people often perceive events to be more predictable <em>after<\/em> they happen.\r\n\r\nWhen something happens, it often seems so obvious that we should have seen it coming, and that may distort our memories so that our earlier predictions conform with this belief. In other words, we tell ourselves \u201cI knew it all along\u201d.<\/figure>\r\nAlthough there is no general agreement on how many types of cognitive bias exist, some common types are:\r\n<ol class=\"ProsemirrorEditor-list\">\r\n \t<li><strong><a href=index.html#anchoring\">Anchoring bias<\/a>\u00a0<\/strong><\/li>\r\n \t<li><strong><a href=index.html#framing\">Framing effect<\/a>\u00a0<\/strong><\/li>\r\n \t<li><strong><a href=index.html#actor-observer\">Actor-observer bias<\/a><\/strong><\/li>\r\n \t<li><strong><a href=index.html#availability\">Availability heuristic (or availability bias)<\/a><\/strong><\/li>\r\n \t<li><strong><a href=index.html#confirmation\">Confirmation bias<\/a>\u00a0<\/strong><\/li>\r\n \t<li><strong><a href=index.html#halo\">Halo effect<\/a><\/strong><\/li>\r\n \t<li><strong><a href=index.html#baader\">The Baader-Meinhof phenomenon<\/a>\u00a0<\/strong><\/li>\r\n<\/ol>\r\n<h3 id=\"anchoring\">Anchoring bias<\/h3>\r\n<a href=index-2964.html><strong>Anchoring bias <\/strong><\/a>is people\u2019s tendency to fixate on the first piece of information they receive, especially when it concerns numbers. This piece of information becomes a reference point or anchor. Because of that, people base all subsequent decisions on this anchor. For example, initial offers have a stronger influence on the outcome of negotiations than subsequent ones.\r\n<figure class=\"kb-textbox border-left purple\"><figcaption>Example: Anchoring bias\u00a0<\/figcaption>Anchoring bias can greatly influence the estimated value of a product.\r\n\r\nIf a car salesperson starts negotiations at $12,000, you\u2019ll likely think you\u2019re getting a good deal when you eventually agree with him to lower the price to $10,500. The real value of the car may be near $10,000, but the first figure you heard influenced your estimation of its value.<\/figure>\r\n<h3 id=\"framing\">Framing effect<\/h3>\r\n<a href=index-3982.html><strong>Framing effect <\/strong><\/a>refers to our tendency to decide based on how the information about the decision is presented to us. In other words, our response depends on whether the option is presented in a negative or positive light, e.g., gain or loss, reward or punishment, etc. This means that the same information can be more or less attractive depending on the wording or what features are highlighted.\r\n<figure class=\"kb-textbox border-left purple\"><figcaption>Example: Framing effect<\/figcaption>The framing effect strongly influences our acceptance of information. A medical procedure with a 90% chance of survival sounds more appealing than one with a 10% chance of mortality. This is because most people prefer an outcome that is presented in a positive rather than a negative way, even though it is the same information or issue, simply cast in a different light.<\/figure>\r\n<h3 id=\"actor-observer\">Actor\u2013observer bias<\/h3>\r\n<strong>Actor\u2013observer bias <\/strong>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.\r\n\r\nIn other words, when you are the <strong>actor<\/strong> in a situation, you are more likely to link events to external factors, such as your surroundings or environment. However, when you are <strong>observing<\/strong> the behavior of others, you are more likely to associate behavior with their personality, nature, or temperament.\r\n<figure class=\"kb-textbox border-left purple\"><figcaption>Example: Actor\u2013observer bias in research<\/figcaption>Suppose you are researching road rage. You are <a href=_-4.html>interviewing<\/a> people about their driving behavior, as well as the behavior of others.\r\n\r\nOne interviewee recalls a morning when it was raining heavily. They were rushing to drop off their kids at school in order to get to work on time. As they were driving down the highway, another car cut them off as they were trying to merge. They tell you how frustrated they felt and exclaim that the other driver must have been a very rude person.\r\n\r\nAt another point, the same interviewee recalls that they did something similar: accidentally cutting off another driver while trying to take the correct exit. However, this time, the interviewee claimed that they always drive very carefully, blaming their mistake on poor visibility due to the rain.\r\n\r\nThis interview was influenced by actor\u2013observer bias. Your interviewee attributed internal factors (rudeness) to others and external factors (rain) to themselves while describing identical behavior (driving dangerously).<\/figure>\r\n<h3 id=\"availability\">Availability heuristic<\/h3>\r\n<strong><a href=index-3277.html>Availability heuristic<\/a> (or availability bias) <\/strong>describes the tendency to evaluate a topic using the information we can quickly recall to our mind, i.e., that is available to us. However, this is not necessarily the best information, rather it\u2019s the most vivid or recent. Even so, due to this mental shortcut, we tend to think that what we can recall must be right and ignore any other information.\r\n<figure class=\"kb-textbox border-left purple\"><figcaption>Example: Availability heuristic\u00a0<\/figcaption>After reading many news stories about shark attacks, you start thinking these attacks always happen. When you go on holiday, you hesitate to swim in the ocean, because you are convinced it\u2019s risky.<\/figure>\r\n<h3 id=\"confirmation\">Confirmation bias<\/h3>\r\n<a href=index-3747.html><strong>Confirmation bias<\/strong><\/a> 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 <a href=index-4155.html>skewed<\/a> results and poor decision-making.\r\n<figure class=\"kb-textbox border-left purple\"><figcaption>Example: Confirmation bias in research<\/figcaption>You are a social scientist researching how military families handle long-term, overseas family separation.\r\n\r\nLet\u2019s say you grew up with a parent in the military. Chances are that you have a lot of complex emotions around overseas deployments. This can lead you to over-emphasize findings that \u201cprove\u201d that your lived experience is the case for most families, neglecting other explanations and experiences.\r\n\r\nAs a researcher, it\u2019s critical to make evidence-based decisions when supporting or rejecting a <a href=index-2943.html>hypothesis<\/a> and to avoid acting with confirmation bias towards a given outcome.<\/figure>\r\n<h3 id=\"halo\">Halo effect<\/h3>\r\nThe <strong>halo effect<\/strong> refers to situations whereby our general impression about a person, a brand, or a product is shaped by a single trait. It happens, for instance, when we automatically make positive assumptions about people based on something positive we notice, while in reality, we know little about them.\r\n<figure class=\"kb-textbox border-left purple\"><figcaption>Example: Halo effect<\/figcaption>When we meet someone for the first time, the information we have about them is limited, so we will use any information available to assess their character. In that process, we focus e.g., on observable behaviors like body language and facial expressions. We may notice that they seem friendly because they smile a lot and from that assume other positive characteristics about that person, such as that they must be kind or intelligent.<\/figure>\r\n<h3 id=\"baader\">The Baader-Meinhof phenomenon<\/h3>\r\n<strong><u><a href=index-3554.html class=\"ProsemirrorEditor-link\">The Baader-Meinhof phenomenon<\/a><\/u> (or frequency illusion) <\/strong>occurs when something that you recently learned seems to appear \u201ceverywhere\u201d soon after it was first brought to your attention. However, this is not the case. What has increased is your awareness of something, such as a new word or an old song you never knew existed, not their frequency.\r\n<figure class=\"kb-textbox border-left purple\"><figcaption>Example: The Baader-Meinhof phenomenon\u00a0<\/figcaption>Soon after hearing the word \u201csimulacrum\u201d in a philosophy class, you experience immediately encountering it again: as the title of a theatrical play, a video game, and a metal band by the same name, over a short period. You start to think this is a strange coincidence. When your brain learns something new and interesting, you simply tend to notice it more.<\/figure>\r\n<h2 id=\"avoid\">How to avoid bias in research<\/h2>\r\nWhile 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.\r\n<ul>\r\n \t<li class=\"pb-2\" aria-level=\"1\">Clearly explain in your<a href=index-1758.html> methodology<\/a> section how your <a href=index-3133.html>research design<\/a> will help you meet the research objectives and why this is the most appropriate research design.<\/li>\r\n \t<li class=\"pb-2\" aria-level=\"1\">In <a href=index-2768.html>quantitative studies<\/a>, make sure that you use probability sampling to select the participants. If you\u2019re running an experiment, make sure you use <a href=index-4153.html>random assignment<\/a> to assign your control and treatment groups.<\/li>\r\n \t<li class=\"pb-2\" aria-level=\"1\">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 <a href=index-3134.html>longitudinal studies<\/a>.<\/li>\r\n \t<li class=\"pb-2\" aria-level=\"1\">Use <a href=index-4157.html>triangulation<\/a> to enhance the <a href=index-4134.html>validity<\/a> and credibility of your findings.<\/li>\r\n \t<li class=\"pb-2\" aria-level=\"1\">Phrase your <a href=_-3.html>survey <\/a>or<a href=_-4.html> interview<\/a> questions in a neutral, non-judgmental tone. Be very careful that your questions do not steer your participants in any particular direction.<\/li>\r\n \t<li class=\"pb-2\" aria-level=\"1\">Consider using a reflexive journal. Here, you can log the details of each <a href=_-4.html>interview<\/a>, paying special attention to any influence you may have had on participants. You can include these in your final analysis.<\/li>\r\n<\/ul>\r\n<h2 id=\"other\">Other types of research bias<\/h2>\r\n<div class=\"container\">\r\n<div class=\"row\">\r\n<div class=\"col-md\" style=\"background-color: #e8f2fc; padding: 1.27rem 1.56rem; line-height: 1.8; border-radius: .5rem; margin-right: 0.5rem; margin-top: 0.5rem;\">\r\n\r\n<strong><a href=indexd548.html?p=442599\%22>Cognitive bias<\/a><\/strong>\r\n<ul style=\"padding: 5px;\">\r\n \t<li><a href=index2bde.html?p=426124\%22>Confirmation bias<\/a><\/li>\r\n \t<li><a href=index6829.html?p=441757\%22>Baader\u2013Meinhof phenomenon<\/a><\/li>\r\n \t<li><a href=index7af2.html?p=458868\%22>Availability heuristic<\/a><\/li>\r\n \t<li><a href=indexd41b.html?p=448068\%22>Halo effect<\/a><\/li>\r\n \t<li><a href=indexeeff.html?p=458884\%22>Framing effect<\/a><\/li>\r\n<\/ul>\r\n<\/div>\r\n<div class=\"col-md\" style=\"background-color: #e8f2fc; padding: 1.27rem 1.56rem; line-height: 1.8; border-radius: .5rem; margin-right: 0.5rem; margin-top: 0.5rem;\">\r\n\r\n<strong><a href=indexdcb7.html?p=427887\%22>Selection bias<\/a><\/strong>\r\n<ul style=\"padding: 5px;\">\r\n \t<li><a href=indexd3d6.html?p=155731\%22>Sampling bias<\/a><\/li>\r\n \t<li><a href=indexe599.html?p=438606\%22>Ascertainment bias<\/a><\/li>\r\n \t<li><a href=index9b5d.html?p=329396\%22>Attrition bias<\/a><\/li>\r\n \t<li><a href=index3395.html?p=443660\%22>Self-selection bias<\/a><\/li>\r\n \t<li><a href=indexc23c.html?p=437125\%22>Survivorship bias<\/a><\/li>\r\n \t<li><a href=index76f7.html?p=442213\%22>Nonresponse bias<\/a><\/li>\r\n \t<li><a href=index9c0f.html?p=442244\%22>Undercoverage bias<\/a><\/li>\r\n<\/ul>\r\n<\/div>\r\n<div class=\"col-md\" style=\"background-color: #e8f2fc; padding: 1.27rem 1.56rem; line-height: 1.8; border-radius: .5rem; margin-right: 0.5rem; margin-top: 0.5rem;\">\r\n\r\n<strong>Other<\/strong>\r\n<ul style=\"padding: 5px;\">\r\n \t<li><a href=indexcff6.html?p=426973\%22>Hawthorne effect<\/a><\/li>\r\n \t<li><a href=index7fab.html?p=335201\%22>Observer bias<\/a><\/li>\r\n \t<li><a href=indexac4d.html?p=441039\%22>Omitted variable bias<\/a><\/li>\r\n \t<li><a href=index07a8.html?p=440951\%22>Publication bias<\/a><\/li>\r\n \t<li><a href=index38ba.html?p=427882\%22>Pygmalion effect<\/a><\/li>\r\n \t<li><a href=index5625.html?p=439541\%22>Recall bias<\/a><\/li>\r\n \t<li><a href=index2ce6.html?p=392447\%22>Social desirability bias<\/a><\/li>\r\n \t<li><a href=index0f1b.html?p=438598\%22>Placebo effect<\/a><\/li>\r\n<\/ul>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n[FAQ-article]","link":"https:\/\/www.scribbr.com\/category\/research-bias\/","name":"Research bias","slug":"research-bias","taxonomy":"category","parent":0,"meta":[],"acf":[],"yoast_head":"<title>Types of Bias in Research | Definition &amp; Examples<\/title>\n
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