Causes and Examples of Undercoverage Bias
Published byat July 18th, 2023 , Revised On October 5, 2023
Have you ever noticed that when you look at online reviews for a certain product, you tend to find excessively good reviews? This is how undercoverage bias works. Usually, people with strong opinions about the product write reviews. Those with more balanced or typical viewpoints may decide not to post a review. Let’s understand the undercoverage bias definition in this blog with a few examples.
What is Undercoverage Bias?
One can define undercoverage bias, often called coverage bias, as, ‘’The bias that occurs when specific population segments or groups are routinely left out or under-represented in a sample or survey.’’
For instance, public opinion polls conducted only via landline phones exclude individuals primarily using mobile phones.
What are the causes of Undercoverage Bias?
The following are some causes of undercoverage bias:
Problems with the Sampling Frame
A sampling frame is a list or illustration of the intended audience from which the sample is chosen. Certain people or groups may be left out of the sampling frame if insufficient or wrong, which could result in under-coverage bias. It will occur if individuals from far-off communities, travelling people, or those without fixed addresses are excluded from the sampling frame.
Non-response occurs when chosen participants in a survey or study choose not to take part or fail to deliver responses. Undercoverage bias may result if the non-response is not random, and some groups are more likely to decline participation. For instance, their viewpoints could not be adequately reflected if people from lower socioeconomic backgrounds were less likely to reply to surveys.
When the method used to pick participants does not sufficiently capture the complete population, selection bias can contribute to undercoverage bias. For instance, undercoverage bias may occur if a study depends on convenience sampling or voluntary participation, which may systematically exclude particular demographic groups.
Specific criteria may be used to exclude particular populations from participation in some research studies or surveys. For instance, by not including people with specific health difficulties, a health study that excludes people with pre-existing diseases may introduce undercoverage bias.
Undercoverage sampling bias results from restricted access to particular groups or communities. For instance, if a poll is performed online but doesn’t include people without internet access, it can underrepresent people with poor connectivity or marginalised by the digital world.
Language or Cultural Obstacles
If the survey or study materials are unavailable in the chosen language or fail to consider cultural sensitivities, language or cultural obstacles may lead to undercoverage bias. People from particular languages or cultural backgrounds may be excluded or have less opportunity to participate.
What are Examples of Undercoverage Bias?
Here are a few examples of undercoverage bias in different settings.
Because it excludes people who need access to landlines or mobile phones, conducting surveys exclusively through telephone interviews may lead to undercoverage bias. This can result in the underrepresentation of specific socioeconomic or age groups.
If a survey is only available online, people with poor internet access or not comfortable utilising digital platforms may not be included. This may result in undercoverage bias, especially for elderly people, people with lesser incomes, or people living in remote areas.
In household surveys, undercoverage bias can happen if particular categories of households, such as homeless populations, group living circumstances, or people living in unusual housing arrangements, are left out of the sampling frame.
If particular workforce groups, such as part-time or remote workers, are not included in the sampling frame, workplace surveys may suffer from undercoverage bias. This can result in an inaccurate portrayal of the traits or experiences of the workforce.
Undercoverage bias may appear if a survey concentrates on a certain geographic area but leaves out some neighbourhoods or regions. This may occur if the target population is not included in the sampling frame within the specified geographic boundaries.
Let’s understand undercoverage bias from daily life examples:
Radio programmes that allow listeners to ring in and voice their opinions on a particular subject are known as “phone-in radio shows,” the sample of callers only includes those who voluntarily choose to participate. As a result, excluding people who do not call in or listen to the radio can introduce coverage sampling bias and skew the general public’s perception of the issue.
Internet Product Reviews
People with strong opinions about the product frequently offer reviews on internet platforms like e-commerce websites or review websites. As result of excluding those who could have a more neutral or average viewpoint but choose not to post a review, this can create coverage sampling bias. The feedback or total rating may not fully reflect.
Suppose researchers fail to consider people who do not have access to phones or the internet. In that case, opinion polls conducted via telephone interviews or online surveys may suffer from coverage sampling bias. This can cause the underrepresentation of particular categories, like elderly people with limited access to technology, and skew the poll findings.
Social Media Surveys
Only people who regularly use and interact with social media platforms like Twitter and Facebook are eligible to participate in surveys or polls there. The exclusion of users who are not active on social media or who do not follow the specific accounts conducting the surveys adds coverage sampling bias. The findings might not fully represent the views of the general public.
When conducting interviews or surveys on the street, researchers risk unintentionally excluding people who aren’t there or who choose to avoid contact. If the chosen area does not reflect the traits or perspectives of the general community, this may induce coverage sampling bias.
How to Avoid Under-Coverage Bias?
Develop a comprehensive sampling frame that considers the full target population of interest. Ensure the sampling frame is correct, current, and includes all necessary population subgroups.
Use Random Sampling Methods
Use random sampling methods to pick participants, such as basic random, stratified, or cluster sampling. Undercoverage bias is less likely due to random sampling’s guarantee that each member of the population has an equal probability of being included.
Unique Data Collection Methods
Utilise various methods of data collection to connect with people who might not be reachable via a single technique. To increase participation and coverage, this might combine telephone polls with internet surveys, in-person interviews, or paper-based questionnaires.
Adjustments and Weighting
Use adjustments or weighting strategies to take into account any groups in the sample that were underrepresented. This may entail giving people from underrepresented communities heavier weights. To account for their smaller representation in the sample
Actively involve underrepresented groups through outreach and engagement to boost their participation. This can be accomplished by making focused recruitment efforts, forming community alliances, or working with organisations that have contacts with the targeted group.
Multilingual and Culturally Sensitive Approaches
To effectively reach various audiences, ensure that survey instruments, materials, and communication are available in many languages and sensitive to cultural differences. This aids in removing linguistic and cultural obstacles that can cause undercoverage bias.
Consider alternate sampling techniques and strategies to include non-institutional populations, such as homeless people, people who live in groups, or those who don’t have permanent addresses. If you want to reach communities, this may entail working with regional organisations or employing focused outreach techniques.
Frequently Asked Questions
Undercoverage bias, often called coverage bias, happens when specific population segments or groups are routinely left out or under-represented in a sample or survey.
Various factors can cause undercoverage bias. These include the following:
- Issues With The Sampling Frame
- Non-Response From Certain Groups
- Selection Bias In Participant Recruitment
- Exclusion Criteria
- Limited Access To Certain Populations
- Language Or Cultural Barriers
- Surveys Conducted Solely Through Telephone Interviews,
- Online Surveys
- Household Surveys
- Employment Surveys
- Geographic Surveys
- Health Surveys
To avoid undercoverage bias, researchers can use the following methods:
- comprehensive sampling frames
- use multiple modes of data collection
- apply adjustments or weighting techniques
- actively engage and outreach to underrepresented populations,
- use multilingual and culturally sensitive approaches