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Introduction
As established in contemporary research practice, qualitative and quantitative variants of research methodology are the leading paradigms that are used in answering topical questions. While the two methods can be mixed within a single study, they are independent, well-defined frameworks that inform data collection and analysis techniques used today. Each type serves its particular purpose that is determined by the aims and objectives of the research. Qualitative research is a fitting model for vast, underexplored concepts that lack systemic knowledge (O’Gorman and Macintosh, 2015). It synthesizes multiple sources of data in the form of narratives, surveys, interviews, and literature reviews to combine a uniform framework that will advance the understanding of the phenomenon. In turn, quantitative methods rely on numerical data, explaining trends, dependencies, and correlation through various analysis formats (Adams, Khan, and Raeside, 2014). They serve to acquire a more profound understanding of a defined concept that provides more concrete knowledge. In mixed studies, the two methods come together within a broad framework that requires a certain level of expertise from the researcher.
Discussion
At the same time, each of these research models is associated with a range of challenges that need to be mitigated. From the qualitative perspective, the materials of the research and the data operated are vaguer. The authors of such projects do not work toward a quantifiable result within a strict framework. While this approach can imply a higher level of researcher creativity, it is also highly demanding in terms of critical and abstract thinking (Mezmir, 2020). The author should be capable of connecting scarce elements of data and finding the connections between them that are often not obvious. Raw qualitative data does not conform to a specific pattern or categorial system, usually being a broad narrative instead. Thus, it requires the ability to venture beyond the surface of the text to seize the crucial idea at its core. In other words, different sources may use varying phrasing to discuss the facets of a single concept (Mezmir, 2020). Accordingly, the challenge consists of eliminating the redundancies and synthesizing the knowledge in a succinct form.
Unlike qualitative frameworks, quantitative data is defined clearly, as it exists in a quantifiable, numerical form. In this regard, it is vital to set the correct variables at the very beginning of the project, determining which are dependent and which are independent (O’Gorman and Macintosh, 2015). Next, the process of raw data analysis commences, which demands an adequate level of technical expertise from the researcher. Such projects often rely on specialized software capable of processing vast arrays of raw data and mastering such programs can be a challenge for less experienced researchers (Datatab, n.d.).
Conclusion
The presentation of quantitative data is also of paramount importance, but it can be difficult to perform optimally. While numerical information is naturally conceivable by the audience, large amounts of it can be hard to read. The challenge consists of selecting a fitting method of data display, which can include graphs, diagrams, and tables. The design of the visual presentation should support its understanding by the reader. Overcoming such challenges is an integral part of a research project, and acknowledging them is a major step toward this goal.
Reference List
O’Gorman, K. and MacIntosh, R. (2015) Research methods for business & management. A guide to writing your dissertation. 2nd ed. Goodfellow Publishers Ltd, Oxford.
Mezmir, E.A. (2020) Qualitative data analysis: An overview of data reduction, data display and interpretation. Research on Humanities and Social Sciences, 10(21), pp. 15-27.
Datatab (n.d.) Descriptive statistics and inferential statistics. Web.
Adams, J., Khan, H.T.A. and Raeside, R. (2014) Research methods for business and social science students. 2nd ed. Sage, New Delhi.
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