What are common types of selection bias?
Common types of selection bias are:
A self-fulfilling prophecy is a belief about a future event that leads people to act a certain way, ultimately bringing about the expected outcome. In other words, we cause something to happen just because we expect it.
This suggests that beliefs have the power to alter people’s behavior in such a way that they become a new reality in the end. Optimism bias can create a self-fulfilling prophecy: when we expect positive things, we are more likely to align our actions with this belief and try harder to influence the outcome.
Although there is no definite answer to what causes the placebo effect, researchers propose a number of explanations such as the power of suggestion, doctor-patient interaction, classical conditioning, etc.
Placebos are used in medical research for new medication or therapies, called clinical trials. In these trials some people are given a placebo, while others are given the new medication being tested.
The purpose is to determine how effective the new medication is: if it benefits people beyond a predefined threshold as compared to the placebo, it’s considered effective and not the result of a placebo effect.
Confirmation bias is the tendency to search, interpret, and recall information in a way that aligns with our pre-existing values, opinions, or beliefs. It refers to the ability to recollect information best when it amplifies what we already believe. Relatedly, we tend to forget information that contradicts our opinions.
Although selective recall is a component of confirmation bias, it should not be confused with recall bias.
On the other hand, recall bias refers to the differences in the ability between study participants to recall past events when self-reporting is used. This difference in accuracy or completeness of recollection is not related to beliefs or opinions. Rather, recall bias relates to other factors, such as the length of the recall period, age, and the characteristics of the disease under investigation.
A funnel plot shows the relation between a study’s effect size and its precision. It is a scatter plot of the treatment effects estimated from individual studies (horizontal axis) against sample size (vertical axis).
Asymmetry in the funnel plot, measured using regression analysis, is an indication of publication bias. In the absence of bias, results from small studies will scatter widely at the bottom of the graph, with the spread narrowing among larger studies.
The idea here is that small studies are more likely to remain unpublished if their results are nonsignificant or unfavorable, whereas larger studies get published regardless. This leads to asymmetry in the funnel plot.
Cognitive bias is an umbrella term used to describe the different ways in which our beliefs and experiences impact our judgment and decision making. These preconceptions are “mental shortcuts” that help us speed up how we process and make sense of new information.
However, this tendency may lead us to misunderstand events, facts, or other people. Cognitive bias can be a source of research bias.
Some common types of cognitive bias are:
The bandwagon effect is a type of cognitive bias. It describes the tendency of people to adopt behaviors or opinions simply because others are doing so, regardless of their own beliefs.
Belief bias and confirmation bias are both types of cognitive bias that impact our judgment and decision-making.
Confirmation bias relates to how we perceive and judge evidence. We tend to seek out and prefer information that supports our preexisting beliefs, ignoring any information that contradicts those beliefs.
Belief bias describes the tendency to judge an argument based on how plausible the conclusion seems to us, rather than how much evidence is provided to support it during the course of the argument.
The availability heuristic can influence our perception of risk in everyday life. One common example occurs when we are considering buying insurance. The sharp increase in purchases of flood insurance in the aftermath of flood events illustrates this phenomenon.
Witnessing such events, knowing someone who was personally affected, or extensive media coverage can make us more aware of floods (or make floods more “available” to us). This can change our risk perception, even though statistically there may not be a change in the probabilities of future flooding.
Heuristics are mental shortcuts or rules of thumb that help people reduce the time and effort required to make a decision. An example of a heuristic in psychology is the availability heuristic (or availability bias). It involves relying on information that comes to mind quickly, (i.e., information that is available to us).
Although both are common types of cognitive bias, they refer to different ways of processing information.
In other words, the availability heuristic gives preference to information that is easy to recall, while confirmation bias gives preference to information that aligns with our existing beliefs. Even though they are different, they both cause us to focus on only a subset of information.
In survey research, such as political polling, the way questions are worded or the order in which answers are presented can influence how respondents answer the questions. This is called the framing effect.
For example, if voters are asked to select which of two candidates they plan to vote for, the order in which the candidates are listed affects the percentage of respondents selecting each candidate. Recognizing the potential for research bias, researchers typically rotate which major candidate is listed first and which is listed second.
The framing effect is often used in advertising to positively influence consumer choice.
One common type of frame is “gain framing.” This shows consumers how they are going to benefit from a product or service. For example, dental care product advertisements use gain framing to display the benefits of using their product: white teeth, healthy gums, fresh breath, etc.
Apart from the obvious benefits, ads using the framing effect often imply other benefits, such as how a better-looking smile makes one more attractive to potential dating partners.
Because of the framing effect, the way information is presented to us influences how attractive a proposition is.
Suppose you are considering joining a gym. A membership at $500 per year sounds like a considerable investment and might prevent you from signing up immediately. However, if they tell you it costs just $1.37 per day and emphasize that this is less than the cost of a cup of coffee, you might think it’s a great offer, even though in reality both offers cost you the same.
The opposite of optimism bias is pessimism bias. Optimism bias occurs when we overestimate our chances of experiencing positive events in our lives, while pessimism bias occurs when we overestimate our chance of experiencing negative events.
For example, pessimism bias could cause someone to think they are going to fail an exam, even though they are well prepared and usually get good grades.
A positive illusion is a form of self-deception under which people have inflated, favorable attitudes about themselves or others close to them.
The most common positive illusions involve:
The planning fallacy refers to people’s tendency to underestimate the resources needed to complete a future task, despite knowing that previous tasks have also taken longer than planned.
For example, people generally tend to underestimate the cost and time needed for construction projects. The planning fallacy occurs due to people’s tendency to overestimate the chances that positive events, such as a shortened timeline, will happen to them. This phenomenon is called optimism bias.
Response bias is a general term used to describe a number of different conditions or factors that cue respondents to provide inaccurate or false answers during surveys or interviews. These factors range from the interviewer’s perceived social position or appearance to the the phrasing of questions in surveys.
Nonresponse bias occurs when the people who complete a survey are different from those who did not, in ways that are relevant to the research topic. Nonresponse can happen because people are either not willing or not able to participate.
Observer bias occurs when the researcher’s assumptions, views, or preconceptions influence what they see and record in a study, while actor–observer bias refers to situations where respondents attribute internal factors (e.g., bad character) to justify other’s behavior and external factors (difficult circumstances) to justify the same behavior in themselves.
Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous, so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous, as units share characteristics.
Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population.
This allows you to gather information from a smaller part of the population (i.e., the sample) and make accurate statements by using statistical analysis. A few sampling methods include simple random sampling, convenience sampling, and snowball sampling.
The observer-expectancy effect occurs when researchers influence the results of their own study through interactions with participants.
Researchers’ own beliefs and expectations about the study results may unintentionally influence participants through demand characteristics.
The observer-expectancy effect is often used synonymously with the Pygmalion or Rosenthal effect.
You can use several tactics to minimize observer bias.
Multiple imputation involves using simulations to replace the missing data with likely values. Alternatively, you can use sample weighting to make up for the uneven balance of participants in your sample.
To avoid attrition, applying some of these measures can help you reduce participant dropout by making it easy and appealing for participants to stay.
Some groups of participants may leave because of bad experiences, unwanted side effects, or inadequate incentives for participation, among other reasons. Attrition is also called subject mortality, but it doesn’t always refer to participants dying!
Use these measures:
These cues may nudge participants to consciously or unconsciously change their responses, and they pose a threat to both internal and external validity. You can’t be sure that your independent variable manipulation worked, or that your findings can be applied to other people or settings.
In research, demand characteristics are cues that might indicate the aim of a study to participants. These cues can lead to participants changing their behaviors or responses based on what they think the research is about.
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