Data Integrity and Management Research Paper

Adequate data management in research has become an increasingly important topic nowadays when the modern tools facilitate the gathering of information, and the pure amount of raw data can become overwhelming. Dealing with issues in data management requires accuracy, efficiency and compliance with certain guidelines and accepted standards, including the rules of ethics.

Maintaining the data integrity demands special effort on the part of the researcher. According to the guidelines published by Medicines and Healthcare products Regulatory Agency (MHRA), the universal, suitable for any type of research data, integrity requirements consist of the following points:

Establishing data criticality and inherent integrity risk … [with] overarching data governance system, which should include relevant policies and staff training in the importance of data integrity … the organizational (e.g. procedures) and technical (e.g. computer system access) controls applied to different areas of the quality systems.. [and] designing systems to assure data quality and integrity … in a way that encourages compliance with the principles of data integrity. (MHRA GMP Data Integrity Definitions and Guidance for Industry, 2015, p. 1-2)

The essential examples of such principles include consistency, legibility, completeness and relevance of the data generated in research, as well as its completeness throughout the time of the research. It is also important to keep in mind the risks, for example, human factor, software and hardware faults, conscious falsification of results, accidental errors, and changes in records or technology, which should be avoided by all means (McDowall, 2013, par. 2-9).

After collecting the data, the researcher may proceed to its interpretation however at that stage it may be difficult to remain unbiased. Focusing on the anticipated results of an experiment can affect the interpretation and even cast discredit on the research in whole. Ted Kaptchuk (2003) describes this effect in his article, claiming that the data-judgment interaction is rarely taken into account as there is no objective way to measure the subjective components of interpretation, while researchers tend to evaluate the evidence supporting their beliefs in a different way than the one that distinctly challenges such convictions (par. 2-3).

However not every research should be considered irrelevant due to the biased interpretation, Robert J. MacCoun (1998) argues that interpretation bias is a common phenomenon with a varied motivational, intentional, and purely cognitive determinants, however there is evidence suggesting that the magnitude of biases is often small and they are subtle; a key role the in identification of biased research interpretation and its sources have played the systematic empirical methods; finally, the biases are not all indefensible and the differing standards of proof are completely acceptable, provided that the researchers are self-conscious about standards and stances, and are explicit about them (p. 36).

Finally, for a researcher there always exists a moral barrier in a form of ethical guidelines. The transfer of research data to fellow researchers or academic institutions should be carried out with great care, and it should be considered whether or not such transfer is necessary.

An advice on data protection and transfer, published at the website of Cardiff University, states that the data should be secured during the transit; sharing the personal data with other organizations should be conducted under the Data Protection Act; access to personal data must be restricted to the minimum number of the research participants, and the issues with identifiability should be avoided as well (FAQs on Data Protection, n.d., par. 10-20). Another guideline sums it all up: “However simple or complex your data set, think about what you might need to do to ensure that your management of the data respects the terms of your consent, and in particular, the confidentiality and anonymity that participants were promised” (Data storage and data security, n.d., par. 14).

Reference List

Data storage and data security. (n.d.). Web.

FAQs on Data Protection and Writing a Research Protocol or Applying for Research Ethics Approval. (n.d.). Web.

Kaptchuk, T. J. (2003). Effect of Interpretive Bias on Research Evidence. BMJ: British Medical Journal, 326(7404), 1453–1455. Web.

MacCoun, R. J. (1998). Biases in the Interpretation and Use of Research Results. Annual Review of Psychology, 49, 259-287. Web.

McDowall, R. D. (2013). FDA’s Focus on Laboratory Data Integrity – Part 1. Web.

MHRA GMP Data Integrity Definitions and Guidance for Industry. (2015). Web.