In this information age, where data are readily accessible and there is both a great demand for accelerated research projects and strict limitations on research funding, using existing data makes sense. Data used in this way are called secondary data; the

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Strengths and Limitations of Secondary Data Sources

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The Population Health Problem

Diabetes is a major population health problem today. According to reports, diabetes affects over 400 million people worldwide and is estimated to escalate to over 600 million in the next 20 years (Al-Lawati, 2017). In the United States, diabetes affects over 29 million people and is among the leading cause of death. It usually occurs when the body cannot generate sufficient insulin or when the body cannot appropriately utilize the insulin produced. Insulin is a hormone that controls blood sugar. When the insulin does not function properly, blood glucose levels increase, causing serious complications in the body. According to reports, diabetes can increase the risk of stroke, heart disease, kidney failure, lower-limb amputation, and blindness. Recent studies have also linked diabetes to hearing loss, cancer, and dementia. Diabetes can also reduce the quality of life and increase the risk of premature death.

Data Set

The three data sets I have selected are 1) Centers for Disease Control and Prevention (CDC), 2) the Clinical Journal of the American Society of Nephrology (CJASN), and 3) Cardiovascular Diabetology. The CDC is a government agency that works to create the tools, information, and expertise that communities and people need to control injury, disease, and disability. The CJASN, on the other hand, is a peer-reviewed journal that covers nephrology. Cardiovascular Diabetology is a peer-reviewed journal that covers the connection between Diabetology and cardiology, which means the connection between cardiovascular diseases, metabolic syndrome, and diabetes.

Variables in each Data Set

Variables can be defined as measurable characteristics that can change over time or can be manipulated. The CDC analyzes how diabetes causes kidney disease. Here, the variables are diabetes and kidney disease. According to the CDC (2021), high blood sugar levels can harm kidney blood vessels, as well as, nephrons. Diabetes can also increase blood pressure. High blood pressure can damage kidneys. The CJASN also analyzes the connection between diabetes and kidney disease. According to the journal, diabetic kidney disease occurs in around 40 percent of people with diabetes. ESRD (End-Stage Renal Disease) is the most recognizable effect of diabetic kidney disease (Alicic et al., 2017). Cardiovascular Diabetology, on the other hand, examines how type 2 diabetes contributes to cardiovascular disease. Here, the variables are diabetes and cardiovascular disease. According to the journal, people with diabetes have a high prevalence rate of cardiovascular disease compared with individuals without diabetes. Cardiovascular disease is a major cause of disability and death among people with type 2 diabetes (Einarson et al., 2018). Research shows that the prevalence of cardiovascular disease has been increasing over time.

The Validity of each Data Set

Validity refers to how well the results of a data set are corresponding to real characteristics, variations, and properties in the social or physical world. In other words, it is the accuracy of a data set. The findings from the CDC are accurate. The CDC has always been a trusted source of information to keep the public safe. It is dedicated to using strict scientific standards to ensure the reliability and accuracy of research results (CDC, 2017). The findings from the CJASN are also accurate. The data set has been used in over 400 articles. Similarly, the findings from the Cardiovascular Diabetology are accurate. The data set has been used in several previous publications.

Challenges in Identifying a Proper Data Set

One major challenge that I might face as a researcher in identifying proper data is the issue of reliability. A data set may not be reliable for my current research needs. This is because it might contain information collected in the past for another reason. It can provide huge amount of information, but a huge quantity does not always reflect appropriateness.

Another major challenge is the availability of data. As a researcher, I need to obtain highly rigorous, valid, and scientific data from secondary sources. This means that I need to work hard since such data is not easily available. Also, sometimes I may need permission to use certain materials (i.e., figures) from another data set.

Another challenge that I might face as a researcher is personal bias. The chances of biasness in secondary sources are higher than in primary sources. Secondary data is gathered by someone else than me. Sometimes these individual may falsify the information to make the situation worse or better.

The other issue is obsolete data. Certain secondary data may contain information related to my research but it might be out-of-date. I cannot use data that was collected 15 years ago or 20 years ago. The data was reliable and valid at the time when it was collected or written but it is obsolete in the current circumstances. It is always recommendable to use data set published within the last five years. Historical data is the only data that can be utilized forever as it represents history – something that cannot be changed or altered.

References

Al-Lawati, J. A. (2017). Diabetes mellitus: a local and global public health emergency!. Oman medical journal, 32(3), 177. DOI: https://doi.org/10.5001%2Fomj.2017.34

Alicic, R. Z., Rooney, M. T., & Tuttle, K. R. (2017). Diabetic kidney disease: challenges, progress, and possibilities. Clinical Journal of the American Society of Nephrology, 12(12), 2032-2045. DOI: https://doi.org/10.2215%2FCJN.11491116

CDC. (2021). Diabetes. Retrieved from: https://www.cdc.gov/diabetes/managing/diabetes-kidney-disease.html

CDC. (2017). Office of Science (OS). Retrieved from: https://www.cdc.gov/os/quality/support/info-qual.htm

Einarson, T. R., Acs, A., Ludwig, C., & Panton, U. H. (2018). Prevalence of cardiovascular disease in type 2 diabetes: a systematic literature review of scientific evidence from across the world in 2007–2017. Cardiovascular diabetology, 17(1), 1-19. DOI: https://doi.org/10.1186/s12933-018-0728-6