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Introduction
Research bias are nearly impossible to eliminate completely, yet their excessive presence implies that the veracity and accuracy of research results reduces extensively. Therefore, addressing the issue of research bias is essential. In qualitative research, bias may occur due to the imperfections in data collection tools. Namely, the interview and survey models allow participants to respond in the manner that they deem as necessary as opposed to voicing their actual opinions or stating the facts. In addition, there is a possibility of an interviewer misinterpreting the collected information in the way that supports the research hypothesis, thus skewing the outcomes and contributing to further confusion.
Bias in Quantitative Research
Quantitative research is not devoid of bias, either, mostly due to the errors made at the stage fo sampling. By using an unnecessarily homogenous research population, one can receive the research results that do not represent the actual situation. In addition, quantitative research bias may occur in the scenarios that involve any systematic error leading to the mismanagement of quantitative information. For instance, if a researcher does not standardize their interactions with a patient, misinterpretation of data and the further introduction of bias into the quantitative analysis will ensue (Krehenwinkel et al. 8). Therefore, using a homogenous approach toward the collection of data is essential in quantitative studies.
Considerations before Creating the Research Protocol
When creating a research protocol, one must ensure that possible bias is addressed accordingly, which can be achieved by considering the weaknesses of the chosen type of research. In addition, before compiling a research protocol, one must set specific research goals and define the milestones for their completion. Additionally, the key characteristics of the target population have to be identified to ensure that the necessary information is gathered properly. Thus, one will ensure that the collected data is accurate and research results are representative of the studied problem.
Work Cited
Krehenwinkel, Henrik, et al. Estimating and Mitigating Amplification Bias in Qualitative and Quantitative Arthropod Metabarcoding. Scientific Reports, vol. 7. no. 1, 2017, pp. 1-12.
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