Quantitative Research Questions and Confounding Variables.

Quantitative Research Questions and Confounding Variables
There is nothing more central to research than the formulation of research questions that address an identified problem and that are helpful in understanding that problem and potential solutions to it. This means being clear on the nature of the problem in its context and the variables that you need to measure. To understand the nature of the problem, an ecological approach is needed, as you saw in Week 1. This approach also alerts you to the existence of confounding variables – extraneous factors that correlate directly or inversely with the variables you plan to measure, but which you do not plan to measure.
For this Discussion, you will develop two potential quantitative research questions for secondary data analysis of a given problem and a given data set. Then, you will discuss potential confounding variables.
To prepare:
Examine the Inpatient Prospective Payment System (IPPS) Provider Summary for the Top 100 Diagnosis-Related Groups (DRG) FY2011 data file at https://data.cms.gov/Medicare/Inpatient-Prospective-Payment-System-IPPS-Provider/97k6-zzx3.
Consider potential health problems and quantitative research questions for which this dataset could provide an answer.
Select variables in the dataset that could be used to answer the research question you have created, and determine potential confounding variables that would also need to be considered.

Post 2 to 3 paragraphs describing one problem and 1 to 2 research questions for which the given dataset could provide an answer. Include key variables that could be measured to answer each research question and two potential confounding variables. Briefly justify your choices.
Selected readings and citations
Creswell, J. (2009). Research design: Qualitative, quantitative, and mixed methods approaches. (Laureate Education, custom ed.).Thousand Oaks, CA: Sage Publications. Chapter 7, Research Questions and Hypotheses (pp. 129143)
Chapter 8, Quantitative Methods (pp. 145171)
Alexander, J., & Hearld, L. (2012). Methods and metrics challenges of delivery-system research. Implementation Science, 7(15). Retrieved from http://www.implementationscience.com/content/7/1/15
Boffetta, P. (2010). Causation in the presence of weak associations. Critical Reviews in Food Science and Nutrition, 50(1), 1316.
Retrieved from the Walden Library databases.
Krieger, N. (2008). Proximal, distal, and the politics of causation: Whats level got to do with it? American Journal of Public Health, 98(2), 221230.
Retrieved from the Walden Library databases.
Data.CMS.gov. (2011). Inpatient prospective payment system (IPPS) provider summary for the top 100 diagnosis-related groups (DRG)FY2011 Data file. Retrieved from https://data.cms.gov/Medicare/Inpatient-Prospective-Payment-System-IPPS-Provider/97k6-zzx3