Undercoverage bias: Causes, Examples and more Product feedback tool

Undercoverage Bias: Causes, Examples, & more


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Gathering information tests in survey research isn’t shaded in high contrast all the time. Now and again, individuals from your research populace might be under-addressed, which prompts what is known as undercoverage bias.

Undercoverage bias is normal in survey research. In the same way as other different entanglements in survey research undercoverage bias can immensely change your review results and influence the legitimacy of your research.

What is Undercoverage Bias?

While collecting data from your sample population, you cannot tell how genuine it is and whether it is influenced by the researcher or not. When they’re a lot of other sampling biases out there happening to the research data, we are going to discuss the one commonly encountered bias in this article. 

Undercoverage bias refers to a type of sampling bias that occurs when a piece of information from your sample responses goes missing or uncovered in the results. This often happens when a large significant entity goes unselected or has zero chance of getting in your representing sample. 

Undercoverage bias: Causes, Examples and more Product feedback tool

For example, a researcher wants to know about the effects of a certain drug on diabetic patients.

There are going to be new patients who have started using the drug and patients that have already been using the drug for years. Not representing any of those groups in your research is going to significantly alter your results, as both their experiences are crucial for the research to accurately determine the effect of the drug on diabetic patients.

And the whole point of conducting research is to arrive at accurate conclusions right? Well, the undercoverage bias work just against that.

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What causes the Undercoverage Bias in Surveys?

Undercoverage bias: Causes, Examples and more Product feedback tool

It is important to say that whenever any undercoverage bias occurs in your research, it is not always the researcher’s fault. There are going to be certain things that cause it that is beyond the researcher’s control. 

We are covering this section where we help you understand common causes of undercoverage bias, so you can identify them easily:

  • Convenient samples

The most commonly made mistake while selecting the samples is choosing them according to the researcher’s convenience. A researcher might interact with only those subjects which he finds are easily available or easy to interact with. 

It is sometimes referred to as non-probability sampling, the reason being it only includes the data that was easily accessible. Such samples selected have a high risk of causing undercoverage bias, due to their unrepresentativeness towards the target population. 

This sampling technique has a chance of having information gaps, further leading to inaccurate results. 

  • Inadequate population knowledge

Another loophole caused by the researcher can be his half-cooked or no cooked at all knowledge of the target population. This happens when the person who is supposed to handle the sampling process, doesn’t have a good idea about what exactly he is sampling for, or who are the potential samples to the research. 

You need to know all the information about WHO to gather your data from and HOW. 

  • Inadequate resources

Sometimes, your data comes from people who are going to get paid for it. And when ‘cost’ enters the research, it brings certain limitations with it. Lack of resources for that matter is the most common reason for such bias. 

Sponsorships, grants, investments matter a lot especially when your data is coming from a place that requires enough funds for it. 

  • Survey design

Sometimes, the way your survey is designed can push the respondents away. The way you use your language, channels, questions and design, matters a lot when it comes to this cause of undercoverage bias. 

Pay attention to how you are outsourcing your survey. Like if it is for a younger population, distributing them over social media platforms is a good idea. But doing the same for old age people, well not so much. Try using an email channel or even paper-pen format for the old age group, and you will have a better chance at getting your information from them.

  • Time limitations

Research cannot keep going forever right? Well, it is meant to find a solution or a conclusion so it needs to be done within certain time limits. This is a reason enough for the researcher to not be able to cover all the potential and significant population samples. 

In such cases, when the time starts to run out, the researcher needs to be ok with what he has and end up drawing results. To solve this hassle, make sure to plan this schedule beforehand and start a background process to interact with the population. 

Examples of Undercoverage Bias

Let us take a look at how actually this undercover bias looks in real-life scenarios:

  • Elections 

Say we made people cast their votes through online ballots to make the process easy. The following people are going to miss out on this survey process:

    • People who have no access to the internet whatsoever. 
    • And individuals who have no knowledge of technology and electronic devices. 

This method is going to form a vast gap in the data that was gathered to elect personnel since a significant part of the data is basically missing. 

  • Statistics 

Let’s assume a researcher wants to count the total number of luxury cars in the city. He then chooses a popular busy road to check the passing cars. In his count, he is going to miss the following luxury cars:

    • The cars that are being driven on other roads. 
    • The cars that are not being driven at all, maybe stay in the home garages. 
    • The cars that are in the repair shops.
    • And the cars that are still in the showrooms. 
  • Research 

A researcher decides to study how many people work in IT. So he decides to go to an IT park and survey the workers working there. But the people he does not survey are:

    • The IT employees working at some other place than the IT Park.
    • And the people who don’t work in IT at all.

This can be a good example of convenient sampling. 

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Disadvantages of Undercoverage Bias

  • It affects the validity of your results and alters them.
  • Your result will not be representative of what can be obtained in the research context. 
  • Undercoverage bias leads to an increase in variability which ultimately compromises your validity of findings. 
  • Due to collecting small responses, the research results are going to be skewed towards the opinion of that small sample population. 
  • It leads to bias in results in a systematic investigation. 

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Ways to Avoid Undercoverage Bias

  • Email invitation – send out email invitations to your respondents and keep track of the coming responses. This minimizes convenience sampling and allows you to access the population easily. This way, you get to keep control over the responses without much of a hassle. 
  • Offline forms – make use of survey tools that allows you to conduct offline surveys and then sync the responses effectively. This eliminates the internet issue that most of the respondents face. 
  • Conditional logic – includes framing your survey in such a way, that the questions and design change the way the respondents respond to the questions. This way you can avoid missing out on certain groups and modify the survey design depending on what the person is responding to. 
  • Mobile forms – outsource your survey on mobile phones which makes it easy for the population to respond to the survey according to their convenience. 
  • Multiple sharing options – make use of an Omni-channel survey software to distribute your data on all the possible channels, from your own website to various social media platforms to reach the maximum population and get significant data.


Undercoverage bias frequently happens because of convenience examining. To dispose of (or if nothing else limits) the impacts of undercoverage bias, a superior type of testing is utilizing a straightforward arbitrary example.

In this kind of test, each individual from a populace has an equivalent possibility of being chosen to be in the example.

The advantage of this approach is that straightforward irregular examples are typically illustrative of the populace we’re keen on since each part has an equivalent possibility of being remembered for the example.

Whenever we utilize this methodology rather than convenience examining, we can be more certain about our capacity to extrapolate the discoveries from the example to the bigger populace since almost certainly, individuals from each (or virtually every) bunch in the populace are remembered for the example.

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