Selection bias is a distortion in measurement introduced by the selection of groups, data, or individuals for analysis. It affects your study’s internal and external legitimacies as it generates false equivalence in your data. It may lead you to perceive the non-existing interactions between variables. Selection bias can be either positive or negative, and the best way to avoid selection bias is randomization. It leads to a false estimation of the connection between variables. The common cause of selection bias is when one fails to address the characteristics of frequent subgroups within the population of interest. Thus, this may form differences between the selected data variables and the systematic analysis of the targeted people. This article will highlight the common types of selection bias that can occur in research.
Types of Selection Bias Used in Research
Selection bias is the systematic error used in research due to a non-random sample of the population. It is important to randomize it at every level of treatment and have a random sample of assessment. There are many types of selection bias used in research. The most common types of it are discussed below;
Sampling Bias
Sampling bias is the most common selection bias, which occurs when some particular participants are systematically selected than others in a sample. In medical terms, it is also known as ascertainment bias. Sampling bias happens when one subset is omitted from the research sample for a reason, leading to a false representation of diverse subsets in a sample population.
How To Avoid Sampling Bias?
- Make the online survey as short and manageable as possible
- Restrain yourself from convenience sampling and clearly mention your target population.
- Clearly mention the sampling frame of your study.
Volunteer Bias
Volunteer bias is the type that indicates when the subject volunteers to participate in a research project are different from the general population. It is also known as self-selection bias as it is a systematic fault due to the difference between the volunteers who choose to participate and those who don’t. In some instances, it also distorts your research outcomes and affects your research investigation’s external validity.
How To Avoid Volunteer Bias?
- Try to ensure that all the responses are collected innominate.
- Try to large your sample population.
- Students must be careful with their data sources.
Survivorship Bias
Survivorship bias is a type of selection bias that occurs when a data set only accepts existing observations in research. It will not accept the observations that have already ceased to exist. It also tends to create an optimistic assumption and may not represent the real-life environment. It may affect the validity of your research study.
How to Avoid Survivorship Bias?
- Verify that the information of your systematic research is relevant
- The aims and objectives of your research sample must be relevant to your study area
- Avoid the observations that no longer exist in order to minimize the risk
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Exclusion Bias
Exclusion bias is the type of selection bias that occurs when a researcher deliberately eliminates some subcategories from the sample population. This is quite unfair and can affect your systematic research’s internal validity. It is also known as ‘’non-response sampling bias’’.
How To Avoid Exclusion Bias?
- Make sure that the excluded sample in your research area exists.
- Include a similar variable if you want to eliminate a specific variable in your research area.
- Make an ideal sampling structure.
Attrition Bias
Attrition bias is another type of selection bias in which some research students or participants exit the study while it is continuing. It may lead to uncertainties and affect the results’ quality in the end. It is a systematic error between those who exit and those who remain. You may still have the attrition bias if a small number of participants leave the study. Even eliminating the attrition bias from your research is impossible, but you can take steps to reduce or minimize the error.
How Do We Reduce Or Minimize Attrition Bias?
- Ensure to communicate with your research participants to be aware of the important points in your study or research.
- Frequently follow up with the research participants.
- Provide them compensation after attending every session.
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Conclusion
Selection bias and its types are very important parameters used in the research study. Students must be familiar with these parameters and how to avoid each selection bias. The types and prevention methods of selection bias mentioned above are very effective and critical in the research study.
