New York University Theory of Aversive Racism Discussion


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When it comes to biases in healthcare, they are classified in many different forms. The epidemiological studies have several biases that reach more than fifty in number. However, there is a simplified version of the biases that classify them in two different categories. Primarily there is information bias that is a result of systematic differences especially on data exposure or outcome from various groups, and there is a selection bias associated to systematic biases resulting from generalization or comparability of groups in participation or the treatment arm of the study and the control groups (Heinze et al., 2016).

In information bias, the observers may enhance preferences due to the misappropriation or misinterpretation of the data collected from the groups. The same case will happen during the interview, in which the interviewer may concentrate on leading questions that influencing the response. There is also the recall bias regarding the quality of data as well as that which the society desires featuring as the only information.

On the selection, biases appear in the form of sampling, with individuals having a particular inclination towards a specific direction. There is also the allocation bias where the systematic difference occurs during the controlled trials and can only be done away with using randomization (Lin & Schneeweiss, 2016). In case there is no follow-up attrition, bias may appear between the different groups and the people who remain in the study.

Peer article

Sabastien Haneuse et al. (2016) looks at a case of Electronic health record-based study where the patient was treated from depression. There were some issues of biases in the survey as the determinants of different available information caused the biases; there were numerous factors that data was not collected in the right manner. There was also a patient attitude towards the variable and some matters of depression that causes the messiness.

Making inferences

Resulting inferences where there are biases is not necessary, as there is an issue with the results. This might be adopted in some way and may continually cause the same errors in other associated studies. It is, therefore, essential to regularly check on the specific issues that protect from such anomalies.


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

Haneuse, S., Bogart, A., Jazic, I., Westbrook, E. O., Boudreau, D., Theis, M. K., … & Arterburn, D. (2016). Learning about missing data mechanisms in electronic health records-based research: a survey-based approach. Epidemiology (Cambridge, Mass.), 27(1), 82.

Heinze, G., Wallisch, C., Kainz, A., Hronsky, M., Leffondré, K., Oberbauer, R., … & Mayer, G. (2016). Chances and challenges of using routine data collections for renal health care research. Nephrology Dialysis Transplantation, 30(suppl_4), iv68- iv75.

Lin, K. J., & Schneeweiss, S. (2016). Considerations for the analysis of longitudinal electronic health records linked to claims data to study the effectiveness and safety of drugs. Clinical Pharmacology & Therapeutics, 100(2), 147-159.

What are the issues in the survey with biases that the authors mentioned and how did they control or minimize them?

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