In the methodology of academic research, understanding and mitigating sampling bias is pivotal to ensuring the validity and reliability of your findings. Sampling bias occurs when certain members of a population are more likely to be included in a sample than others, potentially skewing the results. Vigilance against sampling bias is essential for research that truly reflects your study’s population and stands up to academic scrutiny. Our guide will illuminate how to identify and reduce sampling bias.
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Sampling Bias – In a Nutshell
- Sampling bias occurs when certain samples are systematically more likely to be picked than others.
- It comes in different forms, including non-response, pre-screening bias, and survivorship bias.
- You can avoid sampling bias by using random number generators to select samples.
- You should ensure that all members in the sampling frame have an equal chance of participating in the study.
Definition: Sampling bias
Sampling bias is an issue where a sample is selected in such a way that certain members of the population have a higher or lower probability of being selected. This issue can make it harder to generalize the findings of the research, as it presents a threat to external validity or population validity.
Sampling bias: Causes
It is important to understand the causes of sampling bias so that you avoid them when carrying out research. The problem results from the choice of data collection method and the selected research design.
Sampling bias in probability samples
Probability sampling is a system where every member has the same exact chance of being chosen.
Example
You can use a random number generator to get a simple random sample, and this will give every member a known and equal chance of getting selected.
Probability sampling lowers sampling bias since none of the members has a higher or lower chance of being chosen. However, the researcher may still be biased when creating the sampling frame.
Example
When running a survey research on the level of support for a nation’s dictator, the researcher may get more responses from supporters of the regime since opponents are afraid of airing their opinion. The researcher can use a random number generator to choose a sample from the respondents, and each of them will have a known and equal chance of being picked. However, the sampling frame itself will be biased.
Sampling bias in non-probability samples
A non-probability sample is a technique where the researcher doesn’t use random methods to select the samples. The selection process of the sample is based on the subjective judgment of the researcher, and this makes it easy to introduce sampling bias.
Example
When researching certain products, a researcher may use a sample that includes people from a specific region or school.
Sampling bias: Types
| Type | Explanation | Example |
| Self-selection | A specific type of people are likely to participate in the research. | Outgoing people are likely to participate in studies requiring their physical presence. |
| Non-response | This occurs when a certain type of people are more likely to refuse to participate in the study. | When studying the impact of outdoor activities, people who rarely go outside may not respond. |
| Undercoverage | This occurs when a certain group isn’t adequately represented in the study. | When studying the impacts of a drug, a researcher should adequately represent new users and older users of the medication. |
| Survivorship | This is where the samples that didn’t survive or pass are ignored or inadequately studied. | A researcher studying winning tricks in casino (games may evaluate the winners while ignoring losers who apply similar tactics. |
| Pre-screening or advertising | The sample may be biased because of the method of screening participants. Also, how and where the study is advertised can affect the sample. | In a study on the impact of workload and stress, only people with free time may be able to participate. This can lead to wrong conclusions. |
| Healthy user | This occurs in medical researches as healthy people are generally more likely to volunteer to test preventive medications or interventions. | Volunteers for a medical program are likely to engage in regular exercise and are likely to abstain from drug abuse. |
Sampling bias: Avoiding or correcting it
You can avoid and correct sampling bias by using the right research design and sampling process. Here are four methods of avoiding sampling bias:
- Use simple random sampling or stratified sampling in the research as these do not depend on the judgment of the researcher.
- Ask the right questions to make sure every relevant response is recorded. If necessary, you should use open-ended questions to encourage participants to be more open.
- Make sure all potential respondents have an equal chance of participating in the study.
- Keep the surveys short and easy to access.
- Follow up on the respondents.
Avoiding sampling bias: Oversampling
Oversampling is a good way of reducing sampling bias. With this method, the researcher selects more members from under-represented groups. Since this sample will not represent the population, the researcher has to apply weighting to the under-represented group. Oversampling allows researchers to shed light on groups that would be too small to report on.
An example of oversampling in a study on sexual assault would be the researcher oversampling groups that do not report assaults.
Example
Sexual violence among black women rarely gets reported, so this group would need to be oversampled for this particular study.
FAQs
This bias occurs when a researcher is systematically more likely to pick certain samples over others.
A researcher can eliminate sampling bias by giving all potential respondents an equal chance of taking part in the survey. They can also use simple random sampling.
Bias poses a threat to external validity since the findings will be generalized to the population. It can lead to an overestimation or underestimation of the corresponding parameters in the population.
Sampling bias is commonly introduced by inappropriate sampling methods, so increasing the sample size will not help to lower the bias.