{"id":437591,"date":"2022-08-04T09:00:09","date_gmt":"2022-08-04T16:00:09","guid":{"rendered":"https:\/\/www.questionpro.com\/blog\/?p=437591"},"modified":"2023-08-29T11:00:58","modified_gmt":"2023-08-29T11:00:58","slug":"undercoverage-bias","status":"publish","type":"post","link":"https:\/\/www.questionpro.com\/blog\/undercoverage-bias\/","title":{"rendered":"Undercoverage Bias: How to Avoid it in Survey Research"},"content":{"rendered":"

Convenience sampling<\/a> is one of the most common forms of sample selection bias<\/a> when conducting a research study. This type of sampling is used because it allows researchers to collect data quickly and easily. However, this process can lead to undercoverage bias as researchers will only select certain groups or individuals that are more easily accessible to them.<\/span><\/p>\n

Let’s take the example of a study on bullying among teenagers. In that case, you might not include teenagers who are home-schooled or attend private schools because they would be harder to reach than those who participate in public schools. If you don’t have these groups in your sample, it could affect the validity of your results since these students may experience bullying differently than those who attend public schools.\u00a0<\/span><\/p>\n

Understanding what undercoverage bias is…<\/h2>\n

To put it in simple words, undercoverage happens when a substantial portion of your research population has very few possibilities of getting selected to be part of the sample<\/a> or isn’t satisfactorily represented in your survey population.\u00a0<\/span><\/p>\n

For example, suppose you’re conducting a survey on the preferences of current college students, and you want to understand which movies they like most. To do this, you could select a random sample<\/a> of current college students and ask how many times per week they go to movie theaters. However, if there are no movie theaters near these students’ homes (or if they don’t have cars), they have almost no chance of being selected for this survey.<\/span><\/p>\n

In this case, undercoverage bias would lead to results that underestimate the average number of times per week that current college students go to movie theaters because it doesn’t account for people who don’t have access to movies.<\/span><\/p>\n

Undercoverage Bias Causes<\/span><\/h2>\n

While undercoverage bias is a serious issue, it can also be prevented with the proper technique and understanding of the problem.<\/span><\/p>\n

One of the reasons undercoverage bias occurs is because of survey non-response<\/a>. This means that when a survey is conducted, some people do not respond to it. This can happen for many reasons: maybe they don\u2019t have time, feel like they have nothing important to say, or forget about the survey altogether. Whatever the reason, these people are not included in your results because you did not collect their responses.<\/span><\/p>\n

Another reason for undercoverage bias is non-coverage error\u2014which refers to cases where an individual is selected from your sample but cannot be contacted due to an sampling error<\/a> on behalf of the researcher. For example, if you are conducting a phone survey<\/a> and accidentally call someone who doesn\u2019t speak English well enough to understand your questions, then this person will likely hang up on you before answering anything\u2014meaning that their response was never recorded for later analysis!<\/span><\/p>\n

The last cause of undercoverage bias we\u2019ll discuss here today is coverage error\u2014which refers to cases where individuals who should be included in your sample aren\u2019t.\u00a0<\/span>
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How can we fix undercoverage bias?<\/h3>\n

With QuestionPro Audience<\/a> you can avoid sampling bias using our best tools. Let\u2019s take conditional logic, this feature allows you to use your survey as a tool for validating the experiences of certain groups in your study, thus improving the integrity of your results.\u00a0<\/span><\/p>\n

Conditional logic is particularly useful if you have a small sample size<\/a> or if it is crucial that all members of a particular group be represented in your survey population. This is because conditional logic helps ensure that all members of that group receive the same information on their first question and do not miss out on any important details which might be important for their experience but may not apply to other groups. So suppose you are surveying the experiences of people from different races in America. In that case, conditional logic allows you to present unique questions concerning those experiences to respondents in particular groups.\u00a0<\/span><\/p>\n

Undercoverage Bias Examples<\/strong><\/h2>\n

Undercoverage bias is common in survey research<\/a> and can lead to inaccurate findings. Undercoverage bias occurs when members of your research population cannot complete your survey without any internet access.\u00a0<\/span><\/p>\n