Sampling bias is a huge challenge that can alter your study outcomes and affect the validity of any investigative process. It occurs when you do not have a fair or balanced presentation of the required data samples while carrying out a systematic investigation.
Understanding sampling bias is important for every researcher as it would help you avoid this common pitfall. In this article, we will discuss different types of sampling bias, explain how you can avoid them, and show you how to collect unbiased survey samples with Formplus.
Sampling bias happens when the data sample in a systematic investigation does not accurately represent what is obtainable in the research environment. When you gather data in a way that some members of the intended population have a lower or higher sampling probability than others, the result is sampling bias.
Sampling bias is a common pitfall because, many times, it happens unintentionally; that is, without the knowledge of the researcher. Many times, your research design and research methodology can impose sampling bias on your data gathering process, and alter research outcomes.
Just like the name suggests, self-selection bias happens when individuals with specific characteristics select themselves into the research sample. When self-selection happens, it introduces abnormal or undesirable conditions in the sample that can affect the validity of the entire process.
Sometimes, because of the nature of the study, individuals with certain characteristics or experiences may be more eager to partake in it, and this also results in self-selection. Self-selection bias is common in sociology, criminology, psychology, economics, and other studies in similar fields.
For example, when carrying out a product evaluation survey, individuals who have a positive experience with the product may self-select themselves into the study sample. This will skew the data and prevent a true presentation of consumer and client experiences.
Undercoverage is a common type of sampling bias and it happens when some of the variables in the population are poorly represented or not represented in the study sample. One of the common causes of undercoverage is convenience sampling; that is when you only collect data samples from sources that are easily accessible.
To get the best results from your study, you must have a fair presentation of data samples from the research population. This means that you must be willing to go the extra mile and get the data you need for valid research outcomes.
When you only depend on the data samples you can find easily, there is a high chance that you may miss some important information that can significantly alter your findings. The Literary Digest Poll of 1936 is perhaps the most famous example of undercoverage.
Non-response is the inability of a part of your study population to partake in the study due to a factor that makes them differ greatly from the rest of the population. It can also be referred to as participation bias.
There are different reasons for non-response bias in a systematic investigation. For instance, if your research has bad survey questions or if your survey is poorly constructed, it can be a huge turn-off for some part(s) of your study population.
Also, if you request sensitive information in your survey, you may record high cases of non-response bias. Many survey respondents may not be eager to provide the information they consider personal such as information about family life, sexual preferences, or finances.
For example, a study about ballet techniques will record non-response from individuals who have no knowledge or interest in ballet and even dancing. To avoid non-response sampling bias, ensure that your survey is well-designed, field the right questions, and it targets the right audience.
Survivorship or survivor bias occurs when you ignore research variables that failed to make it past a natural or unnatural selection process while paying attention to the variables that did. It is often regarded as a logical error that ignores certain members of the study population due to a lack of visibility.
For example, when carrying out a study about business performance in a particular industry, you may ignore failed organizations that no longer exist. When you do this, your findings may have a very positive outlook which is not the true representation of what is obtainable in the industry.
Many studies tend to ignore the tales of forgotten failures within the research context. Interestingly, survivorship bias goes beyond research and studies. As humans interact in everyday life, we tend to focus on survivors, ignore failures, and assume that our success tells the whole story.
This type of sampling bias is common in medicine and epidemiologic studies. Healthy user sampling bias simply means that the type of persons who volunteer for medical research and clinical trials are often a far cry from what is obtainable in the general population.
Many times, these persons are healthier and more active than the other individuals in the study population. The result is that you end up studying people who are healthy enough to engage in an activity rather than people who would engage in the activity if they were healthy enough.
When healthy user bias happens, the findings in that study or research cannot be applied to the rest of the population. One way to combat the healthy user effect is to encourage different individuals in the research population to participate in your study.
Pre-screening or advertising bias happens when the selection process deployed in a study results in a sample that is a poor representation of the population. Sometimes, selection criteria in a study can discourage some groups from taking part in the research.
While there may be good reasons for choosing to pre-screen participants in a study, it can greatly distort the investigative process and ultimately; your findings. This is because you can end up selecting participants who share similar characteristics that will affect results.
In research, a sampling method is biased if it favors some research outcomes over others. As we’ve mentioned earlier, sampling bias in research is largely unintentional and it can occur even when you randomly select samples. This does not mean that it cannot be avoided.
To reduce sampling bias in research, you should limit your judgment and try to avoid convenience sampling as much as possible. Also, identify your research variables and define your target audience as accurately as possible.
To find out about voter apathy in a particular region, an organization decides to research to find out why people do not vote. To gather the required data, the researcher decides to administer a survey in one of the most expensive shopping malls in the region.
This sampling method already excludes different sets of people in the region who are eligible to vote. For instance, it excludes people who cannot afford to shop in the mall plus people who would not even be in the mall when the survey is administered.
The data that results from convenience sampling, as we see here, is an inaccurate representation of the thoughts and experiences of the larger population with voter apathy. Hence, the findings from this research will be greatly flawed and cannot be termed valid.
Research and clinical trials in psychology can be affected by different types of sampling bias; especially health user bias and self-selection bias. When this happens, the internal validity of the process is grossly affected and can result in multiple errors.
To reduce sampling bias in psychology, work on gathering data from a well diverse research population. You can create a sampling frame; that is, a list of individuals that the research data will be collected from then match the sampling frame to the target population as closely as possible.
Consider a study that aims at understanding the mental health of individuals in a particular group. To gather the required data, the researcher asks individuals to volunteer for the study.
This action can lead to health user bias where the people who volunteer are individuals with good or great mental health. Hence, the result of this research may not be an accurate representation of what is obtainable in the community.
Your survey design can cause sampling bias just as much as the type of questions you list in your survey. Sometimes, your survey can be crafted in a way that may favor or disfavor collecting data from certain classes of people or individuals in certain conditions.
Something as basic as the type of language used in your survey can automatically exclude a large number of people in your research population. For instance, if you want persons who are illiterate or semi-literate to complete your survey, you must make it easy to understand.
Sampling bias creeps into surveys in different ways. For example, a survey to measure the use of hard drugs amongst teenagers and young adults will be biased if it excludes teenagers and young adults who are poor or uneducated.
The first trick to avoiding sampling bias in your study is to be intentional about the whole process – from choosing research methods to identifying your target audience and everything in between. Many times, sampling bias sneaks in when you're not paying enough attention or when you ignore the most minute details in your research.
Here are some other things you can do:
Be ready to put in the work for your study and source data adequately. You can avoid convenience sampling by clearly mapping out the different groups in your study population and ensuring that you gather sufficient data from each group.
Finding out why people did not respond to your survey or questionnaire can provide insights into what you may be doing wrong. Are you asking the wrong questions? Requesting the wrong information? Or targeting the wrong audience?
Ensure that your survey is easy to understand, concise, and straight to the point. Complex surveys with too many questions can discourage respondents and lead to high survey dropout rates.
Define a target population and a sampling frame. Match the sampling frame to the target population as much as possible to reduce the risk of sampling bias.
This is a method that is used to correct undercoverage sampling bias. Here, you'd deliberately gather more data from groups that are poorly represented in your research population.
After gathering all the data, responses from oversampled groups are weighted to their original share of the study population to remove any form of sampling bias.
With Formplus, you can create beautiful and effective surveys for collecting unbiased data samples. Formplus has numerous features and field response options that help you to gather and process unbiased data samples from your study population. Create your Formplus survey in these easy steps:
Sampling bias is a threat to external validity in research because it generalizes your findings to a broader group of people; which should not be the case. This defeats the purpose of your systematic investigation because its findings will be inaccurate presentations of what is obtainable in the research context.
This is why you should avoid sampling bias or limit its occurrence to the barest minimum. In this article, we've shown you different ways to make sure sampling bias does not ruin your survey. You can use Formplus to create surveys for unbiased sampling.
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