Common types of bias in research studies

Bias in research refers to a systematic error that occurs in the design, conduct, or analysis of a study, resulting in incorrect or misleading results. The following are the main types of bias in research studies:

1. Selection bias: Occurs when the selection of participants is not representative of the population being studied due to inappropriate selection of poor retention of study subjects.

• Ascertainment (sampling) bias: Study population differs from target population due to nonrandom selection methods.

• Nonresponse bias: High nonresponse rate to surveys/questionnaires can cause errors if nonresponders differ in some way from responders.

• Berkson bias: Disease studied using only hospital-based patients may lead to results not applicable to target population.

• Prevalence (Neyman) bias: Exposures that happen long before disease assessment can cause study to miss diseased patients that die early or recover.

• Attrition bias: Significant loss of study participants may cause bias if those lost to follow-up differ significantly from remaining subjects.

2. Observational bias: Inaccurate measurement or classification of disease, exposure, or other variable.

• Recall bias: Common in retrospective studies, subjects with negative outcomes are more likely to report certain exposures than control subjects.

• Observer bias: Observers misclassify data due to individual differences in interpretation or preconceived expectations regarding study.

• Reporting bias: Subjects over- or under-report exposure history due to perceived social stigmatization.

• Surveillance (detection) bias: Risk factor itself causes increased monitoring in exposed group relative to unexposed group, which increases probability of identifying a disease.

• Hawthorne effect: Occurs when participants modify their behavior or performance because they are aware of being observed.

3. Information bias: Occurs when there are errors or inaccuracies in the measurement or classification of the exposure or outcome being studied.

• Confounding bias: Occurs when there is a third factor that is associated with both the exposure and outcome being studied, and this factor distorts the association between them.

• Performance bias: Occurs when there are systematic differences in the way that interventions or treatments are delivered to the exposed and unexposed groups.

• Publication bias: Occurs when studies with statistically significant results are more likely to be published than those with non-significant results, leading to an overestimation of the effect size.

4. Lead-time bias: Occurs when the early detection of a disease through screening results in an apparent increase in survival time, even though the disease may not be cured or may progress at the same rate as if it had been detected later.

5. Length-time bias: Occurs when a screening test is more likely to detect a slower-progressing form of the disease, which leads to an overestimation of the effectiveness of the test in terms of early detection and improved survival rates.

Understanding these types of bias is important for designing studies that are methodologically sound and for critically evaluating the results of studies that have been conducted.

References:

1. Types of Bias in Research | Definition & Examples (scribbr.com) 

2. 5 Types of Bias in Research and How to Make Your Surveys Bias-Free (leadquizzes.com) 

3. Dealing With Bias in Academic Research - Enago Academy