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Data Collection Methodology: Information Relevance


The analysis of the effectiveness of data collection methods used by authors for carrying out certain studies is an important procedure. It helps assess the relevance of the information obtained and their reliability. As the objects of such an analysis, two peer-reviewed articles are examined with different ways of evaluating data. The first paper that is written by Mahalle, Garg, Kulkarni, and Naik (2013) has a qualitative principle for evaluating data and is called “Differences in Traditional and Non-Traditional Risk Factors with Special Reference to Nutritional Factors in Patients with Coronary Artery Disease with or Without Diabetes Mellitus.” The other article written by Licier, Miranda, and Serrano (2016) has a quantitative method of information retrieval and is called “A Quantitative Proteomics Approach to Clinical Research with Non-Traditional Samples.” A critical analysis of the quality of data collection methods will help to compare the two types of information analysis and draw useful conclusions regarding the appropriateness of a specific research technique.

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The relevance of the Data Collection Methods

The relevance of the data collection method presented in the paper by Mahalle et al. (2013) is fully justified. As it is known, the number of patients with coronary heart disease is sufficiently large, and appropriate measures to find new ways of treatment can be sought with the help of questionnaires and surveys. The theme of the diet is very important, and particular patients’ habits can be found out through conversations. For example, Stewart et al. (2016) also describe nutritional principles and emphasize the need for interventions with the help of analyzing the data received during surveys. Moreover, Mozaffarian (2016) claims that overeating is directly related to heart problems and gives some examples of patients who were asked. Therefore, the relevance of the data collection method is quite confirmed.

A quantitative study by Licier et al. (2016) aims at assessing such science as proteomics and considering a non-traditional approach to finding useful clinical interventions. The relevance of the data collection method is not properly explained, and all the practical experiments are described rather ambiguously. For instance, Duarte and Spencer (2016) view personalized proteomics as the science of the future and claim to propose numerical statistical results. Creusot, Battaglia, Roncarolo, and Fathman (2016) share a similar opinion and note the effectiveness of this technology in the treatment of type 1 diabetes, presenting specific accurate data. Therefore, the relevance of the data collection method in this study cannot be evaluated since it is not fully presented and explained.

Quality of the Data Collection Methods

From the standpoint of the quality of the data collection method performed by Mahalle et al. (2013), it can be noted that the research was too narrowly focused since only mature and older adults were analyzed. Nevertheless, Arboix (2015) confirms that the number of young patients suffering from cardiovascular illnesses and, in particular, from coronary heart disease is quite large. Perhaps, more attention could have been paid to not only the most vulnerable segments of the population but also to other categories. However, in general, the data sampling method is rather objective, and the information is analyzed quite competently, without any personal judgments and controversial theories. Therefore, the study by Mahalle et al. (2013) can be considered quite reliable, albeit a limited one.

When evaluating the quality of the data collection method performed in the paper by Licier et al. (2016), it is possible to say that, despite rather interesting ideas, no significant experiments were conducted, and the quality of receiving information cannot be properly discussed. For instance, Dwane, Gallagher, Chonghaile, and O’Connor (2017) focus on the possibility of introducing cellular medicine to combat cancer and cite patients’ feedback. Baetta, Pontremoli, Fernandez, Spickett, and Banfi (2018) claim that “proteomics is a promising tool for research on sex/gender-specific pathophysiology” and give appropriate calculations (p. 62). However, in the analyzed study, there are no significant experiments except for mentioning the effectiveness of proteomics. Therefore, more accurate information would be quite useful.

Appropriateness of the Study Design and the Data Collection Methods

In the study about nutritional factors that affect coronary heart disease, a qualitative analysis is completely appropriate and justified. As Tang and Hazen (2014) remark, cardiovascular diseases are usually caused by individual patient inclinations, and receiving information through a survey is a good way to identify the most common reasons. Furthermore, according to Mangge, Becker, Fuchs, and Gostner (2014), medications for such an illness are also specific, and certain information from patients themselves will be useful for drawing up a clinical picture.

In the case of the study by Licier et al. (2016), the quantitative methodology of the research can hardly be considered valid since no data in the form of numbers and proportions were proposed. For example, Bosman (2016) also mentions the proteome of the red blood cell and does not provide any significant quantitative data. However, this author does not state that the work will be devoted to the research based on the collection of numerical data. Therefore, the methodology of the article under analysis by Licier et al. (2016) can hardly be considered justified and suitable.

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Thus, the comparison of two different studies from the data collection methods can help to draw quite competent conclusions regarding the effectiveness of the described interventions and the obtained results. The analyzed articles have not only advantages but also drawbacks since one of the papers have an unreasonably stated type of methodology. The works of other authors can be a confirmation of the relevance of the conducted studies and their importance for the medical field.


Arboix, A. (2015). Cardiovascular risk factors for acute stroke: Risk profiles in the different subtypes of ischemic stroke. World Journal of Clinical Cases, 3(5), 418-429.

Baetta, R., Pontremoli, M., Fernandez, A. M., Spickett, C. M., & Banfi, C. (2018). Proteomics in cardiovascular diseases: Unveiling sex and gender differences in the era of precision medicine. Journal of Proteomics, 173, 62-76.

Bosman, G. J. (2016). The proteome of the red blood cell: An auspicious source of new insights into membrane-centered regulation of homeostasis. Proteomes, 4(4), 35-46.

Creusot, R. J., Battaglia, M., Roncarolo, M. G., & Fathman, C. G. (2016). Concise review: Cell-based therapies and other non-traditional approaches for type 1 diabetes. Stem Cells, 34(4), 809-819.

Duarte, T. T., & Spencer, C. T. (2016). Personalized proteomics: The future of precision medicine. Proteomes, 4(4), 29-34.

Dwane, L., Gallagher, W. M., Chonghaile, T. N., & O’Connor, D. P. (2017). The emerging role of non-traditional ubiquitination in oncogenic pathways. Journal of Biological Chemistry, 292(9), 3543-3551.

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Licier, R., Miranda, E., & Serrano, H. (2016). A quantitative proteomics approach to clinical research with non-traditional samples. Proteomes, 4(4), 31-48.

Mahalle, N. P., Garg, M. K., Kulkarni, M. V., & Naik, S. S. (2013). Differences in traditional and non-traditional risk factors with special reference to nutritional factors in patients with coronary artery disease with or without diabetes mellitus. Indian Journal of Endocrinology and Metabolism, 17(5), 844-850.

Mangge, H., Becker, K., Fuchs, D., & Gostner, J. M. (2014). Antioxidants, inflammation and cardiovascular disease. World Journal of Cardiology, 6(6), 462-477.

Mozaffarian, D. (2016). Dietary and policy priorities for cardiovascular disease, diabetes, and obesity: A comprehensive review. Circulation, 133(2), 187-225.

Stewart, R. A., Wallentin, L., Benatar, J., Danchin, N., Hagström, E., Held, C.,… White, H. D. (2016). Dietary patterns and the risk of major adverse cardiovascular events in a global study of high-risk patients with stable coronary heart disease. European Heart Journal, 37(25), 1993-2001.

Tang, W. W., & Hazen, S. L. (2014). The contributory role of gut microbiota in cardiovascular disease. The Journal of Clinical Investigation, 124(10), 4204-4211.

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