7 Best Plans to Fight Selection Bias

Selection bias is like a giant that can work silently in the research and data analysis field as well as in decision-making and skew the validity of the outcomes. It happens when the people, population or the data contained in a research or analysis is not representative of the bigger population intended to be studied. Selection bias may skew the results in medical research, the analysis of the market, or in public policy where a biased result may prejudice the further progress. Consequently, it is important to note this bias and fight it as it guarantees credibility and validity of any assessment or research. Seven great ways to fight selection bias in the study and research are discussed below.

1. Random Sampling

The gold standard of diminishment of choice inclination is the arbitrary testing. In this method, all the members of the population will have equal probability of being included in the study. In such a way it reduces the effect of external factors which might otherwise bias the data.

An example of this can be given of a medical trial of a new drug. In case all the patients involved are the younger ones, then the outcome is not going to give the effects of the drug on the older patients. The selection of individuals in the randomized study setting allows the researcher to obtain a more representative sample that is balanced at all ages.

Random sampling tips:

• Participants can be selected using the random number generator or software.

• Establish an effective and broad sampling frame.

• Do not use convenience or self-selection samples unless it is followed by making adjustments.

2. Stratified Sampling

Although random sampling is good, in some cases there are sub-groups of population that are required to be reflected proportionately. That is where stratified sampling plays the role.

The method is especially helpful where there are small subgroups that are of interest to the research question. As an example, when conducting a nationwide survey, there is a need that the rural population should have a proper representation, though they contribute a smaller percentage to the overall population.

Advantages of a stratified sampling:

• Makes estimates more accurate.

• Increments the representation of the minority or underrepresented bunches.

• Control of confounding variables that have already been determined.

3. Control Group use

Control groups are important in the conduct of experimentations most of all in clinical trials to help curb selection bias. Control groups are used as reference points where the efficacy of intervention or treatment is measured.

The control group should also be picked under the same criteria as the treatment group to enable it to be effective. This will guarantee that variations on results can always be explained by the treatment itself, and not by a group difference beforehand.

Best practices:

• Keep the other conditions as much the same as possible between the two groups excepting the variable being tested.

• Make the study blind or double-blind, whichever possible, to minimize the effects of observers or participants.

4. Matching Techniques

Matching may serve to balance groups where it is not possible to apply randomization. The matching ensures that the individuals who are in the treatment group and non-treatment group are exposed to certain essential similarities so that they can be matched.

The technique is usually applicable in observational studies when researchers are not able to manipulate the assignment of treatment. The controlled variables may be confounders that might lead to bias but through matching of the participants, the study can manage to control them.

Kinds of matching:

• Exact matching: Matches subjects on those variables that have identical values.

• Propensity score matching: Symmetrical matching is executed by the probability of treatment that can be obtained because of observed attributes.

• Limitations: The known and measured variables only can be corrected for by matching, there is still the possibility of introducing some bias by unobserved confounders.

5. Blinding, Double-Blinding

In clinical and psychological studies, blinding refers to the actions taken to ensure that either the participants of a research or the researchers themselves (or both) do not know which group of people receives which treatment. This mitigates the chances of alienation either consciously or unconscious as a result of bias.

• Single-blind experiments: It is only the subjects who do not know which group they belong to.

• Double blind: Participants and researchers do not know.

Blinding is also crucial since expectations have the capacity to affect results. As an example, a participant being aware of receiving the actual treatment may feel a placebo effect, a researcher knowing part assignments may end up unintentionally affecting participant behavior.

Effectiveness:

• Eliminates biasness in performance.

• Increases the objectivity of the outcome assessment.

• Enhances credibility, and generalizability of results.

6. Thoughtful Exclusion and Inclusion criteria

A not so obvious and frequent way of introducing bias in the investigation is through criteria of inclusion as exclusion of participants. Since of unreasonably limited or ill-founded criteria, a test may not be an agent of the target populace.

The inclusion/exclusion criteria required for researchers must be:

• Well defined and explained.

• Applying to the objectives of the research.

• Applied in equal terms as all the participants.

As an example selective exclusion of all the patients with comorbid diseases in a heart disease matter may make the analysis less complex but the matter may lack the usefulness of the generalized finding without the inclusion of the patient creature as it is likely to appear in the real world of patients that experience multiple modes.

Bias of criteria avoidance checklist:

• Are the standards too low or too wide?

• Do they systematically marginalize some people?

7. Adjustments to Statistics and Sensitivity Analysis

Despite all controls, a certain amount of selection bias can sneak in the study. Statistical adjustments and sensitivity analysis enters into play there.

In known confounders, it is possible to correct using statistical methods such as multivariable regression, inverse probability weighting and instrumental variable analysis. The methods enable a researcher to make an estimation of the results that would have been achieved in the case of a more representative study population.

The sensitivity analysis tests the reliability of the findings in alternative assumptions. It does the following: it addresses the questions on how our conclusions would differ with other possible distributions of the unmeasured variables.

These are not substitutes to good design, but they are priceless in helping to evaluate and correct remaining biases.

Pro tips:

• Report expectations of statistical models at all times.

• Abuse the utilization of numerous models and decide consistency.

• Make limitations and possible residual bias sources obvious.

Conclusion

Even the best-meaning scientific study can be disrupted by the selection bias that would result in misleading findings with disastrous conclusions. Nonetheless, using a mixture of sound sampling strategies, careful research design, and statistical power, one can majorly diminish or even get rid of its effect.

The most important lesson here is to note that there is no magic bullet strategy that can be applied. The most effective protection against selection bias is a combination of the randomization, use of control groups, and the careful matching, as well as blinding and statistical adjustment, all done with a requisite degree of care and openness. Alike the accuracy of research data is key to its integrity, fighting selection bias is not only a good practice; it is a matter of science.

Author Bio:

Angela Ray is a Marketing Consultant and Technical Writer at RSTech Tales. She has 5+ years of experience in Digital Marketing.