What is self-selection in research?

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Most of the time, researchers have trouble getting results that don’t match up with what happens in the target population. Even though there are many reasons for this, self-selection bias is one of the most important ones. 

When we say “selection bias,” we mean that an experiment went wrong that caused your population of interest to be misrepresented or underrepresented.

This bias is a problem when researching programs or products. Self-selection makes it hard to do market research and evaluate programs.

In this blog,  we will go through the definition of self-selection bias and what methods we should follow to reduce bias, and we will also give some examples of this bias.

What is self-selection bias?

Self-selection bias happens when people choose to join a group on their own. It causes a biased sample when nonprobability sampling is used. It is often used to describe situations in which the traits of the people in the group, which led them to choose to be there, lead to strange or bad things happening in the group. 

It is similar to the non-response bias, which is when the group of people who answered the survey gave different answers than the group who didn’t answer.

Now we will discuss the methods of reducing this bias. We will also give some examples of it as well. To learn more, stay with us till the end.

Methods of reducing self-selection bias

The most obvious way to eliminate self-selection bias is not to let people choose themselves for a survey. To get a sample, a probability sampling technique is ideal.

Probability sampling method

Probability sampling is a method for choosing a population for a systematic study based on probability theory. Here, the researcher picks a small group of people from the whole population whose characteristics they want to estimate.

Probability sampling is based on the randomization principle, which means that all members of the research population have an equal chance of being in the sample population. 

For example, if the population size is 500, every person in the population has a 1 in 500 chance of being in the research sample.

The basic idea behind this method is that if you can pick a random sample representative of the whole, your estimates will be accurate. When the sample population is big enough, you can use statistical techniques to conclude the whole population based on the sample.

Here are some examples of probability sampling methods:

• Simple random sample: The American Community Survey collects information about life in the United States by randomly picking people.

The United States Census Bureau asks a random sample of people in the country for detailed information. They then use this information to conclude the whole population.

• Systematic sampling: Systematic sampling is a type of probability sampling in which the researcher uses a random starting point and fixed intervals to find members of the research population. Here’s what’s happening right now. Example of systematic sampling

Let’s say the people you’re interested in number 800. You can pick the sixth person randomly as your starting point and choose a random sampling interval of 10 people. It means that the research population will be made up of every 10th element in a row.

• Stratified sampling: The idea of stratification is what makes stratified sampling work. When the study population is split into subgroups (called “strata”) based on gender, age, income level, and other similar factors, this is called “stratification.” Each layer is given a weight based on how big it is. Then, a sample is picked by giving each stratum a random place to start.
• Cluster sampling: Cluster sampling is a way to choose research samples from a large population based on chance. In this case, the researcher divides the population into existing groups, such as neighborhoods and cities. It is also referred to as multi-stage sampling.is also known as multi-stage sampling.

To cluster a research sample, the researcher divides the sample into naturally occurring subgroups with different traits. Next, they pick clusters at random to use as samples and get the needed information.

Examples of self-selection bias

The following examples show a few situations where self-selection bias is likely to happen:

Example 1

A teacher wants to know if a new course on how to do well on tests helps students do better. She puts a sign-up sheet outside her classroom and lets students decide for themselves if they want to take the class.

Self-selection bias is likely because students who are more serious about school are more likely to sign up. It means that the sample of students who take the course probably doesn’t look like the whole group who could take it.

Example 2

Imagine that a local government sends out a survey asking people if street signs should also be written in languages other than English to make it easier for people who don’t speak English to get around.

Self-selection bias is likely because only residents who can read English will answer the survey. It means that the opinions of the people who answered the survey are probably not the same as those of all the people who live in the town.

Example 3

If a biologist wants to figure out how tall a specific species of deer are on average, she might put deer food in an open meadow and take pictures of the deer that come to eat it.

In this case, self-selection bias is likely to happen because only the deer who like that type of deer food or are more comfortable being out in the open is likely to enter the meadow and be included in the sample data.

So, it’s unlikely that the average height of the deer in this sample will be the same as the average height of all deer.

Conclusion

We learned about self-selection bias and methods of how to reduce this bias. Also, we gave some examples of it. Self-selection bias is a big problem in research. It makes a biased sample when nonprobability sampling is used. We discussed the probability sampling method, which can help prevent this bias in your business.

QuestionPro is much more than just survey software; we offer a solution for every problem and business. We also have data-management platforms, such as our InsightsHub research library.

Organizations worldwide use knowledge management systems and solutions like InsightsHub to manage data better, save time to acquire insights, and enhance historical data usage while lowering costs and increasing ROI.

Self-selection sampling is a type of non-probability sampling technique. Non-probability sampling focuses on sampling techniques that are based on the judgement of the researcher [see our article Non-probability sampling to learn more about non-probability sampling]. This article explains (a) what self-selection sampling is, (b) how to create a self-selection sample, and (c) the advantages and disadvantages of self-section sampling.

Self-selection sampling is useful when we want to allow units, whether individuals or organisations, for example, to choose to take part in research on their own accord. When we talk about people or organisations that could make up part of our sample, we refer to these as a unit or a case [see our article, Sampling: The basics, if you are unsure about the terms unit, case, sample and population].

As a sampling strategy, self-section sampling can be used with a wide range of research designs and research methods. For example, survey researchers may put a questionnaire online and subsequently invite anyone within a particular organisation to take part. Scientists that conduct experiments using human subjects may advertise the need for volunteers to take part in drug trials or research on physical activity. The key component is that research subjects (or organisations) volunteer to take part in the research on their own accord. They are not approached by the researcher directly.

There may be a wide range of reasons why people (and organisations) volunteer for such studies, including having particularly strong feelings or opinions about the research, a specific interest in the study or its findings, or simply wanting to help out a researcher(s).

The self-selection sample involves two simple steps: (a) publicising your need for units (or cases); and (b) checking the relevance of units (or cases) and either inviting or rejecting them.

You need to let potential applicants or organisations know about your study. This will involve some kind of advertising or promotion, whether print media, the radio, an online notice board, or some other medium. The invitation will need to follow certain ethical guidelines, making it clear what the study involves, but also more practical information, such as the types of applicant that are required (e.g., age, gender, or some other more subject-specific criteria).

Imagine that a researcher wants to understand more about the career goals of students at a particular university. Let?s say that the university has roughly 10,000 students. These 10,000 students are our population (N). Each of the 10,000 students is known as a unit. In order to select a sample (n) of students from this population of 10,000 students, we could choose to use self-selection sampling. Let's imagine that because we have a small budget and limited time, we choose a sample size of 100 students. However, it is important to the sampling strategy that each of the students is in their final year of university. Therefore, you would need to ensure that only final year students took part in the research. To publicise this, the study could run an advertisement on student radio, the student newspaper, or on physical and online notice boards accessed by students at the university.

Not all applicants will be relevant to your study. They may have not all read or understood what the study is about. Furthermore, they may not be the type of applicants you are looking for. For example, students that are not in their final year at the university may still choose to apply to take part in the study. You will need to check this before any particular unit or case, whether an individual or organisation, is invited to become part of your sample.

Since the potential research subjects (or organisations) contact you:

• This can reduce the amount of time necessary to search for appropriate units (or cases); that is, those individuals or organisations that meet the selection criteria needed for your sample.

• The potential units or cases (individuals or organisations) are likely to be committed to take part in the study, which can help in improving attendance (where necessary), and greater willingness to provide more insight into the phenomenon being studied (e.g., a respondent many be more willing to spend the time filling in qualitative, open-ended questions in an online survey, where others may leave them blank).

Since the potential research subjects (or organisations) volunteer to take part in the survey:

• There is likely to be a degree of self-selection bias. For example, the decision to participate in the study may reflect some inherent bias in the characteristics/traits of the participants (e.g., an employee with a 'chip of his shoulder' wanting to give an opinion).

• This can either lead to the sample not being representative of the population being studied, or exaggerating some particular finding from the study.

Despite the potential disadvantages of self-selection sampling, it is a popular sampling technique in many areas of science that require human subjects, as well as human trials within the pharmaceutical industry. As such, whilst self-selection sampling does not benefit from the random choice of subject selection as probability sampling does, or the theoretical drivers of purposive sampling, it is an effective sampling strategy in experimental research settings.