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Qualitative research is, by all means, an asset in terms of scholarly investigation and translation of new ideas to the academic and professional community. However, unlike quantitative approaches to research, qualitative data cannot be interpreted and organized solely with the means of statistical calculation tools. Essentially, qualitative research encompasses such tools as semi-structured in-depth interviews, focus group discussions, and grounded theory research (Wolff, Mahoney, Lohiniva, & Corkum, 2018). As a result, in order to draw tangible conclusions, it is of paramount importance to process the information presented by various respondents, who, in their turn, belong to diverse socio-ethnic backgrounds.
Thus, as far as the data organization is concerned, the creation of a filing system should be addressed by the researcher. When collecting a considerable amount of data in a limited time, some researchers tend to neglect the proper file naming, causing difficulties in finding relevant information and even losing necessary data in the long-term perspective (Suvivuo, 2021). Thus, in order to eliminate such a risk, it is advisable to create a template for document naming that would include the topic, category, and respondent’s identification and organize the files immediately after access.
Another relevant strategy in terms of data management concerns the data volume and the inability to analyze it with no third-party assistance. Emerging trends in data analysis are currently crowdfunding and labeling the data through classifiers (Suvivuo, 2021). The phenomenon of crowdsourcing stands for attracting people to contribute to the research by analyzing a certain amount of data on the matter of specific characteristics outlined in the instructions. Labeling, in its turn, encourages the usage of machine tools programmed to categorize data according to the keywords indicated. Hence, it may be concluded that today’s demand for qualitative research should catalyze more relevant proposals in terms of systematic data systematization, as manual labor capital is not sufficient to conduct large-scale research.
References
Suvivuo, S. (2021). Qualitative big data’s challenges and solutions: An organizing review. In Proceedings of the 54th Hawaii International Conference on System Sciences (pp. 980-988).
Wolff, B., Mahoney, F., Lohiniva, A. L., & Corkum, M. (2018). Collecting and analyzing qualitative data.
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