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Introduction
In contemporary research, experts generally utilize quantitative, qualitative, and mixed-methods designs, depending on the study’s objectives, scope, and area. The former refers to research methodology, emphasizing numerical data and reliable measurements (Goertzen, 2017). As a result, it provides objective information concerning people’s habits, trends, preferences, and other types of observable behavior (Goertzen, 2017). However, quantitative research commonly lacks deep insights into the motives and cannot answer the question of why people behave a certain way (Goertzen, 2017). Experts need to transparently designate their research objectives to utilize quantitative designs effectively and achieve the most reliable and insightful results. From these considerations, quantitative research is practical for empirical and numerical studies but is an inferior choice to learn about respondents’ emotional status (Goertzen, 2017). Nevertheless, quantitative research is a prominent and effective methodology that might be applicable to most sectors.
Quantitative Research in Healthcare
Healthcare is a complex industry that encompasses patient treatment, scientific research, medical financing, information technology improvement, legal regulations, and other areas. From these considerations, all research methods might be applicable, depending on the objectives and scope of the work (Nieswiadomy & Bailey, 2018). For instance, holistic patient treatment studies would require a qualitative approach to reveal the emotional problems of participants and suggest an appropriate solution. On the other hand, a quantitative research design would be most productive for determining the most effective financial strategy in hospital management. The examples show that both methodologies are prominent in contemporary healthcare but are used for diverse objectives.
Consequently, experts need to conduct an interpretation analysis to maximize the effectiveness of quantitative research. As mentioned before, the quantitative approach focuses on numerical data, which is frequently too complex to derive conclusions from directly (Nieswiadomy & Bailey, 2018). As a result, experts need to utilize statistical analysis methods to interpret the findings and positively contribute to the academic community. In turn, if the academics succeed in analyzing the data, the studies might reveal crucial insights for the whole industry. Ultimately, quantitative research is essential in the sector of healthcare.
Implications for Knowledge Generation
Lastly, it is crucial to discuss the benefits of quantitative research to knowledge generation and overall academic contribution. Knowledge generation is defined as “the creation of new knowledge by changing and evolving knowledge based on one’s experience, education, and input from others” (McGonigle & Mastrian, 2017, p. 1521). In other words, the development of the industry is only achievable via continuous research, including both quantitative and qualitative methods. As mentioned before, quantitative research might provide crucial insights and is essential to healthcare research. For instance, at present, a large number of quantitative studies examine the COVID-19 consequences, interpret the numerical data, such as the number of cases or percentage of non-vaccinated people, and offer appropriate solutions (Chen et al., 2022). The quantitative approach is also used directly to measure the infectivity of COVID-19 strains and the effectiveness of vaccines (Chen et al., 2022). Ultimately, the mentioned examples transparently demonstrate the impact of quantitative research on knowledge generation.
Conclusion
A quantitative approach is an effective research methodology that focuses on numerical data and statistical analysis. Consequently, experts interpret the findings to positively contribute to the academic community and advance knowledge generation. The current paper has demonstrated the effectiveness of quantitative research in the healthcare industry and discussed its implications for knowledge generation. Ultimately, the quantitative methodology should be used in combination with qualitative and mixed-methods designs to achieve the best possible results.
References
Chen, J., Wang, R., Gilby, N. B., & Wei, G. W. (2022). Omicron variant (B. 1.1. 529): Infectivity, vaccine breakthrough, and antibody resistance. Journal of chemical information and modeling.
Goertzen, M. J. (2017). Introduction to quantitative research and data. Library Technology Reports, 53(4), 12-18.
McGonigle, D., & Mastrian, K. (2017). Nursing informatics and the foundation of knowledge. Jones & Bartlett Learning.
Nieswiadomy, R. M., & Bailey, C. (2018). Foundations of nursing research. Pearson.
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