How to Improve Ethical Practice in Data Analysis

How to Improve Ethical Practice in Data Analysis

Ethical practice in data analysis acts as a guide in the delineation of various research priorities, the selection of research participants, and credit allocation to researchers for the discoveries that they make (Kromrey, 1993). Based on the facts, this analysis presents different ways that researchers can use to improve ethical practice in data analysis.

Firstly, researchers can improve ethical practice in data analysis by ensuring that research findings are presented in an open and honest manner. This means that researchers should try as much as possible not to report contradictory findings. Secondly, the use of untrue or deceptive statements in study reports should be discouraged. This ensures that the users of research findings are able to identify and use the actual results for specific purposes. Thirdly, all the inference boundaries should be well delineated. For example, considerations for all subjects to be sampled, and the levels of different independent variables should be clearly examined. During the documentation stage, researchers should make sure that all statistical assumptions and procedures are defined in an understandable manner. If possible, researchers should apply the statistical procedures without anticipating for a desirable outcome (Kromrey, 1993).

Mark, Eyssell & Campbell (1999) formulated additional guidelines about ethical practice in data collection and analysis. They assert that it is important for researchers to identify appropriate research techniques to minimize the level of risk exposed to the participants. If a researcher anticipates the emergence of risks, there is a great need to undertake a benefit and risk analysis. If the benefits outweigh the risks, the researcher can go ahead and carry out the research. Little (2013) argues that it is important for researchers to ensure that all the participants are informed about the nature and the method used to undertake the study. The study participants should not be coerced to participate in the research, but should be left to make their individual decisions. If possible, all the potential risks associated with the research should be indentified so that necessary steps can be put in place to manage these risks. As Barton (2006) explains, researchers can deviate from fully informed consent only if the anticipated risks are very minimal, or if it turns out that research cannot be undertaken under a fully informed consent. In instances where it becomes difficult to collect and analyze data anonymously, there is a great need for the researcher to observe maximum confidentiality.

Data Censoring is another good way to improve ethical practice in data analysis. Censoring should be done before collection and analysis of data. An evaluator or other stakeholders who have the required knowledge and skills can carry out this process. It is also important for researchers to avoid reporting anticipated study outcomes. Instead, they should report the findings that were observed when the report was being submitted (Klein & Moeschberger (2005).

Conclusively, it is evident that researchers can observe an ethical practice in data analysis by understanding and respecting the various techniques outlined in this analysis. For example, researchers should ensure that that research findings/results are presented in an open and honest manner to avoid making reports that contain contradictory findings. In situations where a researcher finds it useful to consider possible outcomes from a given experiment, there is a great need to understand the power of the research design applied in the study. When reporting study results, the researcher should provide a solid description of all the analyses that were performed.

 

References

Barton, K. C. (2006). Research methods in social studies education: Contemporary issues and perspectives. Greenwich, Conn: IAP – Information Age Pub.

Klein, J. P., & Moeschberger, M. L. (2005). Survival analysis: Techniques for censored and truncated data. New York, NY: Springer.

Kromrey, J.D. (1993). Ethics and Data Analysis. Educational Researcher, 22(4), 24-27.

Little, T. D. (2013). The Oxford handbook of quantitative methods. New York: Oxford University Press.

Mark, M.M., Eyssell, K.M., & Campbell, B. (1999). The Ethics of Data Collection and Analysis. New Directions for Evaluation, 82, 47-56.

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