Convenience sampling is one of the most common forms of sample selection 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.
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.
Understanding what undercoverage bias is…
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 or isn’t satisfactorily represented in your survey population.
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 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.
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.
Undercoverage Bias Causes
While undercoverage bias is a serious issue, it can also be prevented with the proper technique and understanding of the problem.
One of the reasons undercoverage bias occurs is because of survey non-response. This means that when a survey is conducted, some people do not respond to it. This can happen for many reasons: maybe they don’t 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.
Another reason for undercoverage bias is non-coverage error—which refers to cases where an individual is selected from your sample but cannot be contacted due to an sampling error on behalf of the researcher. For example, if you are conducting a phone survey and accidentally call someone who doesn’t speak English well enough to understand your questions, then this person will likely hang up on you before answering anything—meaning that their response was never recorded for later analysis!
The last cause of undercoverage bias we’ll discuss here today is coverage error—which refers to cases where individuals who should be included in your sample aren’t.
How can we fix undercoverage bias?
With QuestionPro Audience you can avoid sampling bias using our best tools. Let’s 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.
Conditional logic is particularly useful if you have a small sample size 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.
Undercoverage Bias Examples
Undercoverage bias is common in survey research and can lead to inaccurate findings. Undercoverage bias occurs when members of your research population cannot complete your survey without any internet access.
- If you have a part of your population that has no access to the internet, or if they lose their connection while completing your survey, the data collected will be incomplete. This will cause undercoverage bias and affect the outcome of your study.
Our software allows you to gather insights effectively from all parties in your research population, with or without internet access and mobile friendly. Survey participants can fill in data in remote locations without internet access. Let QuestionPro Audience do the hard work for you, avoid undercoverage bias, and collect data from anyone, anywhere and anytime.
- QuestionPro surveys are mobile-friendly and adapt to any internet-enabled device, including mobile phones. This means that you can reach more respondents and deal with the accessibility problem that often leads to undercoverage bias in any systematic investigation.
No matter what device your respondents use, QuestionPro surveys will always look great and be easy to fill out. Respondents can view and answer your questions conveniently without pinching out or zooming in on the form.
- There are many reasons why undercoverage bias might occur; however, one common cause is when data collectors fail to reach out to some groups within the population.
For example, suppose you’re researching gender equality in the workplace but only interview men who work at Fortune 500 companies. In that case, you’ll miss out on women who work at smaller companies or who don’t work at all because they’re caring for children or elderly relatives. The resulting data set might seem skewed toward male perspectives even though it was collected from both genders!
Conclusion
Undercoverage bias, also known as sampling bias, is a common problem in systematic investigations. To avoid undercoverage bias, you must understand why your sample does not represent your target audience. Then you can take steps to eliminate the reasons behind this phenomenon.
In other words, if you are trying to draw conclusions about a large population but only sample a small portion of it, then there will be people in that population who are not represented in your sample—and they may not share any similar characteristics with those who did get included. This can cause issues because your conclusions may not reflect what’s happening in reality.
As stated, undercoverage bias results from convenience sampling and a lack of knowledge and understanding of your target audience. At QuestionPro, we believe targeting an adequate audience will make your research not only accurate but insightful, allowing you to make smart business decisions.
Author: Danielle Figueroa
