Selection bias is a general term describing situations where bias is introduced into the research from factors affecting the study population. Selection bias occurs when individuals or groups in a study differ systematically from the population of interest leading to a systematic error in an association or outcome.
Common types of selection bias in research
Researchers may unintentionally introduce selection bias into a study if participants differ from the target population in ways that affect the results. For example, if a survey is conducted solely on males, then any associations observed will be due to the gender differences between participants and target population, rather than to any inherent differences between men and women. There are two main types of selection bias in research: sampling bias and non-sampling bias. – Sampling bias happens when the study sample does not represent the population of interest. For example, a survey of all university students will very likely be skewed towards students who are more likely to participate in any survey, and so may not represent the people who would have their opinion changed by the results of the survey. This can be reduced by using a stratified sample of participants, or by using a sample that is representative of the target population. – Non-sampling bias happens when the research environment affects the results of the study, either inadvertently or intentionally. For example, a researcher may use a particular method or analyze a dataset in a way that leads to an association that is not necessarily representative of the population of interest. This type of bias can be particularly difficult to identify, as different researchers can often produce different results when using the same dataset.
Identifying Selection Bias in Research
There are many ways to identify selection bias in research, but the most straightforward is to look carefully at the methods and results of a study and see if they describe the target population. This is often done by comparing the results of a study to the demographics of the participants. If a study of university students shows that the participants are disproportionately more likely to be female, or have a lower socioeconomic status, there is a strong possibility that selection bias is occurring. Another way to identify selection bias is to examine the process that led to the study being conducted. If a survey was requested by a particular organization or with a particular goal in mind, then it is likely that the sample was chosen to support the chosen outcome. Selecting a sample in this way may introduce selection bias, although this is often more difficult to identify than sampling bias due to the subtle nature of the process.
Confusion about Confounding and Bias
While these two terms are often used interchangeably, selection bias is different from confounding bias. Selection bias occurs as a result of factors that affect the selection of participants, while confounding occurs as a result of factors that affect the results of the study itself. For example, a study that shows that males are less likely to support a certain policy than females may have a confounding variable, such as the fact that males are often less likely to support certain policies in the first place. However, this is not a case of selection bias, as the factors affecting the study sample (sex, age, socioeconomic status, etc.) are not the factors that affect the participants of the study.
Ways to avoid selection bias in research?
These methods can be used to reduce the risk of selection bias in research, although they are not foolproof. – When conducting a survey, attempt to make the sample as representative as possible of the target population. This can be difficult, as many people in a population are not likely to participate in a survey. To improve the likelihood of a representative sample, try to include people from as many demographic groups as possible, including people who are underrepresented in other surveys. – When reviewing datasets, try to identify when variables were chosen (or when they were not) to create the study dataset. This could help identify when non-representative variables may have been used unintentionally. – When designing a study, try to identify all the potential sources of selection bias. This will help to identify ways to reduce the risk of selection bias.
Selection bias occurs when researchers intentionally or unintentionally select participants who differ systematically from the target population, leading to incorrect conclusions. This bias can be reduced by careful selection of participants, careful data analysis, and careful design of a study. It can also be reduced by careful consideration of the factors that affect the way that participants are selected to participate in a study. Se selection bias in research happens when researchers unintentionally or intentionally select participants who differ systematically from the target population, leading to incorrect conclusions. This bias can be reduced by careful selection of participants, careful data analysis, and careful design of a study. It can also be reduced by careful consideration of the factors that affect the way that participants are selected to participate in a study.