Major Sources of Bias in Research Studies
There are two types of error associated with most forms of research: random and systematic. Random errors, i.e., those due to sampling variability or measurement precision, occur in essentially all quantitative studies and can be minimized but not avoided. Systematic errors, or biases, are reproducible inaccuracies that produce a consistently false pattern of differences between observed and true values. Both random and systematic errors can threaten the validity of any research study. However, random errors can be easily determined and addressed using statistical analysis; most systematic errors or biases cannot. This is because biases can arise from innumerable sources, including complex human factors. For this reason, avoidance of systematic errors or biases is the task of proper research design
Major Categories of Research Bias
There are many different types of biases described in the research literature. The most common categories of bias that can affect the validity of research include the following:
More detail on each of these three categories of bias is provided below, including some specific types and examples. For more detail on the various types of biases, including how they can be controlled, link to the following article:
Selection biases occur when the groups to be compared are different. These differences may influence the outcome. Common types of sample (subject selection) biases include volunteer or referral bias, and nonrespondent bias. By definition, nonequivalent group designs also introduce selection bias.
Volunteer or referral bias. Volunteer or referral bias occurs because people who volunteer to participate in a study (or who are referred to it) are often different than non-volunteers/non-referrals. This bias usually, but not always, favors the treatment group, as volunteers tend to be more motivated and concerned about their health.
Nonrespondentbias. Nonrespondent bias occurs when those who do not respond to a survey differ in important ways from those who respond or participate. This bias can work in either direction.
Measurement biases involve systematic error that can occur in collecting relevant data. Common measurement biases include instrument bias, insensitive measure bias, expectation bias , recall or memory bias, attention bias, and verification or work-up bias.
Instrument bias. Instrument bias occurs when calibration errors lead to inaccurate measurements being recorded, e.g., an unbalanced weight scale.
Insensitive measure bias. Insensitive measure bias occurs when the measurement tool(s) used are not sensitive enough to detect what might be important differences in the variable of interest.
Expectation bias. Expectation bias occurs in the absence of masking or blinding, when observers may err in measuring data toward the expected outcome. This bias usually favors the treatment group
Recall or memory bias. Recall or memory bias can be a problem if outcomes being measured require that subjects recall past events. Often a person recalls positive events more than negative ones. Alternatively, certain subjects may be questioned more vigorously than others, thereby improving their recollections.
Attention bias. Attention bias occurs because people who are part of a study are usually aware of their involvement, and as a result of the attention received may give more favorable responses or perform better than people who are unaware of the study’s intent.
Verification or work-up bias. Verification or work-up bias is associated mainly with test validation studies. In these cases, if the sample used to assess a measurement tool (e.g., diagnostic test) is restricted only to who have the condition of factor being measured, the sensitivity of the measure can be overestimated.
Intervention (Exposure) Biases
Intervention or exposure biases generally are associated with research that compares groups. Common intervention biases include: contamination bias, co-intervention bias, timing bias(es), compliance bias, withdrawal bias, and proficiency bias.
Contamination bias. Contamination bias occurs when members of the 'control' group inadvertently receive the treatment or are exposed to the intervention, thus potentially minimizing the difference in outcomes between the two groups.
Co-intervention bias. Co-intervention bias occurs when some subjects are receiving other (unaccounted for) interventions at the same time as the study treatment.
Timing bias(es). Different issues related to the timing of intervention can bias. If an intervention is provided over a long period of time, maturation alone could be the cause for improvement. If treatment is very short in duration, there may not have been sufficient time for a noticeable effect in the outcomes of interest.
Compliance bias. Compliance bias occurs when differences in subject adherence to the planned treatment regimen or intervention affect the study outcomes..
Withdrawal bias. Withdrawal bias occurs when subjects who leave the study (drop-outs) differ significantly from those that remain.
Proficiency bias. Proficiency bias occurs when the interventions or treatments are not applied equally to subjects. This may be due to skill or training differences among personnel and/or differences in resources or procedures used at different sites.