In order to complete a robust, valid research project, it is vital to implement a fitting methodological vision that will reflect the study’s aims and objectives. Evidently, the contemporary practice of academic research possesses numerous tools that help to address profound topics with due levels of detail and credibility. At the same time, within this vast variety, the key dichotomy is generally observed between qualitative and quantitative methods of research. Each of these types encompasses a particular aspect of academic studies, representing the diversity of challenges faced by modern researchers (Jack and Raturi, 2006). These differences are embedded in the specific tools and formats implemented by the two key methods. On the one hand, qualitative research operates with narratives and facts, synthesizing broad arrays of knowledges into a uniform framework. In other words, it serves to distinguish and formulate causes, effects, and trends of various phenomena. On the other hand, quantitative research works with numerical data in the form of numbers, ratios, coefficients, and percentages. Its purpose is to describe specific relations in a quantifiable format.
The differences in methodology types that are outlined above account for the distribution of their application scopes. Although the body of academic knowledge has seen mixed-method studies where both types work toward a single aim, they do not intersect on the level of particular objectives (Emerald Publishing, 2019). This explains the strengths that are associated with either qualitative or quantitative methodologies. The first type addresses tendencies and facts in a narrative form (Adams, Khan, and Raeside, 2015). It concerns perceptions, opinions, impressions, and theories in a broader format (O’Gorman and MacIntosh, 2015). While it may appear that qualitative research is simpler due to the lack of a specific numerical outcome that can be miscalculated, such an assumption would be false. This type operates with notions and hypotheses that need to be evaluated and synthesized on an in-depth level. It works best with new phenomena that lack an established theoretical framework.
Thus, the key strength of qualitative research is that it consolidates scarce data and helps to explain emerging trends. For example, such methods are actively used to describe the challenges and perspectives of business in the age of Industry 4.0. Digital transformation on its own is new to modern industries. Therefore, prior to measuring specific effects on performance, retention, and other variables, it is necessary to acquire a fundamental understanding of how various business processes interact with the new phenomenon.
In turn, quantitative research conveys solid numerical findings that add a certain tangible dimension to the discussion. This approach is largely mathematical, which is why it actively utilizes statistical analysis and other tools of similar nature to substantiate the data with graphs and ratios (O’Gorman and MacIntosh, 2015). If qualitative research is what creates a theoretical framework that embodies a phenomenon is academic terms, quantitative methods solidify the knowledge by adding numbers (Amolo, Migori, and Ramraj, 2018). In a way, quantitative studies become a logical continuation of the previous qualitative research. As identified prior, a new trend needs to have an evidence-based theoretical framework for subsequent discussion. For example, the process begins with a qualitative study that uses case studies and interviews to confirm that corporate social responsibility is positively correlated with employee retention.
Next, based on this theoretical framework, a quantitative study becomes possible and reveals that strong corporate social responsibility level lowers the employee turnover intention by 35%, on average. This example illustrates how both models of research contribute to a full examination of a particular phenomenon. While both of them have their strength, neither is capable of encompassing a complex phenomenon in its entirety. They complement each other to create true academic knowledge.
Reference List
Adams, J., Khan, H.T.A. and Raeside, R. (2014) Research methods for business and social science students. 2nd ed. Sage, New Delhi.
O’Gorman, K. and MacIntosh, R. (2015) Research methods for business & management. A guide to writing your dissertation. 2nd ed. Goodfellow Publishers Ltd, Oxford.
Amolo, J., Migori, S. & Ramraj, A.B. (2018) The debatable paradigm of mixed methods. European Conference on Research Methodology for Business and Management Studies, Kidmore End, July 2018.
Jack, E.P. & Raturi, A.S. (2006) Lessons learned from methodological triangulation in management research. Management Research News, 29(6), pp. 345-357.
Emerald Publishing (2019) How to use mixed methods research? Web.