Research And Data Analysis In Healthcare
Between Black and White Population
1. Comparing annual percent of Medicare enrollees having at least one ambulatory visit between B and W
2. Comparing average annual percent of diabetic Medicare enrollees age 65-75 having hemoglobin A1c between B and W
3. Comparing average annual percent of diabetic Medicare enrollees age 65-75 having eye examination between B and W
4. Comparing average annual percent of diabetic Medicare enrollees age 65-75 having
Students will develop an analysis report, in five main sections, including introduction, research method (research questions/objective, data set, research method, and analysis), results, conclusion and health policy recommendations. This is a 5-6 page individual project report.
Here are the main steps for this assignment.
Step 1: Students require to submit the topic using topic selection discussion forum by the end of week 1 and wait for instructor approval.
Step 2: Develop the research question and
Step 3: Run the analysis using EXCEL (RStudio for BONUS points) and report the findings using the assignment instruction.
The Report Structure:
Start with the
1.Cover page (1 page, including running head).
Please look at the example http://www.apastyle.org/manual/related/sample-experiment-paper-1.pdf (you can download the file from the class) and http://www.umuc.edu/library/libhow/apa_tutorial.cfm to learn more about the APA style.
In the title page include:
· Title, this is the approved topic by your instructor.
· Student name
· Class name
· Instructor name
Introduce the problem or topic being investigated. Include relevant background information, for example;
· Indicates why this is an issue or topic worth researching;
· Highlight how others have researched this topic or issue (whether quantitatively or qualitatively), and
· Specify how others have operationalized this concept and measured these phenomena
Note: Introduction should not be more than one or two paragraphs.
There is no need for a literature review in this assignment
3.Research Question or Research Hypothesis
What is the Research Question or Research Hypothesis?
***Just in time information: Here are a few points for Research Question or Research Hypothesis
There are basically two kinds of research questions: testable and non-testable. Neither is better than the other, and both have a place in applied research.
Examples of non-testable questions are:
How do managers feel about the reorganization?
What do residents feel are the most important problems facing the community?
Respondents’ answers to these questions could be summarized in descriptive tables and the results might be extremely valuable to administrators and planners. Business and social science researchers often ask non-testable research questions. The shortcoming with these types of questions is that they do not provide objective cut-off points for decision-makers.
In order to overcome this problem, researchers often seek to answer one or more testable research questions. Nearly all testable research questions begin with one of the following two phrases:
Is there a significant difference between …?
Is there a significant relationship between …?
Is there a significant relationship between the age of managers? and their attitudes towards the reorganization?
A research hypothesis is a testable statement of opinion. It is created from the research question by replacing the words “Is there” with the words “There is,” and also replacing the question mark with a period. The hypotheses for the two sample research questions would be:
There is a significant relationship between the age of managers and their attitudes towards the reorganization.
It is not possible to test a hypothesis directly. Instead, you must turn the hypothesis into a null hypothesis. The null hypothesis is created from the hypothesis by adding the words “no” or “not” to the statement. For example, the null hypotheses for the two examples would be:
There is no significant relationship between the age of managers
and their attitudes towards the reorganization.
There is no significant difference between white and minority residents
with respect to what they feel are the most important problems facing the community.
All statistical testing is done on the null hypothesis…never the hypothesis. The result of a statistical test will enable you to either:
1) reject the null hypothesis, or
2) fail to reject the null hypothesis. Never use the words “accept the null hypothesis.”
*Source: StatPac for Windows Tutorial. (2017). User’s Guide; Formulating Hypotheses from Research Questions. Retrieved May 17, 2019 from https://statpac.com/manual/index.htm?turl=formulatinghypothesesfromresearchquestions.htm
What does significance really mean?
“Significance is a statistical term that tells how sure you are that a difference or relationship exists. To say that a significant difference or relationship exists only tells half the story. We might be very sure that a relationship exists, but is it a strong, moderate, or weak relationship? After finding a significant relationship, it is important to evaluate its strength. Significant relationships can be strong or weak. Significant differences can be large or small. It just depends on your sample size.
To determine whether the observed difference is statistically significant, we look at two outputs of our statistical test:
P-value: The primary output of statistical tests is the p-value (probability value). It indicates the probability of observing the difference if no difference exists.
The p-value from above example, 0.9926, indicates that we DO NOT expect to see a meaningless (random) difference of 5% or more in ‘hospital beds’ only about 993 times in 1000 there is no difference (0.9926*1000=992.6 ~ 993).
Note: This is an example from the week1 exercise.
The p-value from above example, 0.0001, indicates that we’d expect to see a meaningless (random) ‘number of the employees on payer’ difference of 5% or more only about 0.1 times in 1000 (0.0001 * 1000=0.1).
CI around Difference: A confidence interval around a difference that does not cross zero also indicates statistical significance. The graph below shows the 95% confidence interval around the difference between hospital beds in 2011 and 2012 (CI: [-40.82 ; 40.44]):
CI around Difference: A confidence interval around a difference that does not cross zero also indicates statistical significance. The graph below shows the 95% confidence interval around the difference between hospital beds in 2011 and 2012 (CI: [-382.16 ; 125.53]):
The boundaries of this confidence interval around the difference also provide a way to see what the upper [40.44] and lower bounds [-40.82].
As a summary:
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