More often than not, researchers struggle with outcomes that are inconsistent with the realities of the target population. While there are many reasons for this, the most prominent of them is selection bias. Selection bias happens when the research sample fails to represent the population of interest, leading to variations in the research outcomes.
To grapple with the effects of selection bias, you need to understand how it works, its common effects, and the best ways to minimize it. In this article, we’ll discuss all of that to help you improve your data collection process.
When we say selection bias, we refer to experimental errors that result in the misrepresentation or underrepresentation of your population of interest in research.
In other words, selection bias is a distortion in the measure of association that occurs when the participants’ pool of your systematic investigation is not representative of the target population. This “selection effect” happens when you fail to achieve proper randomization in your research sample.
A common cause of selection bias is when the researcher fails to account for the characteristics of several subgroups within the population of interest. This results in fundamental differences between the variables selected for the sample data and the actual target population of the systematic investigation.
There are several reasons why selection bias occurs in research. For example, if the researcher uses the wrong criteria for selecting the sample population, they might record several instances of selection bias. It can also occur as a result of factors that influence the continued participation of subjects in a study.
The big question is, “what is the impact of selection bias in your study?”
One implications of selection bias is that it distorts data and leads to unreliable research outcomes. It can affect the external validity of the analysis because the results from a biased sample may not generalize to the population. Since the sample isn’t representative of the research population, any results you get do not reflect the stance of the target population in the study.
Selection bias also affects the internal validity of a study and leads to the inaccurate estimation of relationships between variables. Typically, this results from errors in the sample selection process.
Selection bias manifests in several forms in research. Its most common forms are:
Sampling bias is a type of selection bias caused by the non-random sampling of a population. It happens when some subsets are excluded from the research sample for one reason or the other, leading to a false or imbalanced representation of the different subgroups in the sample population. Sampling biases happen in the process of gathering the sample or cohort.
Sampling bias can happen in several ways. For example, some variables in the research population might have no interest in participating in the study, thus leaving room for a few groups in the population of interest. In some other cases, convenience sampling might exclude some variables from the systematic investigation.
a. Avoid Convenience Sampling: Rather than collecting data from only easily accessible participants, make conscious efforts to gather responses from the different subgroups that make up your population of interest.
b. When people drop out or fail to respond to your survey, do not ignore them. Rather, go the extra mile and find out their reasons for this. You could be asking the wrong questions or targeting the wrong population.
c. Clearly define your target population, parameters for sample selection, and the sampling frame of your study.
Also known as self-selection bias, volunteer bias is a systematic error due to differences between those who choose to participate in studies and those who do not. This happens when the variables that willingly participate in your study are not representative of your research population. In other words, the research sample has significant differences that separate it from your typical population of interest.
For example, suppose you’re researching the career preferences of final-year students. If only white students volunteer to participate in your study, then your data could be affected by volunteer bias. Another instance of voluntary response bias is when your study applies to people of all income levels but you only have participants from the economically advantaged class.
Like sampling bias, volunteer bias distorts your research outcomes—in some instances, you can end up with results that are the exact opposite of what’s obtainable from the target population. It also affects the external validity of your systematic investigation.
a. Aim for a large research sample. The larger your sample population, the more likely you are to represent all subgroups from your population of interest.
b. If possible, ensure that all responses are collected anonymously.
c. Adopt stratified random sampling to help you get a representative research sample.
Exclusion bias happens when the researcher intentionally removes some subgroups from the sample population. It is closely-related to non-response sampling bias, and it affects the internal validity of your systematic investigation.
Experts define exclusion bias as “the collective term covering the various potential biases that can result from the post-randomization exclusion of patients from a trial and subsequent analyses.” When this happens, your research outcomes may establish a false connection between variables.
a. Before excluding any variables from your research sample, make sure they’re already represented in one way or the other.
b. Map out the ideal sampling frame for your study before you kickoff the research.
Survivorship bias means that the researcher subjects variables to some sort of screening contest, and chooses the ones who sail through the process successfully. This preselection process pushes out unsuccessful variables due to their lack of visibility.
When survivorship bias happens, the entire spotlight beams on the most successful variables, even if these participants do not have relevant information for the study. Expectedly, this can affect the validity of your research results. Survivorship bias also tends to create conclusions that are overly optimistic, and that may not be representative of real-life environments.
a. Ensure that your sample population is relevant to the aims and objectives of your systematic investigation
Attrition bias happens when some research participants exit the study while it’s still ongoing. As a result, your research results become ridden with uncertainties, and this affects the quality of the outcomes arrived at in the end.
Most times, the researcher tries to identify any patterns among the drop-out variables. If these patterns exist, then you may be able to discover why the participants exited your survey without notice, and address these reasons.
Typically, a 5% or less attrition rate isn’t much of a concern but rates in excess of 20% pose a problem to your study. While it’s impossible to eliminate attrition from your research process, you can take deliberate steps to scale down its impact on your systematic investigation.
a. Keep close tabs on your research participants, and follow up with them frequently.
b. If possible, offer incentives to encourage participants to complete the systematic investigation.
c. Optimize your communication with research participants so they have important information about your study at every point.
Recall bias affects your research process when some members of your sample cannot remember important details accurately. In this state, they might provide incomplete or incorrect information that distorts your research outcomes. Recall bias mostly affects retrospective surveys that depend on self-reported data.
a. If possible, collect information from respondents when the specific incident is still fresh in their memories
b. Choose the appropriate data collection method for your study.
There are two common methods for discovering selection bias in research—bivariate analysis and multi regression methods.
Bivariate analysis is a type of quantitative analysis used to determine the empirical relationship between two variables. On the other hand, multi-regression methods allow the researcher to assess the strength of the relationship between an outcome (the dependent variable) and several predictor variables.
From all we’ve touched on so far, you should have a fair idea of how selection bias creeps into your data. Let’s look at a few scenarios.
During educational research, the investigator only collects responses from students who arrive at school before a particular time. This is a case of survivorship bias, because it excludes people who do not meet the required time, even if their experience is relevant to the study.
When conducting clinical research, the investigator includes only healthy young adults in the trials whereas the disease predominantly affects the older population.
A study has three separate phases. After the second phase, some of the participants refuse to continue with the process, leading to exclusion bias.
Selection bias affects the internal and external validities of your study. It creates false equivalence in your data, leading you to perceive non-existent relationships between variables. It also makes it difficult for the researcher to extrapolate results from the sample to the target population.
The best way to minimize selection bias in your research is to use randomization or probability sampling. Randomization is a sampling technique where every variable has an equal chance to be part of the sample population. It might be time-consuming but it reduces the interference of irrelevant variables in your systematic investigation.
The first step to dealing with Selection bias is having a clear idea of what it is, its type, and its effect on research outcomes. In this article, we’ve shared important information about Selection bias that would help you identify it and work on minimizing its effects to the barest minimum. Avoid selection bias by collecting quality research data with Formplus.
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