{"id":443247,"date":"2022-10-30T11:00:29","date_gmt":"2022-10-30T18:00:29","guid":{"rendered":"https:\/\/www.questionpro.com\/blog\/?p=443247"},"modified":"2024-02-09T08:44:53","modified_gmt":"2024-02-09T08:44:53","slug":"selection-bias-2","status":"publish","type":"post","link":"https:\/\/www.questionpro.com\/blog\/selection-bias-2\/","title":{"rendered":"Selection Bias: What it is, how to avoid it + practical impact"},"content":{"rendered":"\n<p><span style=\"font-weight: 400;\">Researchers may need help with findings that don&#8217;t match the realities of the target community. There are numerous causes, but one of the most important is selection bias. It occurs when the study sample needs to accurately represent the population of interest, resulting in variations in the research results.<\/span><\/p>\n\n\n\n<p><span style=\"font-weight: 400;\">Understanding selection bias, its practical impacts, and the best ways to avoid it will help you deal with its effects. Everything you need to know about how to enhance your data collection process will be covered in this post.<\/span><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span style=\"font-weight: 400;\">What is selection bias?<\/span><\/h2>\n\n\n\n<p><span style=\"font-weight: 400;\">Selection bias refers to experimental mistakes that lead to an inaccurate representation of your research sample. It arises when the participant pool or data does not represent the target group.<\/span><\/p>\n\n\n\n<p><span style=\"font-weight: 400;\">A significant cause of <a href=\"https:\/\/www.questionpro.com\/blog\/selection-bias\/\">selection bias<\/a> is when the researcher fails to consider subgroup characteristics. It causes fundamental disparities between the sample data variables and the actual research population.<\/span><\/p>\n\n\n\n<p><span style=\"font-weight: 400;\">Selection bias arises in research for several reasons. If the researcher chooses the sample population using the incorrect criteria, they may find numerous examples of this bias. It may also happen due to elements that affect study volunteers&#8217; willingness to continue participating.<\/span><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span style=\"font-weight: 400;\">What are the types of selection bias in research?<\/span><\/h2>\n\n\n\n<p><span style=\"font-weight: 400;\">Several types of selection bias can happen at various points in the research process. The following are some of them:<\/span><\/p>\n\n\n\n<ul>\n<li> <span style=\"font-family: Raleway, sans-serif; font-size: 24px;\">Sampling bias<\/span> <\/li>\n<\/ul>\n\n\n\n<p><span style=\"font-weight: 400;\">Sampling <a href=\"https:\/\/www.questionpro.com\/blog\/sampling-bias\/\">bias<\/a> is a form of selection bias brought on by non-random population sampling. It occurs when specific subsets are removed from the research sample, leading to an inaccurate representation of the subgroups in the sample population.<\/span><\/p>\n\n\n\n<p><i><span style=\"font-weight: 400;\">For example, imagine you are researching the prevalence of heart disease in your area. To collect data, you decide to conduct interviews with shoppers at the shopping mall.<\/span><\/i><\/p>\n\n\n\n<p><i><span style=\"font-weight: 400;\">This strategy excludes hospitalized and heart disease patients. Your sample is biased because many people are not present in that shopping mall but staying at their homes or hospitals.<\/span><\/i><\/p>\n\n\n\n<ul>\n<li> <span style=\"font-family: Raleway, sans-serif; font-size: 24px;\">Self-selection bias<\/span> <\/li>\n<\/ul>\n\n\n\n<p><span style=\"font-weight: 400;\"><a href=\"https:\/\/www.questionpro.com\/blog\/self-selection-bias\/\">Self-selection bias<\/a> is also known as volunteer bias. This arises when the qualities of the people who voluntarily participate in the study are important to the goals of the investigation.<\/span><\/p>\n\n\n\n<p><span style=\"font-weight: 400;\">Self-selection causes biased data if the sample group consists of volunteers rather than the ideal target population. It is quite likely that researchers will likely get biased results.<\/span><\/p>\n\n\n\n<p><i><span style=\"font-weight: 400;\">For example, a pro-auto fan might participate in a study that examines the perception of a new car entering the target market since they see themselves as experts in the field.<\/span><\/i><\/p>\n\n\n\n<p><i><span style=\"font-weight: 400;\">Due to the self-selection bias, they could respond inappropriately or provide more information that wasn&#8217;t asked for.<\/span><\/i><\/p>\n\n\n\n<ul>\n<li> <span style=\"font-family: Raleway, sans-serif; font-size: 24px;\">Nonresponse bias<\/span> <\/li>\n<\/ul>\n\n\n\n<p><span style=\"font-weight: 400;\">Nonresponse bias happens when people don&#8217;t answer a survey or participate in a research project. It often happens in survey research when participants lack the appropriate abilities, lack time, or feel guilt or shame about the topic.<\/span><\/p>\n\n\n\n<p><i><span style=\"font-weight: 400;\">For Example, Researchers are interested in how computer scientists view a new piece of software. They conducted a survey and found many computer scientists didn&#8217;t respond or finish.<\/span><\/i><\/p>\n\n\n\n<p><i><span style=\"font-weight: 400;\">Researchers found that the respondents believe the software is excellent and high-quality after receiving the data. However, they discover that they receive mainly unfavorable criticism after releasing the new software to the full population of computer scientists.<\/span><\/i><\/p>\n\n\n\n<p><i><span style=\"font-weight: 400;\">The survey participants were entry-level computer scientists who couldn&#8217;t spot program flaws. The survey respondents did not reflect the more significant computer scientist population. Hence the results were inaccurate.<\/span><\/i><\/p>\n\n\n\n<ul>\n<li> <span style=\"font-family: Raleway, sans-serif; font-size: 24px;\">Survivorship bias<\/span> <\/li>\n<\/ul>\n\n\n\n<p><span style=\"font-weight: 400;\">Survivorship bias occurs when a researcher subjects variables to a screening contest and selects those who successfully complete the procedure. This preliminary selection method eliminates failed variables because of their lack of visibility.<\/span><\/p>\n\n\n\n<p><span style=\"font-weight: 400;\">Survivorship bias focuses on the most successful factors, even if they don&#8217;t have relevant data. It can alter your research outcomes and lead to unnecessarily positive views that don&#8217;t reflect reality.<\/span><\/p>\n\n\n\n<p><i><span style=\"font-weight: 400;\">Suppose you&#8217;re researching entrepreneur success variables. Most famous entrepreneurs didn&#8217;t finish college. It could make you assume that leaving college with a strong concept is enough to launch a career. But the majority of college dropouts don&#8217;t end up rich.<\/span><\/i><\/p>\n\n\n\n<p><i><span style=\"font-weight: 400;\">Actuality, many more people dropped out of college to launch unsuccessful businesses. In this example, survivorship bias occurs when you only pay attention to dropouts who succeeded and ignore the vast majority of dropouts who failed.<\/span><\/i><\/p>\n\n\n\n<ul>\n<li> <span style=\"font-family: Raleway, sans-serif; font-size: 24px;\">Attrition bias<\/span> <\/li>\n<\/ul>\n\n\n\n<p><span style=\"font-weight: 400;\">Attrition bias occurs when some survey respondents drop out while it is still being conducted. As a result, there are many unknowns in your research findings, which lowers the quality of the conclusions.<\/span><\/p>\n\n\n\n<p><span style=\"font-weight: 400;\">Most of the time, the researcher looks for trends among the drop-out variables. If you can identify these tendencies, you might be able to determine why the respondents left your survey suddenly and take appropriate action.<\/span><\/p>\n\n\n\n<ul>\n<li> <span style=\"font-family: Raleway, sans-serif; font-size: 24px;\">Recall bias<\/span> <\/li>\n<\/ul>\n\n\n\n<p><span style=\"font-weight: 400;\">Recall bias happens when some sample members struggle to recall crucial information, which impacts your research process. It takes place when researchers reject what is in front of them and instead see what they want to see.<\/span><\/p>\n\n\n\n<p><i><span style=\"font-weight: 400;\">You&#8217;ll get a biased result if you just survey those who&#8217;ve seen a new movie. Those who have seen it will say they loved it, while those who haven&#8217;t will say they didn&#8217;t. This is because people who like the movie are more willing to discuss it than those who don&#8217;t.<\/span><\/i><\/p>\n\n\n\n<ul>\n<li> <span style=\"font-family: Raleway, sans-serif; font-size: 24px;\">Undercoverage bias<\/span> <\/li>\n<\/ul>\n\n\n\n<p><span style=\"font-weight: 400;\">Undercoverage bias arises when a representative sample is drawn from a smaller proportion of the target population. Online surveys are especially vulnerable to undercoverage bias.<\/span><\/p>\n\n\n\n<p><i><span style=\"font-weight: 400;\">In an online survey on self-reported health, let&#8217;s say you are focusing on excessive drinking and smoking behaviors. Although, because of your way of conducting the survey, you are deliberately excluding people who don&#8217;t use the Internet.<\/span><\/i><\/p>\n\n\n\n<p><i><span style=\"font-weight: 400;\">This way, older and less educated individuals are left out of your sample. Since internet users and non-users differ significantly, you can&#8217;t draw reliable results from your online survey.<\/span><\/i><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span style=\"font-weight: 400;\">What are the impacts of selection bias?<\/span><\/h2>\n\n\n\n<p><span style=\"font-weight: 400;\">There is always the possibility of random or systematic errors in research that compromise the reliability of research outcomes. Selection bias can have various impacts, and it&#8217;s often hard to tell how significant or in which direction those effects are. The impacts can lead to several issues for businesses, including the following:<\/span><\/p>\n\n\n\n<ul>\n<li> <span style=\"font-family: Raleway, sans-serif; font-size: 24px;\">Risk of losing revenue and reputation<\/span> <\/li>\n<\/ul>\n\n\n\n<p><span style=\"font-weight: 400;\">For business planning and strategy, insights obtained from non-representative samples are significantly less helpful because they don&#8217;t align with the target population. There is a risk of losing money and reputation if business decisions are based on these findings.<\/span><\/p>\n\n\n\n<ul>\n<li> <span style=\"font-family: Raleway, sans-serif; font-size: 24px;\">Impacts the external validity of the analysis<\/span> <\/li>\n<\/ul>\n\n\n\n<p><span style=\"font-weight: 400;\">Research becomes less trustworthy as a result of inaccurate data. Therefore, the analysis&#8217;s external validity compromises because of the biased sample.<\/span><\/p>\n\n\n\n<ul>\n<li> <span style=\"font-family: Raleway, sans-serif; font-size: 24px;\">This leads to inappropriate business decisions<\/span> <\/li>\n<\/ul>\n\n\n\n<p><span style=\"font-weight: 400;\">If the final results are biased and unrepresentative of the topic, it is unsafe to rely on the study&#8217;s findings when making important business decisions.<\/span><\/p>\n\n\n\n<p class=\"has-text-align-center\"><em>We have published a blog that talks about <a href=\"https:\/\/www.questionpro.com\/blog\/subgroup-analysis\/\">subgroup analysis<\/a>; why don\u2019t you check it out for more ideas?<\/em><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span style=\"font-weight: 400;\">How to avoid selection bias?<\/span><\/h2>\n\n\n\n<p><span style=\"font-weight: 400;\">There&#8217;s a good chance you affected your survey results through selection bias. Review the following advice to help you avoid selection bias:<\/span><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Avoid selection bias during survey design<\/h3>\n\n\n\n<p><span style=\"font-weight: 400;\">Try some of these suggestions to avoid selection bias when you are developing the structure for your survey:<\/span><\/p>\n\n\n\n<ul>\n<li><span style=\"font-weight: 400;\">Make sure that your survey objectives are apparent.<\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">Specify the standards that should be met for your intended audience.<\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">Allow every possible participant a fair opportunity to take part in the survey.<\/span><\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Avoid selection bias during sampling<\/h3>\n\n\n\n<p><span style=\"font-weight: 400;\">Consider putting some of these strategies into practice during the process of selecting samples:<\/span><\/p>\n\n\n\n<ul>\n<li><span style=\"font-weight: 400;\">When employing <a href=\"https:\/\/www.questionpro.com\/blog\/simple-random-sampling\/\">random sampling<\/a> in your processes, ensure proper randomization.<\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">Be sure that your list of participants is up to date and accurately represents the intended audience.<\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">Make sure that the subgroups represent the population as a whole and share the essential factors.<\/span><\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Avoid selection bias during the evaluation<\/h3>\n\n\n\n<p><span style=\"font-weight: 400;\">When going through the evaluation and validation process, you need to think about putting some of these ideas into action to avoid selection bias:<\/span><\/p>\n\n\n\n<ul>\n<li><span style=\"font-weight: 400;\">If you want to ensure that your sample selection, procedure, and data collection are free of bias, having a second researcher look over your back is a good idea.<\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">Apply technology to monitor how the data changes so you may identify unexpected outcomes and investigate quickly to repair or avoid inaccurate data.<\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">Check previous basic research data trends to verify if your research is on track for strong <a href=\"https:\/\/www.questionpro.com\/blog\/internal-validity\/\">internal validity<\/a>.<\/span><\/li>\n\n\n\n<li><span style=\"font-weight: 400;\">Invite the people who didn&#8217;t answer the survey to an additional one. A second round might yield more votes for a clearer understanding of the findings.<\/span><\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><span style=\"font-weight: 400;\">Conclusion<\/span><\/h2>\n\n\n\n<p><span style=\"font-weight: 400;\">Understanding selection bias, its types, and how it affects research outcomes is the beginning step in working with it. We&#8217;ve discovered crucial data that will help in identifying it and working to reduce its impacts to a minimum. You can avoid selection bias by using QuestionPro to gather reliable research data.<\/span><\/p>\n\n\n\n<p><span style=\"font-weight: 400;\">Various situations can result in selection bias, such as when non-neutral samples are combined with system problems. An enterprise-grade research tool to use in research and alter experiences is the QuestionPro research suite.<\/span><\/p>\n\n\n\n<p><span style=\"font-weight: 400;\">QuestionPro research suite provides survey templates based on professional research, making it easier to develop surveys. Learn more about surveys and get started with our survey software by creating a free account.<\/span><\/p>\n\n\n\n<p class=\"has-text-align-center\"><a href=\"https:\/\/www.questionpro.com\/research-edition-survey-software\/\" target=\"_blank\" rel=\"noopener\"><button>LEARN MORE<\/button><\/a>&nbsp; &nbsp; &nbsp; &nbsp;<a href=\"https:\/\/www.questionpro.com\/a\/showEntry.do?classID=1053&amp;sourceRef=blog\" target=\"_blank\" rel=\"noopener\"><button>FREE TRIAL<\/button><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Researchers may need help with findings that don&#8217;t match the realities of the target community. There are numerous causes, but [&hellip;]<\/p>\n","protected":false},"author":51,"featured_media":443248,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"","footnotes":""},"categories":[174],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Selection Bias: What it is, how to avoid it + practical impact<\/title>\n<meta name=\"description\" content=\"Selection bias results from improper randomization. The ideal research population is well-defined, accessible, and dependable. 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