Data Strategies
Collecting accurate data to solve problems and help in decision-making is very important in public health. Implementing continuous/interval and discrete data are two strategies for categorizing different types of information. Continuous/Interval data types are occasionally called quantitative or measurement variables. This data type takes on values within a range of reasonable significance such as height, weight, blood pressure, and population growth rate which are examples of measuring variables that are continuous (Burger, 2018). Continuous data advantages and positive aspects show how variables change over time within populations or health outcomes, it takes a countless number of values and makes them accurate, and it helps analysts understand and determine relationships between variables (Burger, 2018). However, the disadvantages are that measuring and collecting continuous data can be expensive, and sometimes the information is difficult to count and read.
Discrete data is a variable that is countable in a limited timeframe. It takes on specific values, and the variables are not dividable (Kazlauskas, 2021). Examples of this strategy would include the number of Cardiovascular cases or students in a classroom. The advantage of discrete data is that the values are easy to count and are non-expensive for collecting data. In addition, it provides valuable understandings to individuals, businesses, and government agencies (Kazlauskas, 2021). However, the disadvantages of discrete data are that measurements are not precise, and it provides fewer details than what one would receive using continuous data.
Since cardiovascular disease accounts for so many deaths today, using continuous data strategies would align best with my project. This is because it provides a lot of information that can be transferred into statistical analysis that can detect differences among patients and their health challenges. Moreover, using this data set can help me easily view and understand age, sex, cholesterol, blood pressure, and many other variables within the population I am reviewing.
References
Burger, T. (2018). Gentle introduction to the statistical foundations of false discovery rate in quantitative proteomics. Journal of proteome research, 17(1), 12-22.
Kazlauskas, B. (2021, July 29). Discrete vs. continuous data: What’s the difference? The Drum. https://www.thedrum.com/profile/whatagraph/news/di…
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References
American Psychological Association. Publication Manual of the American Psychological Association (7th Ed.). Washington, DC: Author.
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