What is Undercoverage Bias?
Undercoverage bias occurs when the population list from which the researchers select their sample (aka the sampling frame) does not include all population members. When that happens, the sample cannot contain the unlisted individuals, potentially producing a biased sample that doesn’t fully represent the population.
Undercoverage bias and nonresponse bias are similar in that both problems produce samples that have insufficient diversity due to a lack of participation amongst particular subpopulations. However, the causes for these two types of sampling biases differ:
- Undercoverage bias: The population list does not include the subpopulation. Consequently, members of that group were never in the sample.
- Nonresponse bias: The list includes the subpopulation, and those group members were in the sample. But they failed to participate, and consequently, the results do not include them.
In short, undercoverage bias occurs when the sampling frame does not cover a subpopulation. While the difference might sound like a technicality, the solutions for minimizing each type of bias differ, making it a crucial distinction.
Undercoverage bias can have significant implications for research and decision-making. If a sample underrepresents certain groups, the results may not accurately reflect the characteristics or experiences of the population. This deficiency can lead to incorrect conclusions or decisions that do not adequately address the needs of all members of the population. That’s a problem common to all nonrepresentative samples. Learn more about Representative Samples: Definition, Uses & Methods.
In this blog post, we’ll take a closer look at undercoverage bias examples, its causes, and how to avoid it.
Related posts: Sampling Bias: Definition & Examples and Selection Bias.
Causes of Undercoverage Bias
Undercoverage bias occurs for two primary reasons—non-probability sampling methods and incomplete population lists.
Non-Probability Sampling Methods
Non-probability sampling methods, such as convenience sampling, tend to be biased because they do not provide an equal chance of selecting all population members for the study. Researchers who choose study participants based on proximity or ease of access cannot generalize their findings to the entire population. These methods typically do not use a complete list of the population. Many don’t use a list at all!
For instance, if a researcher only collects survey responses from attendees at a conference, they may miss out on the perspectives of those who could not attend due to travel or financial constraints.
Learn more about Convenience Sampling: Definition & Examples.
Incomplete Population Lists
Even probability sampling methods like simple random sampling can be susceptible to undercoverage bias if the sampling frame is incomplete. This shortcoming means that the sample may not represent the entire population due to specific segments being underrepresented or not sampled at all. As a result, the findings may not be entirely accurate or generalizable to the larger population.
For example, suppose a researcher wants to survey the opinions of university students but only collects responses from those registered with the student government association. In that case, the sample may not accurately represent the entire population of university students. Students not involved with the student government association may have different perspectives and opinions that the sample does not reflect.
Learn more about Simple Random Sampling: Definition & Examples.
How to Avoid Undercoverage Bias
To minimize the risk of undercoverage bias, you must implement strategies ensuring all segments of the population of interest have an equal chance of being selected for the study. Here are some ways to avoid undercoverage bias in your research.
Use a Comprehensive Sampling Frame
Make sure your sampling frame includes all members of the population of interest rather than just a subset. This approach can help reduce the risk of undercoverage bias by ensuring that all population segments have an equal chance of being selected.
Becoming familiar with your target population is essential to capture all relevant characteristics and subgroups in your research. By understanding the nuances and diversity within your target population, you can ensure that your study is comprehensive and accurately represents the population of interest.
Consider any exclusions or limitations that may be present in your population list. For example, that can include language barriers, geographic restrictions, or other factors that may exclude specific individuals.
For more information about sampling frames and how to develop a good one, read my post Sampling Frames: Definition, Examples & Uses.
Build Your Sampling Frame Using Multiple Sources
Use multiple sources to compile your population list to ensure it represents all segments. For example, if you’re surveying a specific industry, you might use trade association lists, industry directories, and online databases to build your list.
Conduct Pilot Tests
Pilot tests can help you identify sampling frame and sampling method issues before launching the full study. Test runs help you address potential problems and ensure that your sample is representative of the population of interest.
By following these guidelines, you can help ensure that your study’s findings are accurate, reliable, and representative of the larger population.
Learn more about Sampling Methods in Research.
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