Information bias is a type of error that occurs when key study variables are incorrectly measured or classified. Information biases can affect results observational or experimental studies due to systematic differences in how data are obtained from different study groups. Information bias is also called measurement bias or classification error.
Where do information biases come from?
Information bias occurs when the information used in a study is either measured or recorded inaccurately. These measures can take various forms, such as:
- Responses to self-administered questionnaires
- Responses to questions maintenance
- Physical measurements
- Information in medical records
Information bias is one of the most common sources of bias. research . This affects the validity of observational studies, as well as clinical experiments and trials. Information bias can occur when:
The study is not carried out double-blind: the researchers know whether a participant is assigned to the control group or the experimental group.
Researchers use different methods to evaluate the results 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., lung cancer risk) are inaccurately recorded. This may be due to errors in recording an individual's history, different definitions of the disease, or different diagnostic criteria among experts.
Instruments intended for objective measurements (e.g. weight) are not properly calibrated, results are not recorded correctly, or data is switched during the data entry or cleaning phase.
In general, information biases tend to produce erroneous results or conclusions that systematically differ from the truth.
Causes
Information bias can arise due to non-differential misclassification if the experimental group 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 attribute into a category other than that to which it should be assigned.
Nondifferential misclassification is caused by equally inaccurate measurements across study groups. This can occur when study participants in both comparison groups have difficulty accurately remembering something that is not objectively verifiable, such as alcohol consumption levels.
Non-differential misclassification tends to make groups appear more similar than they really are. It also leads researchers to underestimate the association between variables (for example, between alcohol consumption and lung cancer risk).
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 result in underestimation or overestimation of the association between variables.
Types of information bias
Information bias is a general term describing systematic errors in the way data is collected or measured. There are several types of information bias:
Recall bias occurs when participants in one study group are able to remember past events or behaviors better than those in the other.
Observer bias occurs when researchers know the hypothesis being studied or know which group each participant is assigned to. This information can influence how researchers collect, measure, or interpret information.
Performance bias refers to situations in which researchers or participants in a study change their behavior or responses because they are aware of the distribution of groups, that is, they know who is in from the control group and who is part of the treatment group.
Regression toward the mean is a phenomenon in which a variable that has an extreme value (outlier) when first measured (above or below the mean) will tend to be closer to the mean when measured a second time. RTM can lead researchers to believe that an intervention or treatment is more effective than it actually is.
How to avoid information bias?
Information bias arises from the approach used to collect or measure the data in your study. There are several steps you can take to minimize information bias when collecting data:
- Verify information collected from self-report questionnaires or interviews by comparing it with written records, such as medical records.
- Use double blind, if possible. Make sure that anyone involved in the study is unaware of the research hypothesis or who is in which group.
- If blinding is not possible, develop a protocol for collecting, measuring, and interpreting information.
- Use standardized questionnaires and properly calibrated instruments to ensure consistency in data collection.
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