Why is nonresponse bias a problem for researchers it makes it difficult to generalize findings to the larger population?
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What is the difference between belief bias and confirmation bias?
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. What is the bandwagon effect?
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. What are common types of cognitive bias?
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: How does a funnel plot measure publication bias?
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 The idea here is that small studies are more likely to remain unpublished if their results are nonsignificant or unfavorable, whereas larger studies get published regardless.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.  What’s the difference between confirmation bias and recall bias?
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.
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. What causes the placebo effect?
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. What are common types of selection bias? What is the difference between observer bias and actor–observer bias?
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. Why is bias in research a problem?
Research bias affects the validity and reliability of your research findings, leading to false conclusions and a misinterpretation of the truth. This can have serious implications in areas like medical research where, for example, a new form of treatment may be evaluated. What is the difference between stratified and cluster sampling?
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. What is a sampling method? What is the definition of social desirability bias?
Social desirability bias is a type of response bias that occurs when survey respondents provide answers according to society’s expectations, rather than their own beliefs or experiences. It is especially likely to occur in self-report questionnaires, as well as in any type of behavioral research, particularly if the participants know they’re being observed. This research bias can distort your results, leading to over-reporting of socially desirable behaviors or attitudes and under-reporting of socially undesirable behaviors or attitudes. What is the observer-expectancy effect? How can I minimize observer bias in my research?
You can use several tactics to minimize observer bias.
Can I avoid observer bias? What is observer bias?
Observer bias occurs when a researcher’s expectations, opinions, or prejudices influence what they perceive or record in a study. It usually affects studies when observers are aware of the research aims or hypotheses. This type of research bias is also called detection bias or ascertainment bias. How do you overcome attrition bias?
If you have a small amount of attrition bias, you can use some statistical methods to try to make up for it. 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. How do you prevent attrition?
To avoid attrition, applying some of these measures can help you reduce participant dropout by making it easy and appealing for participants to stay.
How does attrition affect external validity?
Attrition bias can skew your sample so that your final sample differs significantly from your original sample. Your sample is biased because some groups from your population are underrepresented. With a biased final sample, you may not be able to generalize your findings to the original population that you sampled from, so your external validity is compromised. How does attrition threaten internal validity? Why is attrition bias a problem? What is attrition bias?
Attrition bias is the selective dropout of some participants who systematically differ from those who remain in the study. 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! What’s the difference between demand characteristics and social desirability bias?
Demand characteristics are aspects of experiments that may give away the research purpose to participants. Social desirability bias is when participants automatically try to respond in ways that make them seem likeable in a study, even if it means misrepresenting how they truly feel. Participants may use demand characteristics to infer social norms or experimenter expectancies and act in socially desirable ways, so you should try to control for demand characteristics wherever possible. How do I prevent demand characteristics?
You can control demand characteristics by taking a few precautions in your research design and materials. Use these measures:
Why do demand characteristics matter in research?
Demand characteristics are a type of extraneous variable that can affect the outcomes of the study. They can invalidate studies by providing an alternative explanation for the results. 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. What are demand characteristics?
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. Demand characteristics are common problems in psychology experiments and other social science studies because they can cause a bias in your research findings. How do you avoid sampling bias? What are some types of sampling bias? What is sampling bias? |
