PUBH 8545 Walden Levels of Measurement Comparison Paper
Mathematics
Secondary data has several different advantages. However, when using secondary data, you must make sure that you are able to define all the attributes of the contained information, in order to be able to identify the appropriate analyses and methods. This “data awareness” will allow you to not only answer your questions, but to also address potential shortfalls with the original data.
Using secondary data may present challenges to the different analyses that you would be able to perform, depending on the levels of measurement contained in your dataset. Having a clear data analysis plan will help you to address most of these challenges before starting your work.
For this Discussion you will compare and contrast levels of measurement for the analysis of secondary data.
To prepare:
- Review the concept of levels of measurement.
- Review this week’s Learning Resources and research the Walden Library and the web for information on how to identify levels of measurement in secondary data.
- Select two different levels of measurement for your comparison.
Post a 4-paragraph comparison of the levels of measurement you selected. Include the following in your post:
- Provide example of a continuous variable and indicate the proper way for providing descriptive statistics for it
- Provide example of a categorical variable and indicate the proper way for providing descriptive statistics for it
- Indicate how would you convert a continuous variable to a categorical variable
- Indicate why would you convert a continuous variable to a categorical variable
Support your post with the Learning Resources and current literature. Use APA formatting for your Discussion and to cite your resources.
References
McPhail, D., Goodwin, I., & Gordon, K. (2006). Reviewing statistical analysis plans—A guide for medical writers.
Drug Information Journal, 40
(2), 197–202.
Cox, E., Martin, B. C., Van Staa, T., Garbe, E., Siebert, U., & Johnson, M. L. (2009). Good research practices for comparative effectiveness research: Approaches to mitigate bias and confounding in the design of nonrandomized studies of treatment effects using secondary data sources: The International Society for Pharmacoeconomics and Outcomes Research Good Research Practices for Retrospective Database Analysis Task Force Report—Part II. Value in Health, 12(8), 1053–1061
