What Is Information Bias? | Definition & Examples
Information bias is a type of error that occurs when key study variables are incorrectly measured or classified. Information bias can affect the findings of observational or experimental studies due to systematic differences in how data is obtained from various study groups.
Information bias is also known as measurement bias or misclassification.
What is information bias?
Information bias occurs when information used in a study is either measured or recorded inaccurately. These measurements can be in various forms, such as:
- Responses to self-administered questionnaires
- Responses to interview questions
- Physical measurements
- Information in medical records
- The study does not have a double-blind design – i.e., the researchers know whether a participant is assigned to the control or the experimental group.
- The researchers use different methods to assess outcomes in each group. For example, using medical records for one group and self-report questionnaires for the other when studying disease status.
- The independent variable (e.g., exposure to toxic substances) and/or the dependent variable (e.g., risk of lung cancer) are recorded inaccurately. This can be due to errors in recording an individual’s history, different disease definitions, or different diagnostic criteria among experts.
- Instruments for objective measurements (e.g., weight) is not correctly calibrated, results are registered incorrectly, or data are switched during the data entry or data cleaning phase.
In general, information bias tends to produce erroneous results or conclusions that differ systematically from the truth.
What causes information bias?
Information bias can arise due to non-differential misclassification if both the experimental and the control group are affected equally, or differential misclassification if it affects one group more than the other. Here, misclassification refers to the classification of an individual or an attribute into a category other than that the one it should be assigned.
Non-differential misclassification is caused by equally inaccurate measurements in all study groups. This can occur when study participants in both comparison groups have difficulty accurately remembering something that is not objectively verifiable, such as levels of alcohol consumption.
Non-differential misclassification tends to make the groups appear more similar than they really are. It also causes researchers to underestimate the association between variables (e.g., between alcohol consumption and the risk of lung cancer).
Differential misclassification is caused by a measurement difference that exists between study groups, such as a case study group and a control group. Because participants in the case group already possess an attribute, such as a specific health condition, they may be able to recall past exposure to risk factors more accurately than the healthy control group.
Differential misclassification can cause either an underestimate or an overestimate of the association between variables.
Types of information bias
Information bias is a broad term describing systematic errors in how data are collected or measured. There are several types of information bias:
- Recall bias occurs when participants in one of the study groups are able to recall past events or behaviors better than those in the other.
- Observer bias happens when researchers are aware of the hypothesis under investigation or know which group each participant is assigned to. Such information may influence how researchers collect, measure, or interpret information.
- Performance bias refers to situations in which researchers or participants in a study modify their behavior or responses because they are aware of group allocation – i.e., they know who is in the control and who is in the treatment group.
- Regression to the mean (RTM) is a phenomenon where a variable that shows an extreme value (outlier) on its first measurement (higher or lower than the mean) will tend to be closer to the mean on a second measurement. RTM can lead researchers to believe that an intervention or treatment is more effective than it really is.
Information bias examples
Researchers’ expectations or opinions can interfere with data collection, resulting in information bias.
Information bias can also cause researchers to miss important data regarding possible factors that contributed to the onset of a disease or condition.
How to minimise information bias
Information bias arises from the approach used to collect or measure data in your study. There are several steps you can take to minimise information bias during data collection:
- Verify information collected from self-report questionnaires or interviews by comparing it with written records, such as medical records.
- Use double blinding, if possible. Make sure that anyone involved in the study is not aware of the research hypothesis or of who is in which group.
- If blinding is not possible, develop a protocol for the collection, measurement, and interpretation of information.
- Use standardised questionnaires and properly calibrated instruments to ensure consistency in data collection.
Other types of research bias
Frequently asked questions
- What is measurement bias?
Measurement bias or information bias refers to the distorted measurement of key study variables. Because there is a systematic (i.e., nonrandom) difference from the truth, measurement bias leads to erroneous results.
Measurement bias can occur, for example, because researchers and/or participants are aware of the research objectives and hypothesis (called observer bias). This awareness can influence how they respond and behave in the study.
- What is bias?
Bias is a systematic error in the design, administration, or analysis of a study. Because of bias, study results deviate from their true value and researchers draw erroneous conclusions.
There are several types of bias and different research designs or fields are susceptible to different types of research bias. For example, in health research, bias arises from two main sources:
- The approach adopted for selecting study participants
- The approach adopted for collecting or measuring data
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