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Bias and confounding

         When it comes to healthcare, bias and confounding are standard terms in the delivery of services related to the sector. They mainly come up in an epidemiological study case. Bias, for instance, in an error or a systematic mishap that happens due to incorrect presentation of estimates regarding an effect of the exposure on a particular interest. Necessarily, the kind of error may give a high or a low estimate regarding some actual value of items. It may also have some issues with the direction of the failure, as it might have been noted.

         Essentially, it is hard to quantify such errors in terms of the reach and the magnitude. It happens in a way that makes the adjustments on the forms as well as the analysis of the necessary information (Sadhra, Kurmi, Sadhra, Lam, & Ayres, 2017). Considerations and the control of the data and control show that bias can be made at the initial stages of design and conduct and will go a long way to affect the results of the study.


         On the other hand, confounding regards the provision of alternative or rather another explanation on the connection between some exposure and the outcome of such data. Some of the disclosure may not correlate with the risk factor. This may be connected in some way to the finding reported in such epidemiology; it is, therefore, essential to understanding the issues or the factors that have a direct link to t the disease as well as those that exist as proxy measures (Kim & Basu, 2016). Such actions are necessary to understand the unknown cases and causes of disease. Proxy causes can be issues of age as well as economic status and the like. Considering a variable as a confounder is essential such as associating some variable to the outcome or, on the other hand, something that lies in the pathway between the exposure and disease itself.


         This is just a way of modification and includes a reduction of the potential of confounding by putting a random assignment that brakes any linkages between the subject and the confounders (Streeter et al., 2017). In such a response, it is possible to generate groups that can be compared with the resulting variable fairly.


American Psychological Association. Publication Manual of the American Psychological Association (6th Ed.). Washington, DC: Author.

Kim, D. D., & Basu, A. (2016). Estimating the medical care costs of obesity in the United States: systematic review, meta-analysis, and empirical analysis. Value in Health, 19(5), 602-613.

Sadhra, S., Kurmi, O. P., Sadhra, S. S., Lam, K. B. H., & Ayres, J. G. (2017). Occupational COPD and job exposure matrices: a systematic review and meta-analysis. International Journal of Chronic Obstructive Pulmonary Disease, 12, 725.

Streeter, A. J., Lin, N. X., Crathorne, L., Haasova, M., Hyde, C., Melzer, D., … & Henley, W. E. (2017). Adjusting for unmeasured confounding in nonrandomized longitudinal studies: a methodological review. Journal of Clinical Epidemiology, 87, 23-34.

What is researcher bias and how to we control it? 

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Researcher bias refers to the tendency of a researcher to favor certain results or interpretations based on their own preconceived notions or personal beliefs. It can occur at various stages of the research process, including study design, data collection, data analysis, and result interpretation. Researcher bias can significantly affect the accuracy and reliability of research findings.

To control researcher bias, several measures can be implemented:

1. Awareness and acknowledgment: Researchers need to be aware of their own biases and potential influences on their observations and interpretations. By acknowledging their biases, they can take steps to minimize their impact on the research.

2. Objective study design: Researchers should strive to design studies that minimize bias. This includes methods such as random assignment, blinding, and control groups, which help ensure that the study results are not influenced by the preferences or expectations of the researchers.

3. Standardized data collection: Researchers should use standardized protocols and procedures for data collection to minimize variability and potential bias. This includes training data collectors, using validated measurement instruments, and ensuring consistency in data collection across study participants.

4. Blinding and double-blinding: Blinding refers to withholding information from the researcher or study participants to prevent bias. Single blinding involves keeping the participants unaware of the study’s purpose or the treatment they are receiving, while double blinding extends this to include the researchers as well.

5. Peer review and collaboration: Research should undergo rigorous peer review to ensure that it meets the standards of scientific rigor and objectivity. Collaborating with other researchers can also help minimize bias by providing different perspectives and critical evaluation of the study design and findings.

6. Transparency and disclosure: Researchers should disclose any potential conflicts of interest that could bias their work. Reporting all study details and findings, even if they contradict the researcher’s expectations or hypotheses, helps ensure transparency and minimize bias.

By implementing these measures, researchers can minimize bias and enhance the quality and validity of their research. It is crucial to prioritize objectivity and scientific integrity to ensure the credibility and reliability of research findings.

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